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The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Flink OpenSource SQL Jobs", + "uri":"dli_09_0006.html", + "doc_type":"devg", + "p_code":"", + "code":"12" + }, + { + "desc":"This guide provides reference for Flink 1.12 only.In this example, we aim to query information about top three most-clicked offerings in each hour from a set of real-time", + "product_code":"dli", + "title":"Reading Data from Kafka and Writing Data to RDS", + "uri":"dli_09_0009.html", + "doc_type":"devg", + "p_code":"12", + "code":"13" + }, + { + "desc":"This guide provides reference for Flink 1.12 only.This example analyzes real-time vehicle driving data and collects statistics on data results that meet specific conditio", + "product_code":"dli", + "title":"Reading Data from Kafka and Writing Data to GaussDB(DWS)", + "uri":"dli_09_0010.html", + "doc_type":"devg", + "p_code":"12", + "code":"14" + }, + { + "desc":"This guide provides reference for Flink 1.12 only.This example analyzes offering purchase data and collects statistics on data results that meet specific conditions. 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Duri", + "product_code":"dli", + "title":"Reading Data from PostgreSQL CDC and Writing Data to GaussDB(DWS)", + "uri":"dli_09_0013.html", + "doc_type":"devg", + "p_code":"12", + "code":"17" + }, + { + "desc":"If you need to configure high reliability for a Flink application, you can set the parameters when creating your Flink jobs.Create an SMN topic and add an email address o", + "product_code":"dli", + "title":"Configuring High-Reliability Flink Jobs (Automatic Restart upon Exceptions)", + "uri":"dli_09_0207.html", + "doc_type":"devg", + "p_code":"12", + "code":"18" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Flink Jar Jobs", + "uri":"dli_09_0202.html", + "doc_type":"devg", + "p_code":"", + "code":"19" + }, + { + "desc":"Built on Flink and Spark, the stream ecosystem is fully compatible with the open-source Flink, Storm, and Spark APIs. It is enhanced in features and improved in performan", + "product_code":"dli", + "title":"Stream Ecosystem", + "uri":"dli_09_0162.html", + "doc_type":"devg", + "p_code":"19", + "code":"20" + }, + { + "desc":"You can perform secondary development based on Flink APIs to build your own Jar packages and submit them to the DLI queues to interact with data sources such as MRS Kafka", + "product_code":"dli", + "title":"Flink Jar Job Examples", + "uri":"dli_09_0150.html", + "doc_type":"devg", + "p_code":"19", + "code":"21" + }, + { + "desc":"DLI allows you to use a custom JAR package to run Flink jobs and write data to OBS. This section describes how to write processed Kafka data to OBS. You need to modify th", + "product_code":"dli", + "title":"Writing Data to OBS Using Flink Jar", + "uri":"dli_09_0191.html", + "doc_type":"devg", + "p_code":"19", + "code":"22" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Spark Jar Jobs", + "uri":"dli_09_0203.html", + "doc_type":"devg", + "p_code":"", + "code":"23" + }, + { + "desc":"DLI is fully compatible with open-source Apache Spark and allows you to import, query, analyze, and process job data by programming. This section describes how to write a", + "product_code":"dli", + "title":"Using Spark Jar Jobs to Read and Query OBS Data", + "uri":"dli_09_0205.html", + "doc_type":"devg", + "p_code":"23", + "code":"24" + }, + { + "desc":"DLI allows you to develop a program to create Spark jobs for operations related to databases, DLI or OBS tables, and table data. This example demonstrates how to develop ", + "product_code":"dli", + "title":"Using the Spark Job to Access DLI Metadata", + "uri":"dli_09_0176.html", + "doc_type":"devg", + "p_code":"23", + "code":"25" + }, + { + "desc":"DLI Spark-submit is a command line tool used to submit Spark jobs to the DLI server. This tool provides command lines compatible with open-source Spark.Getting authorized", + "product_code":"dli", + "title":"Using Spark-submit to Submit a Spark Jar Job", + "uri":"dli_09_0122.html", + "doc_type":"devg", + "p_code":"23", + "code":"26" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Using Spark Jobs to Access Data Sources of Datasource Connections", + "uri":"dli_09_0019.html", + "doc_type":"devg", + "p_code":"23", + "code":"27" + }, + { + "desc":"DLI supports the native Spark DataSource capability and other extended capabilities. You can use SQL statements or Spark jobs to access other data storage services and im", + "product_code":"dli", + "title":"Overview", + "uri":"dli_09_0020.html", + "doc_type":"devg", + "p_code":"27", + "code":"28" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to CSS", + "uri":"dli_09_0089.html", + "doc_type":"devg", + "p_code":"27", + "code":"29" + }, + { + "desc":"The Elasticsearch 6.5.4 and later versions provided by CSS provides the security settings. Once the function is enabled, CSS provides identity authentication, authorizati", + "product_code":"dli", + "title":"CSS Security Cluster Configuration", + "uri":"dli_09_0189.html", + "doc_type":"devg", + "p_code":"29", + "code":"30" + }, + { + "desc":"A datasource connection has been created on the DLI management console.Development descriptionConstructing dependency information and creating a Spark sessionImport depen", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0061.html", + "doc_type":"devg", + "p_code":"29", + "code":"31" + }, + { + "desc":"A datasource connection has been created on the DLI management console.Development descriptionCode implementationImport dependency packages.from __future__ import print_f", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0090.html", + "doc_type":"devg", + "p_code":"29", + "code":"32" + }, + { + "desc":"A datasource connection has been created on the DLI management console.Development descriptionCode implementationConstructing dependency information and creating a Spark ", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0190.html", + "doc_type":"devg", + "p_code":"29", + "code":"33" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to GaussDB(DWS)", + "uri":"dli_09_0086.html", + "doc_type":"devg", + "p_code":"27", + "code":"34" + }, + { + "desc":"This section provides Scala example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.A datasource connection has been create", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0069.html", + "doc_type":"devg", + "p_code":"34", + "code":"35" + }, + { + "desc":"This section provides PySpark example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.A datasource connection has been crea", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0087.html", + "doc_type":"devg", + "p_code":"34", + "code":"36" + }, + { + "desc":"This section provides Java example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.A datasource connection has been created", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0199.html", + "doc_type":"devg", + "p_code":"34", + "code":"37" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to HBase", + "uri":"dli_09_0077.html", + "doc_type":"devg", + "p_code":"27", + "code":"38" + }, + { + "desc":"Create a datasource connection on the DLI management console.Add the /etc/hosts information of MRS cluster nodes to the host file of the DLI queue.For details, see sectio", + "product_code":"dli", + "title":"MRS Configuration", + "uri":"dli_09_0196.html", + "doc_type":"devg", + "p_code":"38", + "code":"39" + }, + { + "desc":"The CloudTable HBase and MRS HBase can be connected to DLI as data sources.PrerequisitesA datasource connection has been created on the DLI management console.Hard-coded ", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0063.html", + "doc_type":"devg", + "p_code":"38", + "code":"40" + }, + { + "desc":"The CloudTable HBase and MRS HBase can be connected to DLI as data sources.PrerequisitesA datasource connection has been created on the DLI management console.Hard-coded ", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0078.html", + "doc_type":"devg", + "p_code":"38", + "code":"41" + }, + { + "desc":"This example applies only to MRS HBase.PrerequisitesA datasource connection has been created and bound to a queue on the DLI management console.Hard-coded or plaintext pa", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0197.html", + "doc_type":"devg", + "p_code":"38", + "code":"42" + }, + { + "desc":"SymptomThe Spark job fails to be executed, and the job log indicates that the Java server connection or container fails to be started.The Spark job fails to be executed, ", + "product_code":"dli", + "title":"Troubleshooting", + "uri":"dli_09_0198.html", + "doc_type":"devg", + "p_code":"38", + "code":"43" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to OpenTSDB", + "uri":"dli_09_0080.html", + "doc_type":"devg", + "p_code":"27", + "code":"44" + }, + { + "desc":"The CloudTable OpenTSDB and MRS OpenTSDB can be connected to DLI as data sources.PrerequisitesA datasource connection has been created on the DLI management console.Hard-", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0065.html", + "doc_type":"devg", + "p_code":"44", + "code":"45" + }, + { + "desc":"The CloudTable OpenTSDB and MRS OpenTSDB can be connected to DLI as data sources.PrerequisitesA datasource connection has been created on the DLI management console.Hard-", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0081.html", + "doc_type":"devg", + "p_code":"44", + "code":"46" + }, + { + "desc":"This example applies only to MRS OpenTSDB.PrerequisitesA datasource connection has been created and bound to a queue on the DLI management console.Hard-coded or plaintext", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0193.html", + "doc_type":"devg", + "p_code":"44", + "code":"47" + }, + { + "desc":"SymptomA Spark job fails to be executed and \"No respond\" is displayed in the job log.A Spark job fails to be executed and \"No respond\" is displayed in the job log.Solutio", + "product_code":"dli", + "title":"Troubleshooting", + "uri":"dli_09_0195.html", + "doc_type":"devg", + "p_code":"44", + "code":"48" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to RDS", + "uri":"dli_09_0083.html", + "doc_type":"devg", + "p_code":"27", + "code":"49" + }, + { + "desc":"PrerequisitesA datasource connection has been created and bound to a queue on the DLI management console.Hard-coded or plaintext passwords pose significant security risks", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0067.html", + "doc_type":"devg", + "p_code":"49", + "code":"50" + }, + { + "desc":"PrerequisitesA datasource connection has been created and bound to a queue on the DLI management console.Hard-coded or plaintext passwords pose significant security risks", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0084.html", + "doc_type":"devg", + "p_code":"49", + "code":"51" + }, + { + "desc":"PrerequisitesA datasource connection has been created and bound to a queue on the DLI management console.Hard-coded or plaintext passwords pose significant security risks", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0187.html", + "doc_type":"devg", + "p_code":"49", + "code":"52" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to Redis", + "uri":"dli_09_0093.html", + "doc_type":"devg", + "p_code":"27", + "code":"53" + }, + { + "desc":"Redis supports only enhanced datasource connections.PrerequisitesAn enhanced datasource connection has been created on the DLI management console and bound to a queue in ", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0094.html", + "doc_type":"devg", + "p_code":"53", + "code":"54" + }, + { + "desc":"Redis supports only enhanced datasource connections.PrerequisitesAn enhanced datasource connection has been created on the DLI management console and bound to a queue in ", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0097.html", + "doc_type":"devg", + "p_code":"53", + "code":"55" + }, + { + "desc":"Redis supports only enhanced datasource connections.PrerequisitesAn enhanced datasource connection has been created on the DLI management console and bound to a queue in ", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0100.html", + "doc_type":"devg", + "p_code":"53", + "code":"56" + }, + { + "desc":"SymptomAfter the code is directly copied to the .py file, unexpected characters may exist after the backslashes (\\).After the code is directly copied to the .py file, une", + "product_code":"dli", + "title":"Troubleshooting", + "uri":"dli_09_0188.html", + "doc_type":"devg", + "p_code":"53", + "code":"57" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Connecting to Mongo", + "uri":"dli_09_0113.html", + "doc_type":"devg", + "p_code":"27", + "code":"58" + }, + { + "desc":"Mongo can be connected only through enhanced datasource connections.DDS is compatible with the MongoDB protocol.An enhanced datasource connection has been created on the ", + "product_code":"dli", + "title":"Scala Example Code", + "uri":"dli_09_0114.html", + "doc_type":"devg", + "p_code":"58", + "code":"59" + }, + { + "desc":"Mongo can be connected only through enhanced datasource connections.DDS is compatible with the MongoDB protocol.PrerequisitesAn enhanced datasource connection has been cr", + "product_code":"dli", + "title":"PySpark Example Code", + "uri":"dli_09_0117.html", + "doc_type":"devg", + "p_code":"58", + "code":"60" + }, + { + "desc":"Mongo can be connected only through enhanced datasource connections.DDS is compatible with the MongoDB protocol.PrerequisitesAn enhanced datasource connection has been cr", + "product_code":"dli", + "title":"Java Example Code", + "uri":"dli_09_0110.html", + "doc_type":"devg", + "p_code":"58", + "code":"61" + }, + { + "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.", + "product_code":"dli", + "title":"Change History", + "uri":"dli_09_00001.html", + "doc_type":"devg", + "p_code":"", + "code":"62" + } +] \ No newline at end of file diff --git a/docs/dli/dev/PARAMETERS.txt b/docs/dli/dev/PARAMETERS.txt new file mode 100644 index 00000000..6da8d5f0 --- /dev/null +++ b/docs/dli/dev/PARAMETERS.txt @@ -0,0 +1,3 @@ +version="" +language="en-us" +type="" \ No newline at end of file diff --git a/docs/dli/dev/dli_05_0044.html b/docs/dli/dev/dli_05_0044.html new file mode 100644 index 00000000..0fc4b9d7 --- /dev/null +++ b/docs/dli/dev/dli_05_0044.html @@ -0,0 +1,175 @@ + + +

Using Spark SQL Jobs to Analyze OBS Data

+

DLI allows you to use data stored on OBS. You can create OBS tables on DLI to access and process data in your OBS bucket.

+

This section describes how to create an OBS table on DLI, import data to the table, and insert and query table data.

+

Prerequisites

  • You have created an OBS bucket. For more information about OBS, see the Object Storage Service Console Operation Guide. In this example, the OBS bucket name is dli-test-021.
  • You have created a DLI SQL queue. For details, see Creating a Queue.

    Note: When you create the DLI queue, set Type to For SQL.

    +
+
+

Preparations

Creating a Database on DLI

+
  1. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark and Queue to the created SQL queue.
  2. Enter the following statement in the SQL editing window to create the testdb database. 
    create database testdb;
    +
+

The following operations in this section must be performed for the testdb database.

+
+

DataSource and Hive Syntax for Creating an OBS Table on DLI

The main difference between DataSource syntax and Hive syntax lies in the range of table data storage formats supported and the number of partitions supported. For the key differences in creating OBS tables using these two syntax, refer to Table 1.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Syntax differences

Syntax

+

Data Types

+

Partitioning

+

Number of Partitions

+

DataSource

+

ORC, PARQUET, JSON, CSV, and AVRO

+

You need to specify the partitioning column in both CREATE TABLE and PARTITIONED BY statements. For details, see Creating a Single-Partition OBS Table Using DataSource Syntax.

+

A maximum of 7,000 partitions can be created in a single table.

+

Hive

+

TEXTFILE, AVRO, ORC, SEQUENCEFILE, RCFILE, and PARQUET

+

Do not specify the partitioning column in the CREATE TABLE statement. Specify the column name and data type in the PARTITIONED BY statement. For details, see Creating an OBS Table Using Hive Syntax.

+

A maximum of 100,000 partitions can be created in a single table.

+
+
+
+

Creating an OBS Table Using the DataSource Syntax

The following describes how to create an OBS table for CSV files. The methods of creating OBS tables for other file formats are similar.

+
  • Create a non-partitioned OBS table.
    • Specify an OBS file and create an OBS table for the CSV data.
      1. Create the test.csv file containing the following content and upload the test.csv file to the root directory of OBS bucket dli-test-021:
        Jordon,88,23
+Kim,87,25
+Henry,76,26
        +
      2. Log in to the DLI management console and choose SQL Editor from the navigation pane on the left. In the SQL editing window, set Engine to spark, Queue to the SQL queue you have created, and Database to testdb. Run the following statement to create an OBS table:
        CREATE TABLE testcsvdatasource (name STRING, score DOUBLE, classNo INT
+) USING csv OPTIONS (path "obs://dli-test-021/test.csv");
        +

        If you create an OBS table using a specified file, you cannot insert data to the table with DLI. The OBS file content is synchronized with the table data.

        +
        +
      3. Run the following statement to query data in the testcsvdatasource table.
        select * from testcsvdatasource;
        +
      4. Open the test.csv file on the local PC, add Aarn,98,20 to the file, and replace the original test.csv file in the OBS bucket.
        Jordon,88,23
+Kim,87,25
+Henry,76,26
+Aarn,98,20
        +
      5. In the DLI SQL Editor, query the testcsvdatasource table for Aarn,98,20. The result is displayed.
        select * from testcsvdatasource;
        +
      +
    • Specify an OBS directory and create an OBS table for CSV data.
      • The specified OBS data directory does not contain files you want to import to the table.
        1. Create the file directory data in the root directory of the OBS bucket dli-test-021.
        2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following statement to create OBS table testcsvdata2source in the testdb database on DLI:
          CREATE TABLE testcsvdata2source (name STRING, score DOUBLE, classNo INT) USING csv OPTIONS (path "obs://dli-test-021/data");
          +
        3. Run the following statement to insert table data:
          insert into testcsvdata2source VALUES('Aarn','98','20');
          +
        4. Run the following statement to query data in the testcsvdata2source table:
          select * from testcsvdata2source;
          +
        5. Refresh the obs://dli-test-021/data directory of the OBS bucket and query the data. A CSV data file is generated, and the data is added to the file.
        +
      • The specified OBS data directory contains files you want to import to the table.
        1. Create file directory data2 in the root directory of the OBS bucket dli-test-021. Create the test.csv file with the following content and upload the file to the obs://dli-test-021/data2 directory:
          Jordon,88,23
+Kim,87,25
+Henry,76,26
          +
        2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following statement to create OBS table testcsvdata3source in the testdb database on DLI:
          CREATE TABLE testcsvdata3source (name STRING, score DOUBLE, classNo INT) USING csv OPTIONS (path "obs://dli-test-021/data2");
          +
        3. Run the following statement to insert table data:
          insert into testcsvdata3source VALUES('Aarn','98','20');
          +
        4. Run the following statement to query data in the testcsvdata3source table:
          select * from testcsvdata3source;
          +
        5. Refresh the obs://dli-test-021/data2 directory of the OBS bucket and query the data. A CSV data file is generated, and the data is added to the file.
        +
      +
    +
  • Create an OBS partitioned table
    • Create a single-partition OBS table
      1. Create file directory data3 in the root directory of the OBS bucket dli-test-021.
      2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following statement to create OBS table testcsvdata4source using data in the specified OBS directory obs://dli-test-021/data3 and partition the table on the classNo column.
        CREATE TABLE testcsvdata4source (name STRING, score DOUBLE, classNo INT) USING csv OPTIONS (path "obs://dli-test-021/data3") PARTITIONED BY (classNo);
        +
      1. Create the classNo=25 directory in the obs://dli-test-021/data3 directory of the OBS bucket. Create the test.csv file based on the following file content and upload the file to the obs://dli-test-021/data3/classNo=25 directory of the OBS bucket.
        Jordon,88,25
+Kim,87,25
+Henry,76,25
        +
      2. Run the following statement in the SQL editor to add the partition data to OBS table testcsvdata4source:
        ALTER TABLE
+  testcsvdata4source
+ADD
+  PARTITION (classNo = 25) LOCATION 'obs://dli-test-021/data3/classNo=25';
        +
      3. Run the following statement to query data in the classNo=25 partition of the testcsvdata4source table:
        select * from testcsvdata4source where classNo = 25;
        +
      4. Run the following statement to insert the following data to the testcsvdata4source table:
        insert into testcsvdata4source VALUES('Aarn','98','25');
+insert into testcsvdata4source VALUES('Adam','68','24');
        +
      5. Run the following statement to query data in the classNo=25 and classNo=24 partitions of the testcsvdata4source table:

        When a partitioned table is queried using the where condition, the partition must be specified. Otherwise, the query fails and "DLI.0005: There should be at least one partition pruning predicate on partitioned table" is reported.

        +
        +
        select * from testcsvdata4source where classNo = 25;
        +
        select * from testcsvdata4source where classNo = 24;
        +
        +
      6. In the obs://dli-test-021/data3 directory of the OBS bucket, click the refresh button. Partition files are generated in the directory for storing the newly inserted table data.

        +
      +
    • Create an OBS table partitioned on multiple columns.
      1. Create file directory data4 in the root directory of the OBS bucket dli-test-021.
      2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following statement to create OBS table testcsvdata5source using data in the specified OBS directory obs://dli-test-021/data4 and partition the table on classNo and dt columns.
        CREATE TABLE testcsvdata5source (name STRING, score DOUBLE, classNo INT, dt varchar(16)) USING csv OPTIONS (path "obs://dli-test-021/data4") PARTITIONED BY (classNo,dt);
        +
      3. Run the following statements to insert the following data into the testcsvdata5source table:
        insert into testcsvdata5source VALUES('Aarn','98','25','2021-07-27');
+insert into testcsvdata5source VALUES('Adam','68','25','2021-07-28');
        +
      4. Run the following statement to query data in the classNo partition of the testcsvdata5source table:
        select * from testcsvdata5source where classNo = 25;
        +
      5. Run the following statement to query data in the dt partition of the testcsvdata5source table:
        select * from testcsvdata5source where dt like '2021-07%';
        +
      6. Refresh the obs://dli-test-021/data4 directory of the OBS bucket. The following data files are generated:
        • File directory 1: obs://dli-test-021/data4/xxxxxx/classNo=25/dt=2021-07-27
        • File directory 2: obs://dli-test-021/data4/xxxxxx/classNo=25/dt=2021-07-28
        +
      7. Create the partition directory classNo=24 in obs://dli-test-021/data4, and then create the subdirectory dt=2021-07-29 in classNo=24. Create the test.csv file using the following file content and upload the file to the obs://dli-test-021/data4/classNo=24/dt=2021-07-29 directory.
        Jordon,88,24,2021-07-29
+Kim,87,24,2021-07-29
+Henry,76,24,2021-07-29
        +
      8. Run the following statement in the SQL editor to add the partition data to OBS table testcsvdata5source:
        ALTER TABLE
+  testcsvdata5source
+ADD
+  PARTITION (classNo = 24,dt='2021-07-29') LOCATION 'obs://dli-test-021/data4/classNo=24/dt=2021-07-29';
        +
      9. Run the following statement to query data in the classNo partition of the testcsvdata5source table:
        select * from testcsvdata5source where classNo = 24;
        +
      10. Run the following statement to query all data in July 2021 in the dt partition:
        select * from testcsvdata5source where dt like '2021-07%';
        +
      +
    +
+
+

Creating an OBS Table Using Hive Syntax

The following describes how to create an OBS table for TEXTFILE files. The methods of creating OBS tables for other file formats are similar.

+
  • Create a non-partitioned OBS table.
    1. Create file directory data5 in the root directory of the OBS bucket dli-test-021. Create the test.txt file based on the following file content and upload the file to the obs://dli-test-021/data5 directory:
      Jordon,88,23
+Kim,87,25
+Henry,76,26
      +
    2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following Hive statement to create an OBS table using data in obs://dli-test-021/data5/test.txt and set the row data delimiter to commas (,):
      CREATE TABLE hiveobstable (name STRING, score DOUBLE, classNo INT) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data5' ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
      +

      ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' indicates that records are separated by commas (,).

      +
      +
    3. Run the following statement to query data in the hiveobstable table:
      select * from hiveobstable;
      +
    4. Run the following statements to insert data into the table:
      insert into hiveobstable VALUES('Aarn','98','25');
+insert into hiveobstable VALUES('Adam','68','25');
      +
    5. Run the following statement to query data in the table to verify that the data has been inserted:
      select * from hiveobstable;
      +
    6. In the obs://dli-test-021/data5 directory, refresh the page and query the data. Two files are generated containing the newly inserted data.
    +

    Create an OBS Table Containing Data of Multiple Formats

    +
    1. Create file directory data6 in the root directory of the OBS bucket dli-test-021. Create the test.txt file based on the following file content and upload the file to the obs://dli-test-021/data6 directory:
      Jordon,88-22,23:21
+Kim,87-22,25:22
+Henry,76-22,26:23
      +
    2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following Hive statement to create an OBS table using data stored in obs://dli-test-021/data6.
      CREATE TABLE hiveobstable2 (name STRING, hobbies ARRAY<string>, address map<string,string>) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data6' 
+ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
+COLLECTION ITEMS TERMINATED BY '-'
+MAP KEYS TERMINATED BY ':';
      +
      • ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' indicates that records are separated by commas (,).
      • COLLECTION ITEMS TERMINATED BY '-' indicates that the second column hobbies is in array format. Elements are separated by hyphens (-).
      • MAP KEYS TERMINATED BY ':' indicates that the address column is in the key-value format. Key-value pairs are separated by colons (:).
      +
      +
    3. Run the following statement to query data in the hiveobstable2 table:
      select * from hiveobstable2;
      +
    +
  • Create a partitioned OBS table.
    1. Create file directory data7 in the root directory of the OBS bucket dli-test-021.
    2. Log in to the DLI management console and click SQL Editor. On the displayed page, set Engine to spark, Queue to the created SQL queue, and Database to testdb. Run the following statement to create an OBS table using data stored in obs://dli-test-021/data7 and partition the table on the classNo column:
      CREATE TABLE IF NOT EXISTS hiveobstable3(name STRING, score DOUBLE) PARTITIONED BY (classNo INT) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data7' ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
      +

      You can specify the partition key in the PARTITIONED BY statement. Do not specify the partition key in the CREATE TABLE IF NOT EXISTS statement. The following is an incorrect example:

      +

      CREATE TABLE IF NOT EXISTS hiveobstable3(name STRING, score DOUBLE, classNo INT) PARTITIONED BY (classNo) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data7';

      +
      +
    3. Run the following statements to insert data into the table:
      insert into hiveobstable3 VALUES('Aarn','98','25');
+insert into hiveobstable3 VALUES('Adam','68','25');
      +
    4. Run the following statement to query data in the table:
      select * from hiveobstable3 where classNo = 25;
      +
    5. Refresh the obs://dli-test-021/data7 directory. A new partition directory classno=25 is generated containing the newly inserted table data.
    6. Create partition directory classno=24 in the obs://dli-test-021/data7 directory. Create the test.txt file using the following file content and upload the file to the obs://dli-test-021/data7/classno=24 directory:
      Jordon,88,24
+Kim,87,24
+Henry,76,24
      +
    7. Run the following statement in the SQL editor to add the partition data to OBS table hiveobstable3:
      ALTER TABLE
+  hiveobstable3
+ADD
+  PARTITION (classNo = 24) LOCATION 'obs://dli-test-021/data7/classNo=24';
      +
    8. Run the following statement to query data in the hiveobstable3 table:
      select * from hiveobstable3 where classNo = 24;
      +
    +
+
+

FAQs

  • Q1: What should I do if the following error is reported when the OBS partition table is queried?
    DLI.0005: There should be at least one partition pruning predicate on partitioned table `xxxx`.`xxxx`.;
    +

    Cause: The partition key is not specified in the query statement of a partitioned table.

    +

    Solution: Ensure that the where condition contains at least one partition key.

    +
  • Q2: What should I do if "DLI.0007: The output path is a file, don't support INSERT...SELECT error" is reported when I use a DataSource statement to insert data in a specified OBS directory into an OBS table and the execution fails?
    The statement is similar to the following:
    CREATE TABLE testcsvdatasource (name string, id int) USING csv OPTIONS (path "obs://dli-test-021/data/test.csv");
    +
    +

    Cause: Data cannot be inserted if a specific file is used in the table creation statement. For example, the OBS file obs://dli-test-021/data/test.csv is used in the preceding example.

    +
    Solution: Replace the OBS file to the file directory. You can insert data using the INSERT statement. The preceding example statement can be modified as follows:
    CREATE TABLE testcsvdatasource (name string, id int) USING csv OPTIONS (path "obs://dli-test-021/data");
    +
    +
  • Q3: What should I do if the syntax of a Hive statement used to create a partitioned OBS table is incorrect? For example, the following statement creates an OBS table partitioned on classNo:
    CREATE TABLE IF NOT EXISTS testtable(name STRING, score DOUBLE, classNo INT) PARTITIONED BY (classNo) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data7';
    +

    Cause: Do not specify the partition key in the list following the table name. Specify the partition key in the PARTITIONED BY statement.

    +
    Solution: Specify the partition key in PARTITIONED BY. For example:
    CREATE TABLE IF NOT EXISTS testtable(name STRING, score DOUBLE) PARTITIONED BY (classNo INT) STORED AS TEXTFILE LOCATION 'obs://dli-test-021/data7';
    +
    +
+
+
+
+
+
Parent topic: SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_05_0062.html b/docs/dli/dev/dli_05_0062.html new file mode 100644 index 00000000..3c820318 --- /dev/null +++ b/docs/dli/dev/dli_05_0062.html @@ -0,0 +1,250 @@ + + +

Calling UDAFs in Spark SQL Jobs

+

Scenario

DLI allows you to use a Hive User Defined Aggregation Function (UDAF) to process multiple rows of data. Hive UDAF is usually used together with groupBy. It is equivalent to SUM() and AVG() commonly used in SQL and is also an aggregation function.

+
+

Constraints

  • To perform UDAF-related operations on DLI, you need to create a SQL queue instead of using the default queue.
  • When UDAFs are used across accounts, other users, except the user who creates them, need to be authorized before using the UDAF.

    To grant required permissions, log in to the DLI console and choose Data Management > Package Management. On the displayed page, select your UDAF Jar package and click Manage Permissions in the Operation column. On the permission management page, click Grant Permission in the upper right corner and select the required permissions.

    +
  • If you use a static class or interface in a UDF, add try catch to capture exceptions. Otherwise, package conflicts may occur.
+
+

Environment Preparations

Before you start, set up the development environment.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Development environment

Item

+

Description

+

OS

+

Windows 7 or later

+

JDK

+

JDK 1.8 (Java downloads).

+

IntelliJ IDEA

+

IntelliJ IDEA is used for application development. The version of the tool must be 2019.1 or later.

+

Maven

+

Basic configuration of the development environment. For details about how to get started, see Downloading Apache Maven and Installing Apache Maven. Maven is used for project management throughout the lifecycle of software development.

+
+
+
+

Development Process

The following figure shows the process of developing a UDAF.

+
Figure 1 Development process
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2 Process description

No.

+

Phase

+

Software Portal

+

Description

+

1

+

Create a Maven project and configure the POM file.

+

IntelliJ IDEA

+

Compile the UDAF function code by referring to the Procedure description.

+

2

+

Editing UDAF code

+

3

+

Debug, compile, and pack the code into a Jar package.

+

4

+

Upload the Jar package to OBS.

+

OBS console

+

Upload the UDAF Jar file to an OBS path.

+

5

+

Create a DLI package.

+

DLI console

+

Select the UDAF Jar file that has been uploaded to OBS for management.

+

6

+

Create a UDAF on DLI.

+

DLI console

+

Create a UDAF on the SQL job management page of the DLI console.

+

7

+

Verify and use the UDAF.

+

DLI console

+

Use the UDAF in your DLI job.

+
+
+
+

Procedure

  1. Create a Maven project and configure the POM file. This step uses IntelliJ IDEA 2020.2 as an example.
    1. Start IntelliJ IDEA and choose File > New > Project.
      Figure 2 Creating a project
      +
    2. Choose Maven, set Project SDK to 1.8, and click Next.
      Figure 3 Configuring the project SDK
      +
    3. Specify the project name and the project path, and click Create. In the displayed page, click Finish.
      Figure 4 Setting project information
      +
    4. Add the following content to the pom.xml file.
      <dependencies> 
+         <dependency> 
+             <groupId>org.apache.hive</groupId> 
+             <artifactId>hive-exec</artifactId> 
+             <version>1.2.1</version> 
+         </dependency> 
+ </dependencies>
      +
      Figure 5 Adding configurations to the POM file
      +
    5. Choose src > main and right-click the java folder. Choose New > Package to create a package and a class file.
      Set Package as required. In this example, set Package to com.dli.demo.
      Figure 6 Creating a package
      +
      +

      Create a Java Class file in the package path. In this example, the Java Class file is AvgFilterUDAFDemo.

      +
      Figure 7 Creating a class
      +
    +
  2. Write UDAF code. Pay attention to the following requirements when you implement the UDAF:
    • The UDAF class must inherit from org.apache.hadoop.hive.ql.exec.UDAF and org.apache.hadoop.hive.ql.exec.UDAFEvaluator classes. The function class must inherit from the UDAF class, and the Evaluator class must implement the UDAFEvaluator interface.
    • The Evaluator class must implement the init, iterate, terminatePartial, merge, and terminate functions of UDAFEvaluator.
      • The init function overrides the init function of the UDAFEvaluator interface.
      • The iterate function receives input parameters for internal iteration.
      • The terminatePartial function has no parameter. It returns the data obtained after the iterate traversal is complete. terminatePartial is similar to Hadoop Combiner.
      • The merge function receives the return values of terminatePartial.
      • The terminate function returns the aggregated result.
      +

      For details about how to implement the UDAF, see the following sample code:

      +
      package com.dli.demo;
+ 
+import org.apache.hadoop.hive.ql.exec.UDAF;
+import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
+ 
+/***
+ * @jdk jdk1.8.0
+ * @version 1.0
+ ***/
+public class AvgFilterUDAFDemo extends UDAF {
+ 
+    /**
+     * Defines the static inner class AvgFilter.
+     */
+    public static class PartialResult
+    {
+        public Long sum;
+    }
+ 
+    public static class VarianceEvaluator implements UDAFEvaluator {
+ 
+        // Initializes the PartialResult object.
+        private AvgFilterUDAFDemo.PartialResult partial;
+ 
+        // Declares a VarianceEvaluator constructor that has no parameters.
+        public VarianceEvaluator(){
+ 
+            this.partial = new AvgFilterUDAFDemo.PartialResult();
+ 
+            init();
+        }
+ 
+        /**
+         * Initializes the UDAF, which is similar to a constructor.
+         */
+        @Override
+        public void init() {
+ 
+            // Sets the initial value of sum.
+            this.partial.sum = 0L;
+        }
+ 
+        /**
+         * Receives input parameters for internal iteration.
+         * @param x
+         * @return
+         */
+        public void iterate(Long x) {
+            if (x == null) {
+                return;
+            }
+            AvgFilterUDAFDemo.PartialResult tmp9_6 = this.partial;
+            tmp9_6.sum = tmp9_6.sum | x;
+        }
+ 
+        /**
+         * Returns the data obtained after the iterate traversal is complete.
+         * terminatePartial is similar to Hadoop Combiner.
+         * @return
+         */
+        public AvgFilterUDAFDemo.PartialResult terminatePartial()
+        {
+            return this.partial;
+        }
+ 
+        /**
+         * Receives the return values of terminatePartial and merges the data.
+         * @param
+         * @return
+         */
+        public void merge(AvgFilterUDAFDemo.PartialResult pr)
+        {
+            if (pr == null) {
+                return;
+            }
+            AvgFilterUDAFDemo.PartialResult tmp9_6 = this.partial;
+            tmp9_6.sum = tmp9_6.sum | pr.sum;
+        }
+ 
+        /**
+         * Returns the aggregated result.
+         * @return
+         */
+        public Long terminate()
+        {
+            if (this.partial.sum == null) {
+                return 0L;
+            }
+            return this.partial.sum;
+        }
+    }
+}
      +
    +
  3. Use IntelliJ IDEA to compile the code and pack it into the JAR package.
    1. Click Maven in the tool bar on the right, and click clean and compile to compile the code.

      After the compilation is successful, click package.

      +
      Figure 8 Exporting the Jar file
      +
    2. The generated JAR package is stored in the target directory. In this example, MyUDAF-1.0-SNAPSHOT.jar is stored in D:\DLITest\MyUDAF\target.
    +
  4. Log in to the OBS console and upload the file to the OBS path.

    The region of the OBS bucket to which the Jar package is uploaded must be the same as the region of the DLI queue. Cross-region operations are not allowed.

    +
    +
  5. (Optional) Upload the file to DLI for package management.
    1. Log in to the DLI management console and choose Data Management > Package Management.
    2. On the Package Management page, click Create in the upper right corner.
    3. In the Create Package dialog, set the following parameters:
      • Type: Select JAR.
      • OBS Path: Specify the OBS path for storing the package.
      • Set Group and Group Name as required for package identification and management.
      +
    4. Click OK.
    +
  6. Create the UDAF on DLI.
    1. Log in to the DLI management console and create a SQL queue and a database.
    2. Log in to the DLI console, choose SQL Editor. Set Engine to spark, and select the created SQL queue and database.
    3. In the SQL editing area, run the following statement to create a UDAF and click Execute.

      If the reloading function of the UDAF is enabled, the create statement changes.

      +
      +
      CREATE FUNCTION AvgFilterUDAFDemo AS 'com.dli.demo.AvgFilterUDAFDemo' using jar 'obs://dli-test-obs01/MyUDAF-1.0-SNAPSHOT.jar';
      +

      Or

      +
      CREATE OR REPLACE FUNCTION AvgFilterUDAFDemo AS 'com.dli.demo.AvgFilterUDAFDemo' using jar 'obs://dli-test-obs01/MyUDAF-1.0-SNAPSHOT.jar';
      +
    +
  7. Restart the original SQL queue for the added function to take effect.
    1. Log in to the DLI management console and choose Resources > Queue Management from the navigation pane. In the Operation column of the SQL queue, click Restart.
    2. In the Restart dialog box, click OK.
    +
  8. Use the UDAF.

    Use the UDAF function created in 6 in the query statement:

    +
    select AvgFilterUDAFDemo(real_stock_rate) AS show_rate FROM dw_ad_estimate_real_stock_rate limit 1000;
    +
  9. (Optional) Delete the UDAF.

    If the UDAF is no longer used, run the following statement to delete it:

    +
    Drop FUNCTION AvgFilterUDAFDemo;
    +
+
+
+
+
+
Parent topic: SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_00001.html b/docs/dli/dev/dli_09_00001.html new file mode 100644 index 00000000..0acb82e0 --- /dev/null +++ b/docs/dli/dev/dli_09_00001.html @@ -0,0 +1,32 @@ + + +

Change History

+
+
+ + + + + + + + + + + + + +

Release Date

+

What's New

+

2024-04-30

+

Modified the following section:

+

In Connecting to Mongo, modified "mongo" in the sample table name and added additional clarification that DDS is compatible with the MongoDB protocol.

+

2024-02-27

+

Modified the following section:

+

Modified the description of the url parameter in the sample code in Scala Example Code.

+

2024-01-05

+

This issue is the first official release.

+
+
+
+ diff --git a/docs/dli/dev/dli_09_0006.html b/docs/dli/dev/dli_09_0006.html new file mode 100644 index 00000000..bbb94176 --- /dev/null +++ b/docs/dli/dev/dli_09_0006.html @@ -0,0 +1,22 @@ + + +

Flink OpenSource SQL Jobs

+

+
+
+ +
+ diff --git a/docs/dli/dev/dli_09_0009.html b/docs/dli/dev/dli_09_0009.html new file mode 100644 index 00000000..2aec6f25 --- /dev/null +++ b/docs/dli/dev/dli_09_0009.html @@ -0,0 +1,143 @@ + + +

Reading Data from Kafka and Writing Data to RDS

+

This guide provides reference for Flink 1.12 only.

+
+

Description

In this example, we aim to query information about top three most-clicked offerings in each hour from a set of real-time click data. Offerings' real-time click data will be sent to Kafka as the input source, and then the analysis result of Kafka data is to be output to RDS.

+

For example, enter the following sample data:

+
{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:01:00", "product_id":"0002", "product_name":"name1"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 08:02:00", "product_id":"0002", "product_name":"name1"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 08:06:00", "product_id":"0004", "product_name":"name2"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:10:00", "product_id":"0003", "product_name":"name3"}
+{"user_id":"0003", "user_name":"Cindy", "event_time":"2021-03-24 08:15:00", "product_id":"0005", "product_name":"name4"}
+{"user_id":"0003", "user_name":"Cindy", "event_time":"2021-03-24 08:16:00", "product_id":"0005", "product_name":"name4"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:56:00", "product_id":"0004", "product_name":"name2"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 09:05:00", "product_id":"0005", "product_name":"name4"} 
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 09:10:00", "product_id":"0006", "product_name":"name5"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 09:13:00", "product_id":"0006", "product_name":"name5"}
+
Expected output:
2021-03-24 08:00:00 - 2021-03-24 08:59:59,0002,name1,2
+2021-03-24 08:00:00 - 2021-03-24 08:59:59,0004,name2,2
+2021-03-24 08:00:00 - 2021-03-24 08:59:59,0005,name4,2
+2021-03-24 09:00:00 - 2021-03-24 09:59:59,0006,name5,2
+2021-03-24 09:00:00 - 2021-03-24 09:59:59,0005,name4,1
+
+
+

Prerequisites

  1. You have created a DMS for Kafka instance.

    When you create the instance, do not enable Kafka SASL_SSL.

    +
    +
  2. You have created an RDS for MySQL DB instance.

    In this example, the RDS for MySQL database version is 8.0.

    +
+
+

Overall Development Process

Overall Process
Figure 1 Job development process
+
+

Step 1: Create a Queue

+

Step 2: Create a Kafka Topic

+

Step 3: Create an RDS Database and Table

+

Step 4: Create an Enhanced Datasource Connection

+

Step 5: Run a Job

+

Step 6: Send Data and Query Results

+
+

Step 1: Create a Queue

  1. Log in to the DLI console. In the navigation pane on the left, choose Resources > Queue Management.
  2. On the displayed page, click Buy Queue in the upper right corner.
  3. On the Buy Queue page, set queue parameters as follows:
    • Billing Mode: .
    • Region and Project: Retain the default values.
    • Name: Enter a queue name.

      The queue name can contain only digits, letters, and underscores (_), but cannot contain only digits or start with an underscore (_). The name must contain 1 to 128 characters.

      +

      The queue name is case-insensitive. Uppercase letters will be automatically converted to lowercase letters.

      +
      +
    • Type: Select For general purpose. Select the Dedicated Resource Mode.
    • AZ Mode and Specifications: Retain the default values.
    • Enterprise Project: Select default.
    • Advanced Settings: Select Custom.
    • CIDR Block: Specify the queue network segment. For example, 10.0.0.0/16.

      The CIDR block of a queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL DB instances. Otherwise, datasource connections will fail to be created.

      +
      +
    • Set other parameters as required.
    +
  4. Click Buy. Confirm the configuration and click Submit.
+
+

Step 2: Create a Kafka Topic

  1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
  2. Choose Topics. On the displayed page, click Create Topic. Configure the following parameters:
    • Topic Name For this example, enter testkafkatopic.
    • Partitions: Set the value to 1.
    • Replicas: Set the value to 1.
    +

    Retain default values for other parameters.

    +
+
+

Step 3: Create an RDS Database and Table

  1. Log in to the RDS console. On the displayed page, locate the target MySQL DB instance and choose More > Log In in the Operation column.
  2. On the displayed login dialog box, enter the username and password and click Log In.
  3. On the Databases page, click Create Database. In the displayed dialog box, enter testrdsdb as the database name and retain default values of rest parameters. Then, click OK.
  4. In the Operation column of row where the created database locates, click SQL Window and enter the following statement to create a table:
    CREATE TABLE clicktop (
+	`range_time` VARCHAR(64) NOT NULL,
+	`product_id` VARCHAR(32) NOT NULL,
+	`product_name` VARCHAR(32),
+	`event_count` VARCHAR(32),
+	PRIMARY KEY (`range_time`,`product_id`)
+)	ENGINE = InnoDB
+	DEFAULT CHARACTER SET = utf8mb4;
    +
+
+

Step 4: Create an Enhanced Datasource Connection

  • Connecting DLI to Kafka
    1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
    2. In the Connection pane, obtain the Instance Address (Private Network). In the Network pane, obtain the VPC and subnet of the instance.
    3. Click the security group name in the Network pane. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    5. In the displayed dialog box, set the following parameters:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_kafka.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue. (Queues that are not added to a resource pool are displayed in this list.)
      • VPC: Select the VPC of the Kafka instance.
      • Subnet: Select the subnet of Kafka instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    6. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    7. In the displayed dialog box, enter Kafka instance address (private network):port in the Address box and click Test to check whether the instance is reachable.
    +
  • Connecting DLI to RDS
    1. Go to the RDS console, click the name of the target RDS DB instance on the Instances page. Basic information of the instance is displayed.
    2. In the Connection Information pane, obtain the floating IP address, database port, VPC, and subnet.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Check whether the Kafka instance and RDS DB instance are in the same VPC and subnet.
      1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
      2. If they are not, go to 5. Create an enhanced datasource connection to connect DLI to the subnet where the RDS DB instance locates.
      +
    5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    6. In the displayed dialog box, set the following parameters:
      • Connection Name: Enter a name of the enhanced datasource connection For this example, enter dli_rds.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue. (Queues that are not added to a resource pool are displayed in this list.)
      • VPC: Select the VPC of the RDS DB instance.
      • Subnet: Select the subnet of RDS DB instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    7. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    8. In the displayed dialog box, enter floating IP address:database port of the RDS DB instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
+
+

Step 5: Run a Job

  1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
  2. In the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkKafkaRds. Click OK.
  3. On the job editing page, set the following parameters and retain the default values of other parameters.
    • Queue: Select the queue created in Step 1: Create a Queue.
    • Flink Version: Select 1.12.
    • Save Job Log: Enable this function.
    • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
    • Enable Checkpointing: Enable this function.
    • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.

