- Enhanced ETL + Real-time BI analysisFigure 1 ETL+BI analysis-
The data warehouse is the pillar of the Business Intelligence (BI) system for collecting, storing, and analyzing massive amounts of data. It provides powerful business analysis support for IoT, mobile Internet, gaming, and Online to Offline (O2O) industries.
-Advantages of GaussDB(DWS) are as follows:
-- Data migration: efficient and real-time data import in batches from multiple data sources
- High performance: cost-effective PB-level data storage and second-level response to correlation analysis of trillions of data records
- Real-time: real-time consolidation of service data for timely optimization and adjustment of operation decision-making
- E-commerceFigure 2 E-commerce-
Data of online retailers is mainly used for marketing recommendation, operating and customer analysis, and full text search.
-Advantages of GaussDB(DWS) are as follows:
-- Multi-dimensional analysis: analysis from products, users, operation, and regions
- Scale-out as the business grows: on-demand cluster scale-out as the business grows
- High reliability: long-term stable running of the e-commerce system
- IoTFigure 3 IoT-
GaussDB(DWS) helps you analyze massive amounts of data from Internet of Things (IoT) in real time and perform optimization based on the results. It is widely used in industrial IoT, O2O service system, and IoV solutions.
-Advantages of GaussDB(DWS) are as follows:
-- Device monitoring and prediction: device monitoring, control, optimization, supply, self-diagnosis, and self-healing based on data analysis and prediction
- Information recommendation: tailed recommendation based on data of users' connected devices
- Enhanced ETL + Real-time BI analysisFigure 1 ETL+BI analysis+
The data warehouse is the pillar of the Business Intelligence (BI) system for collecting, storing, and analyzing massive amounts of data. It provides powerful business analysis support for IoT, mobile Internet, gaming, and Online to Offline (O2O) industries.
+Advantages of GaussDB(DWS) are as follows:
+- Data migration: efficient and real-time data import in batches from multiple data sources
- High performance: cost-effective PB-level data storage and second-level response to correlation analysis of trillions of data records
- Real-time: real-time consolidation of service data for timely optimization and adjustment of operation decision-making
- E-commerceFigure 2 E-commerce+
Data of online retailers is mainly used for marketing recommendation, operating and customer analysis, and full text search.
+Advantages of GaussDB(DWS) are as follows:
+- Multi-dimensional analysis: analysis from products, users, operation, regions, and more
- Scale-out as the business grows: on-demand cluster scale-out as the business grows
- High reliability: long-term stable running of the e-commerce system
- IoTFigure 3 IoT+
GaussDB(DWS) helps you analyze massive amounts of data from Internet of Things (IoT) in real time and perform optimization based on the results. It is widely used in industrial IoT, O2O service system, and IoV solutions.
+Advantages of GaussDB(DWS) are as follows:
+- Device monitoring and prediction: control, optimization, self-diagnosis, and self-healing based on data analysis, device monitoring, and behavior prediction
- Information recommendation: tailed recommendation based on data of users' connected devices
diff --git a/docs/dws/umn/dws_01_00014.html b/docs/dws/umn/dws_01_00014.html index 7e3ef7ef..ecbbabfa 100644 --- a/docs/dws/umn/dws_01_00014.html +++ b/docs/dws/umn/dws_01_00014.html @@ -1,6 +1,6 @@ - + -Databases Monitoring
+Database Monitoring (DMS)
-
diff --git a/docs/dws/umn/dws_01_0002.html b/docs/dws/umn/dws_01_0002.html
index f0339865..36744a8f 100644
--- a/docs/dws/umn/dws_01_0002.html
+++ b/docs/dws/umn/dws_01_0002.html
@@ -1,85 +1,87 @@
-
+
- Application layer
Data loading tools, extract, transform, and load (ETL) tools, business intelligence (BI) tools, as well as data mining and analysis tools, can be integrated with GaussDB(DWS) through standard APIs. GaussDB(DWS) is compatible with the PostgreSQL ecosystem, and the SQL syntax is compatible with Oracle and Teradata. Applications can be smoothly migrated to GaussDB(DWS) with few changes.
- - API
Applications can connect to GaussDB(DWS) through the standard Java Database Connectivity (JDBC) 4.0 and Open Database Connectivity (ODBC) 3.5.
- - GaussDB(DWS) (MPP cluster)
A GaussDB(DWS) cluster contains nodes of the same flavor in the same subnet. These nodes jointly provide services. Datanodes (DNs) in a cluster store data on disks. Coordinators (CNs) receive access requests from applications and return the execution results to clients. In addition, a CN splits and distributes tasks to the DNs for parallel processing.
- - Automatic data backup
Cluster snapshots can be automatically backed up to the EB-level Object Storage Service (OBS), which facilitates periodic backup of the cluster during off-peak hours, ensuring data recovery after a cluster exception occurs.
-A snapshot is a complete backup of GaussDB(DWS) at a specific time point, including the configuration data and service data of a cluster.
- - Tool chain
The parallel data loading tool General Data Service (GDS), SQL syntax migration tool Database Schema Convertor (DSC), and SQL development tool Data Studio are provided. The cluster O&M can be monitored on a console.
+GaussDB(DWS) is an online data analysis and processing database built on the cloud infrastructure and platform. It offers scalable, ready-to-use, and fully managed analytical database services, and is compatible with ANSI/ISO SQL92, SQL99, and SQL 2003 syntax. Additionally, GaussDB(DWS) is interoperable with other database ecosystems such as PostgreSQL, Oracle, Teradata, and MySQL. This makes it a competitive option for petabyte-scale big data analytics across diverse industries.
+- Standard Data Warehouse (DWS 2.0): Oriented to data analysis scenarios, DWS 2.0 provides enterprise-level data warehouse services with high performance, high scalability, high reliability, high security, and easy O&M. It is capable of data analysis at a scale of 2048 nodes and 20 petabytes of data. It supports hot and cold data analysis, elastic scaling of storage and computing resources, and on-demand and pay-per-use pricing, providing users with elastic, flexible, and cost-effective experience. It is applicable to converged analysis services that integrate libraries, warehouses, cities, and lakes.
- Stream data warehouse: The stream data warehouse is built on top of the standard data warehouse. It integrates stream and time sequence engines to provide real-time data ingestion and high-concurrency real-time analysis capabilities. It can be used for IoT real-time analysis.
- Hybrid data warehouse: It provides high-concurrency, high-performance, and low-latency transaction processing capabilities based on large-scale data query and analysis capabilities. It is suitable for hybrid transaction/analytical processing (HTAP). A database can be used for both production and analysis.
-Architecture
GaussDB(DWS) employs the shared-nothing architecture and the massively parallel processing (MPP) engine, and consists of numerous independent logical nodes that do not share the system resources such as CPUs, memory, and storage. In such a system architecture, service data is separately stored on numerous nodes. Data analysis tasks are executed in parallel on the nodes where data is stored. The massively parallel data processing significantly improves response speed.
+Figure 1 Architecture+
- Application layer
Data loading tools, extract, transform, and load (ETL) tools, business intelligence (BI) tools, as well as data mining and analysis tools, can be integrated with GaussDB(DWS) through standard APIs. GaussDB(DWS) is compatible with the PostgreSQL ecosystem, and the SQL syntax is compatible with Oracle and Teradata. Applications can be smoothly migrated to GaussDB(DWS) with few changes.
+ - API
Applications can connect to GaussDB(DWS) through the standard Java Database Connectivity (JDBC) 4.0 and Open Database Connectivity (ODBC) 3.5.
+ - GaussDB(DWS)
A GaussDB(DWS) cluster contains nodes of the same flavor in the same subnet. These nodes jointly provide services. Datanodes (DNs) in a cluster store data on disks. Coordinators (CNs) receive access requests from applications and return the execution results to clients. In addition, a CN splits and distributes tasks to the DNs for parallel processing.
+ - Automatic data backup
Cluster snapshots can be automatically backed up to the EB-level Object Storage Service (OBS), which facilitates periodic backup of the cluster during off-peak hours, ensuring data recovery after a cluster exception occurs.
+A snapshot is a complete backup of GaussDB(DWS) at a specific time point, including the configuration data and service data of a cluster.
+ - Tool chain
The parallel data loading tool General Data Service (GDS), SQL syntax migration tool Database Schema Convertor (DSC), and SQL development tool Data Studio are provided. The cluster O&M can be monitored on a console.
Logical Cluster Architecture
Figure 2 shows the logical architecture of a GaussDB(DWS) cluster. For details about instances, see Table 1.
- +Logical Cluster Architecture
Figure 2 shows the logical architecture of a GaussDB(DWS) cluster. For details about instances, see Table 1.
+ -Table 1 Cluster architecture description Name
+-Table 1 Cluster architecture description Name
Function
+Function
Description
+Description
Cluster Manager (CM)
+Cluster Manager (CM)
Cluster Manager. It manages and monitors the running status of functional units and physical resources in the distributed system, ensuring system stability.
+Cluster Manager. It manages and monitors the running status of functional units and physical resources in the distributed system, ensuring system stability.
The CM consists of CM Agent, OM Monitor, and CM Server.
-- CM Agent monitors the running status of primary and standby GTMs, CNs, and primary and standby DNs on the host, and reports the status to CM Server. In addition, it executes the arbitration instruction delivered by CM Server. A CM Agent process runs on each host.
- OM Monitor monitors scheduled tasks of CM Agent and restarts CM Agent when CM Agent stops. If CM Agent cannot be restarted, the host cannot be used. In this case, manually rectify this fault.NOTE:
CM Agent cannot be restarted probably because of insufficient system resources, which is not a common situation.
+The CM consists of CM Agent, OM Monitor, and CM Server.
+- CM Agent monitors the running status of primary and standby GTMs, CNs, and primary and standby DNs on the host, and reports the status to CM Server. In addition, it executes the arbitration instruction delivered by CM Server. A CM Agent process runs on each host.
- OM Monitor monitors scheduled tasks of CM Agent and restarts CM Agent when CM Agent stops. If CM Agent cannot be restarted, the host cannot be used. In this case, manually rectify this fault.NOTE:
CM Agent cannot be restarted probably because of insufficient system resources, which is not a common situation.
- CM Server checks whether the current system is normal according to the instance status reported by CM Agent. In the case of exceptions, CM Server delivers recovery commands to CM Agent.
GaussDB(DWS) provides the primary/standby CM Server solution to ensure system HA. CM Agent connects to the primary CM Server. If the primary CM Server is faulty, the standby CM Server is promoted to primary to prevent a single point of failure (SPOF).
+- CM Server checks whether the current system is normal according to the instance status reported by CM Agent. In the case of exceptions, CM Server delivers recovery commands to CM Agent.
GaussDB(DWS) provides the primary/standby CM Server solution to ensure system HA. CM Agent connects to the primary CM Server. If the primary CM Server is faulty, the standby CM Server is promoted to primary to prevent a single point of failure (SPOF).
Global Transaction Manager (GTM)
+Global Transaction Manager (GTM)
Generates and maintains the globally unique information, such as the transaction ID, transaction snapshot, and timestamp.
+Generates and maintains the globally unique information, such as the transaction ID, transaction snapshot, and timestamp.
The cluster includes only one pair of GTMs: one primary GTM and one standby GTM.
+The cluster includes only one pair of GTMs: one primary GTM and one standby GTM.
Workload Manager (WLM)
+Workload Manager (WLM)
Workload Manager. It controls allocation of system resources to prevent service congestion and system crash resulting from excessive workload.
+Workload Manager. It controls allocation of system resources to prevent service congestion and system crash resulting from excessive workload.
You do not need to specify names of hosts where WLMs are to be deployed, because the installation program automatically installs a WLM on each host.
+You do not need to specify names of hosts where WLMs are to be deployed, because the installation program automatically installs a WLM on each host.
Coordinator (CN)
+Coordinator (CN)
A CN receives access requests from applications, and returns execution results to the client; splits tasks and allocates task fragments to different DNs for parallel processing.
+A CN receives access requests from applications, and returns execution results to the client; splits tasks and allocates task fragments to different DNs for parallel processing.
CNs in a cluster have equivalent roles and return the same result for the same DML statement. Load balancers can be added between CNs and applications to ensure that CNs are transparent to applications. If a CN is faulty, the load balancer connects its applications to another CN.
-CNs need to connect to each other in the distributed transaction architecture. To reduce heavy load caused by excessive threads on GTMs, no more than 10 CNs should be configured in a cluster.
-GaussDB(DWS) handles the global resource load in a cluster using the Central Coordinator (CCN) for adaptive dynamic load management. When the cluster is started for the first time, the CM selects the CN with the smallest ID as the CCN. If the CCN is faulty, CM replaces it with a new one.
+CNs in a cluster have equivalent roles and return the same result for the same DML statement. Load balancers can be added between CNs and applications to ensure that CNs are transparent to applications. If a CN is faulty, the load balancer automatically connects the application to the other CN. For details, see section "Associating and Disassociating ELB".
+CNs need to connect to each other in the distributed transaction architecture. To reduce heavy load caused by excessive threads on GTMs, no more than 10 CNs should be configured in a cluster.
+GaussDB(DWS) handles the global resource load in a cluster using the Central Coordinator (CCN) for adaptive dynamic load management. When the cluster is started for the first time, the CM selects the CN with the smallest ID as the CCN. If the CCN is faulty, CM replaces it with a new one.
Datanode (DN)
+Datanode (DN)
A DN stores service data by column or row or in the hybrid mode, executes data query tasks, and returns execution results to CNs.
+A DN stores service data by column or row or in the hybrid mode, executes data query tasks, and returns execution results to CNs.
A cluster consists of multiple DNs and each DN stores part of data. If no HA solution is available for DNs, data cannot be accessed when a DN is faulty.
- +A cluster consists of multiple DNs and each DN stores part of data. GaussDB(DWS) provides DN high availability: active DN, standby DN, and secondary DN. The working principles of the three are as follows:
+- During data synchronization, if the active DN suddenly becomes faulty, the standby DN is switched to the active state.
- Before the faulty active DN recovers, the new active DN synchronizes data logs to the secondary DN.
- After the faulty active DN recovers, it becomes the standby DN and uses data logs stored on the secondary DN to restore data generated during its faulty period.
The secondary DN serves exclusively as a backup, never ascending to active or standby status in case of faults. It conserves storage by only holding Xlog data transferred from the new active DN and data replicated during original active DN failures. This efficient approach saves one-third of the storage space compared to conventional tri-backup methods.
Storage
+Storage
Functions as the server's local storage resources to store data permanently.
