Reading MOR Table Views

After the MOR table is synchronized to Hive, the following two tables are synchronized to Hive: Table name_rt and Table name_ro. The table suffixed with rt indicates the real-time view, and the table suffixed with ro indicates the read-optimized view. For example, the name of the Hudi table to be synchronized to Hive is test. After the table is synchronized to Hive, two more tables test_rt and test_ro are generated in the Hive table.

• Reading the real-time view (using Hive and SparkSQL as an example): Directly read the Hudi table with suffix _rt stored in Hive.

  select count(*) from test_rt;

• Reading the real-time view (using the Spark DataSource API as an example): The operations are the same as those for the COW table. For details, see the operations for the COW table.

• Reading the incremental view (using Hive and SparkSQL as an example):

  set hive.input.format=org.apache.hudi.hadoop.hive.HoodieCombineHiveInputFormat; // This parameter does not need to be specified for SparkSQL.
  set hoodie.test.consume.mode=INCREMENTAL;
  set hoodie.test.consume.max.commits=3;
  set hoodie.test.consume.start.timestamp=20201227153030;
  select count(*) from default.test_rt where `_hoodie_commit_time`>'20201227153030';

• Incremental view (using the Spark DataSource API as an example): The operations are the same as those for the COW table. For details, see the operations for the COW table.
• Reading the read-optimized view (using Hive and SparkSQL as an example): Directly read the Hudi table with suffix _ro stored in Hive.

  select count(*) from test_ro;

• Reading the read-optimized view (using the Spark DataSource API as an example): This is similar to reading a common DataSource table.

  QUERY_TYPE_OPT_KEY must be set to QUERY_TYPE_READ_OPTIMIZED_OPT_VAL.

  spark.read.format("hudi")
  .option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_READ_OPTIMIZED_OPT_VAL) // Set the query type to the read-optimized view.
  .load("/tmp/default/mor_bugx/*/*/*/*") // Set the path of the Hudi table to be read. The current table has three levels of partitions.
  .createTempView("mycall")
  spark.sql("select * from mycall").show(100)
  Note:
  Spark SQL cannot query the incremental view of the datasource table but can query Hive tables and dataSource APIs.