      In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to RDS.

      +
      +
      create table click_product(
+    user_id string, --ID of the user
+    user_name string, --Username
+    event_time string, --Click time
+    product_id string, --Offering ID
+    product_name string --Offering name
+) with (
+    "connector" = "kafka",
+    "properties.bootstrap.servers" = " 10.128.0.120:9092,10.128.0.89:9092,10.128.0.83:9092 ",-- Internal network address and port number of the Kafka instance
+    "properties.group.id" = "click",
+    "topic" = " testkafkatopic ",--Name of the created Kafka topic
+    "format" = "json",
+    "scan.startup.mode" = "latest-offset"
+);
+
+--Result table
+create table top_product (
+    range_time string, --Calculated time range
+    product_id string, --Offering ID
+    product_name string --Offering name
+    event_count bigint, --Number of clicks
+    primary key (range_time, product_id) not enforced
+) with (
+    "connector" = "jdbc",
+    "url" = "jdbc:mysql://192.168.12.148:3306/testrdsdb ",--testrdsdb indicates the name of the created RDS database. Replace the IP address and port number with those of the RDS DB instance.
+    "table-name" = "clicktop",
+    "pwd_auth_name"="xxxxx", -- Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+    "sink.buffer-flush.max-rows" = "1000",
+    "sink.buffer-flush.interval" = "1s"
+);
+
+create view current_event_view
+as
+    select product_id, product_name, count(1) as click_count, concat(substring(event_time, 1, 13), ":00:00") as min_event_time, concat(substring(event_time, 1, 13), ":59:59") as max_event_time
+    from click_product group by substring (event_time, 1, 13), product_id, product_name;
+
+insert into top_product
+    select
+        concat(min_event_time, " - ", max_event_time) as range_time,
+        product_id,
+        product_name,
+        click_count
+    from (
+        select *,
+        row_number() over (partition by min_event_time order by click_count desc) as row_num
+        from current_event_view
+    )
+    where row_num <= 3
      +
    +
  4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.
+
+

Step 6: Send Data and Query Results

  1. Use the Kafka client to send data to topics created in Step 2: Create a Kafka Topic to simulate real-time data streams.

    The sample data is as follows:

    +
    {"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:01:00", "product_id":"0002", "product_name":"name1"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 08:02:00", "product_id":"0002", "product_name":"name1"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 08:06:00", "product_id":"0004", "product_name":"name2"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:10:00", "product_id":"0003", "product_name":"name3"}
+{"user_id":"0003", "user_name":"Cindy", "event_time":"2021-03-24 08:15:00", "product_id":"0005", "product_name":"name4"}
+{"user_id":"0003", "user_name":"Cindy", "event_time":"2021-03-24 08:16:00", "product_id":"0005", "product_name":"name4"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 08:56:00", "product_id":"0004", "product_name":"name2"}
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 09:05:00", "product_id":"0005", "product_name":"name4"} 
+{"user_id":"0001", "user_name":"Alice", "event_time":"2021-03-24 09:10:00", "product_id":"0006", "product_name":"name5"}
+{"user_id":"0002", "user_name":"Bob", "event_time":"2021-03-24 09:13:00", "product_id":"0006", "product_name":"name5"}
    +
  2. Log in to the RDS console, click the name of the RDS DB instance. On the displayed page, click the name of the created database, for example, testrdsdb, and click Query SQL Statements in the Operation column of the row that containing the clicktop table.
    select * from `clicktop`;
    +
  3. On the displayed page, click Execute SQL. Check whether data has been written into the RDS table.
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0010.html b/docs/dli/dev/dli_09_0010.html new file mode 100644 index 00000000..aa89ad70 --- /dev/null +++ b/docs/dli/dev/dli_09_0010.html @@ -0,0 +1,124 @@ + + +

Reading Data from Kafka and Writing Data to GaussDB(DWS)

+

This guide provides reference for Flink 1.12 only.

+
+

Description

This example analyzes real-time vehicle driving data and collects statistics on data results that meet specific conditions. The real-time vehicle driving data is stored in the Kafka source table, and then the analysis result is output to GaussDB(DWS).

+

For example, enter the following sample data:

+
{"car_id":"3027", "car_owner":"lilei", "car_age":"7", "average_speed":"76", "total_miles":"15000"}
+{"car_id":"3028", "car_owner":"hanmeimei", "car_age":"6", "average_speed":"92", "total_miles":"17000"}
+{"car_id":"3029", "car_owner":"Ann", "car_age":"10", "average_speed":"81", "total_miles":"230000"}
+
Expected output is vehicles meeting the average_speed <= 90 and total_miles <= 200,000 condition.
{"car_id":"3027", "car_owner":"lilei", "car_age":"7", "average_speed":"76", "total_miles":"15000"}
+
+
+

Prerequisites

  1. You have created a DMS for Kafka instance.

    When you create the instance, do not enable Kafka SASL_SSL.

    +
    +
  2. You have created a GaussDB(DWS) instance.
+
+

Overall Development Process

Overall Process
Figure 1 Job development process
+
+

Step 1: Create a Queue

+

Step 2: Create a Kafka Topic

+

Step 3: Create a GaussDB(DWS) Database and Table

+

Step 4: Create an Enhanced Datasource Connection

+

Step 5: Run a Job

+

Step 6: Send Data and Query Results

+
+

Step 1: Create a Queue

  1. Log in to the DLI console. In the navigation pane on the left, choose Resources > Queue Management.
  2. On the displayed page, click Buy Queue in the upper right corner.
  3. On the Buy Queue page, set queue parameters as follows:
    • Billing Mode: .
    • Region and Project: Retain the default values.
    • Name: Enter a queue name.

      The queue name can contain only digits, letters, and underscores (_), but cannot contain only digits or start with an underscore (_). The name must contain 1 to 128 characters.

      +

      The queue name is case-insensitive. Uppercase letters will be automatically converted to lowercase letters.

      +
      +
    • Type: Select For general purpose. Select the Dedicated Resource Mode.
    • AZ Mode and Specifications: Retain the default values.
    • Enterprise Project: Select default.
    • Advanced Settings: Select Custom.
    • CIDR Block: Specify the queue network segment. For example, 10.0.0.0/16.

      The CIDR block of a queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL DB instances. Otherwise, datasource connections will fail to be created.

      +
      +
    • Set other parameters as required.
    +
  4. Click Buy. Confirm the configuration and click Submit.
+
+

Step 2: Create a Kafka Topic

  1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
  2. Choose Topics in the navigation pane on the left. On the displayed page, click Create Topic. Configure the following parameters:
    • Topic Name: For this example, enter testkafkatopic.
    • Partitions: Set the value to 1.
    • Replicas: Set the value to 1.
    +

    Retain default values for other parameters.

    +
+
+

Step 3: Create a GaussDB(DWS) Database and Table

  1. .
  2. Connect to the default database gaussdb of a GaussDB(DWS) cluster.
    gsql -d gaussdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
    • gaussdb: Default database of the GaussDB(DWS) cluster
    • Connection address of the DWS cluster: If a public network address is used for connection, set this parameter to the public network IP address or domain name. If a private network address is used for connection, set this parameter to the private network IP address or domain name. If an ELB is used for connection, set this parameter to the ELB address.
    • dbadmin: Default administrator username used during cluster creation
    • password: Default password of the administrator
    +
  3. Run the following command to create the testdwsdb database:
    CREATE DATABASE testdwsdb;
    +
  4. Run the following command to exit the gaussdb database and connect to testdwsdb:
    \q
+gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  5. Run the following commands to create a table:
    create schema test;
+set current_schema= test;
+drop table if exists qualified_cars;
+CREATE TABLE qualified_cars
+(
+    car_id VARCHAR, 
+    car_owner VARCHAR,
+    car_age INTEGER ,
+    average_speed FLOAT8,
+    total_miles FLOAT8
+);
    +
+
+

Step 4: Create an Enhanced Datasource Connection

  • Connecting DLI to Kafka
    1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
    2. In the Connection pane, obtain the Instance Address (Private Network). In the Network pane, obtain the VPC and subnet of the instance.
    3. Click the security group name in the Network pane. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    5. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_kafka.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the Kafka instance.
      • Subnet: Select the subnet of Kafka instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    6. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    7. In the displayed dialog box, enter Kafka instance address (private network):port in the Address box and click Test to check whether the instance is reachable.
    +
  • Connecting DLI to GaussDB(DWS)
    1. On the GaussDB(DWS) management console, choose Clusters. On the displayed page, click the name of the created GaussDB(DWS) cluster to view basic information.
    2. In the Basic Information tab, locate the Database Attributes pane and obtain the private IP address and port number of the DB instance. In the Network pane, obtain VPC, and subnet information.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Check whether the Kafka instance and GaussDB(DWS) instance are in the same VPC and subnet.
      1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
      2. If they are not, go to 5. Create an enhanced datasource connection to connect DLI to the subnet where the GaussDB(DWS) instance locates.
      +
    5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    6. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_dws.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the GaussDB(DWS) instance.
      • Subnet: Select the subnet of GaussDB(DWS) instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    7. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    8. In the displayed dialog box, enter floating IP address:database port of the GaussDB(DWS) instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
+
+

Step 5: Run a Job

  1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
  2. In the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkKafkaDWS. Click OK.
  3. On the job editing page, set the following parameters and retain the default values of other parameters.
    • Queue: Select the queue created in Step 1: Create a Queue.
    • Flink Version: Select 1.12.
    • Save Job Log: Enable this function.
    • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
    • Enable Checkpointing: Enable this function.
    • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.

      In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to GaussDB(DWS).

      +
      +
      create table car_infos(
+  car_id STRING,
+  car_owner STRING,
+  car_age INT,
+  average_speed DOUBLE,
+  total_miles DOUBLE
+) with (
+    "connector" = "kafka",
+    "properties.bootstrap.servers" = " 10.128.0.120:9092,10.128.0.89:9092,10.128.0.83:9092 ",-- Internal network address and port number of the Kafka instance
+    "properties.group.id" = "click",
+    "topic" = " testkafkatopic",--Created Kafka topic
+    "format" = "json",
+    "scan.startup.mode" = "latest-offset"
+);
+
+create table qualified_cars (
+  car_id STRING,
+  car_owner STRING,
+  car_age INT,
+  average_speed DOUBLE,
+  total_miles DOUBLE
+)
+WITH (
+  'connector' = 'gaussdb',
+  'driver' = 'com.gauss200.jdbc.Driver',
+  'url'='jdbc:gaussdb://192.168.168.16:8000/testdwsdb ', ---192.168.168.16:8000 indicates the internal IP address and port of the GaussDB(DWS) instance. testdwsdb indicates the name of the created GaussDB(DWS) database.
+  'table-name' = ' test\".\"qualified_cars', ---test indicates the schema of the created GaussDB(DWS) table, and qualified_cars indicates the GaussDB(DWS) table name.
+  'pwd_auth_name'= 'xxxxx', -- Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+  'write.mode' = 'insert'
+);
+
+/** Output information about qualified vehicles **/
+INSERT INTO qualified_cars
+SELECT *
+FROM car_infos
+where average_speed <= 90 and total_miles <= 200000;
      +
    +
  4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.
+
+

Step 6: Send Data and Query Results

  1. Use the Kafka client to send data to topics created in Step 2: Create a Kafka Topic to simulate real-time data streams.

    The sample data is as follows:

    +
    {"car_id":"3027", "car_owner":"lilei", "car_age":"7", "average_speed":"76", "total_miles":"15000"}
+{"car_id":"3028", "car_owner":"hanmeimei", "car_age":"6", "average_speed":"92", "total_miles":"17000"}
+{"car_id":"3029", "car_owner":"Ann", "car_age":"10", "average_speed":"81", "total_miles":"230000"}
    +
  2. Connect to the created GaussDB(DWS) cluster.
  3. Connect to the default database testdwsdb of a GaussDB(DWS) cluster.
    gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  4. Run the following statement to query GaussDB(DWS) table data:
    select * from test.qualified_cars;
    +
    The query result is as follows:
    car_id  car_owner  car_age  average_speed  total_miles
+3027      lilei     7           76.0       15000.0
    +
    +
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0011.html b/docs/dli/dev/dli_09_0011.html new file mode 100644 index 00000000..13fb2fa5 --- /dev/null +++ b/docs/dli/dev/dli_09_0011.html @@ -0,0 +1,202 @@ + + +

Reading Data from Kafka and Writing Data to Elasticsearch

+

This guide provides reference for Flink 1.12 only.

+
+

Description

This example analyzes offering purchase data and collects statistics on data results that meet specific conditions. The offering purchase data is stored in the Kafka source table, and then the analysis result is output to Elasticsearch .

+

For example, enter the following sample data:

+
{"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "pay_time":"2021-03-24 10:02:03", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}
+
+{"order_id":"202103241606060001", "order_channel":"appShop", "order_time":"2021-03-24 16:06:06", "pay_amount":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0002", "user_name":"Jason", "area_id":"330106"}
+

DLI reads data from Kafka and writes the data to Elasticsearch. You can view the result in Kibana of the Elasticsearch cluster.

+
+

Prerequisites

  1. You have created a DMS for Kafka instance.

    When you create a DMS Kafka instance, do not enable Kafka SASL_SSL.

    +
    +
  2. You have created a CSS Elasticsearch cluster.

    In this example, the version of the created CSS cluster is 7.6.2, and security mode is disabled for the cluster.

    +
+
+

Overall Process

Figure 1 shows the overall development process.
Figure 1 Job development process
+
+

Step 1: Create a Queue

+

Step 2: Create a Kafka Topic

+

Step 3: Create an Elasticsearch Index

+

Step 4: Create an Enhanced Datasource Connection

+

Step 5: Run a Job

+

Step 6: Send Data and Query Results

+
+

Step 1: Create a Queue

  1. Log in to the DLI console. In the navigation pane on the left, choose Resources > Queue Management.
  2. On the displayed page, click Buy Queue in the upper right corner.
  3. On the Buy Queue page, set queue parameters as follows:
    • Billing Mode: .
    • Region and Project: Retain the default values.
    • Name: Enter a queue name.

      The queue name can contain only digits, letters, and underscores (_), but cannot contain only digits or start with an underscore (_). The name must contain 1 to 128 characters.

      +

      The queue name is case-insensitive. Uppercase letters will be automatically converted to lowercase letters.

      +
      +
    • Type: Select For general purpose. Select the Dedicated Resource Mode.
    • AZ Mode and Specifications: Retain the default values.
    • Enterprise Project: Select default.
    • Advanced Settings: Select Custom.
    • CIDR Block: Specify the queue network segment. For example, 10.0.0.0/16.

      The CIDR block of a queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL DB instances. Otherwise, datasource connections will fail to be created.

      +
      +
    • Set other parameters as required.
    +
  4. Click Buy. Confirm the configuration and click Submit.
+
+

Step 2: Create a Kafka Topic

  1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
  2. Choose Topics in the navigation pane on the left. On the displayed page, click Create Topic. Configure the following parameters:
    • Topic Name: For this example, enter testkafkatopic.
    • Partitions: Set the value to 1.
    • Replicas: Set the value to 1.
    +

    Retain default values for other parameters.

    +
+
+

Step 3: Create an Elasticsearch Index

  1. Log in to the CSS management console and choose Clusters > Elasticsearch from the navigation pane on the left.
  2. On the Clusters page, click Access Kibana in the Operation column of the created CSS cluster.
  3. On the displayed page, choose Dev Tools in the navigation pane on the left. The Console page is displayed.
  4. On the displayed page, run the following command to create index shoporders:
    PUT /shoporders
+{
+  "settings": {
+    "number_of_shards": 1
+  },
+	"mappings": {
+	  "properties": {
+	    "order_id": {
+	      "type": "text"
+	    },
+	    "order_channel": {
+	      "type": "text"
+	    },
+	    "order_time": {
+	      "type": "text"
+	    },
+	    "pay_amount": {
+	      "type": "double"
+	    },
+	    "real_pay": {
+	      "type": "double"
+	    },
+	    "pay_time": {
+	      "type": "text"
+	    },
+	    "user_id": {
+	      "type": "text"
+	    },
+	    "user_name": {
+	      "type": "text"
+	    },
+	    "area_id": {
+	      "type": "text"
+	    }
+	  }
+	}
+}
    +
+
+

Step 4: Create an Enhanced Datasource Connection

  • Connecting DLI to Kafka
    1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
    2. In the Connection pane, obtain the Instance Address (Private Network). In the Network pane, obtain the VPC and subnet of the instance.
    3. Click the security group name in the Network pane. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    5. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_kafka.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the Kafka instance.
      • Subnet: Select the subnet of Kafka instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    6. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    7. In the displayed dialog box, enter Kafka instance address (private network):port in the Address box and click Test to check whether the instance is reachable.
    +
  • Connecting DLI to CSS
    1. On the CSS management console, choose Clusters > Elasticsearch. On the displayed page, click the name of the created CSS cluster to view basic information.
    2. On the Cluster Information page, obtain the Private Network Address, VPC, AND Subnet.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Check whether the Kafka instance and Elasticsearch instance are in the same VPC and subnet.
      1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
      2. If they are not, go to 5. Create an enhanced datasource connection to connect DLI to the subnet where the Elasticsearch instance locates.
      +
    5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    6. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_css.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the Elasticsearch instance.
      • Subnet: Select the subnet of Elasticsearch instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    7. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    8. In the displayed dialog box, enter floating IP address:database port of the Elasticsearch instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
+
+

Step 5: Run a Job

  1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
  2. In the the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkKafkaES. Click OK.
  3. On the job editing page, set the following parameters and retain the default values of other parameters.
    • Queue: Select the queue created in Step 1: Create a Queue.
    • Flink Version: Select 1.12.
    • Save Job Log: Enable this function.
    • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
    • Enable Checkpointing: Enable this function.
    • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.

      In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to Elasticsearch.

      +
      +
    • Create a Kafka source table and connect DLI to the Kafka data source.
      CREATE TABLE kafkaSource (
+  order_id string,
+  order_channel string,
+  order_time string,
+  pay_amount double,
+  real_pay double,
+  pay_time string,
+  user_id string,
+  user_name string,
+  area_id string
+) with (
+  "connector" = "kafka",
+  "properties.bootstrap.servers" = "10.128.0.120:9092,10.128.0.89:9092,10.128.0.83:9092",-- Internal network address and port number of the Kafka instance
+  "properties.group.id" = "click",
+  "topic" = "testkafkatopic",--Created Kafka topic
+  "format" = "json",
+  "scan.startup.mode" = "latest-offset"
+);
      +
    • Create an Elasticsearch result table to display the data analyzed by DLI.
      CREATE TABLE elasticsearchSink (
+  order_id string,
+  order_channel string,
+  order_time string,
+  pay_amount double,
+  real_pay double,
+  pay_time string,
+  user_id string,
+  user_name string,
+  area_id string
+) WITH (
+  'connector' = 'elasticsearch-7',
+  'hosts' = '192.168.168.125:9200', --Private IP address and port of the CSS cluster
+  'index' = 'shoporders' --Created Elasticsearch engine
+);
+--Write Kafka data to Elasticsearch indexes
+insert into
+  elasticsearchSink
+select
+  *
+from
+  kafkaSource;
      +
    +
  4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.
+
+

Step 6: Send Data and Query Results

  1. Kafaka sends data.

    Use the Kafka client to send data to topics created in Step 2: Create a Kafka Topic to simulate real-time data streams.

    +

    The sample data is as follows:

    +
    {"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "pay_time":"2021-03-24 10:02:03", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}
+
+{"order_id":"202103241606060001", "order_channel":"appShop", "order_time":"2021-03-24 16:06:06", "pay_amount":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0002", "user_name":"Jason", "area_id":"330106"}
    +
  2. View the data processing result on Elasticsearch.
    After the message is sent to Kafka, run the following statement in Kibana for the CSS cluster and check the result:
    GET shoporders/_search
    +
    +
    The query result is as follows:
    {
+  "took" : 0,
+  "timed_out" : false,
+  "_shards" : {
+    "total" : 1,
+    "successful" : 1,
+    "skipped" : 0,
+    "failed" : 0
+  },
+  "hits" : {
+    "total" : {
+      "value" : 2,
+      "relation" : "eq"
+    },
+    "max_score" : 1.0,
+    "hits" : [
+      {
+        "_index" : "shoporders",
+        "_type" : "_doc",
+        "_id" : "6fswzIAByVjqg3_qAyM1",
+        "_score" : 1.0,
+        "_source" : {
+          "order_id" : "202103241000000001",
+          "order_channel" : "webShop",
+          "order_time" : "2021-03-24 10:00:00",
+          "pay_amount" : 100.0,
+          "real_pay" : 100.0,
+          "pay_time" : "2021-03-24 10:02:03",
+          "user_id" : "0001",
+          "user_name" : "Alice",
+          "area_id" : "330106"
+        }
+      },
+      {
+        "_index" : "shoporders",
+        "_type" : "_doc",
+        "_id" : "6vs1zIAByVjqg3_qyyPp",
+        "_score" : 1.0,
+        "_source" : {
+          "order_id" : "202103241606060001",
+          "order_channel" : "appShop",
+          "order_time" : "2021-03-24 16:06:06",
+          "pay_amount" : 200.0,
+          "real_pay" : 180.0,
+          "pay_time" : "2021-03-24 16:10:06",
+          "user_id" : "0002",
+          "user_name" : "Jason",
+          "area_id" : "330106"
+        }
+      }
+    ]
+  }
+}
    +
    +

    +
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0012.html b/docs/dli/dev/dli_09_0012.html new file mode 100644 index 00000000..a0581f9e --- /dev/null +++ b/docs/dli/dev/dli_09_0012.html @@ -0,0 +1,129 @@ + + +

Reading Data from MySQL CDC and Writing Data to GaussDB(DWS)

+

This guide provides reference for Flink 1.12 only.

+
+

Description

Change Data Capture (CDC) can synchronize incremental changes from the source database to one or more destinations. During data synchronization, CDC processes data, for example, grouping (GROUP BY) and joining multiple tables (JOIN).

+

This example creates a MySQL CDC source table to monitor MySQL data changes and insert the changed data into a GaussDB(DWS) database.

+
+

Prerequisites

  1. You have created an RDS for MySQL instance.. In this example, the RDS for MySQL database version is 8.0.
  2. You have created a GaussDB(DWS) cluster.
+
+

Overall Process

Figure 1 shows the overall development process.
Figure 1 Job development process
+
+

Step 1: Create a Queue

+

Step 2: Create an RDS MySQL Database and Table

+

Step 3: Create a GaussDB(DWS) Database and Table

+

Step 4: Create an Enhanced Datasource Connection

+

Step 5: Run a Job

+

Step 6: Send Data and Query Results

+
+

Step 1: Create a Queue

  1. Log in to the DLI console. In the navigation pane on the left, choose Resources > Queue Management.
  2. On the displayed page, click Buy Queue in the upper right corner.
  3. On the Buy Queue page, set queue parameters as follows:
    • Billing Mode: .
    • Region and Project: Retain the default values.
    • Name: Enter a queue name.

      The queue name can contain only digits, letters, and underscores (_), but cannot contain only digits or start with an underscore (_). The name must contain 1 to 128 characters.

      +

      The queue name is case-insensitive. Uppercase letters will be automatically converted to lowercase letters.

      +
      +
    • Type: Select For general purpose. Select the Dedicated Resource Mode.
    • AZ Mode and Specifications: Retain the default values.
    • Enterprise Project: Select default.
    • Advanced Settings: Select Custom.
    • CIDR Block: Specify the queue network segment. For example, 10.0.0.0/16.

      The CIDR block of a queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL DB instances. Otherwise, datasource connections will fail to be created.

      +
      +
    • Set other parameters as required.
    +
  4. Click Buy. Confirm the configuration and click Submit.
+
+

Step 2: Create an RDS MySQL Database and Table

  1. Log in to the RDS console. On the displayed page, locate the target MySQL DB instance and choose More > Log In in the Operation column.
  2. On the displayed login dialog box, enter the username and password and click Log In.
  3. On the Databases page, click Create Database. In the displayed dialog box, enter testrdsdb as the database name and retain default values of rest parameters. Then, click OK.
  4. In the Operation column of row where the created database locates, click SQL Window and enter the following statement to create a table:
    CREATE TABLE mysqlcdc (
+	`order_id` VARCHAR(64) NOT NULL,
+	`order_channel` VARCHAR(32) NOT NULL,
+	`order_time` VARCHAR(32),
+	`pay_amount` DOUBLE,
+	`real_pay` DOUBLE,
+	`pay_time` VARCHAR(32),
+	`user_id` VARCHAR(32),
+	`user_name` VARCHAR(32),
+	`area_id` VARCHAR(32)
+
+)	ENGINE = InnoDB
+	DEFAULT CHARACTER SET = utf8mb4;
    +
+
+

Step 3: Create a GaussDB(DWS) Database and Table

+
  1. Connect to the created GaussDB(DWS) cluster.
  2. Connect to the default database gaussdb of a GaussDB(DWS) cluster.
    gsql -d gaussdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
    • gaussdb: Default database of the GaussDB(DWS) cluster
    • Connection address of the DWS cluster: If a public network address is used for connection, set this parameter to the public network IP address or domain name. If a private network address is used for connection, set this parameter to the private network IP address or domain name. If an ELB is used for connection, set this parameter to the ELB address.
    • dbadmin: Default administrator username used during cluster creation
    • -W: Default password of the administrator
    +
  3. Run the following command to create the testdwsdb database:
    CREATE DATABASE testdwsdb;
    +
  4. Run the following command to exit the gaussdb database and connect to testdwsdb:
    \q
+gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  5. Run the following commands to create a table:
    create schema test;
+set current_schema= test;
+drop table if exists dwsresult;
+CREATE TABLE dwsresult
+(
+    car_id VARCHAR, 
+    car_owner VARCHAR,
+    car_age INTEGER ,
+    average_speed FLOAT8,
+    total_miles FLOAT8
+);
    +
+
+

Step 4: Create an Enhanced Datasource Connection

  • Connecting DLI to RDS
    1. Go to the RDS console, click the name of the target RDS DB instance on the Instances page. Basic information of the instance is displayed.
    2. In the Connection Information pane, obtain the floating IP address, database port, VPC, and subnet.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    5. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_rds.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the RDS DB instance.
      • Subnet: Select the subnet of RDS DB instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    6. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    7. In the displayed dialog box, enter floating IP address:database port of the RDS DB instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
  • Connecting DLI to GaussDB(DWS)
    1. On the GaussDB(DWS) management console, choose Clusters. On the displayed page, click the name of the created GaussDB(DWS) cluster to view basic information.
    2. In the Basic Information tab, locate the Database Attributes pane and obtain the private IP address and port number of the DB instance. In the Network pane, obtain VPC, and subnet information.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Check whether the RDS instance and GaussDB(DWS) instance are in the same VPC and subnet.
      1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
      2. If they are not, go to 5. Create an enhanced datasource connection to connect RDS to the subnet where the GaussDB(DWS) instance locates.
      +
    5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    6. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_dws.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the GaussDB(DWS) instance.
      • Subnet: Select the subnet of GaussDB(DWS) instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    7. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create a Queue. In the Operation column, click More > Test Address Connectivity.
    8. In the displayed dialog box, enter floating IP address:database port of the GaussDB(DWS) instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
+
+

Step 5: Run a Job

  1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
  2. In the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkCDCMySQLDWS. Click OK.
  3. On the job editing page, set the following parameters and retain the default values of other parameters.
    • Queue: Select the queue created in Step 1: Create a Queue.
    • Flink Version: Select 1.12.
    • Save Job Log: Enable this function.
    • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
    • Enable Checkpointing: Enable this function.
    • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.

      In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to Elasticsearch.

      +
      +
      create table mysqlCdcSource(
+  order_id string,
+  order_channel string,
+  order_time string,
+  pay_amount double,
+  real_pay double,
+  pay_time string,
+  user_id string,
+  user_name string,
+  area_id STRING
+) with (
+  'connector' = 'mysql-cdc',
+  'hostname' = ' 192.168.12.148',--IP address of the RDS MySQL instance
+  'port'= ' 3306',--Port number of the RDS MySQL instance
+  'pwd_auth_name'= 'xxxxx', -- Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+  'database-name' = ' testrdsdb',--Database name of the RDS MySQL instance
+  'table-name' = ' mysqlcdc'--Name of the tartet table in the database
+);
+
+create table dwsSink(
+  order_channel string,
+  pay_amount double,
+  real_pay double,
+  primary key(order_channel) not enforced
+) with (
+  'connector' = 'gaussdb',
+  'driver' = 'com.gauss200.jdbc.Driver',
+  'url'='jdbc:gaussdb://192.168.168.16:8000/testdwsdb ', ---192.168.168.16:8000 indicates the internal IP address and port of the GaussDB(DWS) instance. testdwsdb indicates the name of the created GaussDB(DWS) database.
+  'table-name' = ' test\".\"dwsresult', ---test indicates the schema of the created GaussDB(DWS) table, and dwsresult indicates the GaussDB(DWS) table name.
+  'pwd_auth_name'= 'xxxxx', -- Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+  'write.mode' = 'insert'
+);
+
+insert into dwsSink select order_channel, sum(pay_amount),sum(real_pay) from mysqlCdcSource group by order_channel;
      +
    +
  4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.
+
+

Step 6: Send Data and Query Results

  1. Log in to the RDS console. On the displayed page, locate the target MySQL DB instance and choose More > Log In in the Operation column.
  2. On the displayed login dialog box, enter the username and password and click Log In.
  3. In the Operation column of row where the created database locates, click SQL Window and enter the following statement to create a table and insert data to the table:
    insert into mysqlcdc values
+('202103241000000001','webShop','2021-03-24 10:00:00','100.00','100.00','2021-03-24 10:02:03','0001','Alice','330106'),
+('202103241206060001','appShop','2021-03-24 12:06:06','200.00','180.00','2021-03-24 16:10:06','0002','Jason','330106'),
+('202103241403000001','webShop','2021-03-24 14:03:00','300.00','100.00','2021-03-24 10:02:03','0003','Lily','330106'),
+('202103241636060001','appShop','2021-03-24 16:36:06','200.00','150.00','2021-03-24 16:10:06','0001','Henry','330106');
    +
  4. .
  5. Connect to the default database testdwsdb of a GaussDB(DWS) cluster.
    gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  6. Run the following statements to query table data:
    select * from test.dwsresult;
    +
    The query result is as follows:
    order_channel  pay_amount  real_pay 
+appShop         400.0       330.0
+webShop         400.0       200.0
    +
    +
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0013.html b/docs/dli/dev/dli_09_0013.html new file mode 100644 index 00000000..92f1250f --- /dev/null +++ b/docs/dli/dev/dli_09_0013.html @@ -0,0 +1,245 @@ + + +

Reading Data from PostgreSQL CDC and Writing Data to GaussDB(DWS)

+

This guide provides reference for Flink 1.12 only.

+
+

Description

Change Data Capture (CDC) can synchronize incremental changes from the source database to one or more destinations. During data synchronization, CDC processes data, for example, grouping (GROUP BY) and joining multiple tables (JOIN).

+

This example creates a PostgreSQL CDC source table to monitor PostgreSQL data changes and insert the changed data into a GaussDB(DWS) database.

+
+

Prerequisites

  1. You have created an RDS for PostgreSQL DB instance. In this example, the RDS for PostgreSQL database version is 11.

    The version of the RDS PostgreSQL database cannot be earlier than 11.

    +
    +
  2. You have created a GaussDB(DWS) instance.
+
+

Overall Process

Figure 1 shows the overall development process.
Figure 1 Job development process
+
+

Step 1: Create a Queue

+

Step 2: Create an RDS PostgreSQL Database and Table

+

Step 3: Create a GaussDB(DWS) Database and Table

+

Step 4: Create an Enhanced Datasource Connection

+

Step 5: Run a Job

+

Step 6: Send Data and Query Results

+
+

Step 1: Create a Queue

  1. Log in to the DLI console. In the navigation pane on the left, choose Resources > Queue Management.
  2. On the displayed page, click Buy Queue in the upper right corner.
  3. On the Buy Queue page, set queue parameters as follows:
    • Billing Mode: .
    • Region and Project: Retain the default values.
    • Name: Enter a queue name.

      The queue name can contain only digits, letters, and underscores (_), but cannot contain only digits or start with an underscore (_). The name must contain 1 to 128 characters.

      +

      The queue name is case-insensitive. Uppercase letters will be automatically converted to lowercase letters.

      +
      +
    • Type: Select For general purpose. Select the Dedicated Resource Mode.
    • AZ Mode and Specifications: Retain the default values.
    • Enterprise Project: Select default.
    • Advanced Settings: Select Custom.
    • CIDR Block: Specify the queue network segment. For example, 10.0.0.0/16.

      The CIDR block of a queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL DB instances. Otherwise, datasource connections will fail to be created.

      +
      +
    • Set other parameters as required.
    +
  4. Click Buy. Confirm the configuration and click Submit.
+
+

Step 2: Create an RDS PostgreSQL Database and Table

  1. Log in to the RDS console. On the displayed page, locate the target PostgreSQL DB instance and choose More > Log In in the Operation column.
  2. In the login dialog box displayed, enter the username and password and click Log In.
  3. Create a database instance and name it testrdsdb.
  4. Create a schema named test for the testrdsdb database.
  5. Choose SQL Operations > SQL Query. On the page displayed, create a RDS for PostgreSQL table.
    create table test.cdc_order(
+  order_id VARCHAR,
+  order_channel VARCHAR,
+  order_time VARCHAR,
+  pay_amount FLOAT8,
+  real_pay FLOAT8,
+  pay_time VARCHAR,
+  user_id VARCHAR,
+  user_name VARCHAR,
+  area_id VARCHAR,
+  primary key(order_id));
    +
    Run the following statement in the PostgreSQL instance:
    ALTER TABLE test.cdc_order REPLICA IDENTITY FULL;
    +
    +
+
+

Step 3: Create a GaussDB(DWS) Database and Table

  1. Connect to the created GaussDB(DWS) cluster.
  2. Connect to the default database gaussdb of a GaussDB(DWS) cluster.
    gsql -d gaussdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
    • gaussdb: Default database of the GaussDB(DWS) cluster
    • Connection address of the DWS cluster: If a public network address is used for connection, set this parameter to the public network IP address or domain name. If a private network address is used for connection, set this parameter to the private network IP address or domain name. If an ELB is used for connection, set this parameter to the ELB address.
    • dbadmin: Default administrator username used during cluster creation
    • -W: Default password of the administrator
    +
  3. Run the following command to create the testdwsdb database:
    CREATE DATABASE testdwsdb;
    +
  4. Run the following command to exit the gaussdb database and connect to testdwsdb:
    \q
+gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  5. Run the following commands to create a table:
    create schema test;
+set current_schema= test;
+drop table if exists dws_order;
+CREATE TABLE dws_order
+(
+  order_id VARCHAR,
+  order_channel VARCHAR,
+  order_time VARCHAR,
+  pay_amount FLOAT8,
+  real_pay FLOAT8,
+  pay_time VARCHAR,
+  user_id VARCHAR,
+  user_name VARCHAR,
+  area_id VARCHAR
+);
    +
+
+

Step 4: Create an Enhanced Datasource Connection

  • Connecting DLI to RDS
    1. Go to the RDS console, click the name of the target RDS DB instance on the Instances page. Basic information of the instance is displayed.
    2. In the Connection Information pane, obtain the floating IP address, database port, VPC, and subnet.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    5. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_rds.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the RDS DB instance.
      • Subnet: Select the subnet of RDS DB instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    6. In the navigation pane on the left, choose Resources > Queue Management. On the page displayed, locate the queue you created in Step 1: Create a Queue, click More in the Operation column, and select Test Address Connectivity.
    7. In the displayed dialog box, enter floating IP address:database port of the RDS DB instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
  • Connecting DLI to GaussDB(DWS)
    1. On the GaussDB(DWS) management console, choose Clusters. On the displayed page, click the name of the created GaussDB(DWS) cluster to view basic information.
    2. In the Basic Information tab, locate the Database Attributes pane and obtain the private IP address and port number of the DB instance. In the Network pane, obtain VPC, and subnet information.
    3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
    4. Check whether the RDS instance and GaussDB(DWS) instance are in the same VPC and subnet.
      1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
      2. If they are not, go to 5. Create an enhanced datasource connection to connect RDS to the subnet where the GaussDB(DWS) instance locates.
      +
    5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
    6. In the displayed dialog box, set the following parameters: For details, see the following section:
      • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_dws.
      • Resource Pool: Select the name of the queue created in Step 1: Create a Queue.
      • VPC: Select the VPC of the GaussDB(DWS) instance.
      • Subnet: Select the subnet of GaussDB(DWS) instance.
      • Set other parameters as you need.
      +

      Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

      +
    7. In the navigation pane on the left, choose Resources > Queue Management. On the page displayed, locate the queue you created in Step 1: Create a Queue, click More in the Operation column, and select Test Address Connectivity.
    8. In the displayed dialog box, enter floating IP address:database port of the GaussDB(DWS) instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.
    +
+
+

Step 5: Run a Job

  1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
  2. In the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkCDCPostgreDWS. Click OK.
  3. On the job editing page, set the following parameters and retain the default values of other parameters.
    • Queue: Select the queue created in Step 1: Create a Queue.
    • Flink Version: Select 1.12.
    • Save Job Log: Enable this function.
    • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
    • Enable Checkpointing: Enable this function.
    • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.

      In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to Elasticsearch.

      +
      +
      +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Table 1 Job running parameters

      Parameter

      +

      Description

      +

      Queue

      +

      A shared queue is selected by default. You can select a CCE queue with dedicated resources and configure the following parameters:

      +

      UDF Jar: UDF Jar file. Before selecting such a file, upload the corresponding JAR file to the OBS bucket and choose Data Management > Package Management to create a package. For details, see .

      +

      In SQL, you can call a UDF that is inserted into a JAR file.

      +
      NOTE:

      When creating a job, a sub-user can only select the queue that has been allocated to the user.

      +

      If the remaining capacity of the selected queue cannot meet the job requirements, the system automatically scales up the capacity and you will be billed based on the increased capacity. When a queue is idle, the system automatically scales in its capacity.

      +
      +

      CUs

      +

      Sum of the number of compute units and job manager CUs of DLI. One CU equals 1 vCPU and 4 GB.

      +

      The value is the number of CUs required for job running and cannot exceed the number of CUs in the bound queue.

      +

      Job Manager CUs

      +

      Number of CUs of the management unit.

      +

      Parallelism

      +

      Maximum number of Flink OpenSource SQL jobs that can run at the same time.

      +
      NOTE:

      This value cannot be greater than four times the compute units (number of CUs minus the number of JobManager CUs).

      +
      +

      Task Manager Configuration

      +

      Whether to set Task Manager resource parameters.

      +

      If this option is selected, you need to set the following parameters:

      +
      • CU(s) per TM: Number of resources occupied by each Task Manager.
      • Slot(s) per TM: Number of slots contained in each Task Manager.
      +

      OBS Bucket

      +

      OBS bucket to store job logs and checkpoint information. If the selected OBS bucket is not authorized, click Authorize.

      +

      Save Job Log

      +

      Whether to save job run logs to OBS. The logs are saved in Bucket name/jobs/logs/Directory starting with the job ID.

      +
      CAUTION:

      You are advised to configure this parameter. Otherwise, no run log is generated after the job is executed. If the job fails, the run log cannot be obtained for fault locating.

      +
      +

      If this option is selected, you need to set the following parameters:

      +
      OBS Bucket: Select an OBS bucket to store user job logs. If the selected OBS bucket is not authorized, click Authorize.
      NOTE:

      If Enable Checkpointing and Save Job Log are both selected, you only need to authorize OBS once.

      +
      +
      +

      Alarm Generation upon Job Exception

      +

      Whether to notify users of any job exceptions, such as running exceptions or arrears, via SMS or email.

      +

      If this option is selected, you need to set the following parameters:

      +

      SMN Topic

      +

      Select a user-defined SMN topic. For details about how to create a custom SMN topic, see "Creating a Topic" in Simple Message Notification User Guide.

      +

      Enable Checkpointing

      +

      Whether to enable job snapshots. If this function is enabled, jobs can be restored based on checkpoints.

      +
      If this option is selected, you need to set the following parameters:
      • Checkpoint Interval: interval for creating checkpoints, in seconds. The value ranges from 1 to 999999, and the default value is 30.
      • Checkpoint Mode: checkpointing mode, which can be set to either of the following values:
        • At least once: Events are processed at least once.
        • Exactly once: Events are processed only once.
        +
      +
      • OBS Bucket: Select an OBS bucket to store your checkpoints. If the selected OBS bucket is not authorized, click Authorize.
        Checkpoints are saved in Bucket name/jobs/checkpoint/Directory starting with the job ID.
        NOTE:

        If Enable Checkpointing and Save Job Log are both selected, you only need to authorize OBS once.

        +
        +
        +
      +
      +

      Auto Restart upon Exception

      +

      Whether to enable automatic restart. If this function is enabled, jobs will be automatically restarted and restored when exceptions occur.

      +

      If this option is selected, you need to set the following parameters:

      +
      • Max. Retry Attempts: maximum number of retries upon an exception. The unit is times/hour.
        • Unlimited: The number of retries is unlimited.
        • Limited: The number of retries is user-defined.
        +
      • Restore Job from Checkpoint: This parameter is available only when Enable Checkpointing is selected.
      +

      Idle State Retention Time

      +

      How long the state of a key is retained without being updated before it is removed in GroupBy or Window. The default value is 1 hour.

      +

      Dirty Data Policy

      +

      Policy for processing dirty data. The following policies are supported: Ignore, Trigger a job exception, and Save.

      +

      If you set this field to Save, Dirty Data Dump Address must be set. Click the address box to select the OBS path for storing dirty data.