+Functions as the server's local storage resources to store data permanently.
-
+-
DNs in a cluster store data on disks. Figure 3 describes the objects on each DN and the relationships among them logically.
-- A database manages various data objects and is isolated from other databases.
- A datafile segment stores data in only one table. A table containing more than 1 GB of data is stored in multiple data file segments.
- A table belongs only to one database.
- A block is the basic unit of database management, with a default size of 8 KB.
Data can be distributed in replication, round-robin, or hash mode. You can specify the distribution mode during table creation.
- +DNs in a cluster store data on disks. Figure 3 describes the objects on each DN and the relationships among them logically.
+- A database manages various data objects and is isolated from other databases.
- A datafile segment stores data in only one table. A table containing more than 1 GB of data is stored in multiple data file segments.
- A table belongs only to one database.
- A block is the basic unit of database management, with a default size of 8 KB.
Data can be distributed in replication, round-robin, or hash mode. You can specify the distribution mode during table creation.
+diff --git a/docs/dws/umn/dws_01_0003.html b/docs/dws/umn/dws_01_0003.html index 619c124a..2cbcb639 100644 --- a/docs/dws/umn/dws_01_0003.html +++ b/docs/dws/umn/dws_01_0003.html @@ -1,31 +1,31 @@ - +Related Services
--IAM
GaussDB(DWS) uses Identity and Access Management (IAM) for authentication and authorization.
-Users who have the DWS Administrator permissions can fully utilize GaussDB(DWS). To obtain the permissions, contact a user with the Security Administrator permissions or directly create a user with the DWS Administrator permissions. Users granted the DWS Database Access permissions can generate temporary database user credentials based on IAM users to connect to databases in the data warehouse clusters.
++-IAM
GaussDB(DWS) uses Identity and Access Management (IAM) for authentication and authorization.
+Users who have the DWS Administrator permissions can fully utilize GaussDB(DWS). To obtain the permissions, contact a user with the Security Administrator permissions or directly create a user with the DWS Administrator permissions. Users granted the DWS Database Access permissions can generate temporary database user credentials based on IAM users to connect to databases in the data warehouse clusters.
ECS
GaussDB(DWS) uses an ECS as a cluster node.
+-ECS
GaussDB(DWS) uses an ECS as a cluster node.
VPC
GaussDB(DWS) uses the Virtual Private Cloud (VPC) service to provide a network topology for clusters to isolate clusters and control access.
+-VPC
GaussDB(DWS) uses the Virtual Private Cloud (VPC) service to provide a network topology for clusters to isolate clusters and control access.
OBS
GaussDB(DWS) uses OBS to convert cluster data and external data, satisfying the requirements for secure, reliable, and cost-effective storage.
+-OBS
GaussDB(DWS) uses OBS to convert cluster data and external data, satisfying the requirements for secure, reliable, and cost-effective storage.
MRS
Data can be migrated from MRS to GaussDB(DWS) clusters for analysis after the data is processed by Hadoop.
+-MRS
Data can be migrated from MRS to GaussDB(DWS) clusters for analysis after the data is processed by Hadoop.
DRS
You can use Data Replication Service (DRS) to synchronize stream data to GaussDB(DWS) in real time.
+-DRS
You can use Data Replication Service (DRS) to synchronize stream data to GaussDB(DWS) in real time.
Cloud Eye
GaussDB(DWS) uses Cloud Eye to monitor cluster performance metrics, delivering status information in a concise and efficient manner. Cloud Eye supports alarm customization so that you are notified of the exception instantly.
+-Cloud Eye
GaussDB(DWS) uses Cloud Eye to monitor cluster performance metrics, delivering status information in a concise and efficient manner. Cloud Eye supports alarm customization so that you are notified of the exception instantly.
CTS
GaussDB(DWS) uses Cloud Trace Service (CTS) to audit your non-query operations on the management console to ensure that no invalid or unauthorized operations are performed, enhancing service security management.
+-CTS
GaussDB(DWS) uses Cloud Trace Service (CTS) to audit your non-query operations on the management console to ensure that no invalid or unauthorized operations are performed, enhancing service security management.
LTS
GaussDB(DWS) users can view collected cluster logs or dump logs on the Log Tank Service (LTS) console.
+-LTS
GaussDB(DWS) users can view collected cluster logs or dump logs on the Log Tank Service (LTS) console.
TMS
With Tag Management Service (TMS), GaussDB(DWS) can provide centralized tag management and resource classification functions across regions and services. You can customize tags to classify and locate resources.
+-TMS
With Tag Management Service (TMS), GaussDB(DWS) can provide centralized tag management and resource classification functions across regions and services. You can customize tags to classify and locate resources.
DNS
GaussDB(DWS) uses Domain Name Service (DNS) to provide the cluster IP addresses mapped from domain names.
+-DNS
GaussDB(DWS) uses Domain Name Service (DNS) to provide the cluster IP addresses mapped from domain names.
ELB
With Elastic Load Balance (ELB) health checks, the CN requests of a cluster can be quickly forwarded to normal CNs. If a CN is faulty, the workload can be immediately shifted to a healthy node, minimizing cluster access faults.
+ELB
With Elastic Load Balance (ELB) health checks, the CN requests of a cluster can be quickly forwarded to normal CNs. If a CN is faulty, the workload can be immediately shifted to a healthy node, minimizing cluster access faults.
diff --git a/docs/dws/umn/dws_01_0004.html b/docs/dws/umn/dws_01_0004.html index 0292b9cf..0386f162 100644 --- a/docs/dws/umn/dws_01_0004.html +++ b/docs/dws/umn/dws_01_0004.html @@ -1,20 +1,20 @@ - +Concepts
-GaussDB(DWS) Management Concepts
- Cluster
A cluster is a server group that consists of multiple nodes. GaussDB(DWS) is organized using clusters. A data warehouse cluster contains nodes with the same flavor in the same subnet. These nodes work together to provide services.
- - Node
A GaussDB(DWS) cluster can have 3 to 256 nodes. A hybrid data warehouse (standalone) can only have one node. Each node can store and analyze data.
- - Type
You need to specify the node flavors when you create a data warehouse cluster. CPU, memory, and storage resources vary depending on node flavors.
- - Snapshot
You can create snapshots to back up GaussDB(DWS) cluster data. A snapshot is retained until you delete it on the management console. Automated snapshots cannot be manually deleted. Snapshots will occupy your OBS quotas.
- - Project
Projects are used to group and isolate OpenStack resources (computing resources, storage resources, and network resources). A project can be a department or a project team. Multiple projects can be created for one account.
+-GaussDB(DWS) Management Concepts
- Cluster
A cluster is a server group that consists of multiple nodes. GaussDB(DWS) is organized using clusters. A data warehouse cluster contains nodes with the same flavor in the same subnet. These nodes work together to provide services.
+ - Node
A GaussDB(DWS) cluster can have 3 to 256 nodes. A hybrid data warehouse (standalone) can only have one node. Each node can store and analyze data.
+ - Type
You need to specify the node flavors when you create a data warehouse cluster. CPU, memory, and storage resources vary depending on node flavors.
+ - Snapshot
You can create snapshots to back up GaussDB(DWS) cluster data. A snapshot is retained until you delete it on the management console. Automated snapshots cannot be manually deleted. Snapshots will occupy your OBS quotas.
+ - Project
Projects are used to group and isolate OpenStack resources (computing resources, storage resources, and network resources). A project can be a department or a project team. Multiple projects can be created for one account.
diff --git a/docs/dws/umn/dws_01_0007.html b/docs/dws/umn/dws_01_0007.html index c5f4fd5b..870206cb 100644 --- a/docs/dws/umn/dws_01_0007.html +++ b/docs/dws/umn/dws_01_0007.html @@ -1,26 +1,26 @@ - +GaussDB(DWS) Database Concepts
- Databases
A data warehouse cluster is an analysis-oriented relational database platform that supports online analysis.
- - OLAP
OLAP is a major function of data warehouse clusters. It supports complex analysis, provides decision-making support tailored to analysis results, and delivers intuitive query results.
- - MPP
On each node in the data warehouse cluster, memory computing and disk storage systems are independent from each other. With MPP, GaussDB(DWS) distributes service data to different nodes based on the database model and application characteristics. Nodes are connected through the network and collaboratively process computing tasks as a cluster and provide database services that meet service needs.
- - Shared-Nothing Architecture
The shared-nothing architecture is a distributed computing architecture. Each node is independent so that nodes do not compete for resources, which improves work efficiency.
- - Database Version
Each data warehouse cluster has a specific database version. You can check the version when creating a data warehouse cluster.
- - Database Connections
You can use a client to connect to the GaussDB(DWS) cluster. The client can be used for connection on the cloud platform and over the Internet.
- - Database User
You can add and control users who can access the database of a data warehouse cluster by assigning specific permissions to them. The database administrator generated when you create a cluster is the default database user.
+GaussDB(DWS) Database Concepts
- Databases
A data warehouse cluster is an analysis-oriented relational database platform that supports online analysis.
+ - OLAP
OLAP is a major function of data warehouse clusters. It supports complex analysis, provides decision-making support tailored to analysis results, and delivers intuitive query results.
+ - MPP
On each node in the data warehouse cluster, memory computing and disk storage systems are independent from each other. With MPP, GaussDB(DWS) distributes service data to different nodes based on the database model and application characteristics. Nodes are connected through the network and collaboratively process computing tasks as a cluster and provide database services that meet service needs.
+ - Shared-Nothing Architecture
The shared-nothing architecture is a distributed computing architecture. Each node is independent so that nodes do not compete for resources, which improves work efficiency.
+ - Database Version
Each data warehouse cluster has a specific database version. You can check the version when creating a data warehouse cluster.
+ - Database Connections
You can use a client to connect to the GaussDB(DWS) cluster. The client can be used for connection on the cloud platform and over the Internet.
+ - Database User
You can add and control users who can access the database of a data warehouse cluster by assigning specific permissions to them. The database administrator generated when you create a cluster is the default database user.
- Databases
Advantages
-GaussDB(DWS) uses the GaussDB database kernel and is compatible with PostgreSQL 9.2.4. It transforms from a single OLTP database to an enterprise-level distributed OLAP database oriented to massive data analysis based on the massively parallel processing (MPP) architecture.
-Unlike conventional data warehouses, GaussDB(DWS) excels in massive data processing and general platform management with the following benefits:
-Ease of use
-- Visualized one-stop management
GaussDB(DWS) allows you to easily complete the entire process from project concept to production deployment. With the GaussDB(DWS) management console, you can obtain a high-performance and highly available enterprise-level data warehouse cluster within several minutes. Data warehouse software or data warehouse servers are not required.
-With just a few clicks, you can easily connect applications to the data warehouse, back up data, restore data, and monitor data warehouse resources and performance.
- - Seamless integration with big data
Without the need to migrate data, you can use standard SQL statements to directly query data on HDFS and OBS.
- - Heterogeneous database migration tools
GaussDB(DWS) provides various migration tools to migrate SQL scripts of Oracle and Teradata to GaussDB(DWS).
+GaussDB(DWS) uses the GaussDB database kernel and is compatible with PostgreSQL 9.2.4. It transforms from a single OLTP database to an enterprise-level distributed OLAP database oriented to massive data analysis based on the massively parallel processing (MPP) architecture.
+Unlike conventional data warehouses, GaussDB(DWS) excels in massive data processing and general platform management with the following benefits:
+Ease of use
+- Visualized one-stop management
GaussDB(DWS) allows you to easily complete the entire process from project concept to production deployment. With the GaussDB(DWS) management console, you can obtain a high-performance and highly available enterprise-level data warehouse cluster within several minutes. Data warehouse software or data warehouse servers are not required.
+With just a few clicks, you can easily connect applications to the data warehouse, back up data, restore data, and monitor data warehouse resources and performance.
+ - Seamless integration with big data
Without the need to migrate data, you can use standard SQL statements to directly query data on HDFS and OBS.
+ - Heterogeneous database migration tools
GaussDB(DWS) provides various migration tools to migrate SQL scripts of Oracle and Teradata to GaussDB(DWS).
High performance
-- Cloud-based distributed architecture
GaussDB(DWS) adopts the MPP-based database so that service data is separately stored on numerous nodes. Data analysis tasks are executed in parallel on the nodes where data is stored. The massively parallel data processing significantly improves response speed.
- - Query response to trillions of data records within seconds
GaussDB(DWS) improves data query performance by executing multi-thread operators in parallel, running commands in registers in parallel with the vectorized computing engine, and reducing redundant judgment conditions using LLVM.
-GaussDB(DWS) provides you with a better data compression ratio (column-store), better indexing (column-store), and higher point update and query (row-store) performance.
- - Fast data loading
GDS is a tool that helps you with high-speed massively parallel data loading.
+High performance
+- Cloud-based distributed architecture
GaussDB(DWS) adopts the MPP-based database so that service data is separately stored on numerous nodes. Data analysis tasks are executed in parallel on the nodes where data is stored. The massively parallel data processing significantly improves response speed.
+ - Query response to trillions of data records within seconds
GaussDB(DWS) improves data query performance by executing multi-thread operators in parallel, running commands in registers in parallel with the vectorized computing engine, and reducing redundant judgment conditions using LLVM.
+GaussDB(DWS) provides you with a better data compression ratio (column-store), better indexing (column-store), and higher point update and query (row-store) performance.
+ - Fast data loading
GDS is a tool that helps you with high-speed massively parallel data loading.
High scalability
-- On-demand scale-out: With the shared-nothing open architecture, nodes can be added at any time to enhance the data storage, query, and analysis capabilities of the system.
- Enhanced linear performance after scale-out: The capacity and performance increase linearly with the cluster scale. The linear rate is 0.8.
- Service continuity: During scale-out, data can be added, deleted, modified, and queried, and DDL operations (DROP/TRUNCATE/ALTER TABLE) can be performed. Online table-level scale-out ensures service continuity.
Robust reliability
-- ACID
Support for the atomicity, consistency, isolation, and durability (ACID) feature, which ensures strong data consistency for distributed transactions.
- - Comprehensive HA design
All software processes of GaussDB(DWS) are in active/standby mode. Logical components such as the CNs and DNs of each cluster also work in active/standby mode. This ensures data reliability and consistency when any single point of failure (SPOF) occurs.
- - High security
GaussDB(DWS) supports transparent data encryption and can interconnect with the Database Security Service (DBSS) to better protect user privacy and data security with network isolation and security group rule setting options. In addition, GaussDB(DWS) supports automatic full and incremental backup of data, improving data reliability.
+High scalability
+- On-demand scale-out: With the shared-nothing open architecture, nodes can be added at any time to enhance the data storage, query, and analysis capabilities of the system.
- Enhanced linear performance after scale-out: The capacity and performance increase linearly with the cluster scale. The linear rate is 0.8.
- Service continuity: During scale-out, data can be added, deleted, modified, and queried, and DDL operations (DROP/TRUNCATE/ALTER TABLE) can be performed. Online table-level scale-out ensures service continuity.
Robust reliability
+- ACID
Support for the atomicity, consistency, isolation, and durability (ACID) feature, which ensures strong data consistency for distributed transactions.
+ - Comprehensive HA design
All software processes of GaussDB(DWS) are in active/standby mode. Logical components such as the CNs and DNs of each cluster also work in active/standby mode. This ensures data reliability and consistency when any single point of failure (SPOF) occurs.
+ - High security
GaussDB(DWS) supports transparent data encryption and can interconnect with the Database Security Service (DBSS) to better protect user privacy and data security with network isolation and security group rule setting options. In addition, GaussDB(DWS) supports automatic full and incremental backup of data, improving data reliability.
- Cloud-based distributed architecture
diff --git a/docs/dws/umn/dws_01_0008.html b/docs/dws/umn/dws_01_0008.html index 376f83a5..027fcdea 100644 --- a/docs/dws/umn/dws_01_0008.html +++ b/docs/dws/umn/dws_01_0008.html @@ -1,21 +1,29 @@ - +Cluster Upgrade
-After you create a data warehouse cluster, the system automatically configures a random maintenance window for the cluster. Alternatively, you can customize a maintenance window as required. For details about how to view and configure the maintenance window, see Configuring the Maintenance Window.
-The validity period of the maintenance window (maximum maintenance duration) is 4 hours. During this period, you can upgrade the cluster, install operating system patches, and harden the system. If no maintenance tasks are performed within the planned maintenance window, the cluster continues to run properly until the next maintenance window. GaussDB(DWS) will notify you of any cluster O&M operation by sending SMS messages. Exercise caution when performing operations on the cluster during the O&M period.
-If the upgrade affects the current query requests or service running, contact technical support for emergency handling.
-A cluster is charged by hour as long as it is in the Available state, so you will not see any difference in the bills if a faulty node or system upgrade causes a short interruption, for example, 15 minutes. If such events cause major system interruption, which is a very rare case, you will not be charged for those downtime hours.
-Upgrading a Cluster
You do not need to care about GaussDB(DWS) cluster patching or upgrading because GaussDB(DWS) will handle version upgrade automatically. After GaussDB(DWS) is upgraded, the service automatically upgrades the clusters to the latest version within the maintenance window. During the upgrade, the cluster is automatically restarted and cannot provide services for a short period of time. Therefore, you are advised to set a suitable time range when the number of connected users and the number of active tasks are small.
-
- After a cluster is upgraded to 8.1.3 or later, it enters the observation period. During this period, you can check service status and roll back to the earlier version if necessary.
- Upgrading the cluster does not affect the original cluster data.
By default, you do not need to manually upgrade a GaussDB(DWS) cluster. To upgrade a cluster on the console, see Delivering a Cluster Upgrade Task on the Console.
+GaussDB(DWS) will notify you of any cluster O&M operation by sending SMS messages. Exercise caution when performing operations on the cluster during the O&M period.
+If the upgrade affects the current query requests or service running, contact technical support for emergency handling.
+-Upgrading a Cluster
By default, you do not need to care about GaussDB(DWS) cluster patching or upgrading because GaussDB(DWS) will handle version upgrade automatically. After GaussDB(DWS) is upgraded, it will automatically upgrade the cluster to the latest version. During the upgrade, the cluster will be restarted and cannot provide services for a short period of time.
+- - After a cluster is upgraded to 8.1.3 or later, it enters the observation period. During this period, you can check service status and roll back to the earlier version if necessary.
- Upgrading the cluster does not affect the original cluster data or specifications.
The following figure shows the cluster version.
-Figure 1 Version description-
- Service patch upgrade: The last digit of cluster version X.X.X is changed. For example, the cluster is upgraded from 1.1.0 to 1.1.1.
- Duration: The whole process will take less than 10 minutes.
- Impact on services: During this period, services will be interrupted for 1 to 3 minutes. If the source version is 8.1.2 or later, you can install patches online. During the patch upgrade, services do not need to be stopped, but may be interrupted for seconds. You are advised to perform the installation during off-peak hours.
- Service upgrade: The first two digits of cluster version X.X.X are changed. For example, the cluster is upgraded from 1.1.0 to 1.2.0.
- Duration: The whole process will take less than 30 minutes.
- Impact on services: During this period, the database cannot be accessed. If the source version is 8.1.1 or later, you can upgrade it online. During the upgrade, services do not need to be stopped, but may be interrupted for seconds. You are advised to perform the installation during off-peak hours.
The following figure shows the cluster version.
+Figure 1 Version description+
- Service patch upgrade: The last digit of cluster version X.X.X is changed. For example, the cluster is upgraded from 1.1.0 to 1.1.1.
- Duration: The whole process will take less than 10 minutes.
- Impact on services: During this period, if the source version is upgraded to 8.1.3 or later, online patching is supported. During the patch upgrade, you do not have to stop services, but the services will be intermittently interrupted for seconds. If the destination version is earlier than 8.1.3, services will be interrupted for 1 to 3 minutes. Therefore, you are advised to perform this operation during off-peak hours.
- Service upgrade: The first two digits of cluster version X.X.X are changed. For example, the cluster is upgraded from 1.1.0 to 1.2.0.
- Duration: The whole process will take less than 30 minutes.
- Impact on services: Online upgrade is supported for update to 8.1.1 or later. During the upgrade, you are not required to stop services, but services are intermittently interrupted for seconds. You are advised to perform the upgrade during off-peak hours.
Configuring the Maintenance Window
- Log in to the GaussDB(DWS) management console.
- Click Clusters.
- In the cluster list, click the name of the target cluster. The Cluster Information page is displayed.
In the Basic Information area, you can view the maintenance window.
- - Click Configure next to Maintenance Window.
- In the dialog box that is displayed, configure the maintenance window.
- Click OK.
Delivering a Cluster Upgrade Task on the Console
Prerequisites
+For clusters 8.1.1 or later, you need to deliver cluster upgrade operations on the console.
+Procedure
+- Log in to the GaussDB(DWS) console.
- In the cluster list, click the name of a cluster.
- In the navigation pane, choose Upgrade Management.
- On the Upgrade Management page, select a version from the Target Version drop-down list.
+
- Click Upgrade. Click OK in the displayed dialog box.
+
- Check whether the cluster is successfully upgraded.
- If the cluster version is 8.1.3 or later, the cluster enters the service observation period after the upgrade is complete. If you have verified your services, click Submit on the Upgrade Management page to complete the cluster upgrade. If you find your cluster performance affected by the upgrade, you can click Rollback to roll back the upgrade.+
- In versions earlier than 8.1.3, there is neither rollback nor submission button after the upgrade is complete.
- If you do not submit the upgraded version, there will be a wlm thread which occupies the system storage space and affects the performance.
+
- If the cluster upgrade fails, click Rollback to roll back to the original cluster version, or click Retry to deliver the upgrade again.
+
- If the cluster version is 8.1.3 or later, the cluster enters the service observation period after the upgrade is complete. If you have verified your services, click Submit on the Upgrade Management page to complete the cluster upgrade. If you find your cluster performance affected by the upgrade, you can click Rollback to roll back the upgrade.
diff --git a/docs/dws/umn/dws_01_00081.html b/docs/dws/umn/dws_01_00081.html index 6f6b4c60..eae08f4b 100644 --- a/docs/dws/umn/dws_01_00081.html +++ b/docs/dws/umn/dws_01_00081.html @@ -1,15 +1,15 @@ - +DR Overview
-Overview
A homogeneous GaussDB(DWS) disaster recovery (DR) cluster is deployed in another AZ. If the production cluster fails to provide read and write services due to natural disasters in the specified region or cluster internal faults, the DR cluster becomes the production cluster to ensure service continuity. The following figure shows the architecture.
-
+
-Overview
A homogeneous GaussDB(DWS) disaster recovery (DR) cluster is deployed in the same region. If the production cluster fails to provide read and write services due to natural disasters in the specified region or cluster internal faults, the DR cluster becomes the production cluster to ensure service continuity. The following figure shows the architecture.
+
DR Features
- Multi-form DR
- Cross-AZ DR
- Multiple data synchronization modes: synchronization layer based on mutual trust
- Low TCO
- Heterogeneous deployment (logical homogeneity)
- Cluster-level DR
- Visual console
- Automatic and one-click DR drills
-DR Features
Constraints and Limitations
- During data synchronization, the DR cluster cannot provide read or write services.
- When the DR task is stopped or abnormal but the DR cluster is normal, the DR cluster can provide the read service. After the DR switchover is successful, the DR cluster can provide the read and write services.
- When the DR task is created, the snapshot function of the production cluster is normal, but that of the DR cluster is disabled. Besides, snapshot restoration of both clusters is disabled.
- Logical clusters are not supported.
- Resource pools are not supported.
- If cold and hot tables are used, cold data is synchronized using OBS.
- DR does not synchronize data from external sources.
- DR management refers to dual-cluster DR under the same tenant.
- The DR cluster and the production cluster must be logically homogeneous and in the same type and version.
- The production cluster and DR cluster used for cross-AZ DR must be in the same VPC.
- In cross-AZ DR, the DR cluster can be automatically bound to the EIP of the production cluster after a switchover.
Constraints and Limitations
- During data synchronization, a non-fine-grained DR cluster cannot provide read or write services.
- When the DR task is stopped or abnormal but the DR cluster is normal, the DR cluster can provide the read service. After the DR switchover is successful, the DR cluster can provide the read and write services.
- When the DR task is created, the snapshot function of the production cluster is normal, but that of the DR cluster is disabled. Besides, snapshot restoration of both clusters is disabled.
- Logical clusters are not supported.
- Resource pools are not supported.
- If cold and hot tables are used, cold data is synchronized using OBS.
- DR does not synchronize data from external sources.
- DR management refers to dual-cluster DR under the same tenant.
- The DR cluster and the production cluster must be logically homogeneous and in the same type and version.
- The production cluster and DR cluster used for intra-region DR must be in the same VPC.
- In intra-region DR, after services are switched over from the production cluster to the DR cluster, the bound ELB is automatically switched to the new production cluster. During the switchover, the connection is interrupted for a short period of time. Do not run service statements to write data during the switchover.
- During intra-region DR, the EIP, intranet domain name, and connection IP address of the original production cluster are not automatically switched with the cluster switchover. The EIP, domain name, or IP address used for connection in the service system need to be switched to the new cluster.
diff --git a/docs/dws/umn/dws_01_00082.html b/docs/dws/umn/dws_01_00082.html index 4df097e1..db5fe0cc 100644 --- a/docs/dws/umn/dws_01_00082.html +++ b/docs/dws/umn/dws_01_00082.html @@ -1,23 +1,21 @@ - +Creating a DR Task
-Creating a Cross-AZ DR Task
Prerequisites
-You can create a DR task only when the cluster is in the Available or Unbalanced state.
-Procedure
-- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- On the displayed page, click Create.
- Select the type and enter the name of the DR task to be created.
- Type: Cross-AZ DR
- Name: Enter 4 to 64 case-insensitive characters, starting with a letter. Only letters, digits, hyphens (-), and underscores (_) are allowed. - -
- Configure the production cluster. -
- Configure the DR cluster.
- Select the AZ associated with the region where the DR cluster resides.
The production cluster AZ will be filtered out from the available DR cluster AZs.
+Creating an Intra-Region Cluster-Level DR Task
Prerequisites
+You can create a DR task only when the cluster is in the Available or Unbalanced state.
+Procedure
+- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- On the displayed page, click Create.
- Select the type and enter the name of the DR task to be created.
- Type: Intra-region DR
- Name: Enter 4 to 64 case-insensitive characters, starting with a letter. Only letters, digits, hyphens (-), and underscores (_) are allowed.
- Configure the production cluster.
- Select a created production cluster from the drop-down list.
- After a production cluster is selected, the system automatically displays its AZ.
- Configure the DR cluster.
- Select the AZ associated with the region where the DR cluster resides.-
The AZ of the DR cluster can be the same as that of the production cluster. In a 3-AZ cluster, any of the three AZs can be selected for DR.
- After you select an AZ for the DR cluster, homogeneous DR clusters will be displayed. If no DR cluster is available, create a cluster with the same configurations as the production cluster. +
- After you select an AZ for the DR cluster, homogeneous DR clusters will be displayed. If no DR cluster is available, create a cluster with the same configurations as the production cluster.
- Select the AZ associated with the region where the DR cluster resides.
- Configure advanced parameters. Select Default to keep the default values of the advanced parameters. You can also select Custom to modify the values.
- The DR synchronization period indicates the interval for synchronizing incremental data from the production cluster to the DR cluster. Set this parameter based on the actual service data volume.
-diff --git a/docs/dws/umn/dws_01_00083.html b/docs/dws/umn/dws_01_00083.html index d3641f60..1e8da290 100644 --- a/docs/dws/umn/dws_01_00083.html +++ b/docs/dws/umn/dws_01_00083.html @@ -1,13 +1,10 @@ - +
The default DR synchronization period is 30 minutes.
+- Configure advanced parameters. Select Default to keep the default values of the advanced parameters. You can also select Custom to modify the values.
- The DR synchronization period indicates the interval for synchronizing incremental data from the production cluster to the DR cluster. Set this parameter based on the actual service data volume.
The default DR synchronization period is 30 minutes.
- Click OK.
The DR status will then change to Creating. Wait until the creation is complete, and the DR status will change to Not Started.
+- Click OK.
The DR status will then change to Creating. Wait until the creation is complete, and the DR status will change to Not Started.
Viewing DR Information
-- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- In the DR list, click the name of a DR task.
On the page that is displayed, view the following information:
-- DR Information: You can view the DR ID, DR name, DR creation time, and DR status.
- Production Cluster Information: You can view the production cluster ID, cluster name, AZ, used storage capacity, cluster DR status, and the time of the latest successful DR task.
- DR Cluster Information: You can view the DR cluster ID, cluster name, AZ, used storage capacity, cluster DR status, and the time of the latest successful DR task.
- DR Configurations: You can view and modify the DR synchronization period.