      +
      +
      +
      create table PostgreCdcSource(
+  order_id string,
+  order_channel string,
+  order_time string,
+  pay_amount double,
+  real_pay double,
+  pay_time string,
+  user_id string,
+  user_name string,
+  area_id STRING,
+  primary key (order_id) not enforced
+) with (
+  'connector' = 'postgres-cdc',
+  'hostname' = ' 192.168.15.153',--IP address of the PostgreSQL instance
+  'port'= ' 5432',--Port number of the PostgreSQL instance
+  'pwd_auth_name'= 'xxxxx', -- Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+  'database-name' = ' testrdsdb',--Database name of the PostgreSQL instance
+  'schema-name' = ' test',-- Schema in the PostgreSQL database
+  'table-name' = ' cdc_order'--Table name in the PostgreSQL database
+);
+
+create table dwsSink(
+  order_id string,
+  order_channel string,
+  order_time string,
+  pay_amount double,
+  real_pay double,
+  pay_time string,
+  user_id string,
+  user_name string,
+  area_id STRING,  
+  primary key(order_id) not enforced
+) with (
+  'connector' = 'gaussdb',
+  'driver' = 'com.gauss200.jdbc.Driver',
+  'url'='jdbc:gaussdb://192.168.168.16:8000/testdwsdb ', ---192.168.168.16:8000 indicates the internal IP address and port of the GaussDB(DWS) instance. testdwsdb indicates the name of the created GaussDB(DWS) database.
+  'table-name' = ' test\".\"dws_order', ---test indicates the schema of the created GaussDB(DWS) table, and dws_order indicates the GaussDB(DWS) table name.
+  'username' = 'xxxxx',--Username of the GaussDB(DWS) instance
+  'password' = 'xxxxx',--Password of the GaussDB(DWS) instance
+  'write.mode' = 'insert'
+);
+
+insert into dwsSink select * from PostgreCdcSource where pay_amount > 100;
+
      +
      +
    +
  4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.
+
+

Step 6: Send Data and Query Results

  1. Log in to the RDS console. On the displayed page, locate the target PostgreSQL DB instance and choose More > Log In in the Operation column.
  2. On the displayed login dialog box, enter the username and password and click Log In.
  3. In the Operation column of row where the created database locates, click SQL Window and enter the following statement to create a table and insert data to the table:
    insert into test.cdc_order values
+('202103241000000001','webShop','2021-03-24 10:00:00','50.00','100.00','2021-03-24 10:02:03','0001','Alice','330106'),
+('202103251606060001','appShop','2021-03-24 12:06:06','200.00','180.00','2021-03-24 16:10:06','0002','Jason','330106'),
+('202103261000000001','webShop','2021-03-24 14:03:00','300.00','100.00','2021-03-24 10:02:03','0003','Lily','330106'),
+('202103271606060001','appShop','2021-03-24 16:36:06','99.00','150.00','2021-03-24 16:10:06','0001','Henry','330106');
    +
  4. Connect to the created GaussDB(DWS) cluster.
  5. Connect to the default database testdwsdb of a GaussDB(DWS) cluster.
    gsql -d testdwsdb -h Connection address of the GaussDB(DWS) cluster -U dbadmin -p 8000 -W password -r
    +
  6. Run the following statements to query table data:
    select * from test.dws_order;
    +
    The query result is as follows:
    order_channel              order_channel     order_time             pay_amount  real_pay  pay_time              user_id  user_name  area_id
+202103251606060001         appShop         2021-03-24 12:06:06       200.0      180.0   2021-03-24 16:10:06      0002      Jason     330106
+202103261000000001         webShop         2021-03-24 14:03:00       300.0      100.0   2021-03-24 10:02:03      0003      Lily      330106
    +
    +
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0019.html b/docs/dli/dev/dli_09_0019.html new file mode 100644 index 00000000..d231d1ae --- /dev/null +++ b/docs/dli/dev/dli_09_0019.html @@ -0,0 +1,29 @@ + + +

Using Spark Jobs to Access Data Sources of Datasource Connections

+
+
+ + +
+
Parent topic: Spark Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0020.html b/docs/dli/dev/dli_09_0020.html new file mode 100644 index 00000000..8896741d --- /dev/null +++ b/docs/dli/dev/dli_09_0020.html @@ -0,0 +1,13 @@ + + +

Overview

+

DLI supports the native Spark DataSource capability and other extended capabilities. You can use SQL statements or Spark jobs to access other data storage services and import, query, analyze, and process data. Currently, DLI supports the following datasource access services: CloudTable, Cloud Search Service (CSS), Distributed Cache Service (DCS), Document Database Service (DDS), GaussDB(DWS), MapReduce Service (MRS), and Relational Database Service (RDS). To use the datasource capability of DLI, you need to create a datasource connection first.

+

When you use Spark jobs to access other data sources, you can use Scala, PySpark, or Java to program functions for the jobs.

+ +
+
+
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0061.html b/docs/dli/dev/dli_09_0061.html new file mode 100644 index 00000000..f90f8e73 --- /dev/null +++ b/docs/dli/dev/dli_09_0061.html @@ -0,0 +1,759 @@ + + +

Scala Example Code

+

Prerequisites

A datasource connection has been created on the DLI management console.

+
+

CSS Non-Security Cluster

  • Development description
    • Constructing dependency information and creating a Spark session
      1. Import dependencies.
        Maven dependency
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
        +
        Import dependency packages.
        1
+2
        import org.apache.spark.sql.{Row, SaveMode, SparkSession}
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
+
        + +
        +
        +
      2. Create a session.
        1
        val sparkSession = SparkSession.builder().getOrCreate()
+
        + +
        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
        1
+2
+3
+4
        sparkSession.sql("create table css_table(id int, name string) using css options(
+	'es.nodes' 'to-css-1174404221-Y2bKVIqY.datasource.com:9200',
+	'es.nodes.wan.only'='true',
+	'resource' '/mytest/css')")
+
        + +
        + +
        + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Table 1 Parameters for creating a table

        Parameter

        +

        Description

        +

        es.nodes

        +

        CSS connection address. You need to create a datasource connection first.

        +

        If you have created an enhanced datasource connection, use the intranet IP address provided by CSS. The address format is IP1:PORT1,IP2:PORT2.

        +

        resource

        +

        Name of the resource for the CSS datasource connection name. You can use /index/type to specify the resource location (for easier understanding, the index may be seen as database and type as table).

        +
        NOTE:
        • In Elasticsearch 6.X, a single index supports only one type, and the type name can be customized.
        • In Elasticsearch 7.X, a single index uses _doc as the type name and cannot be customized. To access Elasticsearch 7.X, set this parameter to index.
        +
        +

        pushdown

        +

        Whether to enable the pushdown function of CSS. The default value is true. For tables with a large number of I/O requests, the pushdown function help reduce I/O pressure when the where condition is specified.

        +

        strict

        +

        Whether the CSS pushdown is strict. The default value is false. The exact match function can reduce more I/O requests than pushdown.

        +

        batch.size.entries

        +

        Maximum number of entries that can be inserted in a batch. The default value is 1000. If the size of a single data record is so large that the number of data records in the bulk storage reaches the upper limit of the data amount in a single batch, the system stops storing data and submits the data based on the batch.size.bytes parameter.

        +

        batch.size.bytes

        +

        Maximum amount of data in a single batch. The default value is 1 MB. If the size of a single data record is so small that the number of data records in the bulk storage reaches the upper limit of the data amount of a single batch, the system stops storing data and submits the data based on the batch.size.entries parameter.

        +

        es.nodes.wan.only

        +

        Whether to access the Elasticsearch node using only the domain name. The default value is false. If the original internal IP address provided by CSS is used as the es.nodes, you do not need to set this parameter or set it to false.

        +

        es.mapping.id

        +

        Document field name that contains the document ID in the Elasticsearch node.

        +
        NOTE:
        • The document ID in the same /index/type is unique. If a field that contains a document ID has duplicate values, the document with the duplicate ID will be overwritten when the ES is inserted.
        • This feature can be used as a fault tolerance solution. When data is being inserted, the DLI job fails and some data has been inserted into Elasticsearch. The data is redundant. If the document ID is set, the previous data will be overwritten when the DLI job is executed again.
        +
        +
        +
        +

        batch.size.entries and batch.size.bytes limit the number of data records and data volume respectively.

        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(13, 'John'),(22, 'Bob')")
+
        + +
        +
      3. Query data.
        1
+2
        val dataFrame = sparkSession.sql("select * from css_table")
+dataFrame.show()
+
        + +
        +

        Before data is inserted:

        +

        +

        Response:

        +

        +
      4. Delete the datasource connection table.
        1
        sparkSession.sql("drop table css_table")
+
        + +
        +
      +
    • Connecting to data sources through DataFrame APIs
      1. Set connection parameters.
        1
+2
        val resource = "/mytest/css"
+val nodes = "to-css-1174405013-Ht7O1tYf.datasource.com:9200"
+
        + +
        +
      2. Create a schema and add data to it.
        1
+2
        val schema = StructType(Seq(StructField("id", IntegerType, false), StructField("name", StringType, false)))
+val rdd = sparkSession.sparkContext.parallelize(Seq(Row(12, "John"),Row(21,"Bob")))
+
        + +
        +
      3. Import data to CSS.
        1
+2
+3
+4
+5
+6
+7
        val dataFrame_1 = sparkSession.createDataFrame(rdd, schema)
+dataFrame_1.write 
+  .format("css") 
+  .option("resource", resource) 
+  .option("es.nodes", nodes) 
+  .mode(SaveMode.Append) 
+  .save()
+
        + +
        +

        The value of SaveMode can be one of the following:

        +
        • ErrorIfExis: If the data already exists, the system throws an exception.
        • Overwrite: If the data already exists, the original data will be overwritten.
        • Append: If the data already exists, the system saves the new data.
        • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
        +
        +
      4. Read data from CSS.
        1
+2
        val dataFrameR = sparkSession.read.format("css").option("resource",resource).option("es.nodes", nodes).load()
+dataFrameR.show()
+
        + +
        +

        Before data is inserted:

        +

        +

        Response:

        +

        +
      +
    • Submitting a Spark job
      1. Generate a JAR package based on the code and upload the package to DLI.

        +

        +
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

        +

        +
        • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.css when you submit a job.
        • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

          spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +

          spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +
        +
        +
        +
      +
    +
  • Complete example code
    • Maven dependency
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
    • Connecting to data sources through SQL APIs
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
      import org.apache.spark.sql.SparkSession
+
+object Test_SQL_CSS {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    // Create a DLI data table for DLI-associated CSS
+    sparkSession.sql("create table css_table(id long, name string) using css options(
+	'es.nodes' = 'to-css-1174404217-QG2SwbVV.datasource.com:9200',
+	'es.nodes.wan.only' = 'true',
+	'resource' = '/mytest/css')")
+
+    //*****************************SQL model***********************************
+    // Insert data into the DLI data table
+    sparkSession.sql("insert into css_table values(13, 'John'),(22, 'Bob')")
+   
+    // Read data from DLI data table
+    val dataFrame = sparkSession.sql("select * from css_table")
+    dataFrame.show()
+   
+    // drop table
+    sparkSession.sql("drop table css_table")
+
+    sparkSession.close()
+  }
+}
+
      + +
      +
    • Connecting to data sources through DataFrame APIs
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      import org.apache.spark.sql.{Row, SaveMode, SparkSession};
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType};
+
+object Test_SQL_CSS {
+  def main(args: Array[String]): Unit = {
+    //Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    //*****************************DataFrame model***********************************
+    // Setting the /index/type of CSS
+    val resource = "/mytest/css"
+  
+    // Define the cross-origin connection address of the CSS cluster
+    val nodes = "to-css-1174405013-Ht7O1tYf.datasource.com:9200"
+
+    //Setting schema
+    val schema = StructType(Seq(StructField("id", IntegerType, false), StructField("name", StringType, false)))
+  
+    // Construction data
+    val rdd = sparkSession.sparkContext.parallelize(Seq(Row(12, "John"),Row(21,"Bob")))
+  
+    // Create a DataFrame from RDD and schema
+    val dataFrame_1 = sparkSession.createDataFrame(rdd, schema)
+  
+   //Write data to the CSS
+   dataFrame_1.write.format("css") 
+    .option("resource", resource) 
+    .option("es.nodes", nodes) 
+    .mode(SaveMode.Append) 
+    .save()
+  
+    //Read data
+    val dataFrameR = sparkSession.read.format("css").option("resource", resource).option("es.nodes", nodes).load()
+    dataFrameR.show()
+
+    spardSession.close()
+  }
+}
+
      + +
      +
    +
+
+

CSS Security Cluster

  • Development description
    • Constructing dependency information and creating a Spark session
      1. Import dependencies.
        Maven dependency
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
        +
        Import dependency packages.
        1
+2
        import org.apache.spark.sql.{Row, SaveMode, SparkSession}
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
+
        + +
        +
        +
      2. Create a session and set the AKs and SKs.

        Hard-coded or plaintext AK and SK pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.

        +
        +
        1
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        val sparkSession = SparkSession.builder().getOrCreate()
+sparkSession.conf.set("fs.obs.access.key", ak)
+sparkSession.conf.set("fs.obs.secret.key", sk)
+sparkSession.conf.set("fs.obs.endpoint", enpoint)
+sparkSession.conf.set("fs.obs.connecton.ssl.enabled", "false")
+
        + +
        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
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        sparkSession.sql("create table css_table(id int, name string) using css options(
+	'es.nodes' 'to-css-1174404221-Y2bKVIqY.datasource.com:9200',
+	'es.nodes.wan.only'='true',
+	'resource'='/mytest/css',
+ 	'es.net.ssl'='true',
+	'es.net.ssl.keystore.location'='obs://Bucket name/path/transport-keystore.jks',
+	'es.net.ssl.keystore.pass'='***',
+	'es.net.ssl.truststore.location'='obs://Bucket name/path/truststore.jks',
+	'es.net.ssl.truststore.pass'='***',
+	'es.net.http.auth.user'='admin',
+	'es.net.http.auth.pass'='***')")
+
        + +
        + +
        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Table 2 Parameters for creating a table

        Parameter

        +

        Description

        +

        es.nodes

        +

        CSS connection address. You need to create a datasource connection first.

        +

        If you have created an enhanced datasource connection, use the intranet IP address provided by CSS. The address format is IP1:PORT1,IP2:PORT2.

        +

        resource

        +

        Name of the resource for the CSS datasource connection name. You can use /index/type to specify the resource location (for easier understanding, the index may be seen as database and type as table).

        +
        NOTE:

        1. In Elasticsearch 6.X, a single index supports only one type, and the type name can be customized.

        +

        2. In Elasticsearch 7.X, a single index uses _doc as the type name and cannot be customized. To access Elasticsearch 7.X, set this parameter to index.

        +
        +

        pushdown

        +

        Whether to enable the pushdown function of CSS. The default value is true. For tables with a large number of I/O requests, the pushdown function help reduce I/O pressure when the where condition is specified.

        +

        strict

        +

        Whether the CSS pushdown is strict. The default value is false. The exact match function can reduce more I/O requests than pushdown.

        +

        batch.size.entries

        +

        Maximum number of entries that can be inserted in a batch. The default value is 1000. If the size of a single data record is so large that the number of data records in the bulk storage reaches the upper limit of the data amount in a single batch, the system stops storing data and submits the data based on the batch.size.bytes parameter.

        +

        batch.size.bytes

        +

        Maximum amount of data in a single batch. The default value is 1 MB. If the size of a single data record is so small that the number of data records in the bulk storage reaches the upper limit of the data amount of a single batch, the system stops storing data and submits the data based on the batch.size.entries parameter.

        +

        es.nodes.wan.only

        +

        Whether to access the Elasticsearch node using only the domain name. The default value is false. If the original internal IP address provided by CSS is used as the es.nodes, you do not need to set this parameter or set it to false.

        +

        es.mapping.id

        +

        Document field name that contains the document ID in the Elasticsearch node.

        +
        NOTE:
        • The document ID in the same /index/type is unique. If a field that contains a document ID has duplicate values, the document with the duplicate ID will be overwritten when the ES is inserted.
        • This feature can be used as a fault tolerance solution. When data is being inserted, the DLI job fails and some data has been inserted into Elasticsearch. The data is redundant. If the document ID is set, the previous data will be overwritten when the DLI job is executed again.
        +
        +

        es.net.ssl

        +

        Whether to connect to the security CSS cluster. The default value is false.

        +

        es.net.ssl.keystore.location

        +

        OBS bucket location of the keystore file generated by the security CSS cluster certificate.

        +

        es.net.ssl.keystore.pass

        +

        Password of the keystore file generated by the security CSS cluster certificate.

        +

        es.net.ssl.truststore.location

        +

        OBS bucket location of the truststore file generated by the security CSS cluster certificate.

        +

        es.net.ssl.truststore.pass

        +

        Password of the truststore file generated by the security CSS cluster certificate.

        +

        es.net.http.auth.user

        +

        Username of the security CSS cluster.

        +

        es.net.http.auth.pass

        +

        Password of the security CSS cluster.

        +
        +
        +

        batch.size.entries and batch.size.bytes limit the number of data records and data volume respectively.

        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(13, 'John'),(22, 'Bob')")
+
        + +
        +
      3. Query data.
        1
+2
        val dataFrame = sparkSession.sql("select * from css_table")
+dataFrame.show()
+
        + +
        +

        Before data is inserted:

        +

        +

        Response:

        +

        +
      4. Delete the datasource connection table.
        1
        sparkSession.sql("drop table css_table")
+
        + +
        +
      +
    • Connecting to data sources through DataFrame APIs
      1. Set connection parameters.
        1
+2
        val resource = "/mytest/css"
+val nodes = "to-css-1174405013-Ht7O1tYf.datasource.com:9200"
+
        + +
        +
      2. Create a schema and add data to it.
        1
+2
        val schema = StructType(Seq(StructField("id", IntegerType, false), StructField("name", StringType, false)))
+val rdd = sparkSession.sparkContext.parallelize(Seq(Row(12, "John"),Row(21,"Bob")))
+
        + +
        +
      3. Import data to CSS.
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        val dataFrame_1 = sparkSession.createDataFrame(rdd, schema)
+dataFrame_1.write 
+  .format("css") 
+  .option("resource", resource) 
+  .option("es.nodes", nodes) 
+  .option("es.net.ssl", "true")
+  .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+  .option("es.net.ssl.keystore.pass", "***")
+  .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+  .option("es.net.ssl.truststore.pass", "***")
+  .option("es.net.http.auth.user", "admin")
+  .option("es.net.http.auth.pass", "***")
+  .mode(SaveMode.Append) 
+  .save()
+
        + +
        +

        The value of Mode can be one of the following:

        +
        • ErrorIfExis: If the data already exists, the system throws an exception.
        • Overwrite: If the data already exists, the original data will be overwritten.
        • Append: If the data already exists, the system saves the new data.
        • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
        +
        +
      4. Read data from CSS.
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        val dataFrameR = sparkSession.read.format("css")
+        .option("resource",resource)
+        .option("es.nodes", nodes)
+        .option("es.net.ssl", "true")
+        .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+        .option("es.net.ssl.keystore.pass", "***")
+        .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+        .option("es.net.ssl.truststore.pass", "***")
+        .option("es.net.http.auth.user", "admin")
+        .option("es.net.http.auth.pass", "***")
+        .load()
+dataFrameR.show()
+
        + +
        +

        Before data is inserted:

        +

        +

        Response:

        +

        +
      +
    • Submitting a Spark job
      1. Generate a JAR package based on the code and upload the package to DLI.

        +

        +
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
        • When submitting a job, you need to specify a dependency module named sys.datasource.css.
        • For details about how to submit a job on the DLI console, see
        • For details about how to submit a job through an API, see the modules parameter in
        +
        +
      +
    +
  • Complete example code
    • Maven dependency
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
    • Connecting to data sources through SQL APIs
       1
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      import org.apache.spark.sql.SparkSession
+ 
+object csshttpstest {
+  def main(args: Array[String]): Unit = {
+    //Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+    // Create a DLI data table for DLI-associated CSS
+    sparkSession.sql("create table css_table(id long, name string) using css options('es.nodes' = '192.168.6.204:9200','es.nodes.wan.only' = 'false','resource' = '/mytest','es.net.ssl'='true','es.net.ssl.keystore.location' = 'obs://xietest1/lzq/keystore.jks','es.net.ssl.keystore.pass' = '**','es.net.ssl.truststore.location'='obs://xietest1/lzq/truststore.jks','es.net.ssl.truststore.pass'='**','es.net.http.auth.user'='admin','es.net.http.auth.pass'='**')")
+ 
+    //*****************************SQL model***********************************
+    // Insert data into the DLI data table
+    sparkSession.sql("insert into css_table values(13, 'John'),(22, 'Bob')")
+ 
+    // Read data from DLI data table
+    val dataFrame = sparkSession.sql("select * from css_table")
+    dataFrame.show()
+ 
+    // drop table
+    sparkSession.sql("drop table css_table")
+ 
+    sparkSession.close()
+  }
+}
+
      + +
      +
    • Connecting to data sources through DataFrame APIs

      Hard-coded or plaintext AK and SK pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.

      +
      +
       1
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      import org.apache.spark.sql.{Row, SaveMode, SparkSession};
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType};
+
+object Test_SQL_CSS {
+  def main(args: Array[String]): Unit = {
+    //Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+    sparkSession.conf.set("fs.obs.access.key", ak)
+    sparkSession.conf.set("fs.obs.secret.key", sk)
+
+    //*****************************DataFrame model***********************************
+    // Setting the /index/type of CSS
+    val resource = "/mytest/css"
+  
+    // Define the cross-origin connection address of the CSS cluster
+    val nodes = "to-css-1174405013-Ht7O1tYf.datasource.com:9200"
+
+    //Setting schema
+    val schema = StructType(Seq(StructField("id", IntegerType, false), StructField("name", StringType, false)))
+  
+    // Construction data
+    val rdd = sparkSession.sparkContext.parallelize(Seq(Row(12, "John"),Row(21,"Bob")))
+  
+    // Create a DataFrame from RDD and schema
+    val dataFrame_1 = sparkSession.createDataFrame(rdd, schema)
+  
+   //Write data to the CSS
+   dataFrame_1.write .format("css") 
+    .option("resource", resource) 
+    .option("es.nodes", nodes) 
+    .option("es.net.ssl", "true")
+    .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+    .option("es.net.ssl.keystore.pass", "***")
+    .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+    .option("es.net.ssl.truststore.pass", "***")
+    .option("es.net.http.auth.user", "admin")
+    .option("es.net.http.auth.pass", "***")
+    .mode(SaveMode.Append) 
+    .save();
+  
+    //Read data
+    val dataFrameR = sparkSession.read.format("css")
+    .option("resource", resource)
+    .option("es.nodes", nodes)
+    .option("es.net.ssl", "true")
+    .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+    .option("es.net.ssl.keystore.pass", "***")
+    .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+    .option("es.net.ssl.truststore.pass", "***")
+    .option("es.net.http.auth.user", "admin")
+    .option("es.net.http.auth.pass", "***")
+    .load()
+    dataFrameR.show()
+
+    spardSession.close()
+  }
+}
+
      + +
      +
    +
+
+
+
+
+
Parent topic: Connecting to CSS
+
+
+ diff --git a/docs/dli/dev/dli_09_0063.html b/docs/dli/dev/dli_09_0063.html new file mode 100644 index 00000000..32100982 --- /dev/null +++ b/docs/dli/dev/dli_09_0063.html @@ -0,0 +1,450 @@ + + +

Scala Example Code

+

Development Description

The CloudTable HBase and MRS HBase can be connected to DLI as data sources.

+
  • Prerequisites

    A datasource connection has been created on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Constructing dependency information and creating a Spark session
    1. Import dependencies.
      Maven dependency involved
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
      +
      Import dependency packages.
      1
+2
+3
+4
      import scala.collection.mutable
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+
      + +
      +
      +
    2. Create a session.
      1
      val sparkSession = SparkSession.builder().getOrCreate()
+
      + +
      +
    3. Create a table to connect to an HBase data source.
      • The sample code is applicable, if Kerberos authentication is disabled for the interconnected HBase cluster:
        1
+2
+3
+4
+5
+6
+7
+8
+9
        sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
+        'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
+	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+                  cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+                  cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
+	'TableName'='table_DupRowkey1',
+	'RowKey'='id:5,location:6,city:7',
+    'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')"
+)
+
        + +
        +
      • The sample code is applicable, if Kerberos authentication is enabled for the interconnected HBase cluster:
         1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
        sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
+        'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
+	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+                  cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+                  cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
+	'TableName'='table_DupRowkey1',
+	'RowKey'='id:5,location:6,city:7', 
+    'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef',
+    'krb5conf'='./krb5.conf',
+    'keytab' = './user.keytab',
+	'principal' = 'krbtest')")
+
        + +
        +
      + +
      + + + + + + + + + + + + + + + + + + + + + + +
      Table 1 Parameters for creating a table

      Parameter

      +

      Description

      +

      ZKHost

      +

      ZooKeeper IP address of the HBase cluster.

      +

      You need to create a datasource connection first.

      +
      • To access the CloudTable cluster, specify the ZooKeeper connection address in the internal network.
      • To access the MRS cluster, specify the IP addresses and port numbers of the ZooKeeper nodes. The format is as follows: ZK_IP1:ZK_PORT1,ZK_IP2:ZK_PORT2
      +

      RowKey

      +

      Row key field of the table connected to DLI. The single and composite row keys are supported. A single row key can be of the numeric or string type. The length does not need to be specified. The composite row key supports only fixed-length data of the string type. The format is attribute name 1:Length, attribute name 2:Length.

      +

      Cols

      +

      Mapping between the fields in the DLI table and the CloudTable table. In this mapping, the DLI table field is placed before the colon (:) and the CloudTable table field is placed after the colon (:). The period (.) is used to separate the column family and column name of the CloudTable table.

      +

      For example: DLI table field 1:CloudTable table.CloudTable table field 1, DLI table field 2:CloudTable table.CloudTable table field 2, DLI table field 3:CLoudTable table.CloudTable table field 3

      +

      krb5conf

      +

      Path of the krb5.conf file. This parameter is required when Kerberos authentication is enabled. The format is './krb5.conf'. For details, see Completing Configurations for Enabling Kerberos Authentication.

      +

      keytab

      +

      Path of the keytab file. This parameter is required when Kerberos authentication is enabled. The format is './user.keytab.'. For details, see Completing Configurations for Enabling Kerberos Authentication.

      +

      principal

      +

      Username created for Kerberos authentication.

      +
      +
      +
    +
+
+

Accessing a Data Source Using a SQL API

  1. Insert data.
    1
    sparkSession.sql("insert into test_hbase values('12345','abc','guiyang',false,null,3,23,2.3,2.34)")
+
    + +
    +
  2. Query data.
    1
    sparkSession.sql("select * from test_hbase").show ()
+
    + +
    +
+
+

Accessing a Data Source Using a DataFrame API

  1. Construct a schema.
     1
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+10
    val attrId = new StructField("id",StringType)
+val location = new StructField("location",StringType)
+val city = new StructField("city",StringType)
+val booleanf = new StructField("booleanf",BooleanType)
+val shortf = new StructField("shortf",ShortType)
+val intf = new StructField("intf",IntegerType)
+val longf = new StructField("longf",LongType)
+val floatf = new StructField("floatf",FloatType)
+val doublef = new StructField("doublef",DoubleType)
+val attrs = Array(attrId, location,city,booleanf,shortf,intf,longf,floatf,doublef)
+
    + +
    +
  2. Construct data based on the schema type.
    1
+2
    val mutableRow: Seq[Any] = Seq("12345","abc","city1",false,null,3,23,2.3,2.34)
+val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
+
    + +
    +
  3. Import data to HBase.
    1
    sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("test_hbase")
+
    + +
    +
  4. Read data from HBase.
    1
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    val map = new mutable.HashMap[String, String]()
+map("TableName") = "table_DupRowkey1"
+map("RowKey") = "id:5,location:6,city:7"
+map("Cols") = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
+map("ZKHost")="cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+               cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+               cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
+sparkSession.read.schema(new StructType(attrs)).format("hbase").options(map.toMap).load().show()
+
    + +
    +
+
+

Submitting a Spark Job

  1. Generate a JAR package based on the code and upload the package to DLI.

    +

    +
  2. (Optional) Add the krb5.conf and user.keytab files to other dependency files of the job when creating a Spark job in an MRS cluster with Kerberos authentication enabled. Skip this step if Kerberos authentication is not enabled for the cluster.
  3. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

    +
    • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, set Module to sys.datasource.hbase when you submit a job.
    • If the Spark version is 3.1.1, you do not need to select a module. Set Spark parameters (--conf).

      spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

      +

      spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

      +
    +
    +
    +
+
+

Complete Example Code

  • Maven dependency
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    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs
    • Sample code when Kerberos authentication is disabled
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      import org.apache.spark.sql.SparkSession
+
+object Test_SparkSql_HBase {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    /**
+     * Create an association table for the DLI association Hbase table
+     */
+    sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
+        'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
+	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+	          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+	          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
+	'TableName'='table_DupRowkey1',
+	'RowKey'='id:5,location:6,city:7',
+	'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,
+		longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')")
+
+    //*****************************SQL model***********************************
+    sparkSession.sql("insert into test_hbase values('12345','abc','city1',false,null,3,23,2.3,2.34)")
+    sparkSession.sql("select * from test_hbase").collect()
+
+    sparkSession.close()
+  }
+}
+
      + +
      +
    • Sample code when Kerberos authentication is enabled
      import org.apache.spark.SparkFiles
+import org.apache.spark.sql.SparkSession
+ 
+import java.io.{File, FileInputStream, FileOutputStream}
+ 
+object Test_SparkSql_HBase_Kerberos {
+ 
+  def copyFile2(Input:String)(OutPut:String): Unit ={
+    val fis = new FileInputStream(Input)
+    val fos = new FileOutputStream(OutPut)
+    val buf = new Array[Byte](1024)
+    var len = 0
+    while ({len = fis.read(buf);len} != -1){
+      fos.write(buf,0,len)
+    }
+    fos.close()
+    fis.close()
+  }
+ 
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+    val sc = sparkSession.sparkContext
+    sc.addFile("OBS address of krb5.conf")
+    sc.addFile("OBS address of user.keytab")
+    Thread.sleep(10)
+ 
+    val krb5_startfile = new File(SparkFiles.get("krb5.conf"))
+    val keytab_startfile = new File(SparkFiles.get("user.keytab"))
+    val path_user = System.getProperty("user.dir")
+    val keytab_endfile = new File(path_user + "/" + keytab_startfile.getName)
+    val krb5_endfile = new File(path_user + "/" + krb5_startfile.getName)
+    println(keytab_endfile)
+    println(krb5_endfile)
+ 
+    var krbinput = SparkFiles.get("krb5.conf")
+    var krboutput = path_user+"/krb5.conf"
+    copyFile2(krbinput)(krboutput)
+ 
+    var keytabinput = SparkFiles.get("user.keytab")
+    var keytaboutput = path_user+"/user.keytab"
+    copyFile2(keytabinput)(keytaboutput)
+    Thread.sleep(10)
+    /**
+     * Create an association table for the DLI association Hbase table
+     */
+    sparkSession.sql("CREATE TABLE testhbase(id string,booleanf boolean,shortf short,intf int,longf long,floatf float,doublef double) " +
+      "using hbase OPTIONS(" +
+      "'ZKHost'='10.0.0.146:2181'," +
+      "'TableName'='hbtest'," +
+      "'RowKey'='id:100'," +
+      "'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF2.longf,floatf:CF1.floatf,doublef:CF2.doublef'," +
+      "'krb5conf'='" + path_user + "/krb5.conf'," +
+      "'keytab'='" + path_user+ "/user.keytab'," +
+      "'principal'='krbtest') ")
+ 
+  //*****************************SQL model***********************************
+  sparkSession.sql("insert into testhbase values('newtest',true,1,2,3,4,5)")
+  val result = sparkSession.sql("select * from testhbase")
+  result.show()
+ 
+  sparkSession.close()
+  }
+}
      +
    +
  • Connecting to data sources through DataFrame APIs
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    import scala.collection.mutable
+
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+
+object Test_SparkSql_HBase {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    // Create an association table for the DLI association Hbase table
+    sparkSession.sql("CREATE TABLE test_hbase('id' STRING, 'location' STRING, 'city' STRING, 'booleanf' BOOLEAN, 
+        'shortf' SHORT, 'intf' INT, 'longf' LONG, 'floatf' FLOAT,'doublef' DOUBLE) using hbase OPTIONS (
+	'ZKHost'='cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+	          cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+	          cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',
+	'TableName'='table_DupRowkey1',
+	'RowKey'='id:5,location:6,city:7',
+	'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')")
+
+    //*****************************DataFrame model***********************************
+    // Setting schema
+    val attrId = new StructField("id",StringType)
+    val location = new StructField("location",StringType)
+    val city = new StructField("city",StringType)
+    val booleanf = new StructField("booleanf",BooleanType)
+    val shortf = new StructField("shortf",ShortType)
+    val intf = new StructField("intf",IntegerType)
+    val longf = new StructField("longf",LongType)
+    val floatf = new StructField("floatf",FloatType)
+    val doublef = new StructField("doublef",DoubleType)
+    val attrs = Array(attrId, location,city,booleanf,shortf,intf,longf,floatf,doublef)
+
+    // Populate data according to the type of schema
+    val mutableRow: Seq[Any] = Seq("12345","abc","city1",false,null,3,23,2.3,2.34)
+    val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
+
+    // Import the constructed data into Hbase
+    sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("test_hbase")
+
+    // Read data on Hbase
+    val map = new mutable.HashMap[String, String]()
+    map("TableName") = "table_DupRowkey1"
+    map("RowKey") = "id:5,location:6,city:7"
+    map("Cols") = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
+    map("ZKHost")="cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,
+                   cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+                   cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
+    sparkSession.read.schema(new StructType(attrs)).format("hbase").options(map.toMap).load().collect()
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to HBase
+
+
+ diff --git a/docs/dli/dev/dli_09_0065.html b/docs/dli/dev/dli_09_0065.html new file mode 100644 index 00000000..251a1d22 --- /dev/null +++ b/docs/dli/dev/dli_09_0065.html @@ -0,0 +1,274 @@ + + +

Scala Example Code

+

Development Description

The CloudTable OpenTSDB and MRS OpenTSDB can be connected to DLI as data sources.

+
  • Prerequisites

    A datasource connection has been created on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Constructing dependency information and creating a Spark session
    1. Import dependencies.
      Maven dependency involved
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+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
      +
      Import dependency packages.
      1
+2
+3
+4
      import scala.collection.mutable
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+
      + +
      +
      +
    2. Create a session.
      1
      val sparkSession = SparkSession.builder().getOrCreate()
+
      + +
      +
    3. Create a table to connect to an OpenTSDB data source.
      1
+2
+3
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      sparkSession.sql("create table opentsdb_test using opentsdb options(
+	'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',	
+        'metric'='ctopentsdb',
+	'tags'='city,location')")
+
      + +
      + +
      + + + + + + + + + + + + + +
      Table 1 Parameters for creating a table

      Parameter

      +

      Description

      +

      host

      +

      OpenTSDB IP address.

      +
      • To access CloudTable OpenTSDB, specify the OpenTSDB connection address. You can log in to the CloudTable console, choose Cluster Mode and click the target cluster name, and obtain the OpenTSDB connection address from the cluster information.
      • You can also access the MRS OpenTSDB. If you have created an enhanced datasource connection, enter the IP address and port number of the node where the OpenTSDB is located. The format is IP:PORT. If the OpenTSDB has multiple nodes, separate their IP addresses by semicolons (;).
      +

      metric

      +

      Name of the metric in OpenTSDB corresponding to the DLI table to be created.

      +

      tags

      +

      Tags corresponding to the metric, used for operations such as classification, filtering, and quick search. A maximum of 8 tags, including all tagk values under the metric, can be added and are separated by commas (,).

      +
      +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Insert data.
      1
      sparkSession.sql("insert into opentsdb_test values('futian', 'abc', '1970-01-02 18:17:36', 30.0)")
+
      + +
      +
    2. Query data.
      1
      sparkSession.sql("select * from opentsdb_test").show()
+
      + +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Construct a schema.
      1
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      val attrTag1Location = new StructField("location", StringType)
+val attrTag2Name = new StructField("name", StringType)
+val attrTimestamp = new StructField("timestamp", LongType)
+val attrValue = new StructField("value", DoubleType)
+val attrs = Array(attrTag1Location, attrTag2Name, attrTimestamp, attrValue)
+
      + +
      +
    2. Construct data based on the schema type.
      1
+2
      val mutableRow: Seq[Any] = Seq("aaa", "abc", 123456L, 30.0)
+val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
+
      + +
      +
    3. Import data to OpenTSDB.
      1
      sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("opentsdb_test")
+
      + +
      +
    4. Read data from OpenTSDB.
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      val map = new mutable.HashMap[String, String]()
+map("metric") = "ctopentsdb"
+map("tags") = "city,location"
+map("Host") = "opentsdb-3xcl8dir15m58z3.cloudtable.com:4242"
+sparkSession.read.format("opentsdb").options(map.toMap).load().show()
+
      + +
      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.opentsdb when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Maven dependency
    1
+2
+3
+4
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    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs
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    import org.apache.spark.sql.SparkSession
+
+object Test_OpenTSDB_CT {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    // Create a data table for DLI association OpenTSDB
+    sparkSession.sql("create table opentsdb_test using opentsdb options(
+	'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',
+	'metric'='ctopentsdb',
+	'tags'='city,location')")
+
+    //*****************************SQL module***********************************
+    sparkSession.sql("insert into opentsdb_test values('futian', 'abc', '1970-01-02 18:17:36', 30.0)")
+    sparkSession.sql("select * from opentsdb_test").show()
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • Connecting to data sources through DataFrame APIs
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    import scala.collection.mutable
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+
+object Test_OpenTSDB_CT {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    // Create a data table for DLI association OpenTSDB
+    sparkSession.sql("create table opentsdb_test using opentsdb options(
+	'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',
+	'metric'='ctopentsdb',
+	'tags'='city,location')")
+
+    //*****************************DataFrame model***********************************
+    // Setting schema
+    val attrTag1Location = new StructField("location", StringType)
+    val attrTag2Name = new StructField("name", StringType)
+    val attrTimestamp = new StructField("timestamp", LongType)
+    val attrValue = new StructField("value", DoubleType)
+    val attrs = Array(attrTag1Location, attrTag2Name, attrTimestamp,attrValue)
+
+    // Populate data according to the type of schema
+    val mutableRow: Seq[Any] = Seq("aaa", "abc", 123456L, 30.0)
+    val rddData: RDD[Row] = sparkSession.sparkContext.parallelize(Array(Row.fromSeq(mutableRow)), 1)
+
+    //Import the constructed data into OpenTSDB
+    sparkSession.createDataFrame(rddData, new StructType(attrs)).write.insertInto("opentsdb_test")
+
+    //Read data on OpenTSDB
+    val map = new mutable.HashMap[String, String]()
+    map("metric") = "ctopentsdb"
+    map("tags") = "city,location"
+    map("Host") = "opentsdb-3xcl8dir15m58z3.cloudtable.com:4242"
+    sparkSession.read.format("opentsdb").options(map.toMap).load().show()
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to OpenTSDB
+
+
+ diff --git a/docs/dli/dev/dli_09_0067.html b/docs/dli/dev/dli_09_0067.html new file mode 100644 index 00000000..c7905695 --- /dev/null +++ b/docs/dli/dev/dli_09_0067.html @@ -0,0 +1,577 @@ + + +

Scala Example Code

+

Development Description

  • Prerequisites

    A datasource connection has been created and bound to a queue on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Constructing dependency information and creating a Spark session
    1. Import dependencies.

      Maven dependency involved

      +
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
      Import dependency packages.
      1
+2
+3
      import java.util.Properties
+import org.apache.spark.sql.{Row,SparkSession}
+import org.apache.spark.sql.SaveMode
+
      + +
      +
      +
    2. Create a session.
      1
      val sparkSession = SparkSession.builder().getOrCreate()
+
      + +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to an RDS data source and set connection parameters.
      1
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      sparkSession.sql(
+  "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (
+     'url'='jdbc:mysql://to-rds-1174404209-cA37siB6.datasource.com:3306',  // Set this parameter to the actual URL.
+     'dbtable'='test.customer',
+     'user'='root',  // Set this parameter to the actual user.
+     'password'='######',  // Set this parameter to the actual password.
+     'driver'='com.mysql.jdbc.Driver')")
+
      + +
      + +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Table 1 Parameters for creating a table

      Parameter

      +

      Description

      +

      url

      +

      To obtain an RDS IP address, you need to create a datasource connection first. Refer to the Data Lake Insight User Guide for more information.

      +

      If you have created an enhanced datasource connection, use the internal network domain name or internal network address and the database port number provided by RDS to set up the connection. If MySQL is used, the format is Protocol header://Internal IP address:Internal network port number. If PostgreSQL is used, the format is Protocol header://Internal IP address:Internal network port number/Database name.

      +

      For example: jdbc:mysql://192.168.0.193:3306 or jdbc:postgresql://192.168.0.193:3306/postgres.

      +

      dbtable

      +

      To connect to a MySQL cluster, enter Database name.Table name. To connect to a PostgreSQL cluster, enter Mode name.Table name.

      +
      NOTE:

      If the database and table do not exist, create them first. Otherwise, the system reports an error and fails to run.

      +
      +

      user

      +

      RDS database username.

      +

      password

      +

      RDS database password.

      +

      driver

      +

      JDBC driver class name. To connect to a MySQL cluster, enter com.mysql.jdbc.Driver. To connect to a PostgreSQL cluster, enter org.postgresql.Driver.

      +

      partitionColumn

      +

      One of the numeric fields that are required for concurrently reading data.

      +
      NOTE:
      • The partitionColumn, lowerBound, upperBound, and numPartitions parameters must be set at the same time.
      • To improve the concurrent read performance, you are advised to use auto-increment columns.
      +
      +

      lowerBound

      +

      Minimum value of a column specified by partitionColumn. The value is contained in the returned result.

      +

      upperBound

      +

      Maximum value of a column specified by partitionColumn. The value is not contained in the returned result.

      +

      numPartitions

      +

      Number of concurrent read operations.

      +
      NOTE:

      When data is read, lowerBound and upperBound are evenly allocated to each task to obtain data. Example:

      +

      'partitionColumn'='id',

      +

      'lowerBound'='0',

      +

      'upperBound'='100',

      +

      'numPartitions'='2'

      +

      Two concurrent tasks are started in DLI. The execution ID of one task is greater than or equal to 0 and the ID is smaller than 50; the execution ID of the other task is greater than or equal to 50 and the ID is smaller than 100.

      +
      +

      fetchsize

      +

      Number of data records obtained in each batch during data reading. The default value is 1000. If this parameter is set to a large value, the performance is good but more memory is occupied, causing memory overflow as a result.

      +

      batchsize

      +

      Number of data records written in each batch. The default value is 1000. If this parameter is set to a large value, the performance is good but more memory is occupied, causing memory overflow as a result.

      +

      truncate

      +

      Whether to clear the table without deleting the original table when overwrite is executed. The options are as follows:

      +
      • true
      • false
      +

      The default value is false, indicating that the original table is deleted and then a new table is created when the overwrite operation is performed.

      +

      isolationLevel

      +

      Transaction isolation level. The options are as follows:

      +
      • NONE
      • READ_UNCOMMITTED
      • READ_COMMITTED
      • REPEATABLE_READ
      • SERIALIZABLE
      +

      The default value is READ_UNCOMMITTED.

      +
      +
      +
    2. Insert data.
      1
      sparkSession.sql("insert into dli_to_rds values(1, 'John',24),(2, 'Bob',32)")
+
      + +
      +
    3. Query data.
      1
+2
      val dataFrame = sparkSession.sql("select * from dli_to_rds")
+dataFrame.show()
+
      + +
      +

      Before data is inserted

      +

      +

      After data is inserted

      +

      +
    4. Delete the datasource connection table.
      1
      sparkSession.sql("drop table dli_to_rds")
+
      + +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Configure datasource connection parameters.
      1
+2
+3
+4
      val url = "jdbc:mysql://to-rds-1174405057-EA1Kgo8H.datasource.com:3306"
+val username = "root"
+val password = "######"
+val dbtable = "test.customer"
+
      + +
      +
    2. Create a DataFrame, add data, and rename fields.
      1
+2
+3
+4
      var dataFrame_1 = sparkSession.createDataFrame(List((8, "Jack_1", 18)))
+val df = dataFrame_1.withColumnRenamed("_1", "id")
+                    .withColumnRenamed("_2", "name")
+                    .withColumnRenamed("_3", "age")
+
      + +
      +
    3. Import data to RDS.
      1
+2
+3
+4
+5
+6
+7
+8
      df.write.format("jdbc")
+  .option("url", url)
+  .option("dbtable", dbtable)
+  .option("user", username)
+  .option("password", password)
+  .option("driver", "com.mysql.jdbc.Driver")
+  .mode(SaveMode.Append)
+  .save()
+
      + +
      +

      The value of SaveMode can be one of the following:

      +
      • ErrorIfExis: If the data already exists, the system throws an exception.
      • Overwrite: If the data already exists, the original data will be overwritten.
      • Append: If the data already exists, the system saves the new data.
      • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
      +
      +
      +
    4. Read data from RDS.
      • Method 1: read.format()
        1
+2
+3
+4
+5
+6
+7
        val jdbcDF = sparkSession.read.format("jdbc")
+                .option("url", url)
+                .option("dbtable", dbtable)
+                .option("user", username)
+                .option("password", password)
+                .option("driver", "org.postgresql.Driver")
+                .load()
+
        + +
        +
      • Method 2: read.jdbc()
        1
+2
+3
+4
        val properties = new Properties()
+properties.put("user", username)
+properties.put("password", password)
+val jdbcDF2 = sparkSession.read.jdbc(url, dbtable, properties)
+
        + +
        +
      +

      Before data is inserted

      +

      +

      After data is inserted

      +

      +

      The DataFrame read by the read.format() or read.jdbc() method is registered as a temporary table. Then, you can use SQL statements to query data.