- - -
+ On the DR information page of a cross-region DR task, you can only view the disk and AZ information about the current cluster. To check cluster information in the other region, switch to that region first.
-- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- In the DR list, click the name of a DR task.
On the page that is displayed, view the following information:
+- DR Information: You can view the DR ID, DR name, DR creation time, and DR status.
- Production Cluster Information: You can view the production cluster ID, cluster name, AZ, used storage capacity, cluster DR status, and the time of the latest successful DR task.
- DR Cluster Information: You can view the DR cluster ID, cluster name, AZ, used storage capacity, cluster DR status, and the time of the latest successful DR task.
- DR Configuration: Users can view and modify the DR synchronization period.
Figure 1 Viewing the DR information+
diff --git a/docs/dws/umn/dws_01_00084.html b/docs/dws/umn/dws_01_00084.html index dd5d5d74..84763dc9 100644 --- a/docs/dws/umn/dws_01_00084.html +++ b/docs/dws/umn/dws_01_00084.html @@ -1,53 +1,55 @@ - +DR Management
-Starting a DR Task
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Click Start in the Operation column of the target DR task.
-
- In the dialog box that is displayed, click OK.
The DR status will change to Starting. The process will take some time. After the task is started, the DR status will change to Running.
- - You can start a DR task that is in the Not started/Startup failed/Stopped state.
- After you start the DR task, you cannot perform operations, including restoration, scale-out, upgrade, restart, node replacement, and password update, on the production cluster or DR cluster. Backup is also not allowed on the DR cluster. Exercise caution when performing this operation.
-Starting a DR Task
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Click Start in the Operation column of the target DR task.
+
- In the dialog box that is displayed, click OK.
The DR status will change to Starting. The process will take some time. After the task is started, the DR status will change to Running.
+ - You can start a DR task that is in the Not started/Startup failed/Stopped state.
- After you start the DR task, you cannot perform operations, including restoration, scale-out, upgrade, restart, node replacement, and password update, on the production cluster or DR cluster. Backup is also not allowed on the DR cluster. Exercise caution when performing this operation.
Stopping the DR Task
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Click Stop in the Operation column of the target DR task.
-
- In the dialog box that is displayed, click OK.
The DR status will change to Stopping. The process will take some time. After the DR task is stopped, the status will change to Stopped.
- - Only DR tasks in the Running or Stop failed state can be stopped.
- Data cannot be synchronized after a DR task is stopped.
-Stopping the DR Task
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Click Stop in the Operation column of the target DR task.
+
- In the dialog box that is displayed, click OK.
The DR status will change to Stopping. The process will take some time. After the DR task is stopped, the status will change to Stopped.
+ - Only DR tasks in the Running or Stop failed state can be stopped.
- Data cannot be synchronized after a DR task is stopped.
Switching to the DR Cluster
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Click Switch to DR Cluster in the Operation column of the target DR task.
-
- In the dialog box that is displayed, click OK.
The DR status will change to DR switching.
-After the switchover is successful, the DR status will change to the original status.
- - To perform a switchover when the DR cluster is running properly, click Switch to DR Cluster.
- You can perform a DR switchover when the DR task is in the Running state.
- During a switchover, the original production cluster is not available.
- Recovery Point Object (RPO) refers to the point in time to which a system and data must be restored after a disaster occurs. Its value varies by cluster status.
- Production cluster in the Available state: RPO = 0
- Production cluster in the Unavailable state: A zero RPO may not be achieved, but data can at least be restored to that of the latest successful DR synchronization (Last DR Succeeded). For details, see Viewing DR Information.
-Switching to the DR Cluster
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Click Switch to DR Cluster in the Operation column of the target DR task.
+
- In the dialog box that is displayed, click OK.
The DR status will change to DR switching.
+After the switchover is successful, the DR status will change to the original status.
+ - To perform a switchover when the DR cluster is running properly, click Switch to DR Cluster.
- You can perform a DR switchover when the DR task is in the Running state.
- During a switchover, the original production cluster is not available.
- Recovery Point Object (RPO) refers to the point in time to which a system and data must be restored after a disaster occurs. Its value varies by cluster status.
- Production cluster in the Available state: RPO = 0
- Production cluster in the Unavailable state: A zero RPO may not be achieved, but data can at least be restored to that of the latest successful DR synchronization (Last DR Succeeded). For details, see Viewing DR Information.
Abnormal Switchover
Scenario
-The production cluster is unavailable, the DR cluster is normal, and the DR status is Abnormal.
-Procedure
-- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Choose More > Exception Switchover in the Operation column of the target DR task.
-
- In the dialog box that is displayed, click OK.
The Status will change to Switchover in progress.
-After the switchover is successful, the DR status will change to the original status. In this procedure, the DR status will change back to Abnormal.
- - To perform a switchover when the DR cluster is abnormal or the production cluster is faulty, click Exception Switchover.
- The abnormal switchover feature is supported only in cluster version 8.1.2 and later. The agent version must be 8.2.0 or later.
- Before a switchover, check the latest synchronization time in the DR cluster. The DR cluster will serve as a production cluster after an abnormal switchover, but the data that failed to be synchronized from the original production cluster to the DR cluster will not exist in the DR cluster.
- If the DR type is Cross-region DR, the switchover can be performed only in the region where the standby cluster is located.
-Exception Switchover
Scenario
+The production cluster is unavailable, the DR cluster is normal, and the DR status is Abnormal.
+Procedure
+- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Choose More > Exception Switchover in the Operation column of the target DR task.
+
- In the dialog box that is displayed, click OK.
The Status will change to Switchover in progress.
+After the switchover is successful, the DR status will change to the original status. In this procedure, the DR status will change back to Abnormal.
+ - To perform a switchover when the DR cluster is abnormal or the production cluster is faulty, click Exception Switchover.
- DR exception switchover is supported only by clusters of version 8.1.2 or later.
- Before a switchover, check the latest synchronization time in the DR cluster. The DR cluster will serve as a production cluster after an abnormal switchover, but the data that failed to be synchronized from the original production cluster to the DR cluster will not exist in the DR cluster.
- If the DR type is Cross-region DR, the switchover can be performed only in the region where the standby cluster is located.
Performing a DR Switchback
Scenario
-After abnormal switchover, if you have confirmed that the original production cluster was recovered, you can perform a switchback.
-Procedure
-- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Click DR Recovery in the Operation column of a DR task.
-
- In the dialog box that is displayed, click OK.
The Status will change to Recovering.
-After the DR recovery is successful, the Status will change to Running.
- - The switchback feature is supported only in cluster version 8.1.2 and later. The agent version must be 8.2.0 or later.
- During DR recovery, data in the DR cluster will be deleted, and the DR relationship will be re-established with the new production cluster.
- If the DR type is Cross-region DR, the recovery can be performed only in the region where the standby cluster is located.
-Performing a DR Switchback
Scenario
+After abnormal switchover, if you have confirmed that the original production cluster was recovered, you can perform a switchback.
+Procedure
+- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Click DR Recovery in the Operation column of a DR task.
+
- In the displayed dialog box, set Synchronization Mode to Incremental or Full.+
You are advised to set Synchronization Mode to Incremental when updating a DR creation task.
+ - Click OK.
The Status will change to Recovering.
+After the DR recovery is successful, the Status will change to Running.
+ - DR is supported only by clusters of 8.1.2 or later.
- During DR recovery, data in the DR cluster will be deleted, and the DR relationship will be re-established with the new production cluster.
- If the DR type is Cross-region DR, the recovery can be performed only in the region where the standby cluster is located.
Updating DR Configurations
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- In the DR list, click the DR name to go to the DR information page.
- In the DR Configurations area, click Modify.
-
- Only DR tasks in the Not started or Stopped state can be modified.
- The new configuration takes effect after DR is restarted.
-Updating DR Configurations
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- In the DR list, click the DR name to go to the DR information page.
- In the DR Configurations area, click Modify.
+
- Only DR tasks in the Not started or Stopped state can be modified.
- The new configuration takes effect after DR is restarted.
Deleting DR Tasks
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose DR Tasks.
- Click Delete in the Operation column of the target DR task.
-
- In the dialog box that is displayed, click OK.The DR status will change to Deleting.
- You can delete a DR task when DR Status is Creation failed, Not started, Startup failed, Stopped, Stop failed, or Abnormal.
- Data cannot be synchronized after a DR task is deleted, and the deleted task cannot be restored.
Deleting DR Tasks
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose DR Tasks.
- Click Delete in the Operation column of the target DR task.
+
- In the dialog box that is displayed, click OK.The DR status will change to Deleting.
- You can delete a DR task when DR Status is Creation failed, Not started, Startup failed, Stopped, Stop failed, or Abnormal.
- Data cannot be synchronized after a DR task is deleted, and the deleted task cannot be restored.
Mutually Exclusive DR Cases
-Case 1: How Do I Scale out a Cluster in the DR State?
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click Clusters.
- In the cluster list, if Task Information of the cluster you want to scale out is DR not started, perform 5 and 7.
- If the Task Information is other than DR not started, delete the DR task. For details, see Deleting DR Tasks.
- In the Operation column of the production and DR clusters, choose More > Scale Out.
-
- Create a DR task. For details, see Creating a DR Task.
- Start the DR task. For details, see Starting a DR Task.
After scale-out, the number of DNs in the production cluster must be the same as that in the DR cluster.
+diff --git a/docs/dws/umn/dws_01_0009.html b/docs/dws/umn/dws_01_0009.html index 3d4edd8c..6acea8d5 100644 --- a/docs/dws/umn/dws_01_0009.html +++ b/docs/dws/umn/dws_01_0009.html @@ -1,19 +1,21 @@ - +Case 1: How Do I Scale out a Cluster in the DR State?
- Log in to the GaussDB(DWS) console.
- In the navigation pane on the left, choose Clusters > Dedicated Clusters.
- In the cluster list, if Task Information of the cluster you want to scale out is DR not started, perform 5 and 7.
- If the Task Information is other than DR not started, delete the DR task. For details, see Deleting DR Tasks.
- In the Operation column of the production and DR clusters, choose More > Scale Out.
+
- Create a DR task. For details, see Creating a DR Task.
- Start the DR task. For details, see Starting a DR Task.
After scale-out, the number of DNs in the production cluster must be the same as that in the DR cluster.
GaussDB(DWS) Access
-The following figure shows how to use GaussDB(DWS).
-Figure 1 Process for using GaussDB(DWS)-
Accessing a Cluster
GaussDB(DWS) provides a web-based management console and HTTPS-compliant APIs for you to manage data warehouse clusters.
+The following figure shows how to use GaussDB(DWS).
+Figure 1 Process for using GaussDB(DWS)+
-Accessing a Cluster
GaussDB(DWS) provides a web-based management console and HTTPS-compliant APIs for you to manage data warehouse clusters.
+ In cluster deployment, if a single node is faulty, the abnormal node is automatically skipped when GaussDB(DWS) is accessed. However, the cluster performance will be affected.
+Accessing the Database in a Cluster
GaussDB(DWS) supports database access using the following methods:- GaussDB(DWS) clients
Access the cluster database using GaussDB(DWS) clients. For details, see "Connecting to a Cluster" in the Data Warehouse Service (DWS) User Guide.
- - JDBC and ODBC API calling
You can call standard APIs, such as JDBC and ODBC, to access databases in clusters.
-For details, see "Using the JDBC and ODBC Drivers to Connect to a Cluster" in the Data Warehouse Service (DWS) User Guide.
+-Accessing the Database in a Cluster
GaussDB(DWS) supports database access using the following methods:- GaussDB(DWS) clients
Access the cluster database using GaussDB(DWS) clients. For details, see "Connecting to a Cluster" in the Data Warehouse Service (DWS) User Guide.
+ - JDBC and ODBC API calling
You can call standard APIs, such as JDBC and ODBC, to access databases in clusters.
+For details, see "Using the JDBC and ODBC Drivers to Connect to a Cluster" in the Data Warehouse Service (DWS) User Guide.
End-to-End Data Analysis Process
GaussDB(DWS) has been seamlessly integrated with other services on the cloud, helping you rapidly deploy end-to-end data analysis solutions.
-The following figure shows the end-to-end data analysis process. Services in use during each process are also displayed.
-Figure 2 End-to-end data analysis process+
End-to-End Data Analysis Process
GaussDB(DWS) has been seamlessly integrated with other services on cloud, helping you rapidly deploy end-to-end data analysis solutions.
+The following figure shows the end-to-end data analysis process. Services in use during each process are also displayed.
+Figure 2 End-to-end data analysis process
diff --git a/docs/dws/umn/dws_01_00097.html b/docs/dws/umn/dws_01_00097.html index 88eb3076..184fdbfa 100644 --- a/docs/dws/umn/dws_01_00097.html +++ b/docs/dws/umn/dws_01_00097.html @@ -1,13 +1,12 @@ - +Restoring a Snapshot to the Original Cluster
-Scenario
You can use a snapshot to restore data to the original cluster. This function is used when a cluster is faulty or data needs to be rolled back to a specified snapshot version.
-
- This function is supported only by clusters of version 8.1.3.200 or later.
- Snapshots whose backup device is OBS can be backed up.
- Only a snapshot in the Available state can be used for restoration.
- Logical clusters and resource pools cannot be restored to the current cluster.
-Scenario
You can use a snapshot to restore data to the original cluster. This function is used when a cluster is faulty or data needs to be rolled back to a specified snapshot version.
+ - This function is supported only by clusters of version 8.1.3.200 or later.
- Snapshots whose backup device is OBS can be backed up.
- Only a snapshot in the Available state can be used for restoration.
- Logical clusters and resource pools cannot be restored to the current cluster.
Procedure
- Log in to the GaussDB(DWS) management console.
- In the navigation tree on the left, click Clusters.
- Click the name of a cluster and choose Snapshots.
- Click Restore.
-
- Restore the snapshot to the current cluster.
-
If you use a snapshot to restore data to the original cluster, the cluster will be unavailable during the restoration.
+diff --git a/docs/dws/umn/dws_01_0010.html b/docs/dws/umn/dws_01_0010.html index 7fd2a9a5..05eae311 100644 --- a/docs/dws/umn/dws_01_0010.html +++ b/docs/dws/umn/dws_01_0010.html @@ -1,7 +1,74 @@ - +Procedure
- Log in to the GaussDB(DWS) management console.
- In the navigation pane, choose Snapshots. All snapshots are displayed by default.
- In the snapshot list, locate the row that contains the target snapshot and click Restore in the Operation column. The Restore Snapshot to New Cluster page is displayed.
- Restore the snapshot to the current cluster.