      +
      1
+2
      jdbcDF.registerTempTable("customer_test")
+sparkSession.sql("select * from customer_test where id = 1").show()
+
      + +
      +

      Query results

      +

      +
    +
  • DataFrame-related operations

    The data created by the createDataFrame() method and the data queried by the read.format() method and the read.jdbc() method are all DataFrame objects. You can directly query a single record. (In 4, the DataFrame data is registered as a temporary table.)

    +
    • where

      The where statement can be combined with filter expressions such as AND and OR. The DataFrame object after filtering is returned. The following is an example:

      +
      1
      jdbcDF.where("id = 1 or age <=10").show()
+
      + +
      +

      +
    • filter

      The filter statement can be used in the same way as where. The DataFrame object after filtering is returned. The following is an example:

      +
      1
      jdbcDF.filter("id = 1 or age <=10").show()
+
      + +
      +

      +
    • select

      The select statement is used to query the DataFrame object of the specified field. Multiple fields can be queried.

      +
      • Example 1:
        1
        jdbcDF.select("id").show()
+
        + +
        +

        +
      • Example 2:
        1
        jdbcDF.select("id", "name").show()
+
        + +
        +

        +
      • Example 3:
        1
        jdbcDF.select("id","name").where("id<4").show()
+
        + +
        +

        +
      +
    • selectExpr

      selectExpr is used to perform special processing on a field. For example, the selectExpr function can be used to change the field name. The following is an example:

      +

      If you want to set the name field to name_test and add 1 to the value of age, run the following statement:

      +
      1
      jdbcDF.selectExpr("id", "name as name_test", "age+1").show()
+
      + +
      +
    • col

      col is used to obtain a specified field. Different from select, col can only be used to query the column type and one field can be returned at a time. The following is an example:

      +
      1
      val idCol = jdbcDF.col("id")
+
      + +
      +
    • drop

      drop is used to delete a specified field. Specify a field you need to delete (only one field can be deleted at a time), the DataFrame object that does not contain the field is returned. The following is an example:

      +
      1
      jdbcDF.drop("id").show()
+
      + +
      +

      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.rds when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Maven dependency
    1
+2
+3
+4
+5
    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
    import java.util.Properties
+import org.apache.spark.sql.SparkSession
+
+object Test_SQL_RDS {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+  
+    // Create a data table for DLI-associated RDS
+    sparkSession.sql("CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (
+   'url'='jdbc:mysql://to-rds-1174404209-cA37siB6.datasource.com:3306,
+	  'dbtable'='test.customer',
+	  'user'='root',
+	  'password'='######',
+          'driver'='com.mysql.jdbc.Driver')")
+
+    //*****************************SQL model***********************************
+    //Insert data into the DLI data table
+    sparkSession.sql("insert into dli_to_rds values(1,'John',24),(2,'Bob',32)")
+  
+    //Read data from DLI data table
+    val dataFrame = sparkSession.sql("select * from dli_to_rds")
+    dataFrame.show()
+  
+    //drop table
+    sparkSession.sql("drop table dli_to_rds")
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • Connecting to data sources through DataFrame APIs
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
+31
+32
+33
+34
+35
+36
+37
+38
+39
+40
+41
+42
+43
+44
+45
+46
+47
+48
+49
+50
+51
+52
+53
+54
+55
+56
+57
+58
+59
+60
+61
+62
+63
+64
+65
+66
+67
+68
+69
    import java.util.Properties
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.SaveMode
+
+object Test_SQL_RDS {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    //*****************************DataFrame model***********************************
+    // Set the connection configuration parameters. Contains url, username, password, dbtable.
+    val url = "jdbc:mysql://to-rds-1174404209-cA37siB6.datasource.com:3306"
+    val username = "root"
+    val password = "######"
+    val dbtable = "test.customer"
+
+    // Create a DataFrame and initialize the DataFrame data.
+    var dataFrame_1 = sparkSession.createDataFrame(List((1, "Jack", 18)))
+  
+    // Rename the fields set by the createDataFrame() method.
+    val df = dataFrame_1.withColumnRenamed("_1", "id")
+	                .withColumnRenamed("_2", "name")
+	                .withColumnRenamed("_3", "age")
+
+    // Write data to the rds_table_1 table
+    df.write.format("jdbc")
+      .option("url", url)
+      .option("dbtable", dbtable)
+      .option("user", username)
+      .option("password", password)
+      .option("driver", "com.mysql.jdbc.Driver")
+      .mode(SaveMode.Append)
+      .save()
+
+    // DataFrame object for data manipulation
+    //Filter users with id=1
+    var newDF = df.filter("id!=1")
+    newDF.show()
+  
+    // Filter the id column data
+    var newDF_1 = df.drop("id")
+    newDF_1.show()
+
+    // Read the data of the customer table in the RDS database
+    // Way one: Read data from RDS using read.format()
+    val jdbcDF = sparkSession.read.format("jdbc")
+                    .option("url", url)
+                    .option("dbtable", dbtable)
+                    .option("user", username)
+                    .option("password", password)
+                    .option("driver", "com.mysql.jdbc.Driver")
+                    .load()
+    // Way two: Read data from RDS using read.jdbc()
+    val properties = new Properties()
+    properties.put("user", username)
+    properties.put("password", password)
+    val jdbcDF2 = sparkSession.read.jdbc(url, dbtable, properties)
+
+    /**
+     * Register the dateFrame read by read.format() or read.jdbc() as a temporary table, and query the data 
+     * using the sql statement.
+     */
+    jdbcDF.registerTempTable("customer_test")
+    val result = sparkSession.sql("select * from customer_test where id = 1")
+    result.show()
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • DataFrame-related operations
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
      // The where() method uses " and" and "or" for condition filters, returning filtered DataFrame objects
+  jdbcDF.where("id = 1 or age <=10").show()
+
+  // The filter() method is used in the same way as the where() method.
+  jdbcDF.filter("id = 1 or age <=10").show()
+
+  // The select() method passes multiple arguments and returns the DataFrame object of the specified field.
+  jdbcDF.select("id").show()
+  jdbcDF.select("id", "name").show()
+  jdbcDF.select("id","name").where("id<4").show()
+
+  /**
+   * The selectExpr() method implements special handling of fields, such as renaming, increasing or 
+   * decreasing data values.
+   */
+  jdbcDF.selectExpr("id", "name as name_test", "age+1").show()
+
+  // The col() method gets a specified field each time, and the return type is a Column type.
+  val idCol = jdbcDF.col("id")
+
+  /**
+   * The drop() method returns a DataFrame object that does not contain deleted fields, and only one field 
+   * can be deleted at a time.
+   */
+  jdbcDF.drop("id").show()
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to RDS
+
+
+ diff --git a/docs/dli/dev/dli_09_0069.html b/docs/dli/dev/dli_09_0069.html new file mode 100644 index 00000000..1804e177 --- /dev/null +++ b/docs/dli/dev/dli_09_0069.html @@ -0,0 +1,515 @@ + + +

Scala Example Code

+

Scenario

This section provides Scala example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.

+

A datasource connection has been created and bound to a queue on the DLI management console.

+

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

+
+
+

Preparations

Constructing dependency information and creating a Spark session
  1. Import dependencies

    Involved Maven dependency

    +
    1
+2
+3
+4
+5
    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
    Import dependency packages.
    1
+2
+3
    import java.util.Properties
+import org.apache.spark.sql.{Row,SparkSession}
+import org.apache.spark.sql.SaveMode
+
    + +
    +
    +
  2. Create a session.
    1
    val sparkSession = SparkSession.builder().getOrCreate()
+
    + +
    +
+
+
+

Accessing a Data Source Using a SQL API

  1. Create a table to connect to a GaussDB(DWS) data source.
    1
+2
+3
+4
+5
+6
+7
+8
    sparkSession.sql(
+  "CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS (
+     'url'='jdbc:postgresql://to-dws-1174404209-cA37siB6.datasource.com:8000/postgres',
+     'dbtable'='customer',
+     'user'='dbadmin',
+     'passwdauth'='######'// Name of the datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for the job.
+)"
+)
+
    + +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 1 Parameters for creating a table

    Parameter

    +

    Description

    +

    url

    +

    To obtain a GaussDB(DWS) IP address, you need to create a datasource connection first. Refer to Data Lake Insight User Guide for more information.

    +

    After an enhanced datasource connection is created, you can use the JDBC connection string (intranet) provided by GaussDB(DWS) or the intranet IP address and port number to connect to GaussDB(DWS). The format is protocol header://internal IP address:internal network port number/database name, for example: jdbc:postgresql://192.168.0.77:8000/postgres. For details about how to obtain the value, see GaussDB(DWS) cluster information.

    +
    NOTE:

    The GaussDB(DWS) IP address is in the following format: protocol header://IP address:port number/database name

    +

    Example:

    +

    jdbc:postgresql://to-dws-1174405119-ihlUr78j.datasource.com:8000/postgres

    +

    If you want to connect to a database created in GaussDB(DWS), change postgres to the corresponding database name in this connection.

    +
    +

    passwdauth

    +

    Name of datasource authentication of the password type created on DLI. If datasource authentication is used, you do not need to set the username and password for jobs.

    +

    dbtable

    +

    Tables in the PostgreSQL database.

    +

    partitionColumn

    +

    This parameter is used to set the numeric field used concurrently when data is read.

    +
    NOTE:
    • The partitionColumn, lowerBound, upperBound, and numPartitions parameters must be set at the same time.
    • To improve the concurrent read performance, you are advised to use auto-increment columns.
    +
    +

    lowerBound

    +

    Minimum value of a column specified by partitionColumn. The value is contained in the returned result.

    +

    upperBound

    +

    Maximum value of a column specified by partitionColumn. The value is not contained in the returned result.

    +

    numPartitions

    +

    Number of concurrent read operations.

    +
    NOTE:

    When data is read, lowerBound and upperBound are evenly allocated to each task to obtain data. Example:

    +

    'partitionColumn'='id',

    +

    'lowerBound'='0',

    +

    'upperBound'='100',

    +

    'numPartitions'='2'

    +

    Two concurrent tasks are started in DLI. The execution ID of one task is greater than or equal to 0 and the ID is less than 50, and the execution ID of the other task is greater than or equal to 50 and the ID is less than 100.

    +
    +

    fetchsize

    +

    Number of data records obtained in each batch during data reading. The default value is 1000. If this parameter is set to a large value, the performance is good but more memory is occupied. If this parameter is set to a large value, memory overflow may occur.

    +

    batchsize

    +

    Number of data records written in each batch. The default value is 1000. If this parameter is set to a large value, the performance is good but more memory is occupied. If this parameter is set to a large value, memory overflow may occur.

    +

    truncate

    +

    Indicates whether to clear the table without deleting the original table when overwrite is executed. The options are as follows:

    +
    • true
    • false
    +

    The default value is false, indicating that the original table is deleted and then a new table is created when the overwrite operation is performed.

    +

    isolationLevel

    +

    Transaction isolation level. The options are as follows:

    +
    • NONE
    • READ_UNCOMMITTED
    • READ_COMMITTED
    • REPEATABLE_READ
    • SERIALIZABLE
    +

    The default value is READ_UNCOMMITTED.

    +
    +
    +
  2. Insert data
    1
    sparkSession.sql("insert into dli_to_dws values(1, 'John',24),(2, 'Bob',32)")
+
    + +
    +
  3. Query data
    1
+2
    val dataFrame = sparkSession.sql("select * from dli_to_dws")
+dataFrame.show()
+
    + +
    +

    Before data is inserted:

    +

    +

    Response:

    +

    +
  4. Delete the datasource connection table.
    1
    sparkSession.sql("drop table dli_to_dws")
+
    + +
    +
+
+

Accessing a Data Source Using a DataFrame API

  1. Set connection parameters.
    1
+2
+3
+4
    val url = "jdbc:postgresql://to-dws-1174405057-EA1Kgo8H.datasource.com:8000/postgres"
+val username = "dbadmin"
+val password = "######"
+val dbtable = "customer"
+
    + +
    +
  2. Create a DataFrame, add data, and rename fields
    1
+2
+3
+4
    var dataFrame_1 = sparkSession.createDataFrame(List((8, "Jack_1", 18)))
+val df = dataFrame_1.withColumnRenamed("_1", "id")
+                    .withColumnRenamed("_2", "name")
+                    .withColumnRenamed("_3", "age")
+
    + +
    +
  3. Import data to GaussDB(DWS).
    1
+2
+3
+4
+5
+6
+7
    df.write.format("jdbc")
+  .option("url", url)
+  .option("dbtable", dbtable)
+  .option("user", username)
+  .option("password", password)
+  .mode(SaveMode.Append)
+  .save()
+
    + +
    +

    The options of SaveMode can be one of the following:

    +
    • ErrorIfExis: If the data already exists, the system throws an exception.
    • Overwrite: If the data already exists, the original data will be overwritten.
    • Append: If the data already exists, the system saves the new data.
    • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
    +
    +
    +
  4. Read data from GaussDB(DWS).
    • Method 1: read.format()
      1
+2
+3
+4
+5
+6
      val jdbcDF = sparkSession.read.format("jdbc")
+                 .option("url", url)
+                 .option("dbtable", dbtable)
+                 .option("user", username)
+                 .option("password", password)
+                 .load()
+
      + +
      +
    • Method 2: read.jdbc()
      1
+2
+3
+4
      val properties = new Properties()
+ properties.put("user", username)
+ properties.put("password", password)
+ val jdbcDF2 = sparkSession.read.jdbc(url, dbtable, properties)
+
      + +
      +
    +

    Before data is inserted:

    +

    +

    Response:

    +

    +

    The dateFrame read by the read.format() or read.jdbc() method is registered as a temporary table. Then, you can use SQL statements to query data.

    +
    1
+2
    jdbcDF.registerTempTable("customer_test")
+ sparkSession.sql("select * from customer_test where id = 1").show()
+
    + +
    +

    Query results

    +

    +
+
+

DataFrame-Related Operations

The data created by the createDataFrame() method and the data queried by the read.format() method and the read.jdbc() method are all DataFrame objects. You can directly query a single record. (In Accessing a Data Source Using a DataFrame API, the DataFrame data is registered as a temporary table.)

+
  • where

    The where statement can be combined with filter expressions such as AND and OR. The DataFrame object after filtering is returned. The following is an example:

    +
    1
    jdbcDF.where("id = 1 or age <=10").show()
+
    + +
    +

    +
  • filter

    The filter statement can be used in the same way as where. The DataFrame object after filtering is returned. The following is an example:

    +
    1
    jdbcDF.filter("id = 1 or age <=10").show()
+
    + +
    +

    +
  • select

    The select statement is used to query the DataFrame object of the specified field. Multiple fields can be queried.

    +
    • Example 1:
      1
      jdbcDF.select("id").show()
+
      + +
      +

      +
    • Example 2:
      1
      jdbcDF.select("id", "name").show()
+
      + +
      +

      +
    • Example 3:
      1
      jdbcDF.select("id","name").where("id<4").show()
+
      + +
      +

      +
    +
  • selectExpr

    The selectExpr statement is used to perform special processing on a field. For example, it can be used to change the field name. The following is an example:

    +

    If you want to set the name field to name_test and add 1 to the value of age, run the following statement:

    +
    1
    jdbcDF.selectExpr("id", "name as name_test", "age+1").show()
+
    + +
    +
  • col

    col is used to obtain a specified field. Different from select, col can only be used to query the column type and one field can be returned at a time. The following is an example:

    +
    1
    val idCol = jdbcDF.col("id")
+
    + +
    +
  • drop

    drop is used to delete a specified field. Specify a field you need to delete (only one field can be deleted at a time), the DataFrame object that does not contain the field is returned. The following is an example:

    +
    1
    jdbcDF.drop("id").show()
+
    + +
    +

    +
+
+

Submitting a Job

  1. Generate a JAR file based on the code and upload the file to DLI.

    +

    +
  2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

    +

    +
+
+

Complete Example Code

  • Maven dependency
    1
+2
+3
+4
+5
    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
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    import java.util.Properties
+import org.apache.spark.sql.SparkSession
+
+object Test_SQL_DWS {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+    // Create a data table for DLI-associated DWS
+    sparkSession.sql("CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS (
+	  'url'='jdbc:postgresql://to-dws-1174405057-EA1Kgo8H.datasource.com:8000/postgres',
+	  'dbtable'='customer',
+	  'user'='dbadmin',
+	  'password'='######')")
+
+    //*****************************SQL model***********************************
+    //Insert data into the DLI data table
+    sparkSession.sql("insert into dli_to_dws values(1,'John',24),(2,'Bob',32)")
+  
+    //Read data from DLI data table
+    val dataFrame = sparkSession.sql("select * from dli_to_dws")
+    dataFrame.show()
+  
+    //drop table
+    sparkSession.sql("drop table dli_to_dws")
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • Connecting to data sources through DataFrame APIs

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
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    import java.util.Properties
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.SaveMode
+
+object Test_SQL_DWS {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.
+    val sparkSession = SparkSession.builder().getOrCreate()
+
+    //*****************************DataFrame model***********************************
+    // Set the connection configuration parameters. Contains url, username, password, dbtable.
+    val url = "jdbc:postgresql://to-dws-1174405057-EA1Kgo8H.datasource.com:8000/postgres"
+    val username = "dbadmin"
+    val password = "######"
+    val dbtable = "customer"
+
+    //Create a DataFrame and initialize the DataFrame data.
+    var dataFrame_1 = sparkSession.createDataFrame(List((1, "Jack", 18)))
+ 
+    //Rename the fields set by the createDataFrame() method.
+    val df = dataFrame_1.withColumnRenamed("_1", "id")
+	                .withColumnRenamed("_2", "name")
+	                .withColumnRenamed("_3", "age")
+
+    //Write data to the dws_table_1 table
+    df.write.format("jdbc")
+      .option("url", url) 
+      .option("dbtable", dbtable) 
+      .option("user", username) 
+      .option("password", password) 
+      .mode(SaveMode.Append) 
+      .save()
+
+    // DataFrame object for data manipulation
+    //Filter users with id=1
+    var newDF = df.filter("id!=1")
+    newDF.show()
+  
+    // Filter the id column data
+    var newDF_1 = df.drop("id")
+    newDF_1.show()
+
+    // Read the data of the customer table in the RDS database
+    //Way one: Read data from GaussDB(DWS) using read.format()
+    val jdbcDF = sparkSession.read.format("jdbc")
+                    .option("url", url)
+                    .option("dbtable", dbtable)
+                    .option("user", username)
+                    .option("password", password)
+                    .option("driver", "org.postgresql.Driver")
+                    .load()
+    //Way two: Read data from GaussDB(DWS) using read.jdbc()
+    val properties = new Properties()
+    properties.put("user", username)
+    properties.put("password", password)
+    val jdbcDF2 = sparkSession.read.jdbc(url, dbtable, properties)
+
+    /**
+     * Register the dateFrame read by read.format() or read.jdbc() as a temporary table, and query the data 
+     * using the sql statement.
+     */
+    jdbcDF.registerTempTable("customer_test")
+    val result = sparkSession.sql("select * from customer_test where id = 1")
+    result.show()
+
+    sparkSession.close()
+  }
+}
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to GaussDB(DWS)
+
+
+ diff --git a/docs/dli/dev/dli_09_0077.html b/docs/dli/dev/dli_09_0077.html new file mode 100644 index 00000000..0be5a04e --- /dev/null +++ b/docs/dli/dev/dli_09_0077.html @@ -0,0 +1,23 @@ + + +

Connecting to HBase

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0078.html b/docs/dli/dev/dli_09_0078.html new file mode 100644 index 00000000..03093374 --- /dev/null +++ b/docs/dli/dev/dli_09_0078.html @@ -0,0 +1,299 @@ + + +

PySpark Example Code

+

Development Description

The CloudTable HBase and MRS HBase can be connected to DLI as data sources.

+
  • Prerequisites

    A datasource connection has been created on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependency packages.
      1
+2
+3
      from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
+from pyspark.sql import SparkSession
+
      + +
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()
+
      + +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to an HBase data source.
      • The sample code is applicable, if Kerberos authentication is disabled for the interconnected HBase cluster:
        sparkSession.sql(
+    "CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
+    'ZKHost' = '192.168.0.189:2181',\
+    'TableName' = 'hbtest',\
+    'RowKey' = 'id:5',\
+    'Cols' = 'location:info.location,city:detail.city')")
        +
      • The sample code is applicable, if Kerberos authentication is enabled for the interconnected HBase cluster:
        sparkSession.sql(
+    "CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
+    'ZKHost' = '192.168.0.189:2181',\
+    'TableName' = 'hbtest',\
+    'RowKey' = 'id:5',\
+    'Cols' = 'location:info.location,city:detail.city',\
+    'krb5conf' = './krb5.conf',\
+    'keytab'='./user.keytab',\
+    'principal' ='krbtest')")
        +
        If Kerberos authentication is enabled, you need to set three more parameters, as listed in Table 1. +
        + + + + + + + + + + + + + +
        Table 1 Description

        Parameter and Value

        +

        Description

        +

        'krb5conf' = './krb5.conf'

        +

        Path of the krb5.conf file.

        +

        'keytab'='./user.keytab'

        +

        Path of the keytab file.

        +

        'principal' ='krbtest'

        +

        Authentication username.

        +
        +
        +
        +

        For details about how to obtain the krb5.conf and keytab files, see Completing Configurations for Enabling Kerberos Authentication.

        +

        For details about parameters in the table, see Table 1.

        +
        +
      +
    2. Import data to HBase.
      sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")
      +
    3. Read data from HBase.
      sparkSession.sql("select * from testhbase").show()
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Create a table to connect to an HBase data source.
      1
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      sparkSession.sql(\
+  "CREATE TABLE test_hbase(id STRING, location STRING, city STRING, booleanf BOOLEAN, shortf SHORT, intf INT, longf LONG,
+     floatf FLOAT, doublef DOUBLE) using hbase OPTIONS (\
+   'ZKHost' = 'cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,\
+               cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,\
+               cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',\
+   'TableName' = 'table_DupRowkey1',\
+   'RowKey' = 'id:5,location:6,city:7',\
+   'Cols' = 'booleanf:CF1.booleanf, shortf:CF1.shortf, intf:CF1.intf, \  longf:CF1.longf, floatf:CF1.floatf, doublef:CF1.doublef')")
+
      + +
      +
      • For details about the ZKHost, RowKey, and Cols parameters, see Table 1.
      • TableName: Name of a table in the CloudTable file. If no table name exists, the system automatically creates one.
      +
      +
    2. Construct a schema.
      1
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+4
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+8
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      schema = StructType([StructField("id", StringType()),\                     
+                     StructField("location", StringType()),\                     
+                     StructField("city", StringType()),\                     
+                     StructField("booleanf", BooleanType()),\                     
+                     StructField("shortf", ShortType()),\                     
+                     StructField("intf", IntegerType()),\                     
+                     StructField("longf", LongType()),\                     
+                     StructField("floatf", FloatType()),\                     
+                     StructField("doublef", DoubleType())])
+
      + +
      +
    3. Set data.
      1
      dataList = sparkSession.sparkContext.parallelize([("11111", "aaa", "aaa", False, 4, 3, 23, 2.3, 2.34)])
+
      + +
      +
    4. Create a DataFrame.
      1
      dataFrame = sparkSession.createDataFrame(dataList, schema)
+
      + +
      +
    5. Import data to HBase.
      1
      dataFrame.write.insertInto("test_hbase")
+
      + +
      +
    6. Read data from HBase.
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      // Set cross-source connection parameters
+TableName = "table_DupRowkey1"
+RowKey = "id:5,location:6,city:7"
+Cols = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
+ZKHost = "cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+          cloudtable-cf82-zk1- WY09px9l.cloudtable.com:2181"
+
+// select
+jdbcDF = sparkSession.read.schema(schema)\
+                 .format("hbase")\
+                 .option("ZKHost",ZKHost)\
+                 .option("TableName",TableName)\
+                 .option("RowKey",RowKey)\
+                 .option("Cols",Cols)\
+                 .load()
+jdbcDF.filter("id = '12333' or id='11111'").show()
+
      + +
      +

      The length of id, location, and city parameter is limited. When inserting data, you must set the data values based on the required length. Otherwise, an encoding format error occurs during query.

      +
      +
    +
  • Submitting a Spark job
    1. Upload the Python code file to DLI.

      +

      +
    2. (Optional) Add the krb5.conf and user.keytab files to other dependency files of the job when creating a Spark job in an MRS cluster with Kerberos authentication enabled. Skip this step if Kerberos authentication is not enabled for the cluster.
    3. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.hbase when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Connecting to MRS HBase through SQL APIs
    • Sample code when Kerberos authentication is disabled
      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()
+
+  sparkSession.sql(
+    "CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS (\
+    'ZKHost' = '192.168.0.189:2181',\
+    'TableName' = 'hbtest',\
+    'RowKey' = 'id:5',\
+    'Cols' = 'location:info.location,city:detail.city')")
+
+
+  sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")
+
+  sparkSession.sql("select * from testhbase").show()
+  # close session    
+  sparkSession.stop()
      +
    +
    • Sample code when Kerberos authentication is enabled
      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark import SparkFiles
+from pyspark.sql import SparkSession
+import shutil
+import time
+import os
+
+if __name__ == "__main__":
+    # Create a SparkSession session.
+    sparkSession = SparkSession.builder.appName("Test_HBase_SparkSql_Kerberos").getOrCreate()
+    sc = sparkSession.sparkContext
+    time.sleep(10)
+
+    krb5_startfile = SparkFiles.get("krb5.conf")
+    keytab_startfile = SparkFiles.get("user.keytab")
+    path_user = os.getcwd()
+    krb5_endfile = path_user + "/" + "krb5.conf"
+    keytab_endfile = path_user + "/" + "user.keytab"
+    shutil.copy(krb5_startfile, krb5_endfile)
+    shutil.copy(keytab_startfile, keytab_endfile)
+    time.sleep(20)
+
+    sparkSession.sql(
+      "CREATE TABLE testhbase(id string,booleanf boolean,shortf short,intf int,longf long,floatf float,doublef double) " +
+      "using hbase OPTIONS(" +
+      "'ZKHost'='10.0.0.146:2181'," +
+      "'TableName'='hbtest'," +
+      "'RowKey'='id:100'," +
+      "'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF2.longf,floatf:CF1.floatf,doublef:CF2.doublef'," +
+      "'krb5conf'='" + path_user + "/krb5.conf'," +
+      "'keytab'='" + path_user+ "/user.keytab'," +
+      "'principal'='krbtest') ")
+
+      sparkSession.sql("insert into testhbase values('95274','abc','Jinan')")
+
+    sparkSession.sql("select * from testhbase").show()
+    # close session
+    sparkSession.stop()
      +
    +
  • Connecting to HBase through DataFrame APIs
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType, BooleanType, ShortType, LongType, FloatType, DoubleType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-hbase").getOrCreate()
+
+  # Createa data table for DLI-associated ct    
+  sparkSession.sql(\
+   "CREATE TABLE test_hbase(id STRING, location STRING, city STRING, booleanf BOOLEAN, shortf SHORT, intf INT, longf LONG,floatf FLOAT,doublef DOUBLE) using hbase OPTIONS ( \
+    'ZKHost' = 'cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,\
+                cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,\
+                cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181',\
+    'TableName' = 'table_DupRowkey1',\
+    'RowKey' = 'id:5,location:6,city:7',\
+    'Cols' = 'booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef')")
+
+  # Create a DataFrame and initialize the DataFrame data.    
+  dataList = sparkSession.sparkContext.parallelize([("11111", "aaa", "aaa", False, 4, 3, 23, 2.3, 2.34)])
+
+  # Setting schema    
+  schema = StructType([StructField("id", StringType()), 
+                       StructField("location", StringType()), 
+                       StructField("city", StringType()),                         
+                       StructField("booleanf", BooleanType()),                        
+                       StructField("shortf", ShortType()),                     
+                       StructField("intf", IntegerType()),                 
+                       StructField("longf", LongType()),                   
+                       StructField("floatf", FloatType()),              
+                       StructField("doublef", DoubleType())])
+
+  # Create a DataFrame from RDD and schema    
+  dataFrame = sparkSession.createDataFrame(dataList, schema)
+
+  # Write data to the cloudtable-hbase    
+  dataFrame.write.insertInto("test_hbase")
+
+  # Set cross-source connection parameters    
+  TableName = "table_DupRowkey1"
+  RowKey = "id:5,location:6,city:7"
+  Cols = "booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF1.longf,floatf:CF1.floatf,doublef:CF1.doublef"
+  ZKHost = "cloudtable-cf82-zk3-pa6HnHpf.cloudtable.com:2181,cloudtable-cf82-zk2-weBkIrjI.cloudtable.com:2181,
+            cloudtable-cf82-zk1-WY09px9l.cloudtable.com:2181"
+  # Read data on CloudTable-HBase    
+  jdbcDF = sparkSession.read.schema(schema)\
+                       .format("hbase")\
+                       .option("ZKHost", ZKHost)\
+                       .option("TableName",TableName)\
+                       .option("RowKey", RowKey)\
+                       .option("Cols", Cols)\
+                       .load()    
+  jdbcDF.filter("id = '12333' or id='11111'").show()
+
+  # close session    
+  sparkSession.stop()
    +
+
+
+
+
+
Parent topic: Connecting to HBase
+
+
+ diff --git a/docs/dli/dev/dli_09_0080.html b/docs/dli/dev/dli_09_0080.html new file mode 100644 index 00000000..b7c36e21 --- /dev/null +++ b/docs/dli/dev/dli_09_0080.html @@ -0,0 +1,21 @@ + + +

Connecting to OpenTSDB

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0081.html b/docs/dli/dev/dli_09_0081.html new file mode 100644 index 00000000..c14fa840 --- /dev/null +++ b/docs/dli/dev/dli_09_0081.html @@ -0,0 +1,167 @@ + + +

PySpark Example Code

+

Development Description

The CloudTable OpenTSDB and MRS OpenTSDB can be connected to DLI as data sources.

+
  • Prerequisites

    A datasource connection has been created on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependency packages.
      1
+2
+3
      from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
+from pyspark.sql import SparkSession
+
      + +
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()
+
      + +
      +
    3. Create a table to connect to an OpenTSDB data source.
      1
+2
+3
+4
      sparkSession.sql("create table opentsdb_test using opentsdb options(
+  'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',
+  'metric'='ct_opentsdb',
+  'tags'='city,location')")
+
      + +
      +

      For details about the Host, metric, and tags parameters, see Table 1.

      +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Insert data.
      sparkSession.sql("insert into opentsdb_test values('aaa', 'abc', '2021-06-30 18:00:00', 30.0)")
      +
    2. Query data.
      result = sparkSession.sql("SELECT * FROM opentsdb_test")
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Construct a schema.
      1
+2
+3
+4
      schema = StructType([StructField("location", StringType()),\                     
+                     StructField("name", StringType()), \                   
+                     StructField("timestamp", LongType()),\                  
+                     StructField("value", DoubleType())])
+
      + +
      +
    2. Set data.
      1
      dataList = sparkSession.sparkContext.parallelize([("aaa", "abc", 123456L, 30.0)])
+
      + +
      +
    3. Create a DataFrame.
      1
      dataFrame = sparkSession.createDataFrame(dataList, schema)
+
      + +
      +
    4. Import data to OpenTSDB.
      1
      dataFrame.write.insertInto("opentsdb_test")
+
      + +
      +
    5. Read data from OpenTSDB.
      1
+2
+3
+4
+5
+6
+7
      jdbdDF = sparkSession.read
+    .format("opentsdb")\
+    .option("Host","opentsdb-3xcl8dir15m58z3.cloudtable.com:4242")\
+    .option("metric","ctopentsdb")\
+    .option("tags","city,location")\
+    .load()
+jdbdDF.show()
+
      + +
      +
    +
  • Submitting a Spark job
    1. Upload the Python code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.opentsdb when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Connecting to MRS OpenTSDB through SQL APIs
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()
+
+
+  # Create a DLI cross-source association opentsdb data table    
+  sparkSession.sql(\
+    "create table opentsdb_test using opentsdb options(\
+    'Host'='10.0.0.171:4242',\
+    'metric'='cts_opentsdb',\
+    'tags'='city,location')")
+
+  sparkSession.sql("insert into opentsdb_test values('aaa', 'abc', '2021-06-30 18:00:00', 30.0)")
+
+  result = sparkSession.sql("SELECT * FROM opentsdb_test")
+  result.show()
+
+  # close session 
+  sparkSession.stop()
    +
  • Connecting to OpenTSDB through DataFrame APIs
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()
+
+  # Create a DLI cross-source association opentsdb data table    
+  sparkSession.sql(
+    "create table opentsdb_test using opentsdb options(\
+    'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',\
+    'metric'='ct_opentsdb',\
+    'tags'='city,location')")
+
+  # Create a DataFrame and initialize the DataFrame data.    
+  dataList = sparkSession.sparkContext.parallelize([("aaa", "abc", 123456L, 30.0)])
+
+  # Setting schema   
+  schema = StructType([StructField("location", StringType()),\                 
+                       StructField("name", StringType()),\                
+                       StructField("timestamp", LongType()),\               
+                       StructField("value", DoubleType())])
+
+  # Create a DataFrame from RDD and schema   
+  dataFrame = sparkSession.createDataFrame(dataList, schema)
+
+  # Set cross-source connection parameters   
+  metric = "ctopentsdb"   
+  tags = "city,location"  
+  Host = "opentsdb-3xcl8dir15m58z3.cloudtable.com:4242"
+
+  # Write data to the cloudtable-opentsdb   
+  dataFrame.write.insertInto("opentsdb_test")
+  # ******* Opentsdb does not currently implement the ctas method to save data, so the save() method cannot be used.*******   
+  # dataFrame.write.format("opentsdb").option("Host", Host).option("metric", metric).option("tags", tags).mode("Overwrite").save()   
+
+  # Read data on CloudTable-OpenTSDB  
+  jdbdDF = sparkSession.read\       
+      .format("opentsdb")\      
+      .option("Host",Host)\     
+      .option("metric",metric)\  
+      .option("tags",tags)\    
+      .load()   
+  jdbdDF.show()
+
+  # close session 
+  sparkSession.stop()
    +
+
+
+
+
+
Parent topic: Connecting to OpenTSDB
+
+
+ diff --git a/docs/dli/dev/dli_09_0083.html b/docs/dli/dev/dli_09_0083.html new file mode 100644 index 00000000..71f36578 --- /dev/null +++ b/docs/dli/dev/dli_09_0083.html @@ -0,0 +1,19 @@ + + +

Connecting to RDS

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0084.html b/docs/dli/dev/dli_09_0084.html new file mode 100644 index 00000000..df951d2e --- /dev/null +++ b/docs/dli/dev/dli_09_0084.html @@ -0,0 +1,220 @@ + + +

PySpark Example Code

+

Development Description

  • Prerequisites

    A datasource connection has been created and bound to a queue on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependency packages.
      1
+2
+3
      from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
      + +
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder.appName("datasource-rds").getOrCreate()
+
      + +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Configure datasource connection parameters.
      1
+2
+3
+4
+5
      url = "jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306"
+dbtable = "test.customer"
+user = "root"
+password = "######"
+driver = "com.mysql.jdbc.Driver"
+
      + +
      +

      For details about the parameters, see Table 1.

      +
    2. Set data.
      1
      dataList = sparkSession.sparkContext.parallelize([(123, "Katie", 19)])
+
      + +
      +
    3. Configure the schema.
      1
+2
+3
      schema = StructType([StructField("id", IntegerType(), False),\                     
+                     StructField("name", StringType(), False),\                    
+                     StructField("age", IntegerType(), False)])
+
      + +
      +
    4. Create a DataFrame.
      1
      dataFrame = sparkSession.createDataFrame(dataList, schema)
+
      + +
      +
    5. Save data to RDS.
      1
+2
+3
+4
+5
+6
+7
+8
+9
      dataFrame.write \   
+    .format("jdbc") \   
+    .option("url", url) \  
+    .option("dbtable", dbtable) \  
+    .option("user", user) \  
+    .option("password", password) \  
+    .option("driver", driver) \   
+    .mode("Append") \  
+    .save()
+
      + +
      +

      The value of mode can be one of the following:

      +
      • ErrorIfExis: If the data already exists, the system throws an exception.
      • Overwrite: If the data already exists, the original data will be overwritten.
      • Append: If the data already exists, the system saves the new data.
      • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
      +
      +
    6. Read data from RDS.
      1
+2
+3
+4
+5
+6
+7
+8
+9
      jdbcDF = sparkSession.read \ 
+    .format("jdbc") \  
+    .option("url", url) \  
+    .option("dbtable", dbtable) \ 
+    .option("user", user) \  
+    .option("password", password) \ 
+    .option("driver", driver) \  
+    .load()
+jdbcDF.show()
+
      + +
      +
    7. View the operation result.

      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to an RDS data source and set connection parameters.
      1
+2
+3
+4
+5
+6
+7
      sparkSession.sql(
+    "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (\
+    'url'='jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306',\
+    'dbtable'='test.customer',\
+    'user'='root',\
+    'password'='######',\
+    'driver'='com.mysql.jdbc.Driver')")
+
      + +
      +

      For details about the parameters for creating a table, see Table 1.

      +
    2. Insert data.
      1
      sparkSession.sql("insert into dli_to_rds values(3,'John',24)")
+
      + +
      +
    3. Query data.
      1
+2
      jdbcDF_after = sparkSession.sql("select * from dli_to_rds")
+jdbcDF_after.show()
+
      + +
      +
    4. View the operation result.

      +
    +
  • Submitting a Spark job
    1. Upload the Python code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +

      +
    3. After the Spark job is created, click Execute in the upper right corner of the console to submit the job. If the message "Spark job submitted successfully." is displayed, the Spark job is successfully submitted. You can view the status and logs of the submitted job on the Spark Jobs page.
      • The queue you select for creating a Spark job is the one bound when you create the datasource connection.
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.rds when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +
      +
      +
    +
+
+

Complete Example Code

If the following sample code is directly copied to the .py file, note that unexpected characters may exist after the backslashes (\) in the file content. You need to delete the indentations or spaces after the backslashes (\).

+
+
  • Connecting to data sources through DataFrame APIs
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-rds").getOrCreate()
+
+  # Set cross-source connection parameters.   
+  url = "jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306"
+  dbtable = "test.customer"
+  user = "root"
+  password = "######"
+  driver = "com.mysql.jdbc.Driver"
+
+  # Create a DataFrame and initialize the DataFrame data.  
+  dataList = sparkSession.sparkContext.parallelize([(123, "Katie", 19)])
+
+  # Setting schema   
+  schema = StructType([StructField("id", IntegerType(), False),\          
+                       StructField("name", StringType(), False),\               
+                       StructField("age", IntegerType(), False)])
+
+  # Create a DataFrame from RDD and schema   
+  dataFrame = sparkSession.createDataFrame(dataList, schema)
+
+  # Write data to the RDS.
+  dataFrame.write \    
+      .format("jdbc") \     
+      .option("url", url) \     
+      .option("dbtable", dbtable) \   
+      .option("user", user) \    
+      .option("password", password) \     
+      .option("driver", driver) \     
+      .mode("Append") \      
+      .save()
+
+  # Read data  
+  jdbcDF = sparkSession.read \    
+      .format("jdbc") \      
+      .option("url", url) \   
+      .option("dbtable", dbtable) \   
+      .option("user", user) \   
+      .option("password", password) \    
+      .option("driver", driver) \  
+      .load()  
+  jdbcDF.show()
+
+  # close session  
+  sparkSession.stop()
    +
  • Connecting to data sources through SQL APIs
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-rds").getOrCreate()
+
+  # Create a data table for DLI - associated RDS    
+  sparkSession.sql(
+       "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (\
+       'url'='jdbc:mysql://to-rds-1174404952-ZgPo1nNC.datasource.com:3306',\
+       'dbtable'='test.customer',\
+       'user'='root',\
+       'password'='######',\
+       'driver'='com.mysql.jdbc.Driver')")
+
+  # Insert data into the DLI data table   
+  sparkSession.sql("insert into dli_to_rds values(3,'John',24)")
+
+  # Read data from DLI data table    
+  jdbcDF = sparkSession.sql("select * from dli_to_rds")   
+  jdbcDF.show() 
+
+  # close session  
+  sparkSession.stop()
    +
+
+
+
+
+
Parent topic: Connecting to RDS
+
+
+ diff --git a/docs/dli/dev/dli_09_0086.html b/docs/dli/dev/dli_09_0086.html new file mode 100644 index 00000000..4d6a9e47 --- /dev/null +++ b/docs/dli/dev/dli_09_0086.html @@ -0,0 +1,19 @@ + + +

Connecting to GaussDB(DWS)

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0087.html b/docs/dli/dev/dli_09_0087.html new file mode 100644 index 00000000..8199d396 --- /dev/null +++ b/docs/dli/dev/dli_09_0087.html @@ -0,0 +1,298 @@ + + +

PySpark Example Code

+

Scenario

This section provides PySpark example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.

+

A datasource connection has been created and bound to a queue on the DLI management console.

+

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

+
+
+

Preparations

  1. Import dependency packages.
    1
+2
+3
    from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
    + +
    +
  2. Create a session.
    1
    sparkSession = SparkSession.builder.appName("datasource-dws").getOrCreate()
+
    + +
    +
+
+

Accessing a Data Source Using a DataFrame API

  1. Set connection parameters.
    1
+2
+3
+4
+5
    url = "jdbc:postgresql://to-dws-1174404951-W8W4cW8I.datasource.com:8000/postgres"
+dbtable = "customer"
+user = "dbadmin"
+password = "######"
+driver = "org.postgresql.Driver"
+
    + +
    +
  2. Set data.
    1
    dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19)])
+
    + +
    +
  3. Configure the schema.
    1
+2
+3
    schema = StructType([StructField("id", IntegerType(), False),\                
+                     StructField("name", StringType(), False),\            
+                     StructField("age", IntegerType(), False)])
+
    + +
    +
  4. Create a DataFrame.
    1
    dataFrame = sparkSession.createDataFrame(dataList, schema)
+
    + +
    +
  5. Save the data to GaussDB(DWS).
    1
+2
+3
+4
+5
+6
+7
+8
+9
    dataFrame.write \   
+    .format("jdbc") \  
+    .option("url", url) \  
+    .option("dbtable", dbtable) \  
+    .option("user", user) \ 
+    .option("password", password) \ 
+    .option("driver", driver) \ 
+    .mode("Overwrite") \  
+    .save()
+
    + +
    +

    The options of mode can be one of the following:

    +
    • ErrorIfExis: If the data already exists, the system throws an exception.
    • Overwrite: If the data already exists, the original data will be overwritten.
    • Append: If the data already exists, the system saves the new data.
    • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
    +
    +
  6. Read data from GaussDB(DWS).
    1
+2
+3
+4
+5
+6
+7
+8
+9
    jdbcDF = sparkSession.read \
+    .format("jdbc") \
+    .option("url", url) \
+    .option("dbtable", dbtable) \
+    .option("user", user) \
+    .option("password", password) \
+    .option("driver", driver) \
+    .load()
+jdbcDF.show()
+
    + +
    +
  7. View the operation result.