+
If you use a snapshot to restore data to the original cluster, the cluster will be unavailable during the restoration.
Restrictions
-- You can manage clusters only and cannot directly access nodes in a cluster. You can use a cluster's IP address and port to access the database in the cluster.
- Currently, you can only modify the specifications of standard data warehouse clusters and stream data warehouse clusters that only use ECS and EVS resources for computing and storage. If your cluster contains other computing or storage resources but you want to change to a higher node flavor, create a new cluster.
- If you use a client to connect to a cluster, its VPC subnet must be the same as that of the cluster.
- If you copy commands from the document to the operating environment, the text wraps automatically, causing command execution failures. To solve the problem, delete the line break.
This document describes the constraints and precautions of using the key functions of GaussDB(DWS).
+After creating a GaussDB(DWS) cluster, you do not need to perform basic database O&M operations, such as HA and security patch installation. However, you need to pay attention to the following:
+ +
+Table 1 GaussDB (DWS) constraints Item
+Constraint
+Creating a Cluster
+- After a cluster is created, its type cannot be changed.
Connecting to a Cluster
+- If you use a client to connect to a cluster, its VPC subnet must be the same as that of the cluster.
- You can manage clusters only and cannot directly access nodes in a cluster. You can use a cluster's IP address and port to access the database in the cluster.
SQL Syntax Changes
+- The hybrid data warehouse (standalone) does not support full-text search, OBS and HDFS foreign table import and export, automatic partition creation, sequence, and related functions.
- The hybrid data warehouse (standalone) has only one DN. Therefore, the distribution rule is ignored and cannot be modified.
- If you copy commands from the document to the operating environment, the text wraps automatically, causing command execution failures. To solve the problem, delete the line break.
- You are advised to create databases as required. Do not use the default gaussdb database of a cluster.
Changing Flavor
+- Only clusters of version 8.1.1.300 or later support flavor change.
- Currently, only offline flavor chage is supported. The change takes about 10 minutes.
- If your cluster is created using local disks or compute-storage integration, you cannot change the flavor of the cluster. If you need nodes with a higher flavor, create a new cluster. Currently, you can only change the flavors of cloud data warehouse clusters that use SSD cloud disks or hybrid data warehouse clusters.
- Currently, changing all specifications can only be performed for standard data warehouses and hybrid data warehouses (except single-node deployment). The Change all specifications option does not support logical clusters.
- A cluster can have up to 240 nodes. The old and new clusters can have up to 480 nodes in total.
- Disk capacity expansion can be performed only for standard data warehouses using SSD, hybrid data warehouses, or stream data warehouses. Only version 8.1.1.203 and later are supported.
- Disk capacity can be expanded only if the cluster is in Available, To be restarted, Read-only, or Node fault, Unbalanced state.
Scaling Out
+- When you scale out the standard data warehouse cluster, use the same storage specifications as the cluster.
- The cluster redistribution function is supported in cluster versions.
- This function can be manually enabled only when the cluster task information displays To be redistributed after scale-out.
- Scale-in is supported only by clusters of version 8.1.1.300 and later.
- When you scale in a standard data warehouse cluster, you can only modify the same storage specifications as used by the cluster.
Backing Up a Cluster
+- The new GaussDB(DWS) cluster created based on the snapshot must have the same configurations as the original cluster. That is, the number and specifications of nodes, memory, and disks in the new cluster must be the same as those in the original cluster.
- If you create a new cluster based on a snapshot without modifying parameters, the parameters of the new cluster will be the same as those of the snapshot.
- During snapshot creation, do not perform the VACUUM FULL operation, or the cluster may become read-only.
- Snapshot creation affects disk I/O performance. You are advised to create snapshots during off-peak hours.
- During the snapshot creation, some intermediate files are retained, which occupy extra disk space. Therefore, avoid peak hours and ensure that the disk capacity usage is less than 70%.
- Snapshots can be restored to the current cluster, but logical clusters and resource pools cannot be restored to the current cluster.
Version Upgrade
+- Cluster 8.1.1 and later versions allow users to deliver cluster upgrade operations on the console.
- If the cluster is interrupted for a long time due to a node fault or system upgrade, contact technical support.
Data Migration
+- Ensure that no Chinese characters are contained in paths used for importing data to or exporting data from OBS.
- Data cannot be imported to or exported from OBS across regions. Ensure that OBS and the GaussDB(DWS) cluster are in the same region.
Failover
+- When the DR task is created, the snapshot function of the production cluster is normal, but that of the DR cluster is disabled. Besides, snapshot restoration of both clusters is disabled.
- DR does not synchronize data from external sources.
- DR management refers to dual-cluster DR under the same tenant.
- The DR cluster and the production cluster must be logically homogeneous and in the same type and version.
- The production cluster and DR cluster used for intra-region DR must be in the same VPC.
- In intra-region DR, after services are switched over from the production cluster to the DR cluster, the bound ELB is automatically switched to the new production cluster. During the switchover, the connection is interrupted for a short period of time. Do not run service statements to write data during the switchover.
- During intra-region DR, the EIP, intranet domain name, and connection IP address of the original production cluster are not automatically switched with the cluster switchover. The EIP, domain name, or IP address used for connection in the service system need to be switched to the new cluster.
Hot and Cold Data Management
+- The hybrid data warehouse (standalone) does not support cold and hot partition switchover.
- Currently, cold and hot tables support only column-store partitioned tables of version 2.0. Foreign tables do not support cold and hot partitions.
- Only hot data can be switched to cold data. Cold data cannot be switched to hot data.
- A partition on a DN is either hot or cold. For a partition across DNs, its data on some DNs may be hot, and some may be cold.
- Only the cold and hot switchover policies can be modified. The tablespace of cold data in cold and hot tables cannot be modified.
Stream Data Warehouse
+- Time series tables do not support UPDATE, UPSERT, primary keys, or PCKs.
- To create a time series table, you must have the USAGE permission on schema cstore.
- When you modify the enable_delta parameter of a time series table, other ALTER operations cannot be performed.
diff --git a/docs/dws/umn/dws_01_0013.html b/docs/dws/umn/dws_01_0013.html index d74ef5d8..4b40f632 100644 --- a/docs/dws/umn/dws_01_0013.html +++ b/docs/dws/umn/dws_01_0013.html @@ -1,47 +1,47 @@ - +Step 2: Creating a Cluster
-Before using GaussDB(DWS) to analyze data, create a cluster. A cluster consists of multiple nodes in the same subnet. These nodes jointly provide services. This section describes how to create a GaussDB(DWS) cluster.
-Creating a Cluster
- Log in to the GaussDB(DWS) management console.
- Choose Clusters in the navigation pane on the left.
- On the Clusters page, click Create Cluster in the upper right corner.
- Select the region to which the cluster to be created belongs.
- Region: Select the current working area of the cluster.
- AZ: Retain the default value.
- Configure node parameters.
- Resource: For example, Standard.
- CPU Architecture: Select a CPU architecture based on your requirements, for example, x86.
- Node Flavor: Retain the default value.
- Nodes: Retain the default value. At least 3 nodes are required.
- Configure cluster parameters.
- Cluster Name: Enter dws-demo.
- Cluster Version: The current cluster version is displayed and cannot be changed.
- Default Database: The value is gaussdb. which cannot be changed.
- Administrator Account: The default value is dbadmin. Use the default value. After a cluster is created, the client uses this database administrator account and its password to connect to the cluster's database.
- Administrator Password: Enter the password.
- Confirm Password: Enter the database administrator password again.
- Database Port: Use the default port number. This port is used by the client or application to connect to the cluster's database.
Figure 1 Configuring the cluster-
- Configure network parameters.
- VPC: You can select an existing VPC from the drop-down list. If no VPC has been configured, click View VPC to enter the VPC management console to create one, for example, vpc-dws. Then, go back to the page for creating a cluster on the GaussDB(DWS) management console, click
next to the VPC drop-down list, and select the new VPC. - Subnet: When you create a VPC, a subnet is created by default. You can select the corresponding subnet.
- Security Group: Select Automatic creation.
The automatically created security group is named GaussDB(DWS)-<Cluster name>-<GaussDB(DWS) cluster database port>. The outbound allows all access requests, while the inbound enables only Database Port for access requests from clients or applications.
-If you select a custom security group, add an inbound rule to it to enable Database Port for client hosts to access GaussDB(DWS). Table 1 shows an example. For details about how to add an inbound rule, see "Security > Security Group > Adding a Security Group Rule" in the Virtual Private Cloud User Guide.
+Before using GaussDB(DWS) to analyze data, create a cluster. A cluster consists of multiple nodes in the same subnet. These nodes jointly provide services. This section describes how to create a GaussDB(DWS) cluster.
+Creating a Cluster
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, choose Cluster > Dedicated Cluster.
- On the Dedicated Cluster page, click Create Cluster in the upper right corner.
- Select the region to which the cluster to be created belongs.
- Region: Select the current working area of the cluster.
- AZ: Retain the default value.
- Configure node parameters.
- Resource: For example, Standard.
- CPU Architecture: Select a CPU architecture based on your requirements, for example, x86.
- Node Flavor: Retain the default value.
- Nodes: Retain the default value. At least 3 nodes are required.
- Configure cluster parameters.
- Cluster Name: Enter dws-demo.
- Cluster Version: The current cluster version is displayed and cannot be changed.
- Default Database: The value is gaussdb, which cannot be changed.
- Administrator Account: The default value is dbadmin. Use the default value. After a cluster is created, the client uses this database administrator account and its password to connect to the cluster's database.
- Administrator Password: Enter the password.
- Confirm Password: Enter the database administrator password again.
- Database Port: Use the default port number. This port is used by the client or application to connect to the cluster's database.
Figure 1 Configuring the cluster+
- Configure network parameters.
- VPC: You can select an existing VPC from the drop-down list. If no VPC has been configured, click View VPC to enter the VPC management console to create one, for example, vpc-dws. Then, go back to the page for creating a cluster on the GaussDB(DWS) management console, click
next to the VPC drop-down list, and select the new VPC. - Subnet: When you create a VPC, a subnet is created by default. You can select the corresponding subnet.
- Security Group: Select Automatic creation.
The automatically created security group is named GaussDB(DWS)-<Cluster name>-<GaussDB(DWS) cluster database port>. The outbound allows all access requests, while the inbound enables only Database Port for access requests from clients or applications.
+If you select a custom security group, add an inbound rule to it to enable Database Port for client hosts to access GaussDB(DWS). Table 1 shows an example. For details about how to add an inbound rule, see "Security > Security Group > Adding a Security Group Rule" in the Virtual Private Cloud User Guide.
-Table 1 Inbound rule example Parameter
+-Table 1 Inbound rule example Parameter
Example Value
+Example Value
Protocol/Application
+Protocol/Application
TCP
+TCP
Port
+Port
8000
-NOTE:Enter the value of Database Port set when creating the GaussDB(DWS) cluster. This port is used for receiving client connections to GaussDB(DWS). The default port number is 8000.
+8000
+NOTE:Enter the value of Database Port set when creating the GaussDB(DWS) cluster. This port is used for receiving client connections to GaussDB(DWS). The default port number is 8000.
Source
+Source
Select IP address, and enter the IP address and subnet mask of the client host that accesses GaussDB(DWS), for example, 192.168.0.10/16.
+Select IP address, and enter the IP address and subnet mask of the client host that accesses GaussDB(DWS), for example, 192.168.0.10/16.
- EIP: Select Automatically assign to apply for a cluster EIP as the public network IP address of the cluster. In addition, set the EIP bandwidth.
-Figure 2 Configuring the network-
- Configure the enterprise project to which the cluster belongs. You can configure this parameter only when the Enterprise Project Management service is enabled. The default value is default.
An enterprise project facilitates project-level management and grouping of cloud resources and users.
-You can select the default enterprise project (default) or other existing enterprise projects. To create an enterprise project, log in to the Enterprise Management console. For details, see the Enterprise Management User Guide.
-- Select Default for Advanced Settings in this example.
- Default: indicates that the following advanced settings use the default configurations.
- Automated Snapshot: By default, the policy for automatically creating cluster snapshots is enabled.
- CNs: Three CNs are deployed by default.
- Tag: By default, no tag is added to the cluster.
- Custom: Select this option to configure the following advanced parameters: Automated Snapshot, CNs, Tag
- Click Create Now. The Confirm page is displayed.
- Click Submit.
After the submission is successful, the creation starts. Click Back to Cluster List. The Clusters page is displayed. The initial status of the cluster is Creating. Cluster creation takes some time. Wait for a while. Clusters in the Available state are ready for use.
+- EIP: Select Automatically assign to apply for a cluster EIP as the public network IP address of the cluster. In addition, set the EIP bandwidth.
+Figure 2 Configuring the network+
- Configure the enterprise project to which the cluster belongs. You can configure this parameter only when the Enterprise Project Management service is enabled. The default value is default.
An enterprise project facilitates project-level management and grouping of cloud resources and users.
+You can select the default enterprise project default or other existing enterprise projects. To create an enterprise project, log in to the Enterprise Management console. For details, see Enterprise Management User Guide.
+- Select Default for Advanced Settings in this example.
- Default: indicates that the following advanced settings use the default configurations.
- CNs: Three CNs are deployed by default.
- Tag: By default, no tag is added to the cluster.
- Encrypt DataStore: This parameter is disabled by default, indicating that the database is not encrypted.
- Custom: Select this option to configure the following advanced parameters: Automated Snapshot, CNs, Tag
- Click Create Now. The Confirm page is displayed.
- Click Submit.
diff --git a/docs/dws/umn/dws_01_00131.html b/docs/dws/umn/dws_01_00131.html index 9870ed91..857934aa 100644 --- a/docs/dws/umn/dws_01_00131.html +++ b/docs/dws/umn/dws_01_00131.html @@ -1,14 +1,14 @@ - +After the submission is successful, the creation starts. Click Back to Cluster List. The Dedicated Cluster page is displayed. The initial status of the cluster is Creating. Cluster creation takes some time. Wait for a while. Clusters in the Available state are ready for use.
Database Monitoring Overview
-Overview
DMS is provided by GaussDB(DWS) to ensure the fast and stable running of databases. It collects, monitors, and analyzes the disk, network, and OS metric data used by the service database, as well as key performance metric data of cluster running. It also diagnoses database hosts, instances, and service SQL statements based on the collected metrics to expose key faults and performance problems in a database in a timely manner, and guides customers to optimize and resolve the problems.