    +
+
+

Accessing a Data Source Using a SQL API

  1. Create a table to connect to a GaussDB(DWS) data source.
    1
+2
+3
+4
+5
+6
+7
    sparkSession.sql(
+    "CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS (
+    'url'='jdbc:postgresql://to-dws-1174404951-W8W4cW8I.datasource.com:8000/postgres',\
+    'dbtable'='customer',\
+    'user'='dbadmin',\
+    'password'='######',\
+    'driver'='org.postgresql.Driver')")
+
    + +
    +

    For details about table creation parameters, see Table 1.

    +
    +
  2. Insert data.
    1
    sparkSession.sql("insert into dli_to_dws values(2,'John',24)")
+
    + +
    +
  3. Query data.
    1
    jdbcDF = sparkSession.sql("select * from dli_to_dws").show()
+
    + +
    +
  4. View the operation result.

    +
+
+

Submitting a Spark Job

  1. Upload the Python code file to DLI.
  2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
    • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, set Module to sys.datasource.hbase when you submit a job.
    • If the Spark version is 3.1.1, you do not need to select a module. Set Spark parameters (--conf).

      spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/dws/*

      +

      spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/dws/*

      +
    +
    +
+
+

Complete Example Code

  • Connecting to data sources through DataFrame APIs

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
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+40
+41
+42
+43
+44
+45
+46
+47
+48
+49
+50
+51
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.   
+  sparkSession = SparkSession.builder.appName("datasource-dws").getOrCreate()
+
+  # Set cross-source connection parameters  
+  url = "jdbc:postgresql://to-dws-1174404951-W8W4cW8I.datasource.com:8000/postgres"
+  dbtable = "customer" 
+  user = "dbadmin"
+  password = "######"
+  driver = "org.postgresql.Driver"
+
+  # Create a DataFrame and initialize the DataFrame data.   
+  dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19)])
+
+  # Setting schema   
+  schema = StructType([StructField("id", IntegerType(), False),\     
+                       StructField("name", StringType(), False),\    
+                       StructField("age", IntegerType(), False)])
+
+  # Create a DataFrame from RDD and schema   
+  dataFrame = sparkSession.createDataFrame(dataList, schema)
+
+  # Write data to the DWS table  
+  dataFrame.write \ 
+      .format("jdbc") \    
+      .option("url", url) \  
+      .option("dbtable", dbtable) \    
+      .option("user", user) \    
+      .option("password", password) \   
+      .option("driver", driver) \     
+      .mode("Overwrite") \   
+      .save()
+
+  # Read data   
+  jdbcDF = sparkSession.read \  
+      .format("jdbc") \     
+      .option("url", url) \  
+      .option("dbtable", dbtable) \     
+      .option("user", user) \     
+      .option("password", password) \   
+      .option("driver", driver) \  
+      .load()  
+  jdbcDF.show()
+
+  # close session  
+  sparkSession.stop()
+
    + +
    +
  • Connecting to data sources through SQL APIs
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session. 
+  sparkSession = SparkSession.builder.appName("datasource-dws").getOrCreate()
+
+  # Create a data table for DLI - associated GaussDB(DWS)
+  sparkSession.sql(
+      "CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS (\
+      'url'='jdbc:postgresql://to-dws-1174404951-W8W4cW8I.datasource.com:8000/postgres',\
+      'dbtable'='customer',\
+      'user'='dbadmin',\
+      'password'='######',\
+      'driver'='org.postgresql.Driver')")
+
+  # Insert data into the DLI data table  
+  sparkSession.sql("insert into dli_to_dws values(2,'John',24)")
+
+  # Read data from DLI data table  
+  jdbcDF = sparkSession.sql("select * from dli_to_dws").show()
+
+  # close session  
+  sparkSession.stop()
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to GaussDB(DWS)
+
+
+ diff --git a/docs/dli/dev/dli_09_0089.html b/docs/dli/dev/dli_09_0089.html new file mode 100644 index 00000000..16562b9a --- /dev/null +++ b/docs/dli/dev/dli_09_0089.html @@ -0,0 +1,21 @@ + + +

Connecting to CSS

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0090.html b/docs/dli/dev/dli_09_0090.html new file mode 100644 index 00000000..e9bc9d94 --- /dev/null +++ b/docs/dli/dev/dli_09_0090.html @@ -0,0 +1,546 @@ + + +

PySpark Example Code

+

Prerequisites

A datasource connection has been created on the DLI management console.

+
+

CSS Non-Security Cluster

  • Development description
    • Code implementation
      1. Import dependency packages.
        1
+2
+3
        from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType, Row
+from pyspark.sql import SparkSession
+
        + +
        +
      2. Create a session.
        1
        sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+
        + +
        +
      +
    • Connecting to data sources through DataFrame APIs
      1. Set connection parameters.
        1
+2
        resource = "/mytest"
+nodes = "to-css-1174404953-hDTx3UPK.datasource.com:9200"
+
        + +
        +

        resource indicates the name of the resource associated with the CSS. You can specify the resource location in /index/type format. (The index can be the database and type the table.)

        +
        • In Elasticsearch 6.X, a single index supports only one type, and the type name can be customized.
        • In Elasticsearch 7.X, a single index uses _doc as the type name and cannot be customized. To access Elasticsearch 7.X, set this parameter to index.
        +
        +
      2. Create a schema and add data to it.
        1
+2
+3
        schema = StructType([StructField("id", IntegerType(), False),                  
+                     StructField("name", StringType(), False)])
+rdd = sparkSession.sparkContext.parallelize([Row(1, "John"), Row(2, "Bob")])
+
        + +
        +
      3. Construct a DataFrame.
        1
        dataFrame = sparkSession.createDataFrame(rdd, schema)
+
        + +
        +
      4. Save data to CSS.
        1
        dataFrame.write.format("css").option("resource", resource).option("es.nodes", nodes).mode("Overwrite").save()
+
        + +
        +

        The options of mode can be one of the following:

        +
        • ErrorIfExis: If the data already exists, the system throws an exception.
        • Overwrite: If the data already exists, the original data will be overwritten.
        • Append: If the data already exists, the system saves the new data.
        • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
        +
        +
      5. Read data from CSS.
        1
+2
        jdbcDF = sparkSession.read.format("css").option("resource", resource).option("es.nodes", nodes).load()
+jdbcDF.show()
+
        + +
        +
      6. View the operation result.

        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
        1
+2
+3
+4
+5
        sparkSession.sql(
+    "create table css_table(id long, name string) using css options(  
+    'es.nodes'='to-css-1174404953-hDTx3UPK.datasource.com:9200',
+    'es.nodes.wan.only'='true',
+    'resource'='/mytest')")
+
        + +
        +

        For details about the parameters for creating a CSS datasource connection table, see Table 1.

        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(3,'tom')")
+
        + +
        +
      3. Query data.
        1
+2
        jdbcDF = sparkSession.sql("select * from css_table")
+jdbcDF.show()
+
        + +
        +
      4. View the operation result.

        +
      +
    • Submitting a Spark job
      1. Upload the Python code file to DLI.

        +

        +
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

        +
        • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.css when you submit a job.
        • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

          spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +

          spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +
        +
        +
        +
      +
    +
  • Complete example code
    • Connecting to data sources through DataFrame APIs
       1
+ 2
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      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import Row, StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.   
+  sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+
+  # Setting cross-source connection parameters  
+  resource = "/mytest"
+  nodes = "to-css-1174404953-hDTx3UPK.datasource.com:9200"
+
+  # Setting schema  
+  schema = StructType([StructField("id", IntegerType(), False),       
+                       StructField("name", StringType(), False)])
+
+  # Construction data 
+  rdd = sparkSession.sparkContext.parallelize([Row(1, "John"), Row(2, "Bob")])
+
+  # Create a DataFrame from RDD and schema  
+  dataFrame = sparkSession.createDataFrame(rdd, schema)
+
+  # Write data to the CSS 
+  dataFrame.write.format("css").option("resource", resource).option("es.nodes", nodes).mode("Overwrite").save()
+
+  # Read data  
+  jdbcDF = sparkSession.read.format("css").option("resource", resource).option("es.nodes", nodes).load()
+  jdbcDF.show()
+
+  # close session  
+  sparkSession.stop()
+
      + +
      +
    • Connecting to data sources through SQL APIs
       1
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      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.  
+  sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+
+  # Create a DLI data table for DLI-associated CSS   
+  sparkSession.sql(
+      "create table css_table(id long, name string) using css options( \
+      'es.nodes'='to-css-1174404953-hDTx3UPK.datasource.com:9200',\
+      'es.nodes.wan.only'='true',\
+      'resource'='/mytest')")
+
+  # Insert data into the DLI data table  
+  sparkSession.sql("insert into css_table values(3,'tom')")
+
+  # Read data from DLI data table   
+  jdbcDF = sparkSession.sql("select * from css_table")   
+  jdbcDF.show()
+
+  # close session  
+  sparkSession.stop()
+
      + +
      +
    +
+
+

CSS Security Cluster

  • Development description
    • Code implementation
      1. Import dependency packages.
        1
+2
+3
        from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType, Row
+from pyspark.sql import SparkSession
+
        + +
        +
      2. Create a session and set the AKs and SKs.

        Hard-coded or plaintext AK and SK pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.

        +
        +
        1
+2
+3
+4
+5
        sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+sparkSession.conf.set("fs.obs.access.key", ak)
+sparkSession.conf.set("fs.obs.secret.key", sk)
+sparkSession.conf.set("fs.obs.endpoint", enpoint)
+sparkSession.conf.set("fs.obs.connecton.ssl.enabled", "false")
+
        + +
        +
      +
    • Connecting to data sources through DataFrame APIs
      1. Set connection parameters.
        1
+2
        resource = "/mytest";
+nodes = "to-css-1174404953-hDTx3UPK.datasource.com:9200"
+
        + +
        +

        resource indicates the name of the resource associated with the CSS. You can specify the resource location in /index/type format. (The index can be the database and type the table.)

        +
        • In Elasticsearch 6.X, a single index supports only one type, and the type name can be customized.
        • In Elasticsearch 7.X, a single index uses _doc as the type name and cannot be customized. To access Elasticsearch 7.X, set this parameter to index.
        +
        +
      2. Create a schema and add data to it.
        1
+2
+3
        schema = StructType([StructField("id", IntegerType(), False),                  
+                     StructField("name", StringType(), False)])
+rdd = sparkSession.sparkContext.parallelize([Row(1, "John"), Row(2, "Bob")])
+
        + +
        +
      3. Construct a DataFrame.
        1
        dataFrame = sparkSession.createDataFrame(rdd, schema)
+
        + +
        +
      4. Save data to CSS.
         1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
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        dataFrame.write.format("css")
+  .option("resource", resource)
+  .option("es.nodes", nodes)
+  .option("es.net.ssl", "true")
+  .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+  .option("es.net.ssl.keystore.pass", "***")
+  .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+  .option("es.net.ssl.truststore.pass", "***")
+  .option("es.net.http.auth.user", "admin")
+  .option("es.net.http.auth.pass", "***")
+  .mode("Overwrite")
+  .save()
+
        + +
        +

        The options of mode can be one of the following:

        +
        • ErrorIfExis: If the data already exists, the system throws an exception.
        • Overwrite: If the data already exists, the original data will be overwritten.
        • Append: If the data already exists, the system saves the new data.
        • Ignore: If the data already exists, no operation is required. This is similar to the SQL statement CREATE TABLE IF NOT EXISTS.
        +
        +
      5. Read data from CSS.
         1
+ 2
+ 3
+ 4
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+ 9
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        jdbcDF = sparkSession.read.format("css")\
+  .option("resource", resource)\
+  .option("es.nodes", nodes)\
+  .option("es.net.ssl", "true")\
+  .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")\
+  .option("es.net.ssl.keystore.pass", "***")\
+  .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")\
+  .option("es.net.ssl.truststore.pass", "***")\
+  .option("es.net.http.auth.user", "admin")\
+  .option("es.net.http.auth.pass", "***")\
+  .load()
+jdbcDF.show()
+
        + +
        +
      6. View the operation result.

        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
         1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
        sparkSession.sql(
+        "create table css_table(id long, name string) using css options(\  
+        'es.nodes'='to-css-1174404953-hDTx3UPK.datasource.com:9200',\
+        'es.nodes.wan.only'='true',\
+        'resource'='/mytest',\
+	'es.net.ssl'='true',\
+	'es.net.ssl.keystore.location'='obs://Bucket name/path/transport-keystore.jks',\
+	'es.net.ssl.keystore.pass'='***',\
+	'es.net.ssl.truststore.location'='obs://Bucket name/path/truststore.jks',\
+	'es.net.ssl.truststore.pass'='***',\
+	'es.net.http.auth.user'='admin',\
+	'es.net.http.auth.pass'='***')")
+
        + +
        +

        For details about the parameters for creating a CSS datasource connection table, see Table 1.

        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(3,'tom')")
+
        + +
        +
      3. Query data.
        1
+2
        jdbcDF = sparkSession.sql("select * from css_table")
+jdbcDF.show()
+
        + +
        +
      4. View the operation result.

        +
      +
    • Submitting a Spark job
      1. Upload the Python code file to DLI.
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
        • When submitting a job, you need to specify a dependency module named sys.datasource.css.
        • For details about how to submit a job on the DLI console, see
        • For details about how to submit a job through an API, see the modules parameter in
        +
        +
      +
    +
  • Complete example code
    • Connecting to data sources through DataFrame APIs

      Hard-coded or plaintext AK and SK pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.

      +
      +
       1
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      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import Row, StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.   
+  sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+  sparkSession.conf.set("fs.obs.access.key", ak)
+  sparkSession.conf.set("fs.obs.secret.key", sk)
+  sparkSession.conf.set("fs.obs.endpoint", enpoint)
+  sparkSession.conf.set("fs.obs.connecton.ssl.enabled", "false")
+
+  # Setting cross-source connection parameters  
+  resource = "/mytest";
+  nodes = "to-css-1174404953-hDTx3UPK.datasource.com:9200"
+
+  # Setting schema  
+  schema = StructType([StructField("id", IntegerType(), False),       
+                       StructField("name", StringType(), False)])
+
+  # Construction data 
+  rdd = sparkSession.sparkContext.parallelize([Row(1, "John"), Row(2, "Bob")])
+
+  # Create a DataFrame from RDD and schema  
+  dataFrame = sparkSession.createDataFrame(rdd, schema)
+
+  # Write data to the CSS 
+  dataFrame.write.format("css")
+    .option("resource", resource)
+    .option("es.nodes", nodes)
+    .option("es.net.ssl", "true")
+    .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+    .option("es.net.ssl.keystore.pass", "***")
+    .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+    .option("es.net.ssl.truststore.pass", "***")
+    .option("es.net.http.auth.user", "admin")
+    .option("es.net.http.auth.pass", "***")
+    .mode("Overwrite")
+    .save()
+
+  # Read data  
+  jdbcDF = sparkSession.read.format("css")\
+    .option("resource", resource)\
+    .option("es.nodes", nodes)\
+    .option("es.net.ssl", "true")\
+    .option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")\
+    .option("es.net.ssl.keystore.pass", "***")\
+    .option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+    .option("es.net.ssl.truststore.pass", "***")\
+    .option("es.net.http.auth.user", "admin")\
+    .option("es.net.http.auth.pass", "***")\
+    .load()
+  jdbcDF.show()
+
+  # close session  
+  sparkSession.stop()
+
      + +
      +
    • Connecting to data sources through SQL APIs
       1
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      # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql import SparkSession
+import os               
+ 
+if __name__ == "__main__":
+  
+  # Create a SparkSession session.   
+  sparkSession = SparkSession.builder.appName("datasource-css").getOrCreate()
+  # Create a DLI data table for DLI-associated CSS   
+  sparkSession.sql("create table css_table(id int, name string) using css options(\
+                    'es.nodes'='192.168.6.204:9200',\
+                    'es.nodes.wan.only'='true',\
+                    'resource'='/mytest',\
+                    'es.net.ssl'='true',\
+                    'es.net.ssl.keystore.location' = 'obs://xietest1/lzq/keystore.jks',\
+                    'es.net.ssl.keystore.pass' = '**',\
+                    'es.net.ssl.truststore.location'='obs://xietest1/lzq/truststore.jks',\
+                    'es.net.ssl.truststore.pass'='**',\
+                    'es.net.http.auth.user'='admin',\
+                    'es.net.http.auth.pass'='**')")
+ 
+  # Insert data into the DLI data table  
+  sparkSession.sql("insert into css_table values(3,'tom')")
+ 
+  # Read data from DLI data table   
+  jdbcDF = sparkSession.sql("select * from css_table")   
+  jdbcDF.show()
+ 
+  # close session  
+  sparkSession.stop()
+
      + +
      +
    +
+
+
+
+
+
Parent topic: Connecting to CSS
+
+
+ diff --git a/docs/dli/dev/dli_09_0093.html b/docs/dli/dev/dli_09_0093.html new file mode 100644 index 00000000..6ffe961c --- /dev/null +++ b/docs/dli/dev/dli_09_0093.html @@ -0,0 +1,21 @@ + + +

Connecting to Redis

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0094.html b/docs/dli/dev/dli_09_0094.html new file mode 100644 index 00000000..517d40e3 --- /dev/null +++ b/docs/dli/dev/dli_09_0094.html @@ -0,0 +1,603 @@ + + +

Scala Example Code

+

Development Description

Redis supports only enhanced datasource connections.

+
  • Prerequisites

    An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Constructing dependency information and creating a Spark session
    1. Import dependencies.
      Maven dependency involved
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+<dependency>
+  <groupId>redis.clients</groupId>
+  <artifactId>jedis</artifactId>
+  <version>3.1.0</version>
+</dependency>
+<dependency>
+  <groupId>com.redislabs</groupId>
+  <artifactId>spark-redis</artifactId>
+  <version>2.4.0</version>
+</dependency>
+
      + +
      +
      +
      Import dependency packages.
      1
+2
+3
+4
+5
      import org.apache.spark.sql.{Row, SaveMode, SparkSession}
+import org.apache.spark.sql.types._
+import com.redislabs.provider.redis._
+import scala.reflect.runtime.universe._
+import org.apache.spark.{SparkConf, SparkContext}
+
      + +
      +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Create a session.
      1
      val sparkSession = SparkSession.builder().appName("datasource_redis").getOrCreate()
+
      + +
      +
    2. Construct a schema.
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
      //method one
+var schema = StructType(Seq(StructField("name", StringType, false), StructField("age", IntegerType, false)))
+var rdd = sparkSession.sparkContext.parallelize(Seq(Row("abc",34),Row("Bob",19)))
+var dataFrame = sparkSession.createDataFrame(rdd, schema)
+// //method two
+// var jdbcDF= sparkSession.createDataFrame(Seq(("Jack",23)))
+// val dataFrame = jdbcDF.withColumnRenamed("_1", "name").withColumnRenamed("_2", "age")
+// //method three 
+// case class Person(name: String, age: Int)
+// val dataFrame = sparkSession.createDataFrame(Seq(Person("John", 30), Person("Peter", 45)))
+
      + +
      +

      case class Person(name: String, age: Int) must be written outside the object. For details, see Connecting to data sources through DataFrame APIs.

      +
      +
    3. Import data to Redis.
      1
+2
+3
+4
+5
+6
+7
+8
+9
      dataFrame .write
+  .format("redis")
+  .option("host","192.168.4.199")
+  .option("port","6379")
+  .option("table","person")
+  .option("password","******")
+  .option("key.column","name")
+  .mode(SaveMode.Overwrite)
+  .save()
+
      + +
      + +
      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      Table 1 Redis operation parameters

      Parameter

      +

      Description

      +

      host

      +

      IP address of the Redis cluster to be connected.

      +

      To obtain the IP address, log in to the official website, search for redis, go to the console of Distributed Cache Service for Redis, and choose Cache Manager. Select an IP address (including the port information) based on the IP address required by the host name to copy the data.

      +

      port

      +

      Access port.

      +

      password

      +

      Password for the connection. This parameter is optional if no password is required.

      +

      table

      +

      Key or hash key in Redis.

      +
      • This parameter is mandatory when Redis data is inserted.
      • Either this parameter or the keys.pattern parameter when Redis data is queried.
      +

      keys.pattern

      +

      Use a regular expression to match multiple keys or hash keys. This parameter is used only for query. Either this parameter or table is used to query Redis data.

      +

      key.column

      +

      Key value of a column. This parameter is optional. If a key is specified when data is written, the key must be specified during query. Otherwise, the key will be abnormally loaded during query.

      +

      partitions.number

      +

      Number of concurrent tasks during data reading.

      +

      scan.count

      +

      Number of data records read in each batch. The default value is 100. If the CPU usage of the Redis cluster still needs to be improved during data reading, increase the value of this parameter.

      +

      iterator.grouping.size

      +

      Number of data records inserted in each batch. The default value is 100. If the CPU usage of the Redis cluster still needs to be improved during the insertion, increase the value of this parameter.

      +

      timeout

      +

      Timeout interval for connecting to the Redis, in milliseconds. The default value is 2000 (2 seconds).

      +
      +
      +
      • The options of mode are Overwrite, Append, ErrorIfExis, and Ignore.
      • To save nested DataFrames, use .option("model", "binary").
      • Specify the data expiration time by .option("ttl", 1000). The unit is second.
      +
      +
    4. Read data from Redis.
      1
+2
+3
+4
+5
+6
+7
+8
+9
      sparkSession.read
+  .format("redis")
+  .option("host","192.168.4.199")
+  .option("port","6379")
+  .option("table", "person")
+  .option("password","######")
+  .option("key.column","name")
+  .load()
+  .show()
+
      + +
      +
    +
  • Connecting to data sources using Spark RDDs
    1. Create a datasource connection.
      1
+2
+3
+4
+5
+6
      val sparkContext = new SparkContext(new SparkConf()
+   .setAppName("datasource_redis")
+   .set("spark.redis.host", "192.168.4.199")
+   .set("spark.redis.port", "6379")
+   .set("spark.redis.auth", "######")
+   .set("spark.driver.allowMultipleContexts","true"))
+
      + +
      +

      If spark.driver.allowMultipleContexts is set to true, only the current context is used when multiple contexts are started, to prevent context invoking conflicts.

      +
      +
    2. Insert data.
      1. Save data in strings.
        1
+2
        val stringRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("high","111"), ("together","333")))
+sparkContext.toRedisKV(stringRedisData)
+
        + +
        +
      2. Save data in hashes.
        1
+2
        val hashRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("saprk","123"), ("data","222")))
+sparkContext.toRedisHASH(hashRedisData, "hashRDD")
+
        + +
        +
      3. Save data in lists.
        1
+2
+3
        val data = List(("school","112"), ("tom","333"))
+val listRedisData:RDD[String] = sparkContext.parallelize(Seq[(String)](data.toString()))
+sparkContext.toRedisLIST(listRedisData, "listRDD")
+
        + +
        +
      4. Save data in sets.
        1
+2
+3
        val setData = Set(("bob","133"),("kity","322"))
+val setRedisData:RDD[(String)] = sparkContext.parallelize(Seq[(String)](setData.mkString))
+sparkContext.toRedisSET(setRedisData, "setRDD")
+
        + +
        +
      5. Save data in zsets.
        1
+2
        val zsetRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("whight","234"), ("bobo","343")))
+sparkContext.toRedisZSET(zsetRedisData, "zsetRDD")
+
        + +
        +
      +
    3. Query data.
      1. Query data by traversing keys.
        1
+2
+3
+4
+5
+6
        val keysRDD = sparkContext.fromRedisKeys(Array("high","together", "hashRDD", "listRDD", "setRDD","zsetRDD"), 6)
+keysRDD.getKV().collect().foreach(println)
+keysRDD.getHash().collect().foreach(println)
+keysRDD.getList().collect().foreach(println)
+keysRDD.getSet().collect().foreach(println)
+keysRDD.getZSet().collect().foreach(println)
+
        + +
        +
      2. Query data by string.
        1
        sparkContext.fromRedisKV(Array( "high","together")).collect().foreach{println}
+
        + +
        +
      3. Query data by hash.
        1
        sparkContext.fromRedisHash(Array("hashRDD")).collect().foreach{println}
+
        + +
        +
      4. Query data by list.
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        sparkContext.fromRedisList(Array("listRDD")).collect().foreach{println}
+
        + +
        +
      5. Query data by set.
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        sparkContext.fromRedisSet(Array("setRDD")).collect().foreach{println}
+
        + +
        +
      6. Query data by zset.
        1
        sparkContext.fromRedisZSet(Array("zsetRDD")).collect().foreach{println}
+
        + +
        +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to a Redis data source.
      sparkSession.sql(
+     "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (
+     'host' = '192.168.4.199',
+     'port' = '6379',
+     'password' = '######',
+     table  'person')".stripMargin)
      +
    2. Insert data.
      1
      sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin)
+
      + +
      +
    3. Query data.
      1
      sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println)
+
      + +
      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.redis when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Maven dependency
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    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+<dependency>
+  <groupId>redis.clients</groupId>
+  <artifactId>jedis</artifactId>
+  <version>3.1.0</version>
+</dependency>
+<dependency>
+  <groupId>com.redislabs</groupId>
+  <artifactId>spark-redis</artifactId>
+  <version>2.4.0</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs
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    import org.apache.spark.sql.{SparkSession};
+
+object Test_Redis_SQL {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.  
+    val sparkSession = SparkSession.builder().appName("datasource_redis").getOrCreate();  
+   
+    sparkSession.sql(
+      "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (
+         'host' = '192.168.4.199', 'port' = '6379', 'password' = '******',table 'person')".stripMargin)  
+    
+    sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin) 
+   
+    sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println)
+  
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • Connecting to data sources through DataFrame APIs
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    import org.apache.spark.sql.{Row, SaveMode, SparkSession}
+import org.apache.spark.sql.types._
+
+object Test_Redis_SparkSql {
+  def main(args: Array[String]): Unit = {
+  // Create a SparkSession session.  
+  val sparkSession = SparkSession.builder().appName("datasource_redis").getOrCreate()
+
+  // Set cross-source connection parameters.  
+  val host = "192.168.4.199"
+  val port = "6379"
+  val table = "person"
+  val auth = "######"
+  val key_column = "name"
+  
+  // ******** setting DataFrame ********  
+  // method one
+  var schema = StructType(Seq(StructField("name", StringType, false),StructField("age", IntegerType, false)))
+  var rdd = sparkSession.sparkContext.parallelize(Seq(Row("xxx",34),Row("Bob",19)))
+  var dataFrame = sparkSession.createDataFrame(rdd, schema)
+
+// // method two
+// var jdbcDF= sparkSession.createDataFrame(Seq(("Jack",23)))
+// val dataFrame = jdbcDF.withColumnRenamed("_1", "name").withColumnRenamed("_2", "age")
+
+// // method three
+// val dataFrame = sparkSession.createDataFrame(Seq(Person("John", 30), Person("Peter", 45)))
+
+  // Write data to redis  
+  dataFrame.write.format("redis").option("host",host).option("port",port).option("table", table).option("password",auth).mode(SaveMode.Overwrite).save()
+
+  // Read data from redis  
+  sparkSession.read.format("redis").option("host",host).option("port",port).option("table", table).option("password",auth).load().show()
+
+  // Close session  
+  sparkSession.close() 
+  }
+}
+// methoe two
+// case class Person(name: String, age: Int)
+
    + +
    +
  • Connecting to data sources using Spark RDDs
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    import com.redislabs.provider.redis._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{SparkConf, SparkContext}
+
+object Test_Redis_RDD {
+  def main(args: Array[String]): Unit = {
+    // Create a SparkSession session.  
+    val sparkContext = new SparkContext(new SparkConf()
+          .setAppName("datasource_redis")
+          .set("spark.redis.host", "192.168.4.199")
+          .set("spark.redis.port", "6379")
+          .set("spark.redis.auth", "@@@@@@")
+          .set("spark.driver.allowMultipleContexts","true"))
+
+    //***************** Write data to redis **********************  
+    // Save String type data  
+    val stringRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("high","111"), ("together","333")))   
+    sparkContext.toRedisKV(stringRedisData)
+  
+    // Save Hash type data  
+    val hashRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("saprk","123"), ("data","222")))  
+    sparkContext.toRedisHASH(hashRedisData, "hashRDD")
+
+    // Save List type data  
+    val data = List(("school","112"), ("tom","333"));
+    val listRedisData:RDD[String] = sparkContext.parallelize(Seq[(String)](data.toString()))
+    sparkContext.toRedisLIST(listRedisData, "listRDD")
+
+    // Save Set type data  
+    val setData = Set(("bob","133"),("kity","322"))
+    val setRedisData:RDD[(String)] = sparkContext.parallelize(Seq[(String)](setData.mkString))
+    sparkContext.toRedisSET(setRedisData, "setRDD")
+  
+    // Save ZSet type data 
+    val zsetRedisData:RDD[(String,String)] = sparkContext.parallelize(Seq[(String,String)](("whight","234"), ("bobo","343")))  
+    sparkContext.toRedisZSET(zsetRedisData, "zsetRDD")
+
+    // ***************************** Read data from redis *******************************************  
+    // Traverse the specified key and get the value
+    val keysRDD = sparkContext.fromRedisKeys(Array("high","together", "hashRDD", "listRDD", "setRDD","zsetRDD"), 6)  
+    keysRDD.getKV().collect().foreach(println)
+    keysRDD.getHash().collect().foreach(println)
+    keysRDD.getList().collect().foreach(println)
+    keysRDD.getSet().collect().foreach(println)
+    keysRDD.getZSet().collect().foreach(println)
+ 
+    // Read String type data//
+    val stringRDD = sparkContext.fromRedisKV("keyPattern *")
+    sparkContext.fromRedisKV(Array( "high","together")).collect().foreach{println}
+  
+    // Read Hash type data//
+    val hashRDD = sparkContext.fromRedisHash("keyPattern *")
+    sparkContext.fromRedisHash(Array("hashRDD")).collect().foreach{println}
+  
+    // Read List type data//
+    val listRDD = sparkContext.fromRedisList("keyPattern *")
+    sparkContext.fromRedisList(Array("listRDD")).collect().foreach{println}
+  
+    // Read Set type data//
+    val setRDD = sparkContext.fromRedisSet("keyPattern *")
+    sparkContext.fromRedisSet(Array("setRDD")).collect().foreach{println}
+
+    // Read ZSet type data//
+    val zsetRDD = sparkContext.fromRedisZSet("keyPattern *")
+    sparkContext.fromRedisZSet(Array("zsetRDD")).collect().foreach{println}
+  
+    // close session
+    sparkContext.stop()
+  }
+}
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to Redis
+
+
+ diff --git a/docs/dli/dev/dli_09_0097.html b/docs/dli/dev/dli_09_0097.html new file mode 100644 index 00000000..62c9a063 --- /dev/null +++ b/docs/dli/dev/dli_09_0097.html @@ -0,0 +1,217 @@ + + +

PySpark Example Code

+

Development Description

Redis supports only enhanced datasource connections.

+
  • Prerequisites

    An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Connecting to data sources through DataFrame APIs
    1. Import dependencies.
      1
+2
+3
      from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
      + +
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder.appName("datasource-redis").getOrCreate()
+
      + +
      +
    3. Set connection parameters.
      1
+2
+3
+4
      host = "192.168.4.199"
+port = "6379"
+table = "person"
+auth = "@@@@@@"
+
      + +
      +
    4. Create a DataFrame.
      1. Method 1:
        1
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+3
+4
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        dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19),(2,"Tom",20)])
+schema = StructType([StructField("id", IntegerType(), False),          
+                     StructField("name", StringType(), False),
+                     StructField("age", IntegerType(), False)])
+dataFrame = sparkSession.createDataFrame(dataList, schema)
+
        + +
        +
      2. Method 2:
        1
+2
        jdbcDF = sparkSession.createDataFrame([(3,"Jack", 23)])
+dataFrame = jdbcDF.withColumnRenamed("_1", "id").withColumnRenamed("_2", "name").withColumnRenamed("_3", "age")
+
        + +
        +
      +
    5. Import data to Redis.
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      dataFrame.write
+  .format("redis")\
+  .option("host", host)\
+  .option("port", port)\
+  .option("table", table)\
+  .option("password", auth)\
+  .mode("Overwrite")\
+  .save()
+
      + +
      +
      • The options of mode are Overwrite, Append, ErrorIfExis, and Ignore.
      • To specify a key, use .option("key.column", "name"). name indicates the column name.
      • To save nested DataFrames, use .option("model", "binary").
      • If you need to specify the data expiration time, use .option("ttl", 1000). The unit is second.
      +
      +
    6. Read data from Redis.
      1
      sparkSession.read.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).load().show()
+
      + +
      +
    7. View the operation result.

      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to a Redis data source.
      sparkSession.sql(
+     "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (
+     'host' = '192.168.4.199',
+     'port' = '6379',
+     'password' = '######',
+     table  'person')".stripMargin)
      +
    2. Insert data.
      1
      sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin)
+
      + +
      +
    3. Query data.
      1
      sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println)
+
      + +
      +
    +
  • Submitting a Spark job
    1. Upload the Python code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.redis when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Connecting to data sources through DataFrame APIs
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    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+if __name__ == "__main__":
+  # Create a SparkSession session.    
+  sparkSession = SparkSession.builder.appName("datasource-redis").getOrCreate()
+  
+  # Set cross-source connection parameters.
+  host = "192.168.4.199"
+  port = "6379"
+  table = "person"  
+  auth = "######"
+     
+  # Create a DataFrame and initialize the DataFrame data.    
+  # *******   method noe   *********    
+  dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19),(2,"Tom",20)])
+  schema = StructType([StructField("id", IntegerType(), False),StructField("name", StringType(), False),StructField("age", IntegerType(), False)])
+  dataFrame_one = sparkSession.createDataFrame(dataList, schema)
+
+  # ****** method two ******  
+  # jdbcDF = sparkSession.createDataFrame([(3,"Jack", 23)])
+  # dataFrame = jdbcDF.withColumnRenamed("_1", "id").withColumnRenamed("_2", "name").withColumnRenamed("_3", "age")
+  
+  # Write data to the redis table  
+  dataFrame.write.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).mode("Overwrite").save()  
+  # Read data  
+  sparkSession.read.format("redis").option("host", host).option("port", port).option("table", table).option("password", auth).load().show()
+  
+  # close session  
+  sparkSession.stop()
+
    + +
    +
  • Connecting to data sources through SQL APIs
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    # _*_ coding: utf-8 _*_
+from __future__ import print_function
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession  
+  sparkSession = SparkSession.builder.appName("datasource_redis").getOrCreate()
+
+  sparkSession.sql(
+    "CREATE TEMPORARY VIEW person (name STRING, age INT) USING org.apache.spark.sql.redis OPTIONS (\
+    'host' = '192.168.4.199', \
+    'port' = '6379',\
+    'password' = '######',\
+    'table'= 'person')".stripMargin); 
+
+  sparkSession.sql("INSERT INTO TABLE person VALUES ('John', 30),('Peter', 45)".stripMargin)
+  
+  sparkSession.sql("SELECT * FROM person".stripMargin).collect().foreach(println)
+   
+  # close session  
+  sparkSession.stop()
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to Redis
+
+
+ diff --git a/docs/dli/dev/dli_09_0100.html b/docs/dli/dev/dli_09_0100.html new file mode 100644 index 00000000..e15596e3 --- /dev/null +++ b/docs/dli/dev/dli_09_0100.html @@ -0,0 +1,157 @@ + + +

Java Example Code

+

Development Description

Redis supports only enhanced datasource connections.

+
  • Prerequisites

    An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependencies.
      • Maven dependency involved
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
      • Import dependency packages.
        1
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        import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.*;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+import java.util.*;
+
        + +
        +
      +
    2. Create a session.
      1
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      SparkConf sparkConf = new SparkConf();
+sparkConf.setAppName("datasource-redis")
+        .set("spark.redis.host", "192.168.4.199")
+        .set("spark.redis.port", "6379")
+        .set("spark.redis.auth", "******")
+        .set("spark.driver.allowMultipleContexts","true");
+JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
+SQLContext sqlContext = new SQLContext(javaSparkContext);
+
      + +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Read JSON data as DataFrames.
      1
+2
+3
+4
      JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList(
+        "{\"id\":\"1\",\"name\":\"Ann\",\"age\":\"18\"}",
+        "{\"id\":\"2\",\"name\":\"lisi\",\"age\":\"21\"}"));
+Dataset dataFrame = sqlContext.read().json(javaRDD);
+
      + +
      +
    2. Construct the Redis connection parameters.
      1
+2
+3
      Map map = new HashMap<String, String>();
+map.put("table","person");
+map.put("key.column","id");
+
      + +
      +
    3. Save data to Redis.
      1
      dataFrame.write().format("redis").options(map).mode(SaveMode.Overwrite).save();
+
      + +
      +
    4. Read data from Redis.
      1
      sqlContext.read().format("redis").options(map).load().show();
+
      + +
      +
    +
  • Submitting a Spark job
    1. Upload the Java code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.redis when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select the Module module. You need to configure the 'Spark parameter (--conf) '.

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/redis/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

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public class Test_Redis_DaraFrame {
+  public static void main(String[] args) {
+    //create a SparkSession session  
+    SparkConf sparkConf = new SparkConf();  
+    sparkConf.setAppName("datasource-redis")
+             .set("spark.redis.host", "192.168.4.199")
+             .set("spark.redis.port", "6379")
+             .set("spark.redis.auth", "******")
+             .set("spark.driver.allowMultipleContexts","true");
+    JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);
+    SQLContext sqlContext = new SQLContext(javaSparkContext);
+
+    //Read RDD in JSON format to create DataFrame
+    JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList(
+            "{\"id\":\"1\",\"name\":\"Ann\",\"age\":\"18\"}",
+            "{\"id\":\"2\",\"name\":\"lisi\",\"age\":\"21\"}"));
+    Dataset dataFrame = sqlContext.read().json(javaRDD);
+
+    Map map = new HashMap<String, String>();
+    map.put("table","person");
+    map.put("key.column","id");
+    dataFrame.write().format("redis").options(map).mode(SaveMode.Overwrite).save();      
+    sqlContext.read().format("redis").options(map).load().show();
+
+  }
+}
+
+ +
+
+
+
+
+
Parent topic: Connecting to Redis
+
+
+ diff --git a/docs/dli/dev/dli_09_0110.html b/docs/dli/dev/dli_09_0110.html new file mode 100644 index 00000000..3c9d220d --- /dev/null +++ b/docs/dli/dev/dli_09_0110.html @@ -0,0 +1,202 @@ + + +

Java Example Code

+

Development Description

Mongo can be connected only through enhanced datasource connections.

+

DDS is compatible with the MongoDB protocol.

+
+
  • Prerequisites

    An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependencies.
      • Maven dependency involved
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
      • Import dependency packages.
        import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.SaveMode;
        +
      +
    2. Create a session.
      1
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      SparkContext sparkContext = new SparkContext(new SparkConf().setAppName("datasource-mongo"));
+JavaSparkContext javaSparkContext = new JavaSparkContext(sparkContext);
+SQLContext sqlContext = new SQLContext(javaSparkContext);
+
      + +
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Read JSON data as DataFrames.
      JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList("{\"id\":\"5\",\"name\":\"Ann\",\"age\":\"23\"}"));
+Dataset<Row> dataFrame = sqlContext.read().json(javaRDD);
      +
    2. Set connection parameters.
      String url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin";
+String uri = "mongodb://username:pwd@host:8635/db";
+String user = "rwuser";
+String database = "test";
+String collection = "test";
+String password = "######";
      +

      For details about the parameters, see Table 1.

      +
      +
    3. Import data to Mongo.
      dataFrame.write().format("mongo")   
+     .option("url",url)
+     .option("uri",uri)     
+     .option("database",database)  
+     .option("collection",collection)   
+     .option("user",user)    
+     .option("password",password)  
+     .mode(SaveMode.Overwrite)  
+     .save();
      +
    4. Read data from Mongo.
      1
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      sqlContext.read().format("mongo")  
+    .option("url",url)    
+    .option("uri",uri)
+    .option("database",database)   
+    .option("collection",collection)  
+    .option("user",user)   
+    .option("password",password)  
+    .load().show();
+
      + +
      +
    +
  • Submitting a Spark job
    1. Upload the Java code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.mongo when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

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import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.SaveMode;
+import java.util.Arrays;
+
+public class TestMongoSparkSql {
+  public static void main(String[] args) {
+    SparkContext sparkContext = new SparkContext(new SparkConf().setAppName("datasource-mongo"));
+    JavaSparkContext javaSparkContext = new JavaSparkContext(sparkContext);
+    SQLContext sqlContext = new SQLContext(javaSparkContext);
+
+//    // Read json file as DataFrame, read csv / parquet file, same as json file distribution
+//    DataFrame dataFrame = sqlContext.read().format("json").load("filepath");
+
+    // Read RDD in JSON format to create DataFrame
+    JavaRDD<String> javaRDD = javaSparkContext.parallelize(Arrays.asList("{\"id\":\"5\",\"name\":\"Ann\",\"age\":\"23\"}"));
+    Dataset<Row> dataFrame = sqlContext.read().json(javaRDD);
+
+    String url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin";
+    String uri = "mongodb://username:pwd@host:8635/db";
+    String user = "rwuser";
+    String database = "test";
+    String collection = "test";
+    String password = "######";
+
+    dataFrame.write().format("mongo")
+            .option("url",url)
+            .option("uri",uri)
+            .option("database",database) 
+            .option("collection",collection)
+            .option("user",user)
+            .option("password",password)
+            .mode(SaveMode.Overwrite) 
+            .save();
+
+    sqlContext.read().format("mongo")
+            .option("url",url)
+            .option("uri",uri)
+            .option("database",database)
+            .option("collection",collection)
+            .option("user",user)
+            .option("password",password)
+            .load().show();
+    sparkContext.stop();
+    javaSparkContext.close();
+  }
+}
+
+ +
+
+
+
+
+
Parent topic: Connecting to Mongo
+
+
+ diff --git a/docs/dli/dev/dli_09_0113.html b/docs/dli/dev/dli_09_0113.html new file mode 100644 index 00000000..40ee6f3c --- /dev/null +++ b/docs/dli/dev/dli_09_0113.html @@ -0,0 +1,19 @@ + + +

Connecting to Mongo

+
+
+ + +
+
Parent topic: Using Spark Jobs to Access Data Sources of Datasource Connections
+
+
+ diff --git a/docs/dli/dev/dli_09_0114.html b/docs/dli/dev/dli_09_0114.html new file mode 100644 index 00000000..e3525adf --- /dev/null +++ b/docs/dli/dev/dli_09_0114.html @@ -0,0 +1,263 @@ + + +

Scala Example Code

+

Development Description

Mongo can be connected only through enhanced datasource connections.

+

DDS is compatible with the MongoDB protocol.

+
+

An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

+

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

+
+
  • Constructing dependency information and creating a Spark session
    1. Import dependencies.
      Maven dependency involved
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
      +
      Import dependency packages.
      import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
      +
      +
      Create a session.
      val sparkSession = SparkSession.builder().appName("datasource-mongo").getOrCreate()
      +
      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to a Mongo data source.
      sparkSession.sql(
+  "create table test_dds(id string, name string, age int) using mongo options(
+    'url' = '192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin',
+    'uri' = 'mongodb://username:pwd@host:8635/db',
+    'database' = 'test',
+    'collection' = 'test',
+    'user' = 'rwuser',
+    'password' = '######')")
      + +
      + + + + + + + + + + + + + + + + + + + + + + +
      Table 1 Parameters for creating a table

      Parameter

      +

      Description

      +

      url

      +
      • URL format:

        "IP:PORT[,IP:PORT]/[DATABASE][.COLLECTION][AUTH_PROPERTIES]"

        +

        Example:

        +
        "192.168.4.62:8635/test?authSource=admin"
        +
      • The URL needs to be obtained from the Mongo (DDS) connection address..