+-Overview
DMS is provided by GaussDB(DWS) to ensure the fast and stable running of databases. It collects, monitors, and analyzes the disk, network, and OS metric data used by the service database, as well as key performance metric data of cluster running. It also diagnoses database hosts, instances, and service SQL statements based on the collected metrics to expose key faults and performance problems in a database in a timely manner, and guides customers to optimize and resolve the problems.
Entering the Database Monitoring Page
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the target cluster, choose Monitoring Panel. The database monitoring page is displayed.
Entering the Database Monitoring Page
- Log in to the GaussDB(DWS) management console.
- On the Clusters > Dedicated Clusters page, locate the cluster to be monitored.
- In the Operation column of the target cluster, choose Monitoring Panel. The database monitoring page is displayed.
diff --git a/docs/dws/umn/dws_01_00132.html b/docs/dws/umn/dws_01_00132.html index 2077203c..f802e054 100644 --- a/docs/dws/umn/dws_01_00132.html +++ b/docs/dws/umn/dws_01_00132.html @@ -1,38 +1,38 @@ - +-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Cluster Overview
-Cluster Overview
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the target cluster, click Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane on the left, click Cluster Overview.
On the page that is displayed, you can view the cluster status, real-time resource consumption, top SQL statements, cluster resource consumption, and key database metrics.
+Cluster Overview
- Log in to the GaussDB(DWS) management console.
- On the Clusters > Dedicated Clusters page, locate the cluster to be monitored.
- In the Operation column of the target cluster, click Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane on the left, click Cluster Overview.
On the page that is displayed, you can view the cluster status, real-time resource consumption, top SQL statements, cluster resource consumption, and key database metrics.
-Metrics can be collected and displayed on the cluster overview page only if their collection items are enabled. If a collection item is disabled, its metric will not be displayed, and a prompt will be displayed indicating this problem. In this case, you are advised to enable the collection item.
+ Metrics can be collected and displayed on the cluster overview page only if their collection items are enabled. If a collection item is disabled, its metric will not be displayed, and a prompt will be displayed indicating this problem. In this case, you are advised to enable the collection item.
Cluster Status
In the Cluster Status area, you can view the statistics about the current cluster status and instance status, including cluster statistics in the last 24 hours, cluster specifications, available/total CNs and DNs, used/total disk capacity, the number of CCN switchovers in the last 24 hours, and the number of primary/standby DN switchovers in the last 24 hours.
- -
+
-Cluster Status
In the Cluster Status area, you can view the statistics about the current cluster status and instance status, including cluster statistics in the last 24 hours, cluster specifications, available/total CNs and DNs, used/total disk capacity, the number of CCN switchovers in the last 24 hours, and the number of primary/standby DN switchovers in the last 24 hours.
+ +
Alarms
In the Alarms area, you can view all the uncleared alarms of the current cluster and the alarms generated in the last seven days. You can click More in the upper right corner to view details about the existing cluster alarms. For details, see Alarms.
-
+
-Alarms
In the Alarms area, you can view all the uncleared alarms of the current cluster and the alarms generated in the last seven days. You can click More in the upper right corner to view details about the existing cluster alarms. For details, see Alarms.
+
Cluster Resources
In the Cluster Resources area, you can view the resource usage of the current cluster, including the CPU usage, disk I/O, disk usage, memory usage, and network I/O. You can click the metric of a resource to view its trend in the last 24 hours and the top five services that are occupying this resource. You can click More in the upper right corner of the area to go to the Node Monitoring page. Nodes are sorted by the metric value. For details, see Node Monitoring.
- -
+
-Cluster Resources
In the Cluster Resources area, you can view the resource usage of the current cluster, including the CPU usage, disk I/O, disk usage, memory usage, and network I/O. You can click the metric of a resource to view its trend in the last 24 hours and the top five services that are occupying this resource. You can click More in the upper right corner of the area to go to the Node Monitoring page. Nodes are sorted by the metric value. For details, see Node Monitoring.
+ +
Workloads
In the Workloads area, you can view the workload metrics of the current database, including TPS, QPS, stacked SQL queries, and running/queuing tasks in the resource pool. You can also click a workload metric to view its trend in the last 24 hours. The SQL Stack Queries metric depends on the real-time query monitoring function. If this function is disabled, no data will be displayed for the metric.
- -
+
-Workloads
In the Workloads area, you can view the workload metrics of the current database, including TPS, QPS, stacked SQL queries, and running/queuing tasks in the resource pool. You can also click a workload metric to view its trend in the last 24 hours. The SQL Stack Queries metric depends on the real-time query monitoring function. If this function is disabled, no data will be displayed for the metric.
+
diff --git a/docs/dws/umn/dws_01_00134.html b/docs/dws/umn/dws_01_00134.html index 7c35a0d2..eb33ca3e 100644 --- a/docs/dws/umn/dws_01_00134.html +++ b/docs/dws/umn/dws_01_00134.html @@ -1,30 +1,30 @@ - +Databases
In the Database area, you can view the used capacity of the current database and schema. You can click a capacity metric to view the database or schema capacity trend in the last 24 hours and the top five databases or schemas ranked by capacity usage in the current cluster. You can click More in the upper right corner of the area to go to the Database Monitoring page. Databases are sorted by used capacity. For details, see Database Monitoring.
- -
+
-Data Volume
In the Data Volume area, you can view the used capacity of the current database and schema. You can click a capacity metric to view the database or schema capacity trend in the last 24 hours and the top five databases or schemas ranked by capacity usage in the current cluster. You can click More in the upper right corner of the area to go to the Database Monitoring page. Databases are sorted by used capacity. For details, see Database Monitoring.
+ +
+
The database capacity data is collected once a day. Therefore, the data volume fluctuates greatly. To view real-time capacity monitoring information, choose Node Monitoring > Disks.
+Queries
In the Queries area, you can check the average number of queries, sessions, and transactions. You can click a metric to view its trend in the last 24 hours. The Average Queries and Average Sessions metrics depend on the real-time query monitoring function. If this function is disabled, no data will be displayed for the metrics.
- -
+
Queries
In the Queries area, you can check the average number of queries, sessions, and transactions. You can click a metric to view its trend in the last 24 hours. The Average Queries and Average Sessions metrics depend on the real-time query monitoring function. If this function is disabled, no data will be displayed for the metrics.
+
diff --git a/docs/dws/umn/dws_01_00133.html b/docs/dws/umn/dws_01_00133.html index 6932ff70..ddf10ef1 100644 --- a/docs/dws/umn/dws_01_00133.html +++ b/docs/dws/umn/dws_01_00133.html @@ -1,4 +1,4 @@ - +-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Monitoring
@@ -21,7 +21,7 @@-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)SQL Diagnosis
-Prerequisites
To enable SQL diagnosis, enable monitoring on real-time and historical queries on the Queries and History tabs, respectively. For details, see Monitoring Collection.
+-Prerequisites
To enable SQL diagnosis, enable monitoring on real-time and historical queries on the Queries and History tabs, respectively. For details, see Monitoring Collection.
Viewing SQL Diagnosis
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the target cluster, click Monitoring Panel.
- In the navigation pane on the left, choose Utilities > SQL Diagnosis. The metrics include:
- Query ID
- Database
- Schema Name
- User Name
- Client
- Client IP Address
- Running Time (ms)
- CPU Time (ms)
- Scale-Out Started
- Completed
- Details
- On the SQL Diagnosis page, you can view the SQL diagnosis information. In the Details column of a specified query ID, click View to view the detailed SQL diagnosis result, including:
- Diagnosis Type
- Alarm Information
- SQL Statement
- Execution Plan
+
-Viewing SQL Diagnosis
- Log in to the GaussDB(DWS) management console.
- On the Clusters > Dedicated Clusters page, locate the cluster to be monitored.
- In the Operation column of the target cluster, click Monitoring Panel.
- In the navigation pane on the left, choose Utilities > SQL Diagnosis. The metrics include:
- Query ID
- Database
- Schema Name
- User Name
- Client
- Client IP Address
- Running Time (ms)
- CPU Time (ms)
- Scale-Out Started
- Completed
- Details
- On the SQL Diagnosis page, you can view the SQL diagnosis information. In the Details column of a specified query ID, click View to view the detailed SQL diagnosis result, including:
- Diagnosis Type
- Alarm Information
- SQL Statement
- Execution Plan
Setting GUC Parameters
GUC parameters related to SQL diagnosis are as follows. For details, see "GUC Parameters" in the Data Warehouse Service (DWS) Developer Guide.
-- enable_resource_track
- Value range: boolean
- Default value: on
- Expected DMS value: on (for reference only)
- Function: Specifies whether to enable the real-time resource monitoring function.
If this parameter is enabled without other GUC-related parameters correctly configured, real-time resource consumption cannot be recorded.
+Setting GUC Parameters
GUC parameters related to SQL diagnosis are as follows. For details, see "GUC Parameters" in the Data Warehouse Service (DWS) Developer Guide.
+- enable_resource_track
- Value range: boolean
- Default value: on
- Expected DMS value: on (for reference only)
- Function: Specifies whether to enable the real-time resource monitoring function.
If this parameter is enabled without other GUC-related parameters correctly configured, real-time resource consumption cannot be recorded.
- resource_track_cost
- Value range: an integer ranging from –1 to INT_MAX
- Default value: 100000
- Expected DMS value: 0 (for reference only)
- Function: Specifies the minimum execution cost of statement resource monitoring for the current session. This parameter is valid only when enable_resource_track is on.
If this parameter is set to a small value, more statements will be recorded, causing record expansion and affecting cluster performance.
+- resource_track_cost
- Value range: an integer ranging from –1 to INT_MAX
- Default value: 100000
- Expected DMS value: 0 (for reference only)
- Function: Specifies the minimum execution cost of statement resource monitoring for the current session. This parameter is valid only when enable_resource_track is on.
If this parameter is set to a small value, more statements will be recorded, causing record expansion and affecting cluster performance.
- resource_track_level
- Value range: enumerated type
- Default value: query
- Expected DMS value: query (for reference only)
- Function: Specifies the resource monitoring level for the current session. This parameter is valid only when enable_resource_track is on.
If the resource monitoring is set to operator-level, performance will be greatly affected.
+- resource_track_level
- Value range: enumerated type
- Default value: query
- Expected DMS value: query (for reference only)
- Function: Specifies the resource monitoring level for the current session. This parameter is valid only when enable_resource_track is on.
If the resource monitoring is set to operator-level, performance will be greatly affected.
- resource_track_duration
- Value range: an integer ranging from 0 to INT_MAX, in seconds
- Default value: 60.
- Expected DMS value: 0 (for reference only)
- Function: Specifies the minimum statement execution time that determines whether information about jobs of a statement recorded in the real-time view will be dumped to a historical view after the statement is executed. That is, only statements whose execution time exceeds the specified time are recorded in the historical view. This parameter is valid only when enable_resource_track is on.
If this parameter is set to a small value, the batch processing mechanism for dumping kernel statements becomes invalid, affecting the kernel performance.
+- resource_track_duration
- Value range: an integer ranging from 0 to INT_MAX, in seconds
- Default value: 60.
- Expected DMS value: 0 (for reference only)
- Function: Specifies the minimum statement execution time that determines whether information about jobs of a statement recorded in the real-time view will be dumped to a historical view after the statement is executed. That is, only statements whose execution time exceeds the specified time are recorded in the historical view. This parameter is valid only when enable_resource_track is on.
If this parameter is set to a small value, the batch processing mechanism for dumping kernel statements becomes invalid, affecting the kernel performance.
- topsql_retention_time
- Value range: an integer ranging from 0 to 3650, in days
- Default value: 0
- Expected DMS value: 1 (for reference only)
- Function: Specifies the aging time of pgxc_wlm_session_info data in the view.
If this parameter is set to 0, data will not be aged, which will cause storage expansion.
+- topsql_retention_time
- Value range: an integer ranging from 0 to 3650, in days
- Default value: 30
- Expected DMS value: 14 (for reference only)
- Function: Specifies the aging time of pgxc_wlm_session_info data in the view.
If this parameter is set to 0, data will not be aged, which will cause storage expansion.
- enable_resource_record
- Value range: boolean
- Default value: off
- Expected DMS value: on (for reference only)
- Function: Specifies whether to enable the archiving function for resource monitoring records. When this function is enabled, records in the history views (GS_WLM_SESSION_HISTORY and GS_WLM_OPERATOR_HISTORY) are archived to the info views (GS_WLM_SESSION_INFO and GS_WLM_OPERATOR_INFO) every 3 minutes. After the archiving, records in the history views are deleted.
When this parameter is enabled, you are advised to set topsql_retention_time properly to configure the aging time. Otherwise, data in the GS_WLM_SESSION_INFO or GS_WLM_OPERATOR_INFO table will expand.
+- enable_resource_record
diff --git a/docs/dws/umn/dws_01_00135.html b/docs/dws/umn/dws_01_00135.html index 2caf7805..d5d981c2 100644 --- a/docs/dws/umn/dws_01_00135.html +++ b/docs/dws/umn/dws_01_00135.html @@ -1,22 +1,16 @@ - +- Value range: boolean
- Default value: off
- Expected DMS value: on (for reference only)
- Function: Specifies whether to enable the archiving function for resource monitoring records. When this function is enabled, records in the history views (GS_WLM_SESSION_HISTORY and GS_WLM_OPERATOR_HISTORY) are archived to the info views (GS_WLM_SESSION_INFO and GS_WLM_OPERATOR_INFO) every 3 minutes. After the archiving, records in the history views are deleted.
When this parameter is enabled, you are advised to set topsql_retention_time properly to configure the aging time. Otherwise, data in the GS_WLM_SESSION_INFO or GS_WLM_OPERATOR_INFO table will expand.
Settings
-The Monitoring page displays the collection period and data aging period of monitoring metrics.
- - The cluster monitoring function is enabled by default.
- Disable the function if the cluster is being recovered. Enable the function when the fault is rectified.
- When a node in the cluster is powered off or the management IP address of the cluster is unavailable, the cluster monitoring switch and the button for configuring cluster indicator collection are unavailable.
The Monitoring page displays the collection period and data aging period of monitoring metrics.
+- - The cluster monitoring function is enabled by default.
- Disable the function if the cluster is being recovered. Enable the function when the fault is rectified.
- When a node in the cluster is powered off or the IP address of the dedicated cluster is unavailable, the cluster monitoring switch and the button for configuring cluster meitric collection are unavailable.
-Monitoring Collection
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the target cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane on the left, choose Settings > Monitoring. You can reconfigure the collection frequency or disable the collection of the monitoring item.