        The obtained Mongo connection address is in the following format: Protocol header://Username:Password@Connection address:Port number/Database name?authSource=admin

        +

        Example:

        +
        mongodb://rwuser:****@192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin
        +
      +

      uri

      +

      URI format: mongodb://username:pwd@host:8635/db

      +

      Set the following parameters to the actual values:

      +
      • username: username used for creating the Mongo (DDS) database
      • pwd: password of the username for the Mongo (DDS) database
      • host: IP address of the Mongo (DDS) database instance
      • db: name of the created Mongo (DDS) database
      +

      database

      +

      DDS database name. If the database name is specified in the URL, the database name in the URL does not take effect.

      +

      collection

      +

      Collection name in the DDS. If the collection is specified in the URL, the collection in the URL does not take effect.

      +
      NOTE:

      If a collection already exists in DDS, you do not need to specify schema information when creating a table. DLI automatically generates schema information based on data in the collection.

      +
      +

      user

      +

      Username for accessing the DDS cluster.

      +

      password

      +

      Password for accessing the DDS cluster.

      +
      +
      +
    2. Insert data.
      sparkSession.sql("insert into test_dds values('3', 'Ann',23)")
      +
    3. Query data.
      sparkSession.sql("select * from test_dds").show()
      +
    +
  • Connecting to data sources through DataFrame APIs
    1. Set connection parameters.
      val url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin"
+val uri = "mongodb://username:pwd@host:8635/db"
+val user = "rwuser"
+val database = "test"
+val collection = "test"
+val password = "######"
      +
    2. Construct a schema.
      1
      val schema = StructType(List(StructField("id", StringType), StructField("name", StringType), StructField("age", IntegerType)))
+
      + +
      +
    3. Construct a DataFrame.
      val rdd = spark.sparkContext.parallelize(Seq(Row("1", "John", 23), Row("2", "Bob", 32)))
+val dataFrame = spark.createDataFrame(rdd, schema)
      +
    4. Import data to Mongo.
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      dataFrame.write.format("mongo")
+  .option("url", url)
+  .option("uri", uri)
+  .option("database", database)
+  .option("collection", collection)
+  .option("user", user)
+  .option("password", password)
+  .mode(SaveMode.Overwrite)
+  .save()
+
      + +
      +

      The options of mode are Overwrite, Append, ErrorIfExis, and Ignore.

      +
      +
    5. Read data from Mongo.
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      val jdbcDF = spark.read.format("mongo").schema(schema)
+  .option("url", url)
+  .option("uri", uri)
+  .option("database", database)
+  .option("collection", collection)
+  .option("user", user)
+  .option("password", password)
+  .load()
+
      + +
      +

      Operation result

      +

      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.mongo when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Maven dependency
    1
+2
+3
+4
+5
    <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
    + +
    +
  • Connecting to data sources through SQL APIs
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    import org.apache.spark.sql.SparkSession
+
+object TestMongoSql {
+  def main(args: Array[String]): Unit = {
+    val sparkSession = SparkSession.builder().getOrCreate()
+    sparkSession.sql(
+      "create table test_dds(id string, name string, age int) using mongo options(
+        'url' = '192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin',
+        'uri' = 'mongodb://username:pwd@host:8635/db',
+        'database' = 'test',
+        'collection' = 'test',
+        'user' = 'rwuser',
+        'password' = '######')")
+    sparkSession.sql("insert into test_dds values('3', 'Ann',23)")
+    sparkSession.sql("select * from test_dds").show()
+    sparkSession.close()
+  }
+}
+
    + +
    +
  • Connecting to data sources through DataFrame APIs
    import org.apache.spark.sql.{Row, SaveMode, SparkSession}
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
+
+object Test_Mongo_SparkSql {
+  def main(args: Array[String]): Unit = {
+  //  Create a SparkSession session.  
+  val spark = SparkSession.builder().appName("mongodbTest").getOrCreate()
+
+  // Set the connection configuration parameters.  
+  val url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin"
+  val uri = "mongodb://username:pwd@host:8635/db"
+  val user = "rwuser"
+  val database = "test"
+  val collection = "test"
+  val password = "######"
+
+  // Setting up the schema
+  val schema = StructType(List(StructField("id", StringType), StructField("name", StringType), StructField("age", IntegerType)))
+
+  // Setting up the DataFrame
+  val rdd = spark.sparkContext.parallelize(Seq(Row("1", "John", 23), Row("2", "Bob", 32)))
+  val dataFrame = spark.createDataFrame(rdd, schema)
+
+
+  // Write data to mongo
+  dataFrame.write.format("mongo")
+    .option("url", url)
+    .option("uri", uri)
+    .option("database", database)
+    .option("collection", collection)
+    .option("user", user)
+    .option("password", password)
+    .mode(SaveMode.Overwrite)
+    .save()
+
+  // Reading data from mongo
+  val jdbcDF = spark.read.format("mongo").schema(schema)
+    .option("url", url)
+    .option("uri", uri)
+    .option("database", database)
+    .option("collection", collection)
+    .option("user", user)
+    .option("password", password)
+    .load()
+  jdbcDF.show()
+
+  spark.close()
+ }
+}
    +
+
+
+
+
+
Parent topic: Connecting to Mongo
+
+
+ diff --git a/docs/dli/dev/dli_09_0117.html b/docs/dli/dev/dli_09_0117.html new file mode 100644 index 00000000..7701989e --- /dev/null +++ b/docs/dli/dev/dli_09_0117.html @@ -0,0 +1,200 @@ + + +

PySpark Example Code

+

Development Description

Mongo can be connected only through enhanced datasource connections.

+

DDS is compatible with the MongoDB protocol.

+
+
  • Prerequisites

    An enhanced datasource connection has been created on the DLI management console and bound to a queue in packages.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Connecting to data sources through DataFrame APIs
    1. Import dependencies.
      from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder.appName("datasource-mongo").getOrCreate()
+
      + +
      +
    3. Set connection parameters.
      1
+2
+3
+4
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+6
      url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin"
+uri = "mongodb://username:pwd@host:8635/db"
+user = "rwuser"
+database = "test"
+collection = "test"
+password = "######"
+
      + +
      +

      For details about the parameters, see Table 1.

      +
      +
    4. Create a DataFrame.
      1
+2
+3
+4
+5
      dataList = sparkSession.sparkContext.parallelize([(1, "Katie", 19),(2,"Tom",20)])
+schema = StructType([StructField("id", IntegerType(), False),          
+                     StructField("name", StringType(), False),
+                     StructField("age", IntegerType(), False)])
+dataFrame = sparkSession.createDataFrame(dataList, schema)
+
      + +
      +
    5. Import data to Mongo.
      1
+2
+3
+4
+5
+6
+7
+8
+9
      dataFrame.write.format("mongo")
+  .option("url", url)\
+  .option("uri", uri)\
+  .option("user",user)\
+  .option("password",password)\
+  .option("database",database)\
+  .option("collection",collection)\
+  .mode("Overwrite")\
+  .save()
+
      + +
      +
    6. Read data from Mongo.
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
      jdbcDF = sparkSession.read
+  .format("mongo")\
+  .option("url", url)\
+  .option("uri", uri)\
+  .option("user",user)\
+  .option("password",password)\
+  .option("database",database)\
+  .option("collection",collection)\
+  .load()
+jdbcDF.show()
+
      + +
      +
    7. View the operation result.

      +
    +
  • Connecting to data sources through SQL APIs
    1. Create a table to connect to a Mongo data source.
      sparkSession.sql(
+      "create table test_dds(id string, name string, age int) using mongo options(
+      'url' = '192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin',
+      'uri' = 'mongodb://username:pwd@host:8635/db',
+      'database' = 'test',
+      'collection' = 'test', 
+      'user' = 'rwuser', 
+      'password' = '######')")
      +

      For details about the parameters, see Table 1.

      +
      +
    2. Insert data.
      1
      sparkSession.sql("insert into test_dds values('3', 'Ann',23)")
+
      + +
      +
    3. Query data.
      1
      sparkSession.sql("select * from test_dds").show()
+
      + +
      +
    +
  • Submitting a Spark job
    1. Upload the Python code file to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.mongo when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/mongo/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Connecting to data sources through DataFrame APIs
    from __future__ import print_function
+from pyspark.sql.types import StructType, StructField, IntegerType, StringType
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.
+  sparkSession = SparkSession.builder.appName("datasource-mongo").getOrCreate()
+
+  # Create a DataFrame and initialize the DataFrame data.  
+  dataList = sparkSession.sparkContext.parallelize([("1", "Katie", 19),("2","Tom",20)])
+  
+  # Setting schema  
+  schema = StructType([StructField("id", IntegerType(), False),StructField("name", StringType(), False), StructField("age", IntegerType(), False)])
+
+  # Create a DataFrame from RDD and schema  
+  dataFrame = sparkSession.createDataFrame(dataList, schema)
+
+  # Setting connection parameters  
+  url = "192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin"
+  uri = "mongodb://username:pwd@host:8635/db"
+  user = "rwuser"
+  database = "test"
+  collection = "test"
+  password = "######"
+ 
+  # Write data to the mongodb table  
+  dataFrame.write.format("mongo")
+    .option("url", url)\
+    .option("uri", uri)\
+    .option("user",user)\
+    .option("password",password)\
+    .option("database",database)\
+    .option("collection",collection)
+    .mode("Overwrite").save()
+
+  # Read data  
+  jdbcDF = sparkSession.read.format("mongo")
+    .option("url", url)\
+    .option("uri", uri)\
+    .option("user",user)\
+    .option("password",password)\
+    .option("database",database)\
+    .option("collection",collection)\
+    .load()   
+  jdbcDF.show()
+ 
+  # close session  
+  sparkSession.stop()
    +
  • Connecting to data sources through SQL APIs
    from __future__ import print_function
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+  # Create a SparkSession session.  
+  sparkSession = SparkSession.builder.appName("datasource-mongo").getOrCreate()
+
+  # Create a data table for DLI - associated mongo
+    sparkSession.sql(
+      "create table test_dds(id string, name string, age int) using mongo options(\
+      'url' = '192.168.4.62:8635,192.168.5.134:8635/test?authSource=admin',\
+      'uri' = 'mongodb://username:pwd@host:8635/db',\
+      'database' = 'test',\
+      'collection' = 'test', \
+      'user' = 'rwuser', \
+      'password' = '######')")
+
+  # Insert data into the DLI-table  
+  sparkSession.sql("insert into test_dds values('3', 'Ann',23)")
+
+  # Read data from DLI-table  
+  sparkSession.sql("select * from test_dds").show()
+
+  # close session  
+  sparkSession.stop()
    +
+
+
+
+
+
Parent topic: Connecting to Mongo
+
+
+ diff --git a/docs/dli/dev/dli_09_0120.html b/docs/dli/dev/dli_09_0120.html new file mode 100644 index 00000000..7a1e9c02 --- /dev/null +++ b/docs/dli/dev/dli_09_0120.html @@ -0,0 +1,19 @@ + + +

SQL Jobs

+
+
+ +
+ diff --git a/docs/dli/dev/dli_09_0121.html b/docs/dli/dev/dli_09_0121.html new file mode 100644 index 00000000..1daecd83 --- /dev/null +++ b/docs/dli/dev/dli_09_0121.html @@ -0,0 +1,46 @@ + + +

Performing Authentication

+

Scenario

You need to be authenticated when using JDBC to create DLI driver connections.

+
+

Procedure

Currently, the JDBC supports authentication using the Access Key/Secret Key (AK/SK) or token.

+
  • (Recommended) Generating an AK/SK
    1. Log in to the DLI management console.
    2. Click the username in the upper right corner and select My Credentials from the drop-down list.
    3. On the displayed My Credentials page, click Access Keys. By default, the Project List page is displayed.
    4. Click Add Access Key. In the displayed Add Access Key dialog box, specify Login Password and SMS Verification Code.
    5. Click OK to download the certificate.
    6. After the certificate is downloaded, you can obtain the AK and SK information in the credentials file.
    +

    Hard coding AKs and SKs or storing them in code in plaintext poses significant security risks. You are advised to store them in encrypted form in configuration files or environment variables and decrypt them when needed to ensure security.

    +
    +
  • Obtain the token.

    When using token authentication, you need to obtain the user token and configure the token information in the JDBC connection parameters. You can obtain the token as follows:

    +
    1. Send POST https://<IAM_Endpoint>/v3/auth/tokens. To obtain the IAM endpoint, contact the administrator to obtain the region and endpoint information.
      An example request message is as follows:

      Replace content in italic in the sample code with the actual values. For details, see .

      +
      +
      {
+  "auth": {
+    "identity": {
+      "methods": [
+        "password"
+      ],
+      "password": {
+        "user": {
+          "name": "username",
+          "password": "password",
+          "domain": {
+            "name": "domainname"
+          }
+        }
+      }
+    },
+    "scope": {
+      "project": {
+        "id": "0aa253a31a2f4cfda30eaa073fee6477" //Assume that project_id is 0aa253a31a2f4cfda30eaa073fee6477.
+      }
+    }
+  }
+}
      +
      +
    2. After the request is processed, the value of X-Subject-Token in the response header is the token value.
    +
+
+
+
+
+
Parent topic: Submitting a Spark SQL Job Using JDBC
+
+
+ diff --git a/docs/dli/dev/dli_09_0122.html b/docs/dli/dev/dli_09_0122.html new file mode 100644 index 00000000..2c20cf4b --- /dev/null +++ b/docs/dli/dev/dli_09_0122.html @@ -0,0 +1,209 @@ + + +

Using Spark-submit to Submit a Spark Jar Job

+

Introduction to DLI Spark-submit

DLI Spark-submit is a command line tool used to submit Spark jobs to the DLI server. This tool provides command lines compatible with open-source Spark.

+
+

Preparations

  1. Getting authorized.

    DLI uses the Identity and Access Management (IAM) to implement fine-grained permissions for your enterprise-level tenants. IAM provides identity authentication, permissions management, and access control, helping you securely access your resources.

    +

    With IAM, you can use your account to create IAM users for your employees, and assign permissions to the users to control their access to specific resource types.

    +

    Currently, roles (coarse-grained authorization) and policies (fine-grained authorization) are supported.

    +
  2. Create a queue. Choose Resources > Queue Management. On the page displayed, click Buy Queue in the upper right corner. On the Buy Queue page displayed, select For general purpose for Type, that is, the compute resources of the Spark job.

    If the user who creates the queue is not an administrator, the queue can be used only after being authorized by the administrator. For details about how to assign permissions, see .

    +
    +
+
+

Downloading the DLI Client Tool

You can download the DLI client tool from the DLI management console.

+
  1. Log in to the DLI management console.
  2. Obtain the SDK download address from the administrator.
  3. On the DLI SDK DOWNLOAD page, click dli-clientkit-<version> to download the DLI client tool.

    The Beeline client is named dli-clientkit-<version>-bin.tar.gz, which can be used in Linux and depends on JDK 1.8 or later.

    +
    +

+
+

Configuring DLI Spark-submit

Ensure that you have installed JDK 1.8 or later and configured environment variables on the computer where spark-submit is installed. You are advised to use spark-submit on the computer running Linux.

+
+
  1. Download and decompress dli-clientkit-<version>-bin.tar.gz. In this step, set version to the actual version.
  2. Go to the directory where dli-clientkit-<version>-bin.tar.gz is decompressed. In the directory, there are three subdirectories bin, conf, and lib, which respectively store the execution scripts, configuration files, and dependency packages related to Spark-submit.
  3. Go to the conf directory and modify the configuration items in the client.properties file. For details about the configuration items, see Table 1.

    +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 1 DLI client parameters

    Item

    +

    Mandatory

    +

    Default Value

    +

    Description

    +

    dliEndPont

    +

    No

    +

    -

    +

    Domain name of DLI

    +

    If you lef this parameter empty, the program determines the domain name based on region.

    +

    obsEndPoint

    +

    Yes

    +

    -

    +

    OBS service domain name.

    +

    Obtain the OBS domain name from the administrator.

    +

    bucketName

    +

    Yes

    +

    -

    +

    Name of a bucket on OBS. This bucket is used to store JAR files, Python program files, and configuration files used in Spark programs.

    +

    obsPath

    +

    Yes

    +

    dli-spark-submit-resources

    +

    Directory for storing JAR files, Python program files, and configuration files on OBS. The directory is in the bucket specified by Bucket Name. If the directory does not exist, the program automatically creates it.

    +

    localFilePath

    +

    Yes

    +

    -

    +

    The local directory for storing JAR files, Python program files, and configuration files used in Spark programs.

    +

    The program automatically uploads the files on which Spark depends to the OBS path and loads them to the resource package on the DLI server.

    +

    ak

    +

    Yes

    +

    -

    +

    User's Access Key (AK)

    +

    sk

    +

    Yes

    +

    -

    +

    User's Secret Key (SK)

    +

    projectId

    +

    Yes

    +

    -

    +

    Project ID used by a user to access DLI.

    +

    region

    +

    Yes

    +

    -

    +

    Region of interconnected DLI.

    +
    +
    +

    Modify the configuration items in the spark-defaults.conf file based on the Spark application requirements. The configuration items are compatible with the open-source Spark configuration items. For details, see the open-source Spark configuration item description.

    +

+

Using Spark-submit to Submit a Spark Job

  1. Go to the bin directory of the tool file, run the spark-submit command, and carry related parameters.

    The command format is as follows:
    spark-submit [options] <app jar | python file> [app arguments]
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 2 DLI Spark-submit parameters

    Parameter

    +

    Value

    +

    Description

    +

    --class

    +

    <CLASS_NAME>

    +

    Name of the main class of the submitted Java or Scala application.

    +

    --conf

    +

    <PROP=VALUE>

    +

    Spark program parameters can be configured in the spark-defaults.conf file in the conf directory. If both the command and the configuration file are configured, the parameter value specified in the command is preferentially used.

    +
    NOTE:

    If there are multiple conf files, the format is --conf key1=value1 --conf key2=value2.

    +
    +

    --jars

    +

    <JARS>

    +

    Name of the JAR file on which the Spark application depends. Use commas (,) to separate multiple names. The JAR file must be stored in the local path specified by localFilePath in the client.properties file in advance.

    +

    --name

    +

    <NAME>

    +

    Name of a Spark application.

    +

    --queue

    +

    <QUEUE_NAME>

    +

    Name of the Spark queue on the DLI server. Jobs are submitted to the queue for execution.

    +

    --py-files

    +

    <PY_FILES>

    +

    Name of the Python program file on which the Spark application depends. Use commas (,) to separate multiple file names. The Python program file must be saved in the local path specified by localFilePath in the client.properties file in advance.

    +

    -s,--skip-upload-resources

    +

    <all | app | deps>

    +

    Specifies whether to skip. Upload the JAR file, Python program file, and configuration file to OBS and load them to the resource list on the DLI server. If related resource files have been loaded to the DLI resource list, skip this step.

    +

    If this parameter is not specified, all resource files in the command are uploaded and loaded to DLI by default.

    +
    • all: Skips the upload and loading all resource files.
    • app: Skips the upload and loading of Spark application files.
    • deps: skips the upload and loading of all dependent files.
    +

    -h,--help

    +

    -

    +

    Displays command help information.

    +
    +
    +
    +
    Command example:
    ./spark-submit --name <name> --queue <queue_name> --class org.apache.spark.examples.SparkPi spark-examples_2.11-2.1.0.luxor.jar 10
+./spark-submit --name <name> --queue <queue_name> word_count.py
    +

    To use the DLI queue rather than the existing Spark environment, use ./spark-submit instead of spark-submit.

    +
    +
    +

+

+
+
+
+
+
Parent topic: Spark Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0123.html b/docs/dli/dev/dli_09_0123.html new file mode 100644 index 00000000..9e370f59 --- /dev/null +++ b/docs/dli/dev/dli_09_0123.html @@ -0,0 +1,23 @@ + + +

Submitting a Spark SQL Job Using JDBC

+
+
+ + +
+
Parent topic: SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0124.html b/docs/dli/dev/dli_09_0124.html new file mode 100644 index 00000000..057e3c49 --- /dev/null +++ b/docs/dli/dev/dli_09_0124.html @@ -0,0 +1,15 @@ + + +

Obtaining the Server Connection Address

+

Scenario

On DLI, you can connect to the server for data query in the Internet environment. In this case, you need to first obtain the connection information, including the endpoint and project ID by following the following procedure.

+
+

Procedure

The format of the address for connecting to DLI is jdbc:dli://<endPoint>/<projectId>. Therefore, you need to obtain the endpoint and project ID.

+

Obtain the DLI endpoint from the administrator. Specifically, log in to the cloud, click your username, and choose My Credentials from the short-cut menu. You can obtain the project ID on the My Credentials page.

+
+
+
+
+
Parent topic: Submitting a Spark SQL Job Using JDBC
+
+
+ diff --git a/docs/dli/dev/dli_09_0125.html b/docs/dli/dev/dli_09_0125.html new file mode 100644 index 00000000..22376c13 --- /dev/null +++ b/docs/dli/dev/dli_09_0125.html @@ -0,0 +1,18 @@ + + +

Downloading the JDBC Driver Package

+

Scenario

To connect to DLI, JDBC is utilized. You can obtain the JDBC installation package from Maven or download the JDBC driver file from the DLI management console.

+
+

Downloading the JDBC Driver File from the DLI Management Console

  1. Log in to the DLI management console.
  2. Ask the administrator for the download link to the SDK driver package.
  3. On the DLI SDK DOWNLOAD page, select a driver and download it.

    • JDBC driver package

      For example, click dli-jdbc-1.2.5 to download the JDBC driver package of version 1.2.5.

      +

      The JDBC driver package is named dli-jdbc-<version>.zip. It can be used in all versions of all platforms (such as Linux and Windows) and depends on JDK 1.7 or later.

      +
      +
    +

+
+
+
+
+
Parent topic: Submitting a Spark SQL Job Using JDBC
+
+
+ diff --git a/docs/dli/dev/dli_09_0127.html b/docs/dli/dev/dli_09_0127.html new file mode 100644 index 00000000..ac72b703 --- /dev/null +++ b/docs/dli/dev/dli_09_0127.html @@ -0,0 +1,463 @@ + + +

Submitting a Job Using JDBC

+

Scenario

In Linux or Windows, you can connect to the DLI server using JDBC.

+
  • Jobs submitted to DLI using JDBC are executed on the Spark engine.
  • Once JDBC 2.X has undergone function reconstruction, query results can only be accessed from DLI job buckets. To utilize this feature, certain conditions must be met:
    • On the DLI management console, choose Global Configuration > Project to configure the job bucket.
    • Submit a service ticket to request the whitelisting of the feature that allows writing query results to buckets.
    +
+
+
DLI supports 13 data types. Each type can be mapped to a JDBC type. If JDBC is used to connect to the server, you must use the mapped Java type. Table 1 describes the mapping relationships. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 1 Data type mapping

DLI Data Type

+

JDBC Type

+

Java Type

+

INT

+

INTEGER

+

java.lang.Integer

+

STRING

+

VARCHAR

+

java.lang.String

+

FLOAT

+

FLOAT

+

java.lang.Float

+

DOUBLE

+

DOUBLE

+

java.lang.Double

+

DECIMAL

+

DECIMAL

+

java.math.BigDecimal

+

BOOLEAN

+

BOOLEAN

+

java.lang.Boolean

+

SMALLINT/SHORT

+

SMALLINT

+

java.lang.Short

+

TINYINT

+

TINYINT

+

java.lang.Short

+

BIGINT/LONG

+

BIGINT

+

java.lang.Long

+

TIMESTAMP

+

TIMESTAMP

+

java.sql.Timestamp

+

CHAR

+

CHAR

+

Java.lang.Character

+

VARCHAR

+

VARCHAR

+

java.lang.String

+

DATE

+

DATE

+

java.sql.Date

+
+
+
+
+

Prerequisites

Before using JDBC, perform the following operations:

+
  1. Getting authorized.

    DLI uses the Identity and Access Management (IAM) to implement fine-grained permissions for your enterprise-level tenants. IAM provides identity authentication, permissions management, and access control, helping you securely access your resources.

    +

    With IAM, you can use your account to create IAM users for your employees, and assign permissions to the users to control their access to specific resource types.

    +

    Currently, roles (coarse-grained authorization) and policies (fine-grained authorization) are supported.

    +
  2. Create a queue. Choose Resources > Queue Management. On the page displayed, click Buy Queue in the upper right corner. On the Buy Queue page displayed, select For general purpose for Type, that is, the compute resources of the Spark job.

    If the user who creates the queue is not an administrator, the queue can be used only after being authorized by the administrator. For details about how to assign permissions, see .

    +
    +
+
+

Procedure

  1. Install JDK 1.7 or later on the computer where JDBC is installed, and configure environment variables.
  2. Obtain the DLI JDBC driver package dli-jdbc-<version>.zip by referring to Downloading the JDBC Driver Package. Decompress the package to obtain dli-jdbc-<version>-jar-with-dependencies.jar.
  3. On the computer using JDBC, add dli-jdbc-1.1.1-jar-with-dependencies.jar to the classpath path of the Java project.
  4. DLI JDBC provides two authentication modes, namely, token and AK/SK, to connect to DLI. For details about how to obtain the token and AK/SK, see Performing Authentication.
  5. Run the Class.forName() command to load the DLI JDBC driver.

    Class.forName("com.dli.jdbc.DliDriver");

    +

  6. Call the GetConnection method of DriverManager to create a connection.

    Connection conn = DriverManager.getConnection(String url, Properties info);

    +
    JDBC configuration items are passed using the URL. For details, see Table 2. JDBC configuration items can be separated by semicolons (;) in the URL, or you can dynamically set the attribute items using the Info object. For details, see Table 3. +
    + + + + + + + + + + +
    Table 2 Database connection parameters

    Parameter

    +

    Description

    +

    url

    +

    The URL format is as follows:

    +

    jdbc:dli://<endPoint>/projectId? <key1>=<val1>;<key2>=<val2>...

    +
    • EndPoint indicates the DLI domain name. ProjectId indicates the project ID.

      To obtain the endpoint corresponding to DLI, contact the administrator to obtain the region and endpoint information. To obtain the project ID, log in to the cloud, move the mouse on the account, and click My Credentials from the shortcut menu.

      +
    • Other configuration items are listed after ? in the form of key=value. The configuration items are separated by semicolons (;). They can also be passed using the Info object.
    +

    Info

    +

    The Info object passes user-defined configuration items. If Info does not pass any attribute item, you can set it to null. The format is as follows: info.setProperty ("Attribute item", "Attribute value").

    +
    +
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 3 Attribute items

    Item

    +

    Mandatory

    +

    Default Value

    +

    Description

    +

    queuename

    +

    Yes

    +

    -

    +

    Queue name of DLI.

    +

    databasename

    +

    No

    +

    -

    +

    Name of a database.

    +

    authenticationmode

    +

    No

    +

    token

    +

    Authentication mode. Currently, token- and AK/SK-based authentication modes are supported.

    +

    accesskey

    +

    Yes

    +

    -

    +

    AK/SK authentication key. For details about how to obtain the key, see Performing Authentication.

    +

    secretkey

    +

    Yes

    +

    -

    +

    AK/SK authentication key. For details about how to obtain the key, see Performing Authentication.

    +

    servicename

    +

    This parameter must be configured if authenticationmode is set to aksk.

    +

    -

    +

    Indicates the service name, that is, dli.

    +

    token

    +

    This parameter must be configured if authenticationmode is set to token.

    +

    -

    +

    Token authentication. For details about the authentication mode, see Performing Authentication.

    +

    charset

    +

    No

    +

    UTF-8

    +

    JDBC encoding mode.

    +

    usehttpproxy

    +

    No

    +

    false

    +

    Whether to use the access proxy.

    +

    proxyhost

    +

    This parameter must be configured if usehttpproxy is set to true.

    +

    -

    +

    Access proxy host.

    +

    proxyport

    +

    This parameter must be configured if usehttpproxy is set to true.

    +

    -

    +

    Access proxy port.

    +

    dli.sql.checkNoResultQuery

    +

    No

    +

    false

    +

    Whether to allow invoking the executeQuery API to execute statements (for example, DDL) that do not return results.

    +
    • Value false indicates that invoking of the executeQuery API is allowed.
    • Value true indicates that invoking of the executeQuery API is not allowed.
    +

    jobtimeout

    +

    No

    +

    300

    +

    End time of the job submission. Unit: second

    +

    iam.endpoint

    +

    No. By default, the value is automatically combined based on regionName.

    +

    -

    +

    -

    +

    obs.endpoint

    +

    No. By default, the value is automatically combined based on regionName.

    +

    -

    +

    -

    +

    directfetchthreshold

    +

    No

    +

    1000

    +

    Check whether the number of returned results exceeds the threshold based on service requirements.

    +

    The default threshold is 1000.

    +
    +
    +
    +

  7. Create a Statement object, set related parameters, and submit Spark SQL to DLI.

    Statement statement = conn.createStatement();

    +

    statement.execute("SET dli.sql.spark.sql.forcePartitionPredicatesOnPartitionedTable.enabled=true");

    +

    statement.execute("select * from tb1");

    +

  8. Obtain the result.

    ResultSet rs = statement.getResultSet();

    +

  9. Display the result.

    while (rs.next()) {
+int a = rs.getInt(1);
+int b = rs.getInt(2);
+}
    +

  10. Close the connection.

    conn.close();

    +

+
+

Example

  • Hard-coded or plaintext AK and SK pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.
  • In this example, the AK and SK stored in the environment variables are used. Specify the environment variables System.getenv("AK") and System.getenv("SK") in the local environment first.
+
+
import java.sql.*;
+import java.util.Properties;
+
+public class DLIJdbcDriverExample {
+
+    public static void main(String[] args) throws ClassNotFoundException, SQLException {
+        Connection conn = null;
+        try {
+            Class.forName("com.dli.jdbc.DliDriver");
+            String url = "jdbc:dli://<endpoint>/<projectId>?databasename=db1;queuename=testqueue";
+            Properties info = new Properties();
+            info.setProperty("authenticationmode", "aksk");
+            info.setProperty("regionname", "<real region name>");
+            info.setProperty("accesskey", "<System.getenv("AK")>");
+            info.setProperty("secretkey", "<System.getenv("SK")>")
+            conn = DriverManager.getConnection(url, info);
+            Statement statement = conn.createStatement();
+            statement.execute("select * from tb1");
+            ResultSet rs = statement.getResultSet();
+            int line = 0;
+            while (rs.next()) {
+                line ++;
+                int a = rs.getInt(1);
+                int b = rs.getInt(2);
+                System.out.println("Line:" + line + ":" + a + "," + b);
+            }
+            statement.execute("SET dli.sql.spark.sql.forcePartitionPredicatesOnPartitionedTable.enabled=true");
+            statement.execute("describe tb1");
+            ResultSet rs1 = statement.getResultSet();
+            line = 0;
+            while (rs1.next()) {
+                line ++;
+                String a = rs1.getString(1);
+                String b = rs1.getString(2);
+                System.out.println("Line:" + line + ":" + a + "," + b);
+            }
+        } catch (SQLException ex) {
+        } finally {
+            if (conn != null) {
+                conn.close();
+            }
+        }
+    }
+}
+
+

Enabling JDBC Requery

If the JDBC requery function is enabled, the system automatically requeries when the query operation fails.

+
  • To avoid repeated data insertion, non-query statements do not support requery.
  • This function is available in the JDBC driver package of 1.1.5 or later. To use this function, obtain the latest JDBC driver package.
+
+

To enable the requery function, add the attributes listed in Table 4 to the Info parameter.

+ +
+ + + + + + + + + + + + + + + + + + + + + +
Table 4 Requery parameter description

Item

+

Mandatory

+

Default Value

+

Description

+

USE_RETRY_KEY

+

Yes

+

false

+

Whether to enable the requery function. If this parameter is set to True, the requery function is enabled.

+

RETRY_TIMES_KEY

+

Yes

+

3000

+

Requery interval (milliseconds). Set this parameter to 30000 ms.

+

RETRY_INTERVALS_KEY

+

Yes

+

3

+

Requery times. Set this parameter to a value from 3 to 5.

+
+
+

Set JDBC parameters, enable the requery function, and create a link. The following is an example:

+
import com.xxx.dli.jdbs.utils.ConnectionResource;// Introduce "ConnectionResource". Change the class name as needed.
+import java.sql.*;
+import java.util.Properties;
+
+public class DLIJdbcDriverExample {
+
+    private static final String X_AUTH_TOKEN_VALUE = "<realtoken>";
+    public static void main(String[] args) throws ClassNotFoundException, SQLException {
+        Connection conn = null;
+        try {
+            Class.forName("com.dli.jdbc.DliDriver");
+            String url = "jdbc:dli://<endpoint>/<projectId>?databasename=db1;queuename=testqueue";
+            Properties info = new Properties();
+            info.setProperty("token", X_AUTH_TOKEN_VALUE);
+info.setProperty(ConnectionResource.USE_RETRY_KEY, "true"); // Enable the requery function.
+info.setProperty(ConnectionResource.RETRY_TIMES_KEY, "30000");// Requery interval (ms)
+info.setProperty(ConnectionResource.RETRY_INTERVALS_KEY, "5");// Requery Times
+            conn = DriverManager.getConnection(url, info);
+            Statement statement = conn.createStatement();
+            statement.execute("select * from tb1");
+            ResultSet rs = statement.getResultSet();
+            int line = 0;
+            while (rs.next()) {
+                line ++;
+                int a = rs.getInt(1);
+                int b = rs.getInt(2);
+                System.out.println("Line:" + line + ":" + a + "," + b);
+            }
+            statement.execute("describe tb1");
+            ResultSet rs1 = statement.getResultSet();
+            line = 0;
+            while (rs1.next()) {
+                line ++;
+                String a = rs1.getString(1);
+                String b = rs1.getString(2);
+                System.out.println("Line:" + line + ":" + a + "," + b);
+            }
+        } catch (SQLException ex) {
+        } finally {
+            if (conn != null) {
+                conn.close();
+            }
+        }
+    }
+}
+
+
+
+
+
Parent topic: Submitting a Spark SQL Job Using JDBC
+
+
+ diff --git a/docs/dli/dev/dli_09_0129.html b/docs/dli/dev/dli_09_0129.html new file mode 100644 index 00000000..6e8d38da --- /dev/null +++ b/docs/dli/dev/dli_09_0129.html @@ -0,0 +1,14 @@ + + +

Introduction to RDS

+

Basic Concepts

Relational Database Service (RDS) is a cloud-based web service that is reliable, scalable, easy to manage, and immediately ready for use. It can be deployed in single-node, active/standby, or cluster mode.

+
+

Features

  • Hosting: RDS provides upgrade, patch, and backup hosting services.
  • Compatibility: RDS is compatible with MySQL, SQL Server, and PostgreSQL.
  • Scalability: RDS provides elastic capacity expansion to deliver one-click CPU, memory, and disk capacity expansion
  • Security: RDS supports creation of VPCs, subnets, and security groups.
  • Stability: RDS supports automatic or manual backup.
  • High availability: RDS can work in active/standby mode.
+
+
+
+
+
Parent topic: Submitting a Spark SQL Job Using JDBC
+
+
+ diff --git a/docs/dli/dev/dli_09_0150.html b/docs/dli/dev/dli_09_0150.html new file mode 100644 index 00000000..c45599eb --- /dev/null +++ b/docs/dli/dev/dli_09_0150.html @@ -0,0 +1,180 @@ + + +

Flink Jar Job Examples

+

Overview

You can perform secondary development based on Flink APIs to build your own Jar packages and submit them to the DLI queues to interact with data sources such as MRS Kafka, HBase, Hive, HDFS, GaussDB(DWS), and DCS.

+

This section describes how to interact with MRS through a custom job.

+
+

Environment Preparations

  1. Log in to the MRS management console and create an MRS cluster. During the creation, enable Kerberos Authentication and select Kafka, HBase, and HDFS. For details about how to create an MRS cluster, see "Buying a Custom Cluster" in .
  2. Enable the UDP/TCP port in the security group rule. For details, see "Adding a Security Group Rule" in .
  3. Log in to MRS Manager.
    1. Create a machine-machine account. Ensure that you have the hdfs_admin and hbase_admin permissions. Download the user authentication credentials, including the user.keytab and krb5.conf files.

      The .keytab file of a human-machine account becomes invalid when the user password expires. Use a machine-machine account for configuration.

      +
      +
    2. Click Services, download the client, and click OK.
    3. Download the configuration files from the MRS node, including hbase-site.xml and hiveclient.properties.
    +
  4. Create a dedicated DLI queue.
  5. Set up an enhanced datasource connection between the DLI dedicated queue and the MRS cluster and configure security group rules based on the site requirements.

    For details about how to create an enhanced datasource connection, see Enhanced Datasource Connections in the Data Lake Insight User Guide.

    +

    For details about how to configure security group rules, see "Security Group" in Virtual Private Cloud User Guide.

    +
  6. Obtain the IP address and domain name mapping of all nodes in the MRS cluster, and configure the host mapping in the host information of the DLI cross-source connection.

    For details about how to add an IP-domain mapping, see Modifying the Host Information in the Data Lake Insight User Guide.

    +

    If the Kafka server listens on the port using hostname, you need to add the mapping between the hostname and IP address of the Kafka Broker node to the DLI queue. Contact the Kafka service deployment personnel to obtain the hostname and IP address of the Kafka Broker node.

    +
    +
+
+

Prerequisites

  • Ensure that a dedicated queue has been created.
  • When running a Flink Jar job, you need to build the secondary development application code into a JAR package and upload it to the created OBS bucket. On the DLI console, choose Data Management > Package Management to create a package.

    DLI does not support the download function. If you need to modify the uploaded data file, edit the local file and upload it again.

    +
    +
  • Flink dependencies have been built in the DLI server and security hardening has been performed based on the open-source community version. To prevent dependency compatibility issues or log output and dump issues, exclude the following files during packaging:
    • Built-in dependencies (or set the package dependency scope to "provided" in Maven or sbt)
    • Log configuration files (for example, log4j.properties or logback.xml)
    • JAR packages for log output implementation (for example, log4j)
    +
+
+

How to Use

  1. In the left navigation pane of the DLI management console, choose Job Management > Flink Jobs. The Flink Jobs page is displayed.
  2. In the upper right corner of the Flink Jobs page, click Create Job.
  3. Configure job parameters.

    +

    + + + + + + + + + + + + + +
    Table 1 Job parameters

    Parameter

    +

    Description

    +

    Type

    +

    Select Flink Jar.

    +

    Name

    +

    Job name, which contains 1 to 57 characters and consists of only letters, digits, hyphens (-), and underscores (_).

    +
    NOTE:

    The job name must be globally unique.

    +
    +

    Description

    +

    Description of the job, which contains 0 to 512 characters.

    +
    +
    +

  4. Click OK to enter the Edit page.
  5. Select a queue. A Flink Jar job can run only on general queues.

    • A Flink Jar job can run only on a pre-created dedicated queue.
    • If no dedicated queue is available in the Queue drop-down list, create a dedicated queue and bind it to the current user.
    +
    +

  6. Upload the JAR package.

    The Flink version must be the same as that specified in the JAR package. +
    + + + + + + + + + + + + + + + + + + + + + + +
    Table 2 Description

    Parameter

    +

    Description

    +

    Application

    +

    User-defined package. Before selecting a package, upload the corresponding JAR package to the OBS bucket and create a package on the Data Management > Package Management page.

    +

    Main Class

    +

    Name of the main class of the JAR package to be loaded, for example, KafkaMessageStreaming.

    +
    • Default: The value is specified based on the Manifest file in the JAR package.
    • Manually assign: You must enter the class name and confirm the class arguments (separate arguments with spaces).
    +
    NOTE:

    When a class belongs to a package, the package path must be carried, for example, packagePath.KafkaMessageStreaming.

    +
    +

    Class Arguments

    +

    List of arguments of a specified class. The arguments are separated by spaces.

    +

    JAR Package Dependencies

    +

    User-defined dependencies. Before selecting a package, upload the corresponding JAR package to the OBS bucket and create a JAR package on the Data Management > Package Management page.

    +

    Other Dependencies

    +

    User-defined dependency files. Before selecting a file, upload the corresponding file to the OBS bucket and create a package of any type on the Data Management > Package Management page.

    +

    You can add the following content to the application to access the corresponding dependency file: fileName indicates the name of the file to be accessed, and ClassName indicates the name of the class that needs to access the file.

    +
    ClassName.class.getClassLoader().getResource("userData/fileName")
    +

    Flink Version

    +

    Before selecting a Flink version, you need to select the queue to which the Flink version belongs. Currently, the following versions are supported: 1.10.

    +
    +
    +
    +

  7. Configure job parameters.

    +

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 3 Parameter description

    Parameter

    +

    Description

    +

    CUs

    +

    One CU has one vCPU and 4 GB memory. The number of CUs ranges from 2 to 400.

    +

    Job Manager CUs

    +

    Set the number of CUs on a management unit. The value ranges from 1 to 4. The default value is 1.

    +

    Parallelism

    +

    Maximum number of parallel operators in a job.

    +
    NOTE:
    • The value must be less than or equal to four times the number of compute units (CUs minus the number of job manager CUs).
    • You are advised to set this parameter to a value greater than that configured in the code. Otherwise, job submission may fail.
    +
    +

    Task Manager Configuration

    +

    Whether to set Task Manager resource parameters.

    +

    If this option is selected, you need to set the following parameters:

    +
    • CU(s) per TM: Number of resources occupied by each Task Manager.
    • Slot(s) per TM: Number of slots contained in each Task Manager.
    +

    Save Job Log

    +

    Whether to save the job running logs to OBS.

    +

    If this option is selected, you need to set the following parameters:

    +

    OBS Bucket: Select an OBS bucket to store user job logs. If the selected OBS bucket is not authorized, click Authorize.

    +

    Alarm Generation upon Job Exception

    +

    Whether to report job exceptions, for example, abnormal job running or exceptions due to an insufficient balance, to users via SMS or email.

    +

    If this option is selected, you need to set the following parameters:

    +

    SMN Topic

    +

    Select a user-defined SMN topic. For details about how to customize SMN topics, see "Creating a Topic" in the Simple Message Notification User Guide.

    +

    Auto Restart upon Exception

    +

    Whether to enable automatic restart. If this function is enabled, jobs will be automatically restarted and restored when exceptions occur.

    +

    If this option is selected, you need to set the following parameters:

    +
    • Max. Retry Attempts: maximum number of retry times upon an exception. The unit is Times/hour.
      • Unlimited: The number of retries is unlimited.
      • Limited: The number of retries is user-defined.
      +
    • Restore Job from Checkpoint: Restore the job from the latest checkpoint.

      If you select this parameter, you also need to set Checkpoint Path.

      +

      Checkpoint Path: Select the checkpoint saving path. The value must be the same as the checkpoint path you set in the application package. Note that the checkpoint path for each job must be unique. Otherwise, the checkpoint cannot be obtained.

      +
    +
    +
    +

  8. Click Save on the upper right of the page.
  9. Click Start on the upper right side of the page. On the displayed Start Flink Job page, confirm the job specifications, and click Start Now to start the job.

    After the job is started, the system automatically switches to the Flink Jobs page, and the created job is displayed in the job list. You can view the job status in the Status column. After a job is successfully submitted, the job status will change from Submitting to Running. After the execution is complete, the message Completed is displayed.

    +

    If the job status is Submission failed or Running exception, the job submission failed or the job did not execute successfully. In this case, you can move the cursor over the status icon in the Status column of the job list to view the error details. You can click to copy these details. After handling the fault based on the provided information, resubmit the job.

    +

    Other buttons are as follows:

    +

    Save As: Save the created job as a new job.

    +
    +

+
+

Related Operations

  • How Do I Configure Job Parameters?
    1. In the Flink job list, select your desired job.
    2. Click Edit in the Operation column.
    3. Configure the parameters as needed.

      List of parameters of a specified class. The parameters are separated by spaces.

      +

      Parameter input format: --Key 1 Value 1 --Key 2 Value 2

      +

      For example, if you enter the following parameters on the console:

      +

      --bootstrap.server 192.168.168.xxx:9092

      +

      The parameters are parsed by ParameterTool as follows:

      +
      Figure 1 Parsed parameters
      +
    +
  • How Do I View Job Logs?
    1. In the Flink job list, click a job name to access its details page.
    2. Click the Run Log tab and view job logs on the console.