-
- Click Update of DDL Audit to reset the automatic audit frequency or audit items.
-
-
Collection Storage
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the target cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane on the left, choose Settings > Monitoring and switch to the Collection Storage tab page. Update the retention days.
+
Monitoring Collection
- Log in to the GaussDB(DWS) management console.
- On the Clusters > Dedicated Clusters page, locate the cluster to be monitored.
- In the Operation column of the target cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane on the left, choose Monitoring Settings > Monitoring Collection. You can reconfigure the collection frequency or disable the collection of the monitoring item.
diff --git a/docs/dws/umn/dws_01_00136.html b/docs/dws/umn/dws_01_00136.html index b9d05d29..ab5924ff 100644 --- a/docs/dws/umn/dws_01_00136.html +++ b/docs/dws/umn/dws_01_00136.html @@ -1,1755 +1,1755 @@ - +-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Monitoring Metrics
-You can check the status and available resources of a cluster and learn about its real-time resource consumption through the GaussDB(DWS) monitoring items.
-Table 1 describes GaussDB(DWS) monitoring metrics.
+You can check the status and available resources of a cluster and learn about its real-time resource consumption through the GaussDB(DWS) monitoring items.
+Table 1 describes GaussDB(DWS) monitoring metrics.
-Table 1 GaussDB(DWS) monitoring metrics Monitored Object
+Table 1 GaussDB(DWS) monitoring metrics Monitored Object
Metric
+Metric
Description
+Description
Value Range
+Value Range
Monitoring Period (Raw Data)
+Monitoring Period (Raw Data)
Cluster Overview
- - - +Cluster Overview
+ + +Cluster Status
+Cluster Status
Status of a cluster.
+Status of a cluster.
Normal/Abnormal/Degraded
+Normal/Abnormal/Degraded
30s
+30s
Nodes
+Nodes
Number of available nodes and total number of nodes (Available/Total) in a cluster.
+Number of available nodes and total number of nodes (Available/Total) in a cluster.
≥ 0
+≥ 0
60s
+60s
CNs
+CNs
Number of CNs in a cluster.
+Number of CNs in a cluster.
≥ 0
+≥ 0
60s
+60s
Databases
+Databases
Number of created databases in a cluster.
+Number of created databases in a cluster.
≥ 0
+≥ 0
90s
+90s
Resource Consumption
- - - - +Resource Consumption
+ + + +CPU Usage
+CPU Usage
Average real-time CPU usage of all nodes in a cluster.
+Average real-time CPU usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Memory Usage
+Memory Usage
Average real-time memory usage of all nodes in a cluster.
+Average real-time memory usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Disk Usage
+Disk Usage
Average real-time disk usage of all nodes in a cluster.
+Average real-time disk usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Disk I/O
+Disk I/O
Average real-time disk I/O of all nodes in a cluster.
+Average real-time disk I/O of all nodes in a cluster.
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
Network I/O
+Network I/O
Average real-time network I/O of all NICs in a cluster.
+Average real-time network I/O of all NICs in a cluster.
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
Top 5 Time-Consuming Queries
- - +Top 5 Time-Consuming Queries
+ +Query ID
+Query ID
ID of a query, which is automatically generated by the database.
+ID of a query, which is automatically generated by the database.
≥ 0
+≥ 0
180s
+180s
SQL Statement
+SQL Statement
Query statement executed by a user.
+Query statement executed by a user.
String
+String
180s
+180s
Execution Time
+Execution Time
Execution time of a query statement (unit: ms).
+Execution time of a query statement (unit: ms).
≥ 0 ms
+≥ 0 ms
180s
+180s
Top 5 Queries with Most Data Written to Disk
- - +Top 5 Queries with Most Data Written to Disk
+ +Query ID
+Query ID
ID of a query, which is automatically generated by the database.
+ID of a query, which is automatically generated by the database.
≥ 0
+≥ 0
180s
+180s
SQL Statement
+SQL Statement
Query statement executed by a user.
+Query statement executed by a user.
String
+String
180s
+180s
Data Written to Disk
+Data Written to Disk
Data to be written to disks after a user runs a statement (unit: MB).
+Data to be written to disks after a user runs a statement (unit: MB).
≥ 0 MB
+≥ 0 MB
180s
+180s
Cluster Resource Metrics
- - - - - - - - - - +Cluster Resource Metrics
+ + + + + + + + + +CPU Usage
+CPU Usage
Average CPU usage and skew ratio of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average CPU usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Memory Usage
+Memory Usage
Average memory usage and skew ratio of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average memory usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Disk Usage
+Disk Usage
Average usage and skew ratio of all disks in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average usage of all disks in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Disk I/O Usage
+Disk I/O Usage
Average I/O usage and skew rate of all disks in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average I/O usage of all disks in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Network I/O Usage
+Network I/O Usage
Average I/O usage and skew rate of all NICs in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average I/O usage of all NICs in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Key Database Metrics
+Key Database Metrics
Cluster Status
+Cluster Status
Cluster running status.
+Cluster running status.
Normal/Degraded/Abnormal
+Normal/Degraded/Abnormal
30s
+30s
Cluster Abnormal CNs
+Cluster Abnormal CNs
Number of abnormal CNs in the cluster
+Number of abnormal CNs in the cluster
≥ 0
+≥ 0
60s
+60s
Cluster Read-only
+Cluster Read-only
Whether the cluster is in the read-only state
+Whether the cluster is in the read-only state
Yes/No
+Yes/No
30s
+30s
Concurrent Sessions
+Concurrent Sessions
Number of concurrent sessions in a cluster within a specified period.
+Number of concurrent sessions in a cluster within a specified period.
≥ 0
+≥ 0
30s
+30s
Concurrent Queries
+Concurrent Queries
Number of concurrent queries in a cluster within a specified period.
+Number of concurrent queries in a cluster within a specified period.
≥ 0
+≥ 0
30s
+30s
Node Monitoring-Overview
- - - - - - +Node Monitoring-Overview
+ + + + + +Node Name
+Node Name
Name of a node in a cluster.
+Name of a node in a cluster.
String
+String
30s
+30s
CPU Usage
+CPU Usage
CPU usage of a host.
+CPU usage of a host.
0% to 100%
+0% to 100%
30s
+30s
Memory Usage
+Memory Usage
Memory usage of a host.
+Memory usage of a host.
0% to 100%
+0% to 100%
30s
+30s
Average Disk Usage (%)
+Average Disk Usage (%)
Disk usage of a host.
+Disk usage of a host.
0% to 100%
+0% to 100%
30s
+30s
IP Address
+IP Address
Service IP address of a host.
+Service IP address of a host.
String
+String
30s
+30s
Disk I/O
+Disk I/O
Disk I/O of a host (unit: KB/s)
+Disk I/O of a host (unit: KB/s)
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
TCP Protocol Stack Retransmission Rate
+TCP Protocol Stack Retransmission Rate
Retransmission rate of TCP packets per unit time.
+Retransmission rate of TCP packets per unit time.
0% to 100%
+0% to 100%
30s
+30s
Status
+Status
Running status of a host
+Running status of a host
Online/Offline
+Online/Offline
30s
+30s
Node Monitoring-Disks
- - - - - - - - +Node Monitoring-Disks
+ + + + + + + +Node Name
+Node Name
Name of a node in a cluster.
+Name of a node in a cluster.
String
+String
30s
+30s
Disk Name
+Disk Name
Name of a disk on a host.
+Name of a disk on a host.
String
+String
30s
+30s
Disk Capacity
+Disk Capacity
Disk capacity of the host (unit: GB)
+Disk capacity of the host (unit: GB)
≥ 0 GB
+≥ 0 GB
30s
+30s
Disk Usage
+Disk Usage
Disk usage of a host.
+Disk usage of a host.
0% to 100%
+0% to 100%
30s
+30s
Disk Read Rate
+Disk Read Rate
Disk read rate of the host (unit: KB/s)
+Disk read rate of the host (unit: KB/s)
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
Disk Write Rate
+Disk Write Rate
Disk write rate of the host (unit: KB/s)
+Disk write rate of the host (unit: KB/s)
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
I/O Wait Time (await, ms)
+I/O Wait Time (await, ms)
Average waiting time for each I/O request (unit: ms)
+Average waiting time for each I/O request (unit: ms)
≥ 0 ms
+≥ 0 ms
30s
+30s
I/O Service Time (svctm, ms)
+I/O Service Time (svctm, ms)
Average processing time for each I/O request (unit: ms)
+Average processing time for each I/O request (unit: ms)
≥ 0 ms
+≥ 0 ms
30s
+30s
I/O Utility (util, %)
+I/O Utility (util, %)
Disk I/O usage of a host.
+Disk I/O usage of a host.
0% to 100%
+0% to 100%
30s
+30s
Node Monitoring-Network
- - - - - - - - +Node Monitoring-Network
+ + + + + + + +Node Name
+Node Name
Name of a node in a cluster.
+Name of a node in a cluster.
String
+String
30s
+30s
NIC Name
+NIC Name
Name of the NIC on a host.
+Name of the NIC on a host.
String
+String
30s
+30s
NIC Status
+NIC Status
NIC status.
+NIC status.
up/down
+up/down
30s
+30s
NIC Speed
+NIC Speed
Working rate of a NIC, in Mbit/s.
+Working rate of a NIC, in Mbit/s.
≥ 0
+≥ 0
30s
+30s
Received Packets
+Received Packets
Number of received packets of a NIC.
+Number of received packets of a NIC.
≥ 0
+≥ 0
30s
+30s
Sent Packets
+Sent Packets
Number of sent packets of a NIC.
+Number of sent packets of a NIC.
≥ 0
+≥ 0
30s
+30s
Lost Packets Received
+Lost Packets Received
Number of received lost packets of a NIC.
+Number of received lost packets of a NIC.
≥ 0
+≥ 0
30s
+30s
Receive Rate
+Receive Rate
Number of bytes received by a NIC per unit of time (KB/s).
+Number of bytes received by a NIC per unit of time (KB/s).
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
Transmit Rate
+Transmit Rate
Number of bytes sent by a NIC per unit of time (unit: KB/s)
+Number of bytes sent by a NIC per unit of time (unit: KB/s)
≥ 0 KB/s
+≥ 0 KB/s
30s
+30s
Database Monitoring
- - - - - - - - - - - - - - - - - +Database Monitoring
+ + + + + + + + + + + + + + + + +Database Name
+Database Name
Name of the database created by a user in a cluster.
+Name of the database created by a user in a cluster.
String
+String
60s
+60s
Usage
+Usage
Used capacity of the current database (unit: GB).
+Used capacity of the current database (unit: GB).
≥ 0 GB
+≥ 0 GB
86400s
+86400s
Users
+Users
Number of users in the current database.
+Number of users in the current database.
≥ 0
+≥ 0
30s
+30s
Sessions
+Sessions
Number of sessions in the current database.
+Number of sessions in the current database.
≥ 0
+≥ 0
30s
+30s
Applications
+Applications
Number of applications in the current database.
+Number of applications in the current database.
≥ 0
+≥ 0
30s
+30s
Queries
+Queries
Number of active queries in the current database.
+Number of active queries in the current database.
≥ 0
+≥ 0
30s
+30s
Scanning Rows
+Scanning Rows
Number of rows returned by the full table scan query in the current database.
+Number of rows returned by the full table scan query in the current database.
≥ 0
+≥ 0
60s
+60s
Index Query Rows
+Index Query Rows
Number of rows returned by the index query in the current database.
+Number of rows returned by the index query in the current database.
≥ 0
+≥ 0
60s
+60s
Inserted Rows
+Inserted Rows
Number of rows inserted in the current database.
+Number of rows inserted in the current database.
≥ 0
+≥ 0
60s
+60s
Updated Rows
+Updated Rows
Number of rows updated in the current database.
+Number of rows updated in the current database.
≥ 0
+≥ 0
60s
+60s
Deleted Rows
+Deleted Rows
Number of rows deleted from the current database.
+Number of rows deleted from the current database.
≥ 0
+≥ 0
60s
+60s
Executed Transactions
+Executed Transactions
Number of transaction executions on the current database.
+Number of transaction executions on the current database.
≥ 0
+≥ 0
60s
+60s
Transaction Rollbacks
+Transaction Rollbacks
Number of transactions in the current database that have been rolled back.
+Number of transactions in the current database that have been rolled back.
≥ 0
+≥ 0
60s
+60s
Deadlocks
+Deadlocks
Number of deadlocks detected in the current database.
+Number of deadlocks detected in the current database.
≥ 0
+≥ 0
60s
+60s
Temporary Files
+Temporary Files
Number of temporary files created in the current database.
+Number of temporary files created in the current database.
≥ 0
+≥ 0
60s
+60s
Temporary File Capacity
+Temporary File Capacity
Size of temporary files written by the current database, in GB.
+Size of temporary files written by the current database, in GB.
≥ 0
+≥ 0
60s
+60s
Performance Monitoring
- - - - - - - - - - - - - - - - - - - - - - - - +Performance Monitoring
+ + + + + + + + + + + + + + + + + + + + + + + +Cluster CPU Usage
+Cluster CPU Usage
Historical trend of the average CPU usage and skew of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average CPU usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Cluster Memory Usage
+Cluster Memory Usage
Historical trend of the average memory usage and skew of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average memory usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Cluster Disk Usage
+Cluster Disk Usage
Historical trend of the average disk usage and skew of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average disk usage of all nodes in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Cluster Disk I/O
+Cluster Disk I/O
Historical trend of the average disk I/O and skew of all disks in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average I/O of all disks in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Cluster Network I/O
+Cluster Network I/O
Historical trend of the average network I/O value and skew of all NICs in the cluster. The formula for calculating the skew is (max-avg)/max.
+Average I/O of all NICs in a cluster.
0% to 100%
+0% to 100%
30s
+30s
Cluster Status
+Cluster Status
Historical trend of the cluster status.
+Historical trend of the cluster status.
Normal/Abnormal/Degraded
+Normal/Abnormal/Degraded
30s
+30s
Cluster Read-only
+Cluster Read-only
Historical trend of the cluster read-only status change trend.
+Historical trend of the cluster read-only status change trend.
Yes/No
+Yes/No
30s
+30s
Cluster Abnormal CNs
+Cluster Abnormal CNs
Historical trend of the number of abnormal CNs in the cluster.
+Historical trend of the number of abnormal CNs in the cluster.
≥ 0
+≥ 0
60s
+60s
Cluster Abnormal DNs
+Cluster Abnormal DNs
Historical trend of the number of abnormal DNs in the cluster.