      Only the latest run logs are displayed. For more information, see the OBS bucket that stores logs.

      +
    +
+
+
+
+
+
Parent topic: Flink Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0162.html b/docs/dli/dev/dli_09_0162.html new file mode 100644 index 00000000..3ecee099 --- /dev/null +++ b/docs/dli/dev/dli_09_0162.html @@ -0,0 +1,20 @@ + + +

Stream Ecosystem

+

Overview

Built on Flink and Spark, the stream ecosystem is fully compatible with the open-source Flink, Storm, and Spark APIs. It is enhanced in features and improved in performance to provide easy-to-use DLI with low latency and high throughput.

+
The DLI stream ecosystem includes the cloud service ecosystem, open source ecosystem, and custom stream ecosystem.
  • Cloud service ecosystems

    DLI can be interconnected with other services by using Stream SQLs. You can directly use SQL statements to read and write data from various cloud services, such as Data Ingestion Service (DIS), Object Storage Service (OBS), CloudTable Service (CloudTable), MapReduce Service (MRS), Relational Database Service (RDS), Simple Message Notification (SMN), and Distributed Cache Service (DCS).

    +
  • Open-source ecosystems

    After connections to other VPCs are established through VPC peering connections, you can access all data sources and output targets (such as Kafka, HBase, and Elasticsearch) supported by Flink and Spark in your dedicated DLI queues.

    +
  • Custom stream ecosystems

    You can compile code to obtain data from the desired cloud ecosystem or open-source ecosystem as the input data of Flink jobs.

    +
+
+
+

Supported Data Formats

DLI Flink jobs support the following data formats:

+

Avro, Avro_merge, BLOB, CSV, EMAIL, JSON, ORC, Parquet, and XML.

+
+
+
+
+
Parent topic: Flink Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0171.html b/docs/dli/dev/dli_09_0171.html new file mode 100644 index 00000000..0dfcc1e0 --- /dev/null +++ b/docs/dli/dev/dli_09_0171.html @@ -0,0 +1,153 @@ + + +

Calling UDFs in Spark SQL Jobs

+

Scenario

DLI allows you to use Hive user-defined functions (UDFs) to query data. UDFs take effect only on a single row of data and are applicable to inserting and deleting a single data record.

+
+

Constraints

  • To perform UDF-related operations on DLI, you need to create a SQL queue instead of using the default queue.
  • When UDFs are used across accounts, other users, except the user who creates them, need to be authorized before using the UDF. The authorization operations are as follows:

    Log in to the DLI console and choose Data Management > Package Management. On the displayed page, select your UDF Jar package and click Manage Permissions in the Operation column. On the permission management page, click Grant Permission in the upper right corner and select the required permissions.

    +
  • If you use a static class or interface in a UDF, add try catch to capture exceptions. Otherwise, package conflicts may occur.
+
+

Environment Preparations

Before you start, set up the development environment.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Development environment

Item

+

Description

+

OS

+

Windows 7 or later

+

JDK

+

JDK 1.8.

+

IntelliJ IDEA

+

This tool is used for application development. The version of the tool must be 2019.1 or other compatible versions.

+

Maven

+

Basic configurations of the development environment. Maven is used for project management throughout the lifecycle of software development.

+
+
+
+

Development Process

The process of developing a UDF is as follows:
Figure 1 Development process
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2 Process description

No.

+

Phase

+

Software Portal

+

Description

+

1

+

Create a Maven project and configure the POM file.

+

IntelliJ IDEA

+

+

Write UDF code by referring the steps in Procedure.

+

+

2

+

Write UDF code.

+

3

+

Debug, compile, and pack the code into a Jar package.

+

4

+

Upload the Jar package to OBS.

+

OBS console

+

Upload the UDF Jar file to an OBS directory.

+

5

+

Create the UDF on DLI.

+

DLI console

+

Create a UDF on the SQL job management page of the DLI console.

+

6

+

Verify and use the UDF on DLI.

+

DLI console

+

Use the UDF in your DLI job.

+
+
+
+
+

Procedure

  1. Create a Maven project and configure the POM file. This step uses IntelliJ IDEA 2020.2 as an example.
    1. Start IntelliJ IDEA and choose File > New > Project.
      Figure 2 Creating a project
      +
    2. Choose Maven, set Project SDK to 1.8, and click Next.
      Figure 3 Choosing Maven
      +
    3. Set the project name, configure the storage path, and click Finish.
      Figure 4 Creating a project
      +
    4. Add the following content to the pom.xml file.
      <dependencies>
+        <dependency>
+            <groupId>org.apache.hive</groupId>
+            <artifactId>hive-exec</artifactId>
+            <version>1.2.1</version>
+        </dependency>
+</dependencies>
      +
      Figure 5 Adding configurations to the POM file
      +
      +
    5. Choose src > main and right-click the java folder. Choose New > Package to create a package and a class file.
      Figure 6 Creating a package and a class file
      +

      Set the package name as you need. Then, press Enter.

      +

      Create a Java Class file in the package path. In this example, the Java Class file is SumUdfDemo.

      +
    +
  2. Write UDF code.
    1. The UDF must inherit org.apache.hadoop.hive.ql.UDF.
    2. You must implement the evaluate function, which can be reloaded.
    +

    For details about how to implement the UDF, see the following sample code:

    +
    package com.demo;
+import org.apache.hadoop.hive.ql.exec.UDF;
+  public class SumUdfDemo extends UDF {
+    public int evaluate(int a, int b) {
+     return a + b;
+  }
+ }
    +
  3. Use IntelliJ IDEA to compile the code and pack it into the JAR package.
    1. Click Maven in the tool bar on the right, and click clean and compile to compile the code.

      After the compilation is successful, click package.

      +

      The generated JAR package is stored in the target directory. In this example, MyUDF-1.0-SNAPSHOT.jar is stored in D:\DLITest\MyUDF\target.

      +
    +
  4. Log in to the OBS console and upload the file to the OBS path.

    The region of the OBS bucket to which the Jar package is uploaded must be the same as the region of the DLI queue. Cross-region operations are not allowed.

    +
    +
  5. (Optional) Upload the file to DLI for package management.
    1. Log in to the DLI management console and choose Data Management > Package Management.
    2. On the Package Management page, click Create in the upper right corner.
    3. In the Create Package dialog, set the following parameters:
      1. Type: Select JAR.
      2. OBS Path: Specify the OBS path for storing the package.
      3. Set Group and Group Name as required for package identification and management.
      +
    4. Click OK.
    +
  6. Create the UDF on DLI.
    1. Log in to the DLI console, choose SQL Editor. Set Engine to spark, and select the created SQL queue and database.
    2. In the SQL editing area, run the following statement to create a UDF and click Execute.
      CREATE FUNCTION TestSumUDF AS 'com.demo.SumUdfDemo' using jar 'obs://dli-test-obs01/MyUDF-1.0-SNAPSHOT.jar';
      +
    +
  7. Restart the original SQL queue for the added function to take effect.
    1. Log in to the DLI console and choose Queue Management from the navigation pane. In the Operation column of the SQL queue job, click Restart.
    2. In the Restart dialog box, click OK.
    +
  8. Call the UDF.

    Use the UDF created in 6 in the SELECT statement as follows:

    +
    select TestSumUDF(1,2);
    +
  9. (Optional) Delete the UDF.

    If the UDF is no longer used, run the following statement to delete it:

    +
    Drop FUNCTION TestSumUDF;
    +
+
+
+
+
+
Parent topic: SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0176.html b/docs/dli/dev/dli_09_0176.html new file mode 100644 index 00000000..0e3f8dc7 --- /dev/null +++ b/docs/dli/dev/dli_09_0176.html @@ -0,0 +1,372 @@ + + +

Using the Spark Job to Access DLI Metadata

+

Scenario

DLI allows you to develop a program to create Spark jobs for operations related to databases, DLI or OBS tables, and table data. This example demonstrates how to develop a job by writing a Java program, and use a Spark job to create a database and table and insert table data.

+
+

Constraints

  • You must create a queue to use Spark 3.1 for metadata access.
  • The following cases are not supported:
    • If you create a database with a SQL job, you cannot write a program to create tables in that database.

      For example, the testdb database is created using the SQL editor of DLI. A program package for creating the testTable table in the testdb database does not work after it is submitted to a Spark Jar job.

      +
    +
  • The following cases are supported:
    • You can create databases and tables in a SQL job, and read and insert data using SQL statements or a Spark program.
    • You can create databases and tables in a Spark job, and read and insert data using SQL statements or a Spark program.
    +
+
+

Environment Preparations

Before developing a Spark job to access DLI metadata, set up a development environment that meets the following requirements.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Development environment

Item

+

Description

+

OS

+

Windows 7 or later

+

JDK

+

JDK 1.8.

+

IntelliJ IDEA

+

This tool is used for application development. The version of the tool must be 2019.1 or other compatible versions.

+

Maven

+

Basic configurations of the development environment. Maven is used for project management throughout the lifecycle of software development.

+
+
+
+

Development Process

The following figure shows the process for developing a Spark job to access DLI metadata.
Figure 1 Development process
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2 Process description

No.

+

Phase

+

Software Portal

+

Description

+

1

+

Create a queue for general use.

+

DLI console

+

The DLI queue is created for running your job.

+

2

+

Configure the OBS file.

+

OBS console

+
  • To create an OBS table, you need to upload the file to the OBS bucket.
  • Configure the path for storing DLI metadata. This folder is used to store DLI metadata in spark.sql.warehouse.dir.
+

3

+

Create a Maven project and configure the POM file.

+

IntelliJ IDEA

+

+

Write a program to create a DLI or OBS table by referring to the sample code.

+

+

4

+

Write code.

+

5

+

Debug, compile, and pack the code into a Jar package.

+

6

+

Upload the Jar package to OBS and DLI.

+

OBS console

+

You can upload the generated Spark Jar package to an OBS directory and DLI program package.

+

7

+

Create a Spark JAR job.

+

DLI console

+

The Spark Jar job is created and submitted on the DLI console.

+

8

+

Check execution result of the job.

+

DLI console

+

You can view the job running status and run logs.

+
+
+
+
+

Step 1: Create a Queue for General Purpose

If you submit a Spark job for the first time, you need to create a queue first. For example, create a queue, name it sparktest, and set Queue Usage to For general purpose.
  1. In the navigation pane of the DLI management console, choose Queue Management.
  2. In the upper right corner of the Queue Management page, click Create Queue to create a queue.
  3. Create a queue, name it sparktest, and set the queue usage to for general purpose. For details about how to create a queue, see Creating a Queue.
  4. Click Create Now to create a queue.
+
+
+

Step 2: Configure the OBS Bucket File

  1. To create an OBS table, upload data to the OBS bucket directory.
    Use the following sample data to create the testdata.csv file and upload it to an OBS bucket.
    12,Michael
+27,Andy
+30,Justin
    +
    +
  2. Log in to the OBS Console. In the Bucket page, click the name of the created OBS bucket. In this example, the bucket name is dli-test-obs01. The overview page is displayed.
  3. In the navigation pane on the left, choose Objects. Click Upload Object to upload the testdata.csv file to the root directory of the OBS bucket.
  4. In the root directory of the OBS bucket, click Create Folder to create a folder and name it warehousepath. This folder is used to store DLI metadata in spark.sql.warehouse.dir.
+
+

Step 3: Create a Maven Project and Configure the POM Dependency

This step uses IntelliJ IDEA 2020.2 as an example.
  1. Start IntelliJ IDEA and choose File > New > Project.
    Figure 2 Creating a project
    +
  2. Choose Maven, set Project SDK to 1.8, and click Next.
    Figure 3 Selecting an SDK
    +
  3. Set the project name, configure the storage path, and click Finish.
    Figure 4 Creating a project
    +

    In this example, the Maven project name is SparkJarMetadata, and the project storage path is D:\DLITest\SparkJarMetadata.

    +
  4. Add the following content to the pom.xml file.
    <dependencies>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+        </dependency>
+</dependencies>
    +
    Figure 5 Modifying the pom.xml file
    +
    +
  5. Choose src > main and right-click the java folder. Choose New > Package to create a package and a class file.
    Figure 6 Creating a package
    +

    Set the package name as you need. In this example, set Package to com.dli.demo and press Enter.

    +

    Create a Java Class file in the package path. In this example, the Java Class file is DliCatalogTest.

    +
+
+
+

Step 4: Write Code

Write the DliCatalogTest program to create a database, DLI table, and OBS table.

+

For the sample code, see Java Example Code.

+
  1. Import the dependency.
    import org.apache.spark.sql.SparkSession;
    +
  2. Create a SparkSession instance.

    When you create a SparkSession, you need to specify spark.sql.session.state.builder, spark.sql.catalog.class, and spark.sql.extensions parameters as configured in the following example.

    +
    • Spark 2.x and 3.1.x
      SparkSession spark = SparkSession
+                .builder()
+                .config("spark.sql.session.state.builder", "org.apache.spark.sql.hive.UQueryHiveACLSessionStateBuilder")
+                .config("spark.sql.catalog.class", "org.apache.spark.sql.hive.UQueryHiveACLExternalCatalog")
+                 .config("spark.sql.extensions","org.apache.spark.sql.DliSparkExtension")
+                .appName("java_spark_demo")
+                .getOrCreate();
      +
    • Spark 3.3.x
      SparkSession spark = SparkSession
+           .builder()           
+           .config("spark.sql.session.state.builder", "org.apache.spark.sql.hive.DliLakeHouseBuilder")           
+           .config("spark.sql.catalog.class", "org.apache.spark.sql.hive.DliLakeHouseCatalog")           
+           .appName("java_spark_demo")           
+           .getOrCreate();
      +
    +
  3. Create a database.
    The following sample code shows how to create a database and named test_sparkapp.
    spark.sql("create database if not exists test_sparkapp").collect();
    +
    +
  4. Create a DLI table and insert test data.
    spark.sql("drop table if exists test_sparkapp.dli_testtable").collect();
+spark.sql("create table test_sparkapp.dli_testtable(id INT, name STRING)").collect();
+spark.sql("insert into test_sparkapp.dli_testtable VALUES (123,'jason')").collect();
+spark.sql("insert into test_sparkapp.dli_testtable VALUES (456,'merry')").collect();
    +
  5. Create an OBS Table. Replace the OBS path in the following example with the path you set in Step 2: Configure the OBS Bucket File.
    spark.sql("drop table if exists test_sparkapp.dli_testobstable").collect();
+spark.sql("create table test_sparkapp.dli_testobstable(age INT, name STRING) using csv options (path 'obs://dli-test-obs01/testdata.csv')").collect();
    +
  6. Disable the spark session.
    spark.stop();
    +
+
+

Step 5: Debug, Compile, and Pack the Code into a Jar Package.

  1. Double-click Maven in the tool bar on the right, and double-click clean and compile to compile the code.

    After the compilation is successful, double-click package.

    +

    The generated JAR package is stored in the target directory. In this example, SparkJarMetadata-1.0-SNAPSHOT.jar is stored in D:\DLITest\SparkJarMetadata\target.

    +
+
+

Step 6: Upload the JAR Package to OBS and DLI

  1. Log in to the OBS console and upload the SparkJarMetadata-1.0-SNAPSHOT.jar file to the OBS path.
  2. Upload the file to DLI for package management.
    1. Log in to the DLI management console and choose Data Management > Package Management.
    2. On the Package Management page, click Create in the upper right corner.
    3. In the Create Package dialog, set the following parameters:
      1. Type: Select JAR.
      2. OBS Path: Specify the OBS path for storing the package.
      3. Set Group and Group Name as required for package identification and management.
      +
    4. Click OK.
    +
+
+

Step 7: Create a Spark Jar Job

  1. Log in to the DLI console. In the navigation pane, choose Job Management > Spark Jobs.
  2. On the Spark Jobs page, click Create Job.
  3. On the displayed page, configure the following parameters: +
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 3 Spark Jar job parameters

    Parameter

    +

    Value

    +

    Queue

    +

    Select the DLI queue created for general purpose. For example, select the queue sparktest created in Step 1: Create a Queue for General Purpose.

    +

    Spark Version

    +

    Select a Spark version. Select a supported Spark version from the drop-down list. The latest version is recommended.

    +

    Job Name (--name)

    +

    Name of a custom Spark Jar job. For example, SparkTestMeta.

    +

    Application

    +

    Select the package uploaded to DLI in Step 6: Upload the JAR Package to OBS and DLI. For example, select SparkJarObs-1.0-SNAPSHOT.jar.

    +

    Main Class (--class)

    +

    The format is program package name + class name.

    +

    Spark Arguments (--conf)

    +

    spark.dli.metaAccess.enable=true

    +

    spark.sql.warehouse.dir=obs://dli-test-obs01/warehousepath

    +
    NOTE:

    Set spark.sql.warehouse.dir to the OBS path that is specified in Step 2: Configure the OBS Bucket File.

    +
    +

    Access Metadata

    +

    Select Yes.

    +
    +
    +

    Retain default values for other parameters.

    +
  4. Click Execute to submit the Spark Jar job. On the Job management page, view the running status.

    +
+
+

Step 8: View Job Execution Result

  1. On the Job management page, view the running status. The initial status is Starting.
  2. If the job is successfully executed, the job status is Finished. Perform the following operations to view the created database and table:
    1. On the DLI console, choose SQL Editor in the left navigation pane. The created database test_sparkapp is displayed in the database list.
    2. Double-click the database name to view the created DLI and OBS tables in the database.
    3. Double-click dli_testtable and click Execute to query data in the table.
    4. Comment out the statement for querying the DLI table, double-click the OBS table dli_testobstable, and click Execute to query the OBS table data.
    +
  3. If the job fails, the job status is Failed. Click More in the Operation column and select Driver Logs to view the running log.

    After the fault is rectified, click Edit in the Operation column of the job, modify job parameters, and click Execute to run the job again.

    +
+
+

Follow-up Guide

  • For details about the syntax for creating DLI tables, see "SQL Syntax of Batch Jobs" > "Creating a DLI Table" in Data Lake Insight SQL Syntax Reference. For details about the syntax for creating OBS tables, see "SQL Syntax of Batch Jobs" > "Creating an OBS Table" in Data Lake Insight SQL Syntax Reference.
  • If you submit the job by calling an API, perform the following operations:

    Call the API for creating a batch processing job. The following table describes the request parameters.

    +
    • Set catalog_name in the request to dli.
    • Add "spark.dli.metaAccess.enable":"true" to the CONF file.

      Configure "spark.sql.warehouse.dir": "obs://bucket/warehousepath" in the CONF file if you need to run the DDL.

      +

      The following example provided you with the complete API request.

      +
      {
+    "queue":"citest",
+    "file":"SparkJarMetadata-1.0-SNAPSHOT.jar",
+    "className":"DliCatalogTest",
+    "conf":{"spark.sql.warehouse.dir": "obs://bucket/warehousepath",
+    "spark.dli.metaAccess.enable":"true"},
+    "sc_type":"A",
+    "executorCores":1,
+    "numExecutors":6,
+    "executorMemory":"4G",
+    "driverCores":2,
+    "driverMemory":"7G",
+    "catalog_name": "dli"
+}
      +
    +
+
+

Java Example Code

This example uses Java for coding. The complete sample code is as follows:

+
package com.dli.demo;
+
+import org.apache.spark.sql.SparkSession;
+
+public class DliCatalogTest {
+    public static void main(String[] args) {
+
+        SparkSession spark = SparkSession
+                .builder()
+                .config("spark.sql.session.state.builder", "org.apache.spark.sql.hive.UQueryHiveACLSessionStateBuilder")
+                .config("spark.sql.catalog.class", "org.apache.spark.sql.hive.UQueryHiveACLExternalCatalog")
+                .config("spark.sql.extensions","org.apache.spark.sql.DliSparkExtension")
+                .appName("java_spark_demo")
+                .getOrCreate();
+
+        spark.sql("create database if not exists test_sparkapp").collect();
+        spark.sql("drop table if exists test_sparkapp.dli_testtable").collect();
+        spark.sql("create table test_sparkapp.dli_testtable(id INT, name STRING)").collect();
+        spark.sql("insert into test_sparkapp.dli_testtable VALUES (123,'jason')").collect();
+        spark.sql("insert into test_sparkapp.dli_testtable VALUES (456,'merry')").collect();
+
+        spark.sql("drop table if exists test_sparkapp.dli_testobstable").collect();
+        spark.sql("create table test_sparkapp.dli_testobstable(age INT, name STRING) using csv options (path 'obs://dli-test-obs01/testdata.csv')").collect();
+
+
+        spark.stop();
+
+    }
+}
+
+

Scala Example Code

object DliCatalogTest {
+  def main(args:Array[String]): Unit = {
+    val sql = args(0)
+    val runDdl =
+Try(args(1).toBoolean).getOrElse(true)
+    System.out.println(s"sql is $sql
+runDdl is $runDdl")
+    val sparkConf = new SparkConf(true)
+    sparkConf    
+      .set("spark.sql.session.state.builder","org.apache.spark.sql.hive.UQueryHiveACLSessionStateBuilder")
+      .set("spark.sql.catalog.class","org.apache.spark.sql.hive.UQueryHiveACLExternalCatalog") 
+    sparkConf.setAppName("dlicatalogtester")
+
+    val spark = SparkSession.builder
+      .config(sparkConf)
+      .enableHiveSupport()
+      .config("spark.sql.extensions","org.apache.spark.sql.DliSparkExtension")
+      .appName("SparkTest")
+      .getOrCreate()
+
+    System.out.println("catalog is "
++ spark.sessionState.catalog.toString)
+    if (runDdl) {
+      val df = spark.sql(sql).collect()
+    } else {
+      spark.sql(sql).show()
+    }
+
+    spark.close()
+  }
+
+}
+
+

Example Python Code

#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+from __future__ import print_function
+
+import sys
+
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+    url = sys.argv[1]
+    creatTbl = "CREATE TABLE test_sparkapp.dli_rds USING JDBC OPTIONS ('url'='jdbc:mysql://%s'," \
+              "'driver'='com.mysql.jdbc.Driver','dbtable'='test.test'," \
+              " 'passwdauth' = 'DatasourceRDSTest_pwd','encryption' = 'true')" % url
+
+    spark = SparkSession \
+        .builder \
+        .enableHiveSupport() \
+.config("spark.sql.session.state.builder","org.apache.spark.sql.hive.UQueryHiveACLSessionStateBuilder") \       
+.config("spark.sql.catalog.class", "org.apache.spark.sql.hive.UQueryHiveACLExternalCatalog") \  
+.config("spark.sql.extensions","org.apache.spark.sql.DliSparkExtension") \
+        .appName("python Spark test catalog") \
+        .getOrCreate()
+
+    spark.sql("CREATE database if not exists test_sparkapp").collect()
+    spark.sql("drop table if exists test_sparkapp.dli_rds").collect()
+    spark.sql(creatTbl).collect()
+    spark.sql("select * from test_sparkapp.dli_rds").show()
+    spark.sql("insert into table test_sparkapp.dli_rds select 12,'aaa'").collect()
+    spark.sql("select * from test_sparkapp.dli_rds").show()
+    spark.sql("insert overwrite table test_sparkapp.dli_rds select 1111,'asasasa'").collect()
+    spark.sql("select * from test_sparkapp.dli_rds").show()
+    spark.sql("drop table test_sparkapp.dli_rds").collect()
+    spark.stop()
+
+
+
+
+
Parent topic: Spark Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0187.html b/docs/dli/dev/dli_09_0187.html new file mode 100644 index 00000000..848c2ff6 --- /dev/null +++ b/docs/dli/dev/dli_09_0187.html @@ -0,0 +1,124 @@ + + +

Java Example Code

+

Development Description

  • Prerequisites

    A datasource connection has been created and bound to a queue on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependencies.
      • Maven dependency involved
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
      • Import dependency packages.
        1
        import org.apache.spark.sql.SparkSession;
+
        + +
        +
      +
    2. Create a session.
      1
      SparkSession sparkSession = SparkSession.builder().appName("datasource-rds").getOrCreate();
+
      + +
      +
    +
  • Connecting to data sources through SQL APIs
    • Create a table to connect to an RDS data source and set connection parameters.
      1
+2
+3
+4
+5
+6
+7
      sparkSession.sql(
+  "CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS (
+     'url'='jdbc:mysql://to-rds-1174404209-cA37siB6.datasource.com:3306',  // Set this parameter to the actual URL.
+     'dbtable'='test.customer',
+     'user'='root',  // Set this parameter to the actual user.
+     'password'='######',  // Set this parameter to the actual password.
+     'driver'='com.mysql.jdbc.Driver')")
+
      + +
      +

      For details about the parameters for creating a table, see Table 1.

      +
    • Insert data.
      1
      sparkSession.sql("insert into dli_to_rds values (1,'John',24)");
+
      + +
      +
    • Query data.
      1
      sparkSession.sql("select * from dli_to_rd").show();
+
      + +
      +

      Response

      +

      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +

      +
    3. After the Spark job is created, click Execute in the upper right corner of the console to submit the job. If the message "Spark job submitted successfully." is displayed, the Spark job is successfully submitted. You can view the status and logs of the submitted job on the Spark Jobs page.
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.rds when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/rds/*

        +
      +
      +
    +
+
+

Complete Example Code

Connecting to data sources through SQL APIs

+
 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
import org.apache.spark.sql.SparkSession;
+ 
+public class java_rds {
+ 
+    public static void main(String[] args) {
+        SparkSession sparkSession = SparkSession.builder().appName("datasource-rds").getOrCreate();
+ 
+        // Create a data table for DLI-associated RDS
+        sparkSession.sql("CREATE TABLE IF NOT EXISTS dli_to_rds USING JDBC OPTIONS ('url'='jdbc:mysql://192.168.6.150:3306','dbtable'='test.customer','user'='root','password'='**','driver'='com.mysql.jdbc.Driver')");
+ 
+        //*****************************SQL model***********************************
+        //Insert data into the DLI data table
+        sparkSession.sql("insert into dli_to_rds values(3,'Liu',21),(4,'Joey',34)");
+ 
+        //Read data from DLI data table
+        sparkSession.sql("select * from dli_to_rds");
+ 
+        //drop table
+        sparkSession.sql("drop table dli_to_rds");
+ 
+        sparkSession.close();
+    }
+}
+
+ +
+
+
+
+
+
Parent topic: Connecting to RDS
+
+
+ diff --git a/docs/dli/dev/dli_09_0188.html b/docs/dli/dev/dli_09_0188.html new file mode 100644 index 00000000..f5705a4d --- /dev/null +++ b/docs/dli/dev/dli_09_0188.html @@ -0,0 +1,14 @@ + + +

Troubleshooting

+

Problem 1

  • Symptom

    After the code is directly copied to the .py file, unexpected characters may exist after the backslashes (\).

    +
  • Solution

    Delete the indentations or spaces after the backslashes (\).

    +
+
+
+
+
+
Parent topic: Connecting to Redis
+
+
+ diff --git a/docs/dli/dev/dli_09_0189.html b/docs/dli/dev/dli_09_0189.html new file mode 100644 index 00000000..f662db92 --- /dev/null +++ b/docs/dli/dev/dli_09_0189.html @@ -0,0 +1,33 @@ + + +

CSS Security Cluster Configuration

+

Preparations

The Elasticsearch 6.5.4 and later versions provided by CSS provides the security settings. Once the function is enabled, CSS provides identity authentication, authorization, and encryption for users. Before connecting DLI to the CSS security cluster, you need to perform certain preparations.
  1. Select CSS Elasticsearch 6.5.4 or a later cluster version, create a CSS security cluster, and download the security cluster certificate (CloudSearchService.cer).
    1. Log in to the CSS management console, click Clusters, and select the cluster for which you want to create a datasource connection.
    2. Click Download Certificate next to Security Mode to download the security certificate.
    +
  2. Use keytool to generate the keystore and truststore files.
    1. Security certificate CloudSearchService.cer of the security cluster is required when you use keytool to generate the keystore and truststore files. You can set other keytool parameters as required.
      1. Open the cmd command window and run the following command to generate a keystore file that contains a private key:
        keytool -genkeypair -alias certificatekey -keyalg RSA -keystore transport-keystore.jks
        +
      2. After the keystore and truststore files are generated using keytool, you can view the transport-keystore.jks file in the folder. Run the following command to verify the keystore file and certificate information:
        keytool -list -v -keystore transport-keystore.jks
        +

        After you enter the correct keystore password, the corresponding information is displayed.

        +
      3. Run the following commands to create the truststore.jks file and verify it:
        keytool -import -alias certificatekey -file CloudSearchService.cer  -keystore truststore.jks
+keytool -list -v -keystore truststore.jks
        +
      +
    2. Upload the generated keystore and truststore files to an OBS bucket.
    +
+
+
+

CSS Security Cluster Parameter Configuration

For details about the parameters, see Table 1. This part describes the precautions for configuring the connection parameters of the CSS security cluster.

+
.option("es.net.http.auth.user", "admin") .option("es.net.http.auth.pass", "***")
+

The parameters are the identity authentication account and password, which are also the account and password for logging in to Kibana.

+
.option("es.net.ssl", "true")
+
  • If HTTPS access is enabled for the CSS security cluster, set this parameter to true and then set parameters such as the security certificate and file address.
  • If HTTPS access is not enabled for the CSS security cluster, set this parameter to false. In this case, you do not need to set parameters such as the security certificate and file address.
+
.option("es.net.ssl.keystore.location", "obs://Bucket name/path/transport-keystore.jks")
+.option("es.net.ssl.keystore.pass", "***")
+

Set the location of the keystore.jks file and the key for accessing the file. Place the keystore.jks file generated in Preparations in the OBS bucket, and then enter the AK, SK, and location of the keystore.jks file. Enter the key for accessing the file in es.net.ssl.keystore.pass.

+
.option("es.net.ssl.truststore.location", "obs://Bucket name/path/truststore.jks")
+.option("es.net.ssl.truststore.pass", "***")
+

The parameters in the truststore.jks file are basically the same as those in the keystore.jks file. You can refer to the preceding procedure to set parameters.

+
+
+
+
+
Parent topic: Connecting to CSS
+
+
+ diff --git a/docs/dli/dev/dli_09_0190.html b/docs/dli/dev/dli_09_0190.html new file mode 100644 index 00000000..045d4f12 --- /dev/null +++ b/docs/dli/dev/dli_09_0190.html @@ -0,0 +1,277 @@ + + +

Java Example Code

+

Prerequisites

A datasource connection has been created on the DLI management console.

+
+

CSS Non-Security Cluster

  • Development description
    • Code implementation
      • Constructing dependency information and creating a Spark session
        1. Import dependencies.

          Maven dependency

          +
          <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
          +
          Import dependency packages.
          1
          import org.apache.spark.sql.SparkSession;
+
          + +
          +
          +
        2. Create a session.
          1
          SparkSession sparkSession = SparkSession.builder().appName("datasource-css").getOrCreate();
+
          + +
          +
        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
        sparkSession.sql("create table css_table(id long, name string) using css options( 'es.nodes' = '192.168.9.213:9200', 'es.nodes.wan.only' = 'true','resource' ='/mytest')");
        +
      2. Insert data.
        sparkSession.sql("insert into css_table values(18, 'John'),(28, 'Bob')");
        +
      3. Query data.
        sparkSession.sql("select * from css_table").show();
        +
      4. Delete the datasource connection table.
        sparkSession.sql("drop table css_table");
        +
      +
    • Submitting a Spark job
      1. Generate a JAR package based on the code file and upload the package to DLI.

        +

        +
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

        +
        • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.css when you submit a job.
        • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

          spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +

          spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/css/*

          +
        +
        +
        +
      +
    +
  • Complete example code
    • Maven dependency
      <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
      +
    • Connecting to data sources through SQL APIs
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
      import org.apache.spark.sql.*;
+ 
+public class java_css_unsecurity {
+ 
+    public static void main(String[] args) {
+        SparkSession sparkSession = SparkSession.builder().appName("datasource-css-unsecurity").getOrCreate();
+ 
+        // Create a DLI data table for DLI-associated CSS
+        sparkSession.sql("create table css_table(id long, name string) using css options( 'es.nodes' = '192.168.15.34:9200', 'es.nodes.wan.only' = 'true', 'resource' = '/mytest')");
+ 
+        //*****************************SQL model***********************************
+        // Insert data into the DLI data table
+        sparkSession.sql("insert into css_table values(18, 'John'),(28, 'Bob')");
+ 
+        // Read data from DLI data table
+        sparkSession.sql("select * from css_table").show();
+ 
+        // drop table
+        sparkSession.sql("drop table css_table");
+ 
+        sparkSession.close();
+    }
+}
+
      + +
      +
    +
+
+

CSS Security Cluster

  • Preparations

    Generate the keystore.jks and truststore.jks files and upload them to the OBS bucket. For details, see CSS Security Cluster Configuration.

    +
  • Description of the development with HTTPS disabled
    If HTTPS is disabled, keystore.jks and truststore.jks files are not required. You only need to set SSL access parameters and credentials.
    • Constructing dependency information and creating a Spark session
      1. Import dependencies.

        Maven dependency

        +
        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
        +
        Import dependency packages.
        1
        import org.apache.spark.sql.SparkSession;
+
        + +
        +
        +
      2. Create a session.
        1
        SparkSession sparkSession = SparkSession.builder().appName("datasource-css").getOrCreate();
+
        + +
        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
        1
        sparkSession.sql("create table css_table(id long, name string) using css options( 'es.nodes' = '192.168.9.213:9200', 'es.nodes.wan.only' = 'true', 'resource' = '/mytest','es.net.ssl'='false','es.net.http.auth.user'='admin','es.net.http.auth.pass'='*******')");
+
        + +
        +
        • For details about the parameters for creating a CSS datasource connection table, see Table 1.
        • In the preceding example, HTTPS access is disabled for the CSS security cluster. Therefore, you need to set es.net.ssl to false. es.net.http.auth.user and es.net.http.auth.pass are the username and password set during cluster creation, respectively.
        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(18, 'John'),(28, 'Bob')");
+
        + +
        +
      3. Query data.
        1
        sparkSession.sql("select * from css_table").show();
+
        + +
        +
      4. Delete the datasource connection table.
        sparkSession.sql("drop table css_table");
        +
      +
    • Submitting a Spark job
      1. Generate a JAR package based on the code file and upload the package to DLI.

        +

        +
      2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

        +
        • When submitting a job, you need to specify a dependency module named sys.datasource.css.
        • For details about how to submit a job on the console, see
        • For details about how to submit a job through an API, see the modules parameter in
        +
        +
        +
      +
    • Complete example code
      • Maven dependency
        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
        +
      +
    +
    +
  • Description of development with HTTPS enabled
    • Constructing dependency information and creating a Spark session
      1. Import dependencies.

        Maven dependency

        +
        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
        +

        Import dependency packages.

        +
        1
        import org.apache.spark.sql.SparkSession;
+
        + +
        +
      2. Create a session.
        1
        SparkSession sparkSession = SparkSession.builder().appName("datasource-css").getOrCreate();
+
        + +
        +
      +
    • Connecting to data sources through SQL APIs
      1. Create a table to connect to a CSS data source.
        1
+2
+3
        sparkSession.sql("create table css_table(id long, name string) using css options( 'es.nodes' = '192.168.13.189:9200', 'es.nodes.wan.only' = 'true', 'resource' = '/mytest','es.net.ssl'='true','es.net.ssl.keystore.location' = 'obs://Bucket name/Address/transport-keystore.jks','es.net.ssl.keystore.pass' = '**',
+'es.net.ssl.truststore.location'='obs://Bucket name/Address/truststore.jks,
+'es.net.ssl.truststore.pass'='***','es.net.http.auth.user'='admin','es.net.http.auth.pass'='**')");
+
        + +
        +

        For details about the parameters for creating a CSS datasource connection table, see Table 1.

        +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into css_table values(18, 'John'),(28, 'Bob')");
+
        + +
        +
      3. Query data.
        1
        sparkSession.sql("select * from css_table").show();
+
        + +
        +
      4. Delete the datasource connection table.
        sparkSession.sql("drop table css_table");
        +
      +
    • Submitting a Spark job
      1. Generate a JAR package based on the code file and upload the package to DLI.

        +

        +
      2. If HTTPS access is enabled, you need to upload the dependency file hadoop-site.xml when creating a Spark job. The content of the hadoop-site.xml file is as follows:
        <?xml version="1.0" encoding="UTF-8"?>
+<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
+<!--
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License. See accompanying LICENSE file.
+-->
+
+<!-- Put site-specific property overrides in this file. -->
+
+<configuration>
+<property>
+    <name>fs.obs.bucket.Bucket name.access.key</name>
+    <value>AK</value>
+  </property>
+<property>
+    <name>fs.obs.bucket.Bucket name.secret.key </name>
+    <value>SK</value>
+  </property>
+</configuration>
        +

        <name>fs.obs.bucket.Bucket name.access.key</name> is used to better locate the bucket address. The bucket name is the name of the bucket where the keystore.jks and truststore.jks files are stored.

        +
        +
      3. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

        +
        • When submitting a job, you need to specify a dependency module named sys.datasource.css.
        • For details about how to submit a job on the console, see
        • For details about how to submit a job through an API, see the modules parameter in
        +
        +
        +
      +
    • Complete example code
      • Maven dependency
        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
        +
      • Connecting to data sources through SQL APIs
         1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
        import org.apache.spark.sql.SparkSession;
+ 
+public class java_css_security_httpson {
+    public static void main(String[] args) {
+        SparkSession sparkSession = SparkSession.builder().appName("datasource-css").getOrCreate();
+ 
+        // Create a DLI data table for DLI-associated CSS
+        sparkSession.sql("create table css_table(id long, name string) using css options( 'es.nodes' = '192.168.13.189:9200', 'es.nodes.wan.only' = 'true', 'resource' = '/mytest','es.net.ssl'='true','es.net.ssl.keystore.location' = 'obs://Bucket name/Address/transport-keystore.jks','es.net.ssl.keystore.pass' = '**','es.net.ssl.truststore.location'='obs://Bucket name/Address/truststore.jks','es.net.ssl.truststore.pass'='**','es.net.http.auth.user'='admin','es.net.http.auth.pass'='**')");
+ 
+        //*****************************SQL model***********************************
+        // Insert data into the DLI data table
+        sparkSession.sql("insert into css_table values(34, 'Yuan'),(28, 'Kids')");
+ 
+        // Read data from DLI data table
+        sparkSession.sql("select * from css_table").show();
+ 
+        // drop table
+        sparkSession.sql("drop table css_table");
+ 
+        sparkSession.close();
+    }
+}
+
        + +
        +
      +
    +

    +
+
+
+
+
+
Parent topic: Connecting to CSS
+
+
+ diff --git a/docs/dli/dev/dli_09_0191.html b/docs/dli/dev/dli_09_0191.html new file mode 100644 index 00000000..f34989ba --- /dev/null +++ b/docs/dli/dev/dli_09_0191.html @@ -0,0 +1,311 @@ + + +

Writing Data to OBS Using Flink Jar

+

Overview

DLI allows you to use a custom JAR package to run Flink jobs and write data to OBS. This section describes how to write processed Kafka data to OBS. You need to modify the parameters in the example Java code based on site requirements.

+
+

Environment Preparations

Development tools such as IntelliJ IDEA and other development tools, JDK, and Maven have been installed and configured.

+
  • For details about how to configure the pom.xml file of the Maven project, see "POM file configurations" in Java Example Code.
  • Ensure that you can access the public network in the local compilation environment.
+
+
+

Constraints

  • In the left navigation pane of the DLI console, choose Global Configuration > Service Authorization. On the page displayed, select Tenant Administrator(Global service) and click Update.
  • The bucket to which data is written must be an OBS bucket created by a main account.
+
+

Java Example Code

  • POM file configurations
    <?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <parent>
+        <artifactId>Flink-demo</artifactId>
+        <version>1.0-SNAPSHOT</version>
+    </parent>
+    <modelVersion>4.0.0</modelVersion>
+
+    <artifactId>flink-kafka-to-obs</artifactId>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <!--Flink version-->
+        <flink.version>1.12.2</flink.version>
+        <!--JDK version -->
+        <java.version>1.8</java.version>
+        <!--Scala 2.11 -->
+        <scala.binary.version>2.11</scala.binary.version>
+        <slf4j.version>2.13.3</slf4j.version>
+        <log4j.version>2.10.0</log4j.version>
+        <maven.compiler.source>8</maven.compiler.source>
+        <maven.compiler.target>8</maven.compiler.target>
+    </properties>
+
+    <dependencies>
+        <!-- flink -->
+        <dependency>
+            <groupId>org.apache.flink</groupId>
+            <artifactId>flink-java</artifactId>
+            <version>${flink.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.flink</groupId>
+            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
+            <version>${flink.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.flink</groupId>
+            <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
+            <version>${flink.version}</version>
+            <scope>provided</scope>
+        </dependency>
+
+        <!--  kafka  -->
+        <dependency>
+            <groupId>org.apache.flink</groupId>
+            <artifactId>flink-connector-kafka_2.11</artifactId>
+            <version>${flink.version}</version>
+        </dependency>
+
+        <!--  logging  -->
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-slf4j-impl</artifactId>
+            <version>${slf4j.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-api</artifactId>
+            <version>${log4j.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-core</artifactId>
+            <version>${log4j.version}</version>
+            <scope>provided</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-jcl</artifactId>
+            <version>${log4j.version}</version>
+            <scope>provided</scope>
+        </dependency>
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-assembly-plugin</artifactId>
+                <version>3.3.0</version>
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>single</goal>
+                        </goals>
+                    </execution>
+                </executions>
+                <configuration>
+                    <archive>
+                        <manifest>
+                            <mainClass>com.dli.FlinkKafkaToObsExample</mainClass>
+                        </manifest>
+                    </archive>
+                    <descriptorRefs>
+                        <descriptorRef>jar-with-dependencies</descriptorRef>
+                    </descriptorRefs>
+                </configuration>
+            </plugin>
+        </plugins>
+        <resources>
+            <resource>
+                <directory>../main/config</directory>
+                <filtering>true</filtering>
+                <includes>
+                    <include>**/*.*</include>
+                </includes>
+            </resource>
+        </resources>
+    </build>
+</project>
    +
  • Example code
    import org.apache.flink.api.common.serialization.SimpleStringEncoder; 
+import org.apache.flink.api.common.serialization.SimpleStringSchema; 
+import org.apache.flink.api.java.utils.ParameterTool; 
+import org.apache.flink.contrib.streaming.state.RocksDBStateBackend; 
+import org.apache.flink.core.fs.Path; 
+import org.apache.flink.runtime.state.filesystem.FsStateBackend; 
+import org.apache.flink.streaming.api.datastream.DataStream; 
+import org.apache.flink.streaming.api.environment.CheckpointConfig; 
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; 
+import org.apache.flink.streaming.api.functions.sink.filesystem.StreamingFileSink; 
+import org.apache.flink.streaming.api.functions.sink.filesystem.bucketassigners.DateTimeBucketAssigner; 
+import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.OnCheckpointRollingPolicy; 
+import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer; 
+import org.apache.kafka.clients.consumer.ConsumerConfig; 
+import org.slf4j.Logger; 
+import org.slf4j.LoggerFactory; 
+
+import java.util.Properties; 
+
+/** 
+ * @author xxx 
+ * @date 6/26/21 
+ */ 
+public class FlinkKafkaToObsExample { 
+    private static final Logger LOG = LoggerFactory.getLogger(FlinkKafkaToObsExample.class); 
+
+    public static void main(String[] args) throws Exception { 
+        LOG.info("Start Kafka2OBS Flink Streaming Source Java Demo."); 
+        ParameterTool params = ParameterTool.fromArgs(args); 
+        LOG.info("Params: " + params.toString()); 
+
+        // Kafka connection address
+        String bootstrapServers; 
+        // Kafka consumer group
+        String kafkaGroup; 
+        // Kafka topic 
+        String kafkaTopic; 
+        // Consumption policy. This policy is used only when the partition does not have a checkpoint or the checkpoint expires.
+        // If a valid checkpoint exists, consumption continues from this checkpoint.
+        // When the policy is set to LATEST, the consumption starts from the latest data. This policy will ignore the existing data in the stream.
+        // When the policy is set to EARLIEST, the consumption starts from the earliest data. This policy will obtain all valid data in the stream.
+        String offsetPolicy; 
+        // OBS file output path, in the format of obs://bucket/path.
+        String outputPath; 
+        // Checkpoint output path, in the format of obs://bucket/path.
+        String checkpointPath; 
+
+        bootstrapServers = params.get("bootstrap.servers", "xxxx:9092,xxxx:9092,xxxx:9092"); 
+        kafkaGroup = params.get("group.id", "test-group"); 
+        kafkaTopic = params.get("topic", "test-topic"); 
+        offsetPolicy = params.get("offset.policy", "earliest"); 
+        outputPath = params.get("output.path", "obs://bucket/output"); 
+       checkpointPath = params.get("checkpoint.path", "obs://bucket/checkpoint"); 
+
+        try { 
+            //Create an execution environment.
+            StreamExecutionEnvironment streamEnv = StreamExecutionEnvironment.getExecutionEnvironment();
+            streamEnv.setParallelism(4);
+            RocksDBStateBackend rocksDbBackend = new RocksDBStateBackend(checkpointPath, true);
+            RocksDBStateBackend rocksDbBackend = new RocksDBStateBackend(new FsStateBackend(checkpointPath), true); 
+            streamEnv.setStateBackend(rocksDbBackend); 
+            // Enable Flink checkpointing mechanism. If enabled, the offset information will be synchronized to Kafka.
+            streamEnv.enableCheckpointing(300000); 
+            // Set the minimum interval between two checkpoints.
+            streamEnv.getCheckpointConfig().setMinPauseBetweenCheckpoints(60000); 
+            // Set the checkpoint timeout duration.
+            streamEnv.getCheckpointConfig().setCheckpointTimeout(60000); 
+            // Set the maximum number of concurrent checkpoints.
+            streamEnv.getCheckpointConfig().setMaxConcurrentCheckpoints(1); 
+            // Retain checkpoints when a job is canceled.
+            streamEnv.getCheckpointConfig().enableExternalizedCheckpoints( 
+                    CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION); 
+
+            // Source: Connect to the Kafka data source.
+            Properties properties = new Properties(); 
+            properties.setProperty("bootstrap.servers", bootstrapServers); 
+            properties.setProperty("group.id", kafkaGroup); 
+            properties.setProperty(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, offsetPolicy); 
+            String topic = kafkaTopic; 
+
+            // Create a Kafka consumer.
+            FlinkKafkaConsumer<String> kafkaConsumer = 
+                    new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties); 
+            /** 
+             * Read partitions from the offset submitted by the consumer group (specified by group.id in the consumer attribute) in Kafka brokers.
+             * If the partition offset cannot be found, set it by using the auto.offset.reset parameter.
+             * For details, see https://ci.apache.org/projects/flink/flink-docs-release-1.13/zh/docs/connectors/datastream/kafka/.
+             */ 
+            kafkaConsumer.setStartFromGroupOffsets(); 
+
+            // Add Kafka to the data source.
+            DataStream<String> stream = streamEnv.addSource(kafkaConsumer).setParallelism(3).disableChaining(); 
+
+            // Create a file output stream.
+            final StreamingFileSink<String> sink = StreamingFileSink 
+                    // Specify the file output path and row encoding format.
+                    .forRowFormat(new Path(outputPath), new SimpleStringEncoder<String>("UTF-8")) 
+                    // Specify the file output path and bulk encoding format. Files are output in parquet format.
+                    //.forBulkFormat(new Path(outputPath), ParquetAvroWriters.forGenericRecord(schema)) 
+                    // Specify a custom bucket assigner.
+                    .withBucketAssigner(new DateTimeBucketAssigner<>()) 
+                    // Specify the rolling policy.
+                    .withRollingPolicy(OnCheckpointRollingPolicy.build()) 
+                    .build(); 
+
+            // Add sink for DIS Consumer data source 
+            stream.addSink(sink).disableChaining().name("obs"); 
+
+            // stream.print(); 
+            streamEnv.execute(); 
+        } catch (Exception e) { 
+            LOG.error(e.getMessage(), e); 
+        } 
+    } 
+}
+
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table 1 Parameter description

    Parameter

    +

    Description

    +

    Example

    +

    bootstrap.servers

    +

    Kafka connection address

    +

    IP address of the Kafka service 1:9092, IP address of the Kafka service 2:9092, IP address of the Kafka service 3:9092

    +

    group.id

    +

    Kafka consumer group

    +

    test-group

    +

    topic

    +

    Kafka consumption topic

    +

    test-topic

    +

    offset.policy

    +

    Kafka offset policy

    +

    earliest

    +

    output.path

    +

    OBS path to which data will be written

    +

    obs://bucket/output

    +

    checkpoint.path

    +

    Checkpoint OBS path

    +

    obs://bucket/checkpoint

    +
    +
    +
    +
+
+

Compiling and Running the Application

After the application is developed, upload the JAR package to DLI by referring to Flink Jar Job Examples and check whether related data exists in the OBS path.

+
+
+
+
+
Parent topic: Flink Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0193.html b/docs/dli/dev/dli_09_0193.html new file mode 100644 index 00000000..aaeef07a --- /dev/null +++ b/docs/dli/dev/dli_09_0193.html @@ -0,0 +1,113 @@ + + +

Java Example Code

+

Development Description

This example applies only to MRS OpenTSDB.

+
  • Prerequisites

    A datasource connection has been created and bound to a queue on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependencies.
      • Maven dependency involved
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
      • Import dependency packages.
        1
        import org.apache.spark.sql.SparkSession;
+
        + +
        +
      +
    2. Create a session.
      1
      sparkSession = SparkSession.builder().appName("datasource-opentsdb").getOrCreate();
+
      + +
      +
    +
  • Connecting to data sources through SQL APIs
    • Create a table to connect to an MRS OpenTSDB data source and set connection parameters.
      1
      sparkSession.sql("create table opentsdb_new_test using opentsdb options('Host'='10.0.0.171:4242','metric'='ctopentsdb','tags'='city,location')");
+
      + +
      +

      For details about the Host, metric, and tags parameters, see Table 1.

      +
      +
    • Insert data.
      1
      sparkSession.sql("insert into opentsdb_new_test values('Penglai', 'abc', '2021-06-30 18:00:00', 30.0)");
+
      + +
      +
    • Query data.
      1
      sparkSession.sql("select * from opentsdb_new_test").show();
+
      + +
      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code file and upload the package to DLI.

      +

      +
    2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.opentsdb when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Maven dependency involved
    <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+</dependency>
    +
  • Connecting to data sources through SQL APIs
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
    import org.apache.spark.sql.SparkSession;
+ 
+public class java_mrs_opentsdb {
+ 
+    private static SparkSession sparkSession = null;
+ 
+    public static void main(String[] args) {
+        //create a SparkSession session
+        sparkSession = SparkSession.builder().appName("datasource-opentsdb").getOrCreate();
+ 
+        sparkSession.sql("create table opentsdb_new_test using opentsdb options('Host'='10.0.0.171:4242','metric'='ctopentsdb','tags'='city,location')");
+ 
+        //*****************************SQL module***********************************
+        sparkSession.sql("insert into opentsdb_new_test values('Penglai', 'abc', '2021-06-30 18:00:00', 30.0)");
+        System.out.println("Penglai new timestamp");
+        sparkSession.sql("select * from opentsdb_new_test").show();
+ 
+       sparkSession.close();
+ 
+    }
+}
+
    + +
    +
+
+
+
+
+
Parent topic: Connecting to OpenTSDB
+
+
+ diff --git a/docs/dli/dev/dli_09_0195.html b/docs/dli/dev/dli_09_0195.html new file mode 100644 index 00000000..436d6a2f --- /dev/null +++ b/docs/dli/dev/dli_09_0195.html @@ -0,0 +1,15 @@ + + +

Troubleshooting

+

A Spark Job Fails to Be Executed and "No respond" Is Displayed in the Job Log

  • Symptom

    A Spark job fails to be executed and "No respond" is displayed in the job log.

    +
  • Solution

    Create a Spark job again. When creating the job, add the spark.sql.mrs.opentsdb.ssl.enabled=true configuration item to the Spark parameters (--conf).

    +

    +
+
+
+
+
+
Parent topic: Connecting to OpenTSDB
+
+
+ diff --git a/docs/dli/dev/dli_09_0196.html b/docs/dli/dev/dli_09_0196.html new file mode 100644 index 00000000..326eb23d --- /dev/null +++ b/docs/dli/dev/dli_09_0196.html @@ -0,0 +1,22 @@ + + +

MRS Configuration

+

Configuring MRS Host Information in DLI Datasource Connection

  1. Create a datasource connection on the DLI management console.
  2. Add the /etc/hosts information of MRS cluster nodes to the host file of the DLI queue.

    +

    For details, see section "Modifying the Host Information" in the Data Lake Insight User Guide.

    +
+
+

Completing Configurations for Enabling Kerberos Authentication

  1. Create a cluster with Kerberos authentication enabled by referring to section "Creating a Security Cluster and Logging In to MRS Manager" in . Add a user and grant permissions to the user by referring to section "Creating Roles and Users".
  2. Use the user created in 1 for login authentication. For details, see . A human-machine user must change the password upon the first login.
  3. Log in to Manager and choose System. In the navigation pane on the left, choose Permission > User, locate the row where the new user locates, click More, and select Download Authentication Credential. Save the downloaded package and decompress it to obtain the keytab and krb5.conf files.
+
+

Creating an MRS HBase Table

Before creating an MRS HBase table to be associated with the DLI table, ensure that the HBase table exists. The following provides example code to describe how to create an MRS HBase table:

+
  1. Remotely log in to the ECS and use the HBase Shell command to view table information. In this command, hbtest indicates the name of the table to be queried.
    describe 'hbtest'
    +
  2. (Optional) If the HBase table does not exist, run the following command to create one:
    create 'hbtest', 'info', 'detail'
    +

    In this command, hbtest indicates the table name, and other parameters indicate the column family names.

    +
  3. Configure the connection information. TableName corresponds to the name of the HBase table.
+
+
+
+
+
Parent topic: Connecting to HBase
+
+
+ diff --git a/docs/dli/dev/dli_09_0197.html b/docs/dli/dev/dli_09_0197.html new file mode 100644 index 00000000..aad426c9 --- /dev/null +++ b/docs/dli/dev/dli_09_0197.html @@ -0,0 +1,270 @@ + + +

Java Example Code

+

Development Description

This example applies only to MRS HBase.

+
  • Prerequisites

    A datasource connection has been created and bound to a queue on the DLI management console.

    +

    Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

    +
    +
  • Code implementation
    1. Import dependencies.
      • Maven dependency involved
        1
+2
+3
+4
+5
        <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
        + +
        +
      • Import dependency packages.
        1
        import org.apache.spark.sql.SparkSession;
+
        + +
        +
      +
    2. Create a session.
      1
      parkSession = SparkSession.builder().appName("datasource-HBase-MRS").getOrCreate();
+
      + +
      +
    +
  • Connecting to data sources through SQL APIs
    • For clusters with Kerberos authentication disabled
      1. Create a table to connect to an MRS HBase data source and set connection parameters.
        1
        sparkSession.sql("CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS('ZKHost'='10.0.0.63:2181','TableName'='hbtest','RowKey'='id:5','Cols'='location:info.location,city:detail.city') ");
+
        + +
        +
      2. Insert data.
        1
        sparkSession.sql("insert into testhbase values('12345','abc','xxx')");
+
        + +
        +
      3. Query data.
        1
        sparkSession.sql("select * from testhbase").show();
+
        + +
        +
      +
    • For clusters with Kerberos authentication enabled
      1. Create a table to connect to an MRS HBase data source and set connection parameters.
        1
        sparkSession.sql("CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS('ZKHost'='10.0.0.63:2181','TableName'='hbtest','RowKey'='id:5','Cols'='location:info.location,city:detail.city,'krb5conf'='./krb5.conf','keytab'='./user.keytab','principal'='krbtest') ");
+
        + +
        +
        If Kerberos authentication is enabled, you need to set three more parameters, as listed in Table 1. +
        + + + + + + + + + + + + + +
        Table 1 Parameter description

        Parameter and Value

        +

        Description

        +

        'krb5conf' = './krb5.conf'

        +

        Path of the krb5.conf file.

        +

        'keytab'='./user.keytab'

        +

        Path of the keytab file.

        +

        'principal' ='krbtest'

        +

        Authentication username.

        +
        +
        +
        +

        For details about how to obtain the krb5.conf and keytab files, see Completing Configurations for Enabling Kerberos Authentication.

        +
      2. Insert data.
        1
        sparkSession.sql("insert into testhbase values('95274','abc','Hongkong')");
+
        + +
        +
      3. Query data.
        1
        sparkSession.sql("select * from testhbase").show();
+
        + +
        +
      +
    +
  • Submitting a Spark job
    1. Generate a JAR package based on the code file and upload the package to DLI.

      +

      +
    2. (Optional) Add the krb5.conf and user.keytab files to the dependency files of the job when creating a Spark job in an MRS cluster with Kerberos authentication enabled. Skip this step if Kerberos authentication is not enabled for the cluster.
    3. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

      +
      • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.hbase when you submit a job.
      • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

        spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

        +

        spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/hbase/*

        +
      +
      +
      +
    +
+
+

Complete Example Code

  • Connecting to data sources through SQL APIs
    • Complete example code for the cluster with Kerberos authentication disabled
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
      import org.apache.spark.sql.SparkSession;
+ 
+public class java_mrs_hbase {
+ 
+    public static void main(String[] args) {
+        //create a SparkSession session
+        SparkSession sparkSession = SparkSession.builder().appName("datasource-HBase-MRS").getOrCreate();
+ 
+        sparkSession.sql("CREATE TABLE testhbase(id STRING, location STRING, city STRING) using hbase OPTIONS('ZKHost'='10.0.0.63:2181','TableName'='hbtest','RowKey'='id:5','Cols'='location:info.location,city:detail.city') ");
+ 
+        //*****************************SQL model***********************************
+        sparkSession.sql("insert into testhbase values('95274','abc','Hongkong')");
+        sparkSession.sql("select * from testhbase").show();
+ 
+        sparkSession.close();
+    }
+}
+
      + +
      +
    • Complete example code for the cluster with Kerberos authentication enabled
       1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
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+20
+21
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+63
+64
      import org.apache.spark.SparkContext;
+import org.apache.spark.SparkFiles;
+import org.apache.spark.sql.SparkSession;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+
+public class Test_HBase_SparkSql_Kerberos {
+
+    private static void copyFile(File src,File dst) throws IOException {
+        InputStream input  = null;
+        OutputStream output = null;
+        try {
+            input = new FileInputStream(src);
+            output = new FileOutputStream(dst);
+            byte[] buf = new byte[1024];
+            int bytesRead;
+            while ((bytesRead = input.read(buf)) > 0) {
+                output.write(buf, 0, bytesRead);
+            }
+        } finally { 
+            input.close();
+            output.close();
+        }
+    }
+
+    public static void main(String[] args) throws InterruptedException, IOException {
+        SparkSession sparkSession = SparkSession.builder().appName("Test_HBase_SparkSql_Kerberos").getOrCreate();
+        SparkContext sc = sparkSession.sparkContext();
+        sc.addFile("obs://xietest1/lzq/krb5.conf");
+        sc.addFile("obs://xietest1/lzq/user.keytab");
+        Thread.sleep(20);
+
+        File krb5_startfile = new File(SparkFiles.get("krb5.conf"));
+        File keytab_startfile = new File(SparkFiles.get("user.keytab"));
+        String path_user = System.getProperty("user.dir");
+        File keytab_endfile = new File(path_user + "/" + keytab_startfile.getName());
+        File krb5_endfile = new File(path_user + "/" + krb5_startfile.getName());
+        copyFile(krb5_startfile,krb5_endfile);
+        copyFile(keytab_startfile,keytab_endfile);
+        Thread.sleep(20);
+
+        /**
+         * Create an association table for the DLI association Hbase table
+         */
+        sparkSession.sql("CREATE TABLE testhbase(id string,booleanf boolean,shortf short,intf int,longf long,floatf float,doublef double) " +
+                "using hbase OPTIONS(" +
+                "'ZKHost'='10.0.0.146:2181'," +
+                "'TableName'='hbtest'," +
+                "'RowKey'='id:100'," +
+                "'Cols'='booleanf:CF1.booleanf,shortf:CF1.shortf,intf:CF1.intf,longf:CF2.longf,floatf:CF1.floatf,doublef:CF2.doublef'," +
+                "'krb5conf'='" + path_user + "/krb5.conf'," +
+                "'keytab'='" + path_user+ "/user.keytab'," +
+                "'principal'='krbtest') ");
+
+        //*****************************SQL model***********************************
+        sparkSession.sql("insert into testhbase values('newtest',true,1,2,3,4,5)");
+        sparkSession.sql("select * from testhbase").show();
+        sparkSession.close();
+    }
+}
+
      + +
      +
    +
+
+
+
+
+
Parent topic: Connecting to HBase
+
+
+ diff --git a/docs/dli/dev/dli_09_0198.html b/docs/dli/dev/dli_09_0198.html new file mode 100644 index 00000000..f329a73c --- /dev/null +++ b/docs/dli/dev/dli_09_0198.html @@ -0,0 +1,18 @@ + + +

Troubleshooting

+

Problem 1

  • Symptom

    The Spark job fails to be executed, and the job log indicates that the Java server connection or container fails to be started.

    +
  • Solution

    Check whether the host information of the datasource connection has been modified. If not, modify the host information by referring to Configuring MRS Host Information in DLI Datasource Connection. Then create and submit a Spark job again.

    +
+
+

Problem 2

  • Symptom

    A Spark job fails to be executed and "KrbException: Message stream modified (41)" is displayed in the job log.

    +
  • Solution

    Delete all renew_lifetime = xxx configurations from the krb5.conf configuration file. Then create and submit a Spark job again.

    +
+
+
+
+
+
Parent topic: Connecting to HBase
+
+
+ diff --git a/docs/dli/dev/dli_09_0199.html b/docs/dli/dev/dli_09_0199.html new file mode 100644 index 00000000..2af44992 --- /dev/null +++ b/docs/dli/dev/dli_09_0199.html @@ -0,0 +1,85 @@ + + +

Java Example Code

+

Scenario

This section provides Java example code that demonstrates how to use a Spark job to access data from the GaussDB(DWS) data source.

+

A datasource connection has been created and bound to a queue on the DLI management console.

+

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

+
+
+

Preparations

  1. Import dependencies.
    • Maven dependency involved
      1
+2
+3
+4
+5
      <dependency>
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-sql_2.11</artifactId>
+  <version>2.3.2</version>
+</dependency>
+
      + +
      +
    • Import dependency packages.
      1
      import org.apache.spark.sql.SparkSession;
+
      + +
      +
    +
  2. Create a session.
    1
    SparkSession sparkSession = SparkSession.builder().appName("datasource-dws").getOrCreate();
+
    + +
    +
+
+

Accessing a Data Source Through a SQL API

  1. Create a table to connect to a GaussDB(DWS) data source and set connection parameters.
    1
    sparkSession.sql("CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS ('url'='jdbc:postgresql://10.0.0.233:8000/postgres','dbtable'='test','user'='dbadmin','password'='**')");
+
    + +
    +
  2. Insert data.
    1
    sparkSession.sql("insert into dli_to_dws values(3,'L'),(4,'X')");
+
    + +
    +
  3. Query data.
    1
    sparkSession.sql("select * from dli_to_dws").show();
+
    + +
    +
+
+

Submitting a Spark Job

  1. Generate a JAR package based on the code file and upload the package to DLI.

    +

    +
  2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.

    +
    • If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.dws when you submit a job.
    • If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).

      spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/dws/*

      +

      spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/dws/*

      +
    +
    +
    +
+
+

Complete Example Code

Accessing GaussDB(DWS) tables through SQL APIs

+
import org.apache.spark.sql.SparkSession;
+ 
+public class java_dws {
+    public static void main(String[] args) {
+        SparkSession sparkSession = SparkSession.builder().appName("datasource-dws").getOrCreate();
+ 
+        sparkSession.sql("CREATE TABLE IF NOT EXISTS dli_to_dws USING JDBC OPTIONS ('url'='jdbc:postgresql://10.0.0.233:8000/postgres','dbtable'='test','user'='dbadmin','password'='**')");
+ 
+        //*****************************SQL model***********************************
+        //Insert data into the DLI data table
+        sparkSession.sql("insert into dli_to_dws values(3,'Liu'),(4,'Xie')");
+ 
+        //Read data from DLI data table
+        sparkSession.sql("select * from dli_to_dws").show();
+ 
+        //drop table
+        sparkSession.sql("drop table dli_to_dws");
+ 
+        sparkSession.close();
+    }
+}
+
+
+
+
+
Parent topic: Connecting to GaussDB(DWS)
+
+
+ diff --git a/docs/dli/dev/dli_09_0202.html b/docs/dli/dev/dli_09_0202.html new file mode 100644 index 00000000..10fe980d --- /dev/null +++ b/docs/dli/dev/dli_09_0202.html @@ -0,0 +1,15 @@ + + +

Flink Jar Jobs

+
+
+ +
+ diff --git a/docs/dli/dev/dli_09_0203.html b/docs/dli/dev/dli_09_0203.html new file mode 100644 index 00000000..30e7a2ed --- /dev/null +++ b/docs/dli/dev/dli_09_0203.html @@ -0,0 +1,21 @@ + + + +

Spark Jar Jobs

+ +

+
+ +
+ +
+ diff --git a/docs/dli/dev/dli_09_0204.html b/docs/dli/dev/dli_09_0204.html new file mode 100644 index 00000000..6f2440a3 --- /dev/null +++ b/docs/dli/dev/dli_09_0204.html @@ -0,0 +1,232 @@ + + +

Calling UDTFs in Spark SQL Jobs

+

Scenario

You can use Hive User-Defined Table-Generating Functions (UDTF) to customize table-valued functions. Hive UDTFs are used for the one-in-multiple-out data operations. UDTF reads a row of data and output multiple values.

+
+

Constraints

  • To perform UDTF-related operations on DLI, you need to create a SQL queue instead of using the default queue.
  • When UDTFs are used by multiple accounts, other users, except the user who creates them, need to be authorized before using the UDTF. The authorization operations are as follows:

    Log in to the DLI console and choose Data Management > Package Management. On the displayed page, select your UDTF Jar package and click Manage Permissions in the Operation column. On the permission management page, click Grant Permission in the upper right corner and select the required permissions.

    +
  • If you use a static class or interface in a UDF, add try catch to capture exceptions. Otherwise, package conflicts may occur.
+
+

Environment Preparations

Before you start, set up the development environment.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Development environment

Item

+

Description

+

OS

+

Windows 7 or later

+

JDK

+

JDK 1.8.

+

IntelliJ IDEA

+

This tool is used for application development. The version of the tool must be 2019.1 or other compatible versions.

+

Maven

+

Basic configurations of the development environment. Maven is used for project management throughout the lifecycle of software development.

+
+
+
+

Development Process

The process of developing a UDTF is as follows:
Figure 1 Development process
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2 Process description

No.

+

Phase

+

Software Portal

+

Description

+

1

+

Create a Maven project and configure the POM file.

+

IntelliJ IDEA

+

+

Write UDTF code by referring the steps in Procedure.

+

+

2

+

Write UDTF code.

+

3

+

Debug, compile, and pack the code into a Jar package.

+

4

+

Upload the Jar package to OBS.

+

OBS console

+

Upload the UDTF Jar file to an OBS directory.

+

5

+

Create the UDTF on DLI.

+

DLI console

+

Create a UDTF on the SQL job management page of the DLI console.

+

6

+

Verify and use the UDTF on DLI.

+

DLI console

+

Use the UDTF in your DLI job.

+
+
+
+
+

Procedure

  1. Create a Maven project and configure the POM file. This step uses IntelliJ IDEA 2020.2 as an example.
    1. Start IntelliJ IDEA and choose File > New > Project.
      Figure 2 Creating a project
      +
    2. Choose Maven, set Project SDK to 1.8, and click Next.
      Figure 3 Choosing Maven
      +
    3. Set the project name, configure the storage path, and click Finish.
      Figure 4 Creating a project
      +
    4. Add the following content to the pom.xml file.
      <dependencies>
+        <dependency>
+            <groupId>org.apache.hive</groupId>
+            <artifactId>hive-exec</artifactId>
+            <version>1.2.1</version>
+        </dependency>
+</dependencies>
      +
      Figure 5 Adding configurations to the POM file
      +
      +
    5. Choose src > main and right-click the java folder. Choose New > Package to create a package and a class file.
      Figure 6 Creating a package and a class file
      +

      Set the package name as you need. Then, press Enter.

      +

      Create a Java Class file in the package path. In this example, the Java Class file is UDTFSplit.

      +
    +
  2. Write UDTF code. For sample code, see Sample Code.

    The UDTF class must inherit org.apache.hadoop.hive.ql.udf.generic.GenericUDTF to implement the initialize, process, and close methods.

    +
    1. Call the initialize method in the UDTF. This method returns the information about the returned data rows of the UDTF, such as the number and type.
    2. Call the process method to process data. Each time forward() is called in the process method, a row is generated.
      If multiple columns are generated, you can put the values in an array and pass the array to the forward() function.
      public void process(Object[] args) throws HiveException {
+        // TODO Auto-generated method stub
+        if(args.length == 0){
+            return;
+        }
+        String input = args[0].toString();
+        if(StringUtils.isEmpty(input)){
+            return;
+        }
+        String[] test = input.split(";");
+        for (int i = 0; i < test.length; i++) {
+            try {
+                String[] result = test[i].split(":");
+                forward(result);
+            } catch (Exception e) {
+                continue;
+            }
+        }
+
+    }
      +
      +
    3. Call the close method to clear methods that need to be closed.
    +
  3. Use IntelliJ IDEA to compile the code and pack it into the JAR package.
    1. Click Maven in the tool bar on the right, and click clean and compile to compile the code.

      After the compilation is successful, click package.

      +

      The generated JAR package is stored in the target directory. In this example, MyUDTF-1.0-SNAPSHOT.jar is stored in D:\MyUDTF\target.

      +
    +
  4. Log in to the OBS console and upload the file to the OBS path.

    The region of the OBS bucket to which the Jar package is uploaded must be the same as the region of the DLI queue. Cross-region operations are not allowed.

    +
    +
  5. (Optional) Upload the file to DLI for package management.
    1. Log in to the DLI management console and choose Data Management > Package Management.
    2. On the Package Management page, click Create in the upper right corner.
    3. In the Create Package dialog, set the following parameters:
      1. Type: Select JAR.
      2. OBS Path: Specify the OBS path for storing the package.
      3. Set Group and Group Name as required for package identification and management.
      +
    4. Click OK.
    +
  6. Create the UDTF on DLI.
    1. Log in to the DLI console, choose SQL Editor. Set Engine to spark, and select the created SQL queue and database.
    2. In the SQL editing area, enter the path of the JAR file to be uploaded to create a UDTF and click Execute.
      CREATE FUNCTION mytestsplit AS 'com.demo.UDTFSplit' using jar 'obs://dli-test-obs01/MyUDTF-1.0-SNAPSHOT.jar';
      +
    +
  7. Restart the original SQL queue for the added function to take effect.
    1. Log in to the DLI management console and choose Resources > Queue Management from the navigation pane. In the Operation column of the SQL queue job, click Restart.
    2. In the Restart dialog box, click OK.
    +
  8. Verify and use the UDTF on DLI.

    Use the UDTF created in 6 in the SELECT statement as follows:

    +
    select mytestsplit('abc:123\;efd:567\;utf:890');
    +

    +
  9. (Optional) Delete the UDTF.

    If this function is no longer used, run the following statement to delete the function:

    +
    Drop FUNCTION mytestsplit;
    +
+
+

Sample Code

The complete UDTFSplit.java code is as follows:

+
import java.util.ArrayList;
+
+import org.apache.commons.lang.StringUtils;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
+import org.apache.hadoop.hive.ql.exec.UDFArgumentLengthException;
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
+import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
+import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
+
+public class UDTFSplit extends GenericUDTF {
+
+    @Override
+    public void close() throws HiveException {
+        // TODO Auto-generated method stub
+
+    }
+
+    @Override
+    public void process(Object[] args) throws HiveException {
+        // TODO Auto-generated method stub
+        if(args.length == 0){
+            return;
+        }
+        String input = args[0].toString();
+        if(StringUtils.isEmpty(input)){
+            return;
+        }
+        String[] test = input.split(";");
+        for (int i = 0; i < test.length; i++) {
+            try {
+                String[] result = test[i].split(":");
+                forward(result);
+            } catch (Exception e) {
+                continue;
+            }
+        }
+
+    }
+
+    @Override
+    public StructObjectInspector initialize(ObjectInspector[] args) throws UDFArgumentException {
+        if (args.length != 1) {
+            throw new UDFArgumentLengthException("ExplodeMap takes only one argument");
+        }
+        if (args[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
+            throw new UDFArgumentException("ExplodeMap takes string as a parameter");
+        }
+
+        ArrayList<String> fieldNames = new ArrayList<String>();
+        ArrayList<ObjectInspector> fieldOIs = new ArrayList<ObjectInspector>();
+        fieldNames.add("col1");
+        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
+        fieldNames.add("col2");
+        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
+
+        return ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs);
+    }
+
+}
+
+
+
+
+
Parent topic: SQL Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0205.html b/docs/dli/dev/dli_09_0205.html new file mode 100644 index 00000000..125cffdc --- /dev/null +++ b/docs/dli/dev/dli_09_0205.html @@ -0,0 +1,292 @@ + + +

Using Spark Jar Jobs to Read and Query OBS Data

+

Scenario

DLI is fully compatible with open-source Apache Spark and allows you to import, query, analyze, and process job data by programming. This section describes how to write a Spark program to read and query OBS data, compile and package the code, and submit it to a Spark Jar job.

+
+

Environment Preparations

Before you start, set up the development environment.

+ +
+ + + + + + + + + + + + + + + + +
Table 1 Spark Jar job development environment

Item

+

Description

+

OS

+

Windows 7 or later

+

JDK

+

JDK 1.8.

+

IntelliJ IDEA

+

This tool is used for application development. The version of the tool must be 2019.1 or other compatible versions.

+

Maven

+

Basic configurations of the development environment. Maven is used for project management throughout the lifecycle of software development.

+
+
+
+

Development Process

The following figure shows the process of developing a Spark Jar job.
Figure 1 Development process
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2 Process description

No.

+

Phase

+

Software Portal

+

Description

+

1

+

Create a queue for general use.

+

DLI console

+

The DLI queue is created for running your job.

+

2

+

Upload data to an OBS bucket.

+

OBS console

+

The test data needs to be uploaded to your OBS bucket.

+

3

+

Create a Maven project and configure the POM file.

+

IntelliJ IDEA

+

+

Write your code by referring to the sample code for reading data from OBS.

+

+

4

+

Write code.

+

5

+

Debug, compile, and pack the code into a Jar package.

+

6

+

Upload the Jar package to OBS and DLI.

+

OBS console

+

You can upload the generated Spark JAR package to an OBS directory and DLI program package.

+

7

+

Create a Spark Jar Job.

+

DLI console

+

The Spark Jar job is created and submitted on the DLI console.

+

8

+

Check execution result of the job.

+

DLI console

+

You can view the job running status and run logs.

+
+
+
+
+

Step 1: Create a Queue for General Purpose

If you submit a Spark job for the first time, you need to create a queue first. For example, create a queue, name it sparktest, and set Queue Usage to For general purpose.
  1. In the navigation pane of the DLI management console, choose Queue Management.
  2. In the upper right corner of the Queue Management page, click Create Queue to create a queue.
  3. Create a queue, name it sparktest, and set the queue usage to for general purpose. For details about how to create a queue, see Creating a Queue.
  4. Click Create Now to create a queue.
+
+
+

Step 2: Upload Data to OBS

  1. Create the people.json file containing the following content:
    {"name":"Michael"}
+{"name":"Andy", "age":30}
+{"name":"Justin", "age":19}
    +
  2. Log in to the OBS Console. In the Bucket page, click the name of the created OBS bucket. In this example, the bucket name is dli-test-obs01. The overview page is displayed.
  3. In the navigation pane on the left, choose Objects. Click Upload Object to upload the file to the root directory of the OBS bucket.
  4. In the root directory of the OBS bucket, click Create Folder to create a folder and name it result.
  5. Click the result folder, click Create Folder on the displayed page to create a folder and name it parquet.
+
+

Step 3: Create a Maven Project and Configure the pom Dependency

This step uses IntelliJ IDEA 2020.2 as an example.
  1. Start IntelliJ IDEA and choose File > New > Project.
    Figure 2 Creating a project
    +
  2. Choose Maven, set Project SDK to 1.8, and click Next.
    Figure 3 Creating a project
    +
  3. Set the project name, configure the storage path, and click Finish.
    Figure 4 Creating a project
    +

    In this example, the Maven project name is SparkJarObs, and the project storage path is D:\DLITest\SparkJarObs.

    +
  4. Add the following content to the pom.xml file.
    <dependencies>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.11</artifactId>
+            <version>2.3.2</version>
+        </dependency>
+</dependencies>
    +
    Figure 5 Modifying the pom.xml file
    +
    +
  5. Choose src > main and right-click the java folder. Choose New > Package to create a package and a class file.
    Figure 6 Creating a package
    +

    Set the package name as you need. Then, press Enter.

    +

    Create a Java Class file in the package path. In this example, the Java Class file is SparkDemoObs.

    +
+
+
+

Step 4: Write Code

Code the SparkDemoObs program to read the people.json file from the OBS bucket, create the temporary table people, and query data.

+

For the sample code, see Sample Code.

+
  1. Import dependencies.
    import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SaveMode;
+import org.apache.spark.sql.SparkSession;
+
+import static org.apache.spark.sql.functions.col;
    +
  2. Create Spark session spark using the AK and SK of the current account.
    SparkSession spark = SparkSession
+                .builder()
+                .config("spark.hadoop.fs.obs.access.key", "xxx")
+                .config("spark.hadoop.fs.obs.secret.key", "yyy")
+                .appName("java_spark_demo")
+                .getOrCreate();
    +
    • Replace xxx of "spark.hadoop.fs.obs.access.key" with the AK of the account.
    • Replace yyy of "spark.hadoop.fs.obs.secret.key" with the SK of the account.
    +
  3. Read the people.json file from the OBS bucket.
    dli-test-obs01 is the name of the sample OBS bucket. Replace it with the actual OBS bucket name.
    Dataset<Row> df = spark.read().json("obs://dli-test-obs01/people.json");
+df.printSchema();
    +
    +
  4. Create temporary table people to read data.
    df.createOrReplaceTempView("people");
    +
  5. Query data in the people table.
    Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
+sqlDF.show();
    +
  6. Export people table data in Parquet format to the result/parquet directory of the OBS bucket.
    sqlDF.write().mode(SaveMode.Overwrite).parquet("obs://dli-test-obs01/result/parquet");
+spark.read().parquet("obs://dli-test-obs01/result/parquet").show();
    +
  7. Disable the spark session.
    spark.stop();
    +
+
+

Step 5: Debug, compile, and pack the code into a JAR package.

  1. Double-click Maven in the tool bar on the right, and double-click clean and compile to compile the code.

    After the compilation is successful, double-click package.

    +

    The generated JAR package is stored in the target directory. In this example, SparkJarObs-1.0-SNAPSHOT.jar is stored in D:\DLITest\SparkJarObs\target.

    +
+
+

Step 6: Upload the JAR Package to OBS and DLI

  • Spark 3.3 or later:

    You can only set the Application parameter when creating a Spark job and select the required JAR file from OBS.

    +
    1. Log in to the OBS console and upload the SparkJarObs-1.0-SNAPSHOT.jar file to the OBS path.
    2. Log in to the DLI console. In the navigation pane, choose Job Management > Spark Jobs.
    3. Locate the row containing a desired job and click Edit in the Operation column.
    4. Set Application to the OBS path in 1.
    +
  • Versions earlier than Spark 3.3:

    Upload the JAR file to OBS and DLI.

    +
    1. Log in to the OBS console and upload the SparkJarObs-1.0-SNAPSHOT.jar file to the OBS path.
    2. Upload the file to DLI for package management.
      1. Log in to the DLI management console and choose Data Management > Package Management.
      2. On the Package Management page, click Create in the upper right corner.
      3. In the Create Package dialog, set the following parameters:
        1. Type: Select JAR.
        2. OBS Path: Specify the OBS path for storing the package.
        3. Set Group and Group Name as required for package identification and management.
        +
      4. Click OK.
      +
    +
+
+

Step 7: Create a Spark Jar Job

  1. Log in to the DLI console. In the navigation pane, choose Job Management > Spark Jobs.
  2. On the Spark Jobs page, click Create Job.
  3. On the displayed page, configure the following parameters:
    • Queue: Select the created queue. For example, select the queue sparktest created in Step 1: Create a Queue for General Purpose.
    • Select a supported Spark version from the drop-down list. The latest version is recommended.
    • Job Name (--name): Name of the Spark Jar job. For example, SparkTestObs.
    • Application: Select the package uploaded in Step 6: Upload the JAR Package to OBS and DLI. For example, select SparkJarObs-1.0-SNAPSHOT.jar.
    • Main Class (--class): The format is program package name + class name.
    +

    You do not need to set other parameters.

    +
  4. Click Execute to submit the Spark Jar job. On the Job management page, view the running status.
+
+

Step 8: View Job Execution Result

  1. On the Job management page, view the running status. The initial status is Starting.
  2. If the job is successfully executed, the job status is Finished. Click More in the Operation column and select Driver Logs to view the running log.
    Figure 7 Driver logs
    +
  3. If the job is successfully executed, go to the result/parquet directory in the OBS bucket to view the generated parquet file.
  4. If the job fails to be executed, choose More > Driver Logs in the Operation column to view the detailed error information.
    For example, the following figure shows that when you create the Spark Jar job, you did not add the package path to the main class name.
    Figure 8 Error information
    +
    +

    In the Operation column, click Edit, change the value of Main Class to com.SparkDemoObs, and click Execute to run the job again.

    +
+
+

Sample Code

Hard-coded or plaintext access.key and secret.key pose significant security risks. To ensure security, encrypt your AK and SK, store them in configuration files or environment variables, and decrypt them when needed.

+
+
package com.dli.demo;
+
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SaveMode;
+import org.apache.spark.sql.SparkSession;
+
+import static org.apache.spark.sql.functions.col;
+
+public class SparkDemoObs {
+    public static void main(String[] args) {
+        SparkSession spark = SparkSession
+                .builder()
+                .config("spark.hadoop.fs.obs.access.key", "xxx")
+                .config("spark.hadoop.fs.obs.secret.key", "yyy")
+                .appName("java_spark_demo")
+                .getOrCreate();
+        // can also be used --conf to set the ak sk when submit the app
+
+        // test json data:
+        // {"name":"Michael"}
+        // {"name":"Andy", "age":30}
+        // {"name":"Justin", "age":19}
+        Dataset<Row> df = spark.read().json("obs://dli-test-obs01/people.json");
+        df.printSchema();
+        // root
+        // |-- age: long (nullable = true)
+        // |-- name: string (nullable = true)
+
+        // Displays the content of the DataFrame to stdout
+        df.show();
+        // +----+-------+
+        // | age|   name|
+        // +----+-------+
+        // |null|Michael|
+        // |  30|   Andy|
+        // |  19| Justin|
+        // +----+-------+
+
+        // Select only the "name" column
+        df.select("name").show();
+        // +-------+
+        // |   name|
+        // +-------+
+        // |Michael|
+        // |   Andy|
+        // | Justin|
+        // +-------+
+
+        // Select people older than 21
+        df.filter(col("age").gt(21)).show();
+        // +---+----+
+        // |age|name|
+        // +---+----+
+        // | 30|Andy|
+        // +---+----+
+
+        // Count people by age
+        df.groupBy("age").count().show();
+        // +----+-----+
+        // | age|count|
+        // +----+-----+
+        // |  19|    1|
+        // |null|    1|
+        // |  30|    1|
+        // +----+-----+
+
+        // Register the DataFrame as a SQL temporary view
+        df.createOrReplaceTempView("people");
+
+        Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
+        sqlDF.show();
+        // +----+-------+
+        // | age|   name|
+        // +----+-------+
+        // |null|Michael|
+        // |  30|   Andy|
+        // |  19| Justin|
+        // +----+-------+
+
+        sqlDF.write().mode(SaveMode.Overwrite).parquet("obs://dli-test-obs01/result/parquet");
+        spark.read().parquet("obs://dli-test-obs01/result/parquet").show();
+
+        spark.stop();
+    }
+}
+

+
+
+
+
+
Parent topic: Spark Jar Jobs
+
+
+ diff --git a/docs/dli/dev/dli_09_0207.html b/docs/dli/dev/dli_09_0207.html new file mode 100644 index 00000000..a6f01869 --- /dev/null +++ b/docs/dli/dev/dli_09_0207.html @@ -0,0 +1,24 @@ + + +

Configuring High-Reliability Flink Jobs (Automatic Restart upon Exceptions)

+

Scenario

If you need to configure high reliability for a Flink application, you can set the parameters when creating your Flink jobs.

+
+

Procedure

  1. Create an SMN topic and add an email address or mobile number to subscribe to the topic. You will receive a subscription notification by an email or message. Click the confirmation link to complete the subscription.
  2. Log in to the DLI console, create a Flink SQL job, write SQL statements for the job, and configure running parameters. In this example, key parameters are described. Set other parameters based on your requirements. For details about how to create a Flink SQL job, see .

    The reliability configuration of a Flink Jar job is the same as that of a SQL job, which will not be described in this section.

    +
    +
    1. Set CUs, Job Manager CUs, and Max Concurrent Jobs based on the following formulas:

      Total number of CUs = Number of manager CUs + (Total number of concurrent operators / Number of slots of a TaskManager) x Number of TaskManager CUs

      +

      For example, with a total of 9 CUs (1 manager CU) and a maximum of 16 concurrent jobs, the number of compute-specific CUs is 8.

      +

      If you do not configure TaskManager specifications, a TaskManager occupies 1 CU by default and has no slot. To ensure a high reliability, set the number of slots of the TaskManager to 2, according to the preceding formula.

      +

      Set the maximum number of concurrent jobs be twice the number of CUs.

      +
    2. Select Save Job Log and select an OBS bucket. If you are not authorized to access the bucket, click Authorize. This allows job logs be saved to your OBS bucket. If a job fails, the logs can be used for fault locating.
    3. Select Alarm Generation upon Job Exception and select the SMN topic created in 1. This allows DLI to send notifications to your email box or phone when a job exception occurs, so you can be notified of any exceptions in time.
    4. Select Enable Checkpointing and set the checkpoint interval and mode as needed. This function ensures that a failed Flink task can be restored from the latest checkpoint.
      • Checkpoint interval indicates the interval between two triggers. Checkpointing hurts real-time computing performance. To minimize the performance loss, you need to allow for the recovery duration when configuring the interval. It is recommended that the checkpoint interval be greater than the checkpointing duration. The recommended value is 5 minutes.
      • The Exactly once mode ensures that each piece of data is consumed only once, and the At least once mode ensures that each piece of data is consumed at least once. Select a mode as you need.
      +
      +
    5. Select Auto Restart upon Exception and Restore Job from Checkpoint, and set the number of retry attempts as needed.
    6. Configure Dirty Data Policy. You can select Ignore, Trigger a job exception, or Save based on your service requirements.
    7. Select a queue, and then submit and run the job.
    +
  3. Log in to the Cloud Eye console. In the navigation pane on the left, choose Cloud Service Monitoring > Data Lake Insight. Locate the target Flink job and click Create Alarm Rule.

    DLI provides various monitoring metrics for Flink jobs. You can define alarm rules as required using different monitoring metrics for fine-grained job monitoring.

    +
+
+
+
+
+
Parent topic: Flink OpenSource SQL Jobs
+
+
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