+Historical trend of the number of abnormal DNs in the cluster.
≥ 0
+≥ 0
60s
+60s
Cluster CPU Usage of DNs
+Cluster CPU Usage of DNs
Historical trends of the average CPU usage and skew ratio changes of all DNs in the cluster. The formula for calculating the skew ratio is (max – avg)/max.
+Average CPU usage of all DNs in a cluster.
0% to 100%
+0% to 100%
60s
+60s
Cluster Sessions
+Cluster Sessions
Historical trend of the number of sessions in a cluster.
+Historical trend of the number of sessions in a cluster.
≥ 0
+≥ 0
30s
+30s
Cluster Queries
+Cluster Queries
Historical change trend of the number of queries in the cluster.
+Historical change trend of the number of queries in the cluster.
≥ 0
+≥ 0
30s
+30s
Cluster Deadlocks
+Cluster Deadlocks
Historical trend of the number of deadlocks in a cluster.
+Historical trend of the number of deadlocks in a cluster.
≥ 0
+≥ 0
60s
+60s
Cluster TPS
+Cluster TPS
Average number of transactions per second of all databases in a cluster. Formula: (delta_xact_commit + delta_xact_rollback)/current_collect_rate
+Average number of transactions per second of all databases in a cluster. Formula: (delta_xact_commit + delta_xact_rollback)/current_collect_rate
≥0
+≥0
60s
+60s
Cluster QPS
+Cluster QPS
Average number of concurrent requests per second of all databases in a cluster. Formula: delta_query_count/current_collect_rate
+Average number of concurrent requests per second of all databases in a cluster. Formula: delta_query_count/current_collect_rate
≥ 0
+≥ 0
60s
+60s
Database Sessions
+Database Sessions
Historical trend of the number of sessions on a single database in a cluster.
+Historical trend of the number of sessions on a single database in a cluster.
≥ 0
+≥ 0
30s
+30s
Database Queries
+Database Queries
Historical trend of the number of queries on a single database in a cluster.
+Historical trend of the number of queries on a single database in a cluster.
≥ 0
+≥ 0
30s
+30s
Database Inserted Rows
+Database Inserted Rows
Historical trend of the number of rows inserted into a single database in a cluster.
+Historical trend of the number of rows inserted into a single database in a cluster.
≥ 0
+≥ 0
60s
+60s
Database Updated Rows
+Database Updated Rows
Historical trend of the number of updated rows in a single database in a cluster.
+Historical trend of the number of updated rows in a single database in a cluster.
≥ 0
+≥ 0
60s
+60s
Database Deleted Rows
+Database Deleted Rows
Historical trend of the number of deleted rows in a single database in a cluster.
+Historical trend of the number of deleted rows in a single database in a cluster.
≥ 0
+≥ 0
60s
+60s
Database Capacity
+Database Capacity
Historical trend of the capacity in a single database in a cluster.
+Historical trend of the capacity in a single database in a cluster.
≥ 0
+≥ 0
86400s
+86400s
Live Session
- - - - - - - - +Live Session
+ + + + + + + +Session ID
+Session ID
ID of the current session (query thread ID).
+ID of the current session (query thread ID).
String
+String
30s
+30s
User Name
+User Name
Name of the user who executes the current session.
+Name of the user who executes the current session.
String
+String
30s
+30s
Database Name
+Database Name
Name of the database connected to the current session.
+Name of the database connected to the current session.
String
+String
30s
+30s
Session Duration
+Session Duration
Duration of the current session (unit: ms).
+Duration of the current session (unit: ms).
≥ 0 ms
+≥ 0 ms
30s
+30s
Application Name
+Application Name
Name of the application that creates the current session.
+Name of the application that creates the current session.
String
+String
30s
+30s
Queries
+Queries
Number of SQL statements executed in the current session.
+Number of SQL statements executed in the current session.
≥ 0
+≥ 0
30s
+30s
Latest Query Duration
+Latest Query Duration
Duration for executing the previous SQL statement in the current session.
+Duration for executing the previous SQL statement in the current session.
≥ 0 ms
+≥ 0 ms
30s
+30s
Client IP Address
+Client IP Address
IP address of the client that initiates the current session.
+IP address of the client that initiates the current session.
String
+String
30s
+30s
Connected CN
+Connected CN
Connected CN of the current session.
+Connected CN of the current session.
String
+String
30s
+30s
Session Status
+Session Status
Execution status of the current session.
+Execution status of the current session.
Running/Idle/Retry
+Running/Idle/Retry
30s
+30s
Real-Time Query
- - - - - - - - - - - - - - - - - +Real-Time Query
+ + + + + + + + + + + + + + + + +Query ID
+Query ID
Query ID of a current query statement, which is a unique identifier allocated by the kernel to each query statement.
+Query ID of a current query statement, which is a unique identifier allocated by the kernel to each query statement.
String
+String
30s
+30s
User Name
+User Name
Name of the user who submits the current query statement.
+Name of the user who submits the current query statement.
String
+String
30s
+30s
Database Name
+Database Name
Name of the database corresponding to the current query statement.
+Name of the database corresponding to the current query statement.
String
+String
30s
+30s
Application Name
+Application Name
Name of the application corresponding to the current query statement.
+Name of the application corresponding to the current query statement.
String
+String
30s
+30s
Resource Pool
+Resource Pool
Name of the resource pool for the current query statement.
+Name of the resource pool for the current query statement.
String
+String
30s
+30s
Submitted
+Submitted
Timestamp when the current query statement is submitted.
+Timestamp when the current query statement is submitted.
String
+String
30s
+30s
Blocking Time
+Blocking Time
Waiting time before the current query statement is executed, in ms.
+Waiting time before the current query statement is executed, in ms.
≥ 0
+≥ 0
30s
+30s
Execution Time
+Execution Time
Execution time of the current query statement, in ms.
+Execution time of the current query statement, in ms.
≥ 0
+≥ 0
30s
+30s
CPU Time
+CPU Time
Total CPU time spent by the current query statement on all DNs, in ms.
+Total CPU time spent by the current query statement on all DNs, in ms.
≥ 0
+≥ 0
30s
+30s
CPU Time Skew
+CPU Time Skew
CPU time skew of the current query statement among all DNs.
+CPU time skew of the current query statement among all DNs.
0% to 100%
+0% to 100%
30s
+30s
Statement
+Statement
Query statement that is being executed.
+Query statement that is being executed.
String
+String
30s
+30s
Connected CN
+Connected CN
Name of the CN that submits the current query statement.
+Name of the CN that submits the current query statement.
String
+String
30s
+30s
Client IP Address
+Client IP Address
IP address of the client that submits the current query statement.
+IP address of the client that submits the current query statement.
String
+String
30s
+30s
Lane
+Lane
Lane where the current query statement is located.
+Lane where the current query statement is located.
Fast Lane/Slow Lane
+Fast Lane/Slow Lane
30s
+30s
Query Status
+Query Status
Query status of the statement that is being executed.
+Query status of the statement that is being executed.
String
+String
30s
+30s
Session ID
+Session ID
Session ID of the current query statement, which is a unique identifier allocated by the kernel to each client connection.
+Session ID of the current query statement, which is a unique identifier allocated by the kernel to each client connection.
String
+String
30s
+30s
Queuing Status
+Queuing Status
Status of the current query execution in the database, indicating whether the query is queued in the resource pool.
+Status of the current query execution in the database, indicating whether the query is queued in the resource pool.
Yes/No
+Yes/No
30s
+30s
Historical Query
- - - - - - - - - - - +Historical Query
+ + + + + + + + + + +Query ID
+Query ID
Query ID of a query statement, which is a unique identifier allocated by the kernel to each query statement.
+Query ID of a query statement, which is a unique identifier allocated by the kernel to each query statement.
String
+String
180s
+180s
User Name
+User Name
Name of the user who submits a query statement.
+Name of the user who submits a query statement.
String
+String
180s
+180s
Application Name
+Application Name
Application name corresponding to a query statement.
+Application name corresponding to a query statement.
String
+String
180s
+180s
Database Name
+Database Name
Name of the database corresponding to a query statement.
+Name of the database corresponding to a query statement.
String
+String
180s
+180s
Resource Pool
+Resource Pool
Name of the resource pool for the current query statement.
+Name of the resource pool for the current query statement.
String
+String
180s
+180s
Submitted
+Submitted
Timestamp when a query statement is submitted.
+Timestamp when a query statement is submitted.
String
+String
180s
+180s
Blocking Time
+Blocking Time
Waiting time before the query statement is executed, in ms.
+Waiting time before the query statement is executed, in ms.
≥ 0
+≥ 0
180s
+180s
Execution Time
+Execution Time
Execution time of the query statement, in ms.
+Execution time of the query statement, in ms.
≥ 0
+≥ 0
180s
+180s
CPU Time
+CPU Time
Total CPU time spent by the query statement on all DNs, in ms.
+Total CPU time spent by the query statement on all DNs, in ms.
≥ 0
+≥ 0
180s
+180s
CPU Time Skew
+CPU Time Skew
CPU time skew of a query statement executed on all DNs.
+CPU time skew of a query statement executed on all DNs.
0% to 100%
+0% to 100%
180s
+180s
Statement
+Statement
Query statements to be parsed
+Query statements to be parsed
String
+String
180s
+180s
Slow Instance Monitoring
- - - - +Slow Instance Monitoring
+ + + +Slow Instance
+Slow Instance
Number of slow instances detected at the current time point.
+Number of slow instances detected at the current time point.
≥ 0
+≥ 0
240s
+240s
Detected
+Detected
Time when a slow instance is detected for the first time.
+Time when a slow instance is detected for the first time.
String
+String
240s
+240s
Node Name
+Node Name
Name of the node where the slow instance is deployed.
+Name of the node where the slow instance is deployed.
String
+String
240s
+240s
Instance
+Instance
Name of an instance.
+Name of an instance.
String
+String
240s
+240s
Slow Node Detections (within 24 hours)
+Slow Node Detections (within 24 hours)
Number of times that a slow instance is detected within 24 hours.
+Number of times that a slow instance is detected within 24 hours.
≥ 0
+≥ 0
240s
+240s
Resource Pool Monitoring
- - - - - - - - - - - - - - - - +Resource Pool Monitoring
+ + + + + + + + + + + + + + + +Resource Pool
+Resource Pool
Name of a resource pool in a cluster.
+Name of a resource pool in a cluster.
String
+String
120s
+120s
CPU Usage
+CPU Usage
Real-time CPU usage of a resource pool.
+Real-time CPU usage of a resource pool.
0% to 100%
+0% to 100%
120s
+120s
CPU Resource
+CPU Resource
CPU usage quota of a resource pool.
+CPU usage quota of a resource pool.
0% to 100%
+0% to 100%
120s
+120s
Real-Time Concurrent Short Queries
+Real-Time Concurrent Short Queries
Simple concurrency in a resource pool.
+Simple concurrency in a resource pool.
≥ 0
+≥ 0
120s
+120s
Concurrent Short Queries
+Concurrent Short Queries
Quota for simple concurrency in a resource pool.
+Quota for simple concurrency in a resource pool.
≥ 0
+≥ 0
120s
+120s
Real-Time Concurrent Queries
+Real-Time Concurrent Queries
Real-time complex concurrency in a resource pool.
+Real-time complex concurrency in a resource pool.
≥ 0
+≥ 0
120s
+120s
Query Concurrency
+Query Concurrency
Quota for complex concurrency in a resource pool.
+Quota for complex concurrency in a resource pool.
≥ 0
+≥ 0
120s
+120s
Storage
+Storage
Storage resource quota of a resource pool.
+Storage resource quota of a resource pool.
≥ 0
+≥ 0
120s
+120s
Disk Usage
+Disk Usage
Disk usage of a resource pool.
+Disk usage of a resource pool.
0% to 100%
+0% to 100%
120s
+120s
Memory
+Memory
Memory quota of a resource pool.
+Memory quota of a resource pool.
≥ 0
+≥ 0
120s
+120s
Memory Usage
+Memory Usage
Memory usage of a resource pool.
+Memory usage of a resource pool.
0% to 100%
+0% to 100%
120s
+120s
Queries Waiting in a Resource Pool
- - - - - - +Queries Waiting in a Resource Pool
+ + + + + +User
+User
Name of the user of waiting queries
+Name of the user of waiting queries
String
+String
120s
+120s
Application
+Application
Name of the application to be queried.
+Name of the application to be queried.
String
+String
120s
+120s
Database
+Database
Name of the database to be queried.
+Name of the database to be queried.
String
+String
120s
+120s
Queuing Status
+Queuing Status
Execution status of a query in the database (CCN/CN/DN).
+Execution status of a query in the database (CCN/CN/DN).
String
+String
120s
+120s
Wait Time
+Wait Time
Waiting time for a waiting query (unit: ms).
+Waiting time for a waiting query (unit: ms).
≥ 0 ms
+≥ 0 ms
120s
+120s
Resource Pool
+Resource Pool
Resource pool of the waiting query.
+Resource pool of the waiting query.
String
+String
120s
+120s
Statement
+Statement
Query statement for the waiting status.
+Query statement for the waiting status.
String
+String
120s
+120s
Circuit Breaking Queries
- - - - - - - +Circuit Breaking Queries
+ + + + + + +Query ID
+Query ID
Query ID of the circuit breaking query statement.
+Query ID of the circuit breaking query statement.
String
+String
120s
+120s
Query Statement
+Query Statement
Query statement for the circuit breaking status.
+Query statement for the circuit breaking status.
String
+String
120s
+120s
Blocking Time
+Blocking Time
Blocking time before the query statement triggers circuit breaking, in ms.
+Blocking time before the query statement triggers circuit breaking, in ms.
≥ 0
+≥ 0
120s
+120s
Execution Time
+Execution Time
Execution time before the query statement triggers circuit breaking, in ms.
+Execution time before the query statement triggers circuit breaking, in ms.
≥ 0
+≥ 0
120s
+120s
CPU Time
+CPU Time
Average CPU time consumed by each DN before the query statement triggers circuit breaking, in ms.
+Average CPU time consumed by each DN before the query statement triggers circuit breaking, in ms.
≥ 0
+≥ 0
120s
+120s
CPU Skew
+CPU Skew
Skew rate of CPU time consumed by each DN before the query statement triggers circuit breaking.
+Skew rate of CPU time consumed by each DN before the query statement triggers circuit breaking.
0% to 100%
+0% to 100%
120s
+120s
Exception Handling
+Exception Handling
Handling method after the query statement triggers circuit breaking.
+Handling method after the query statement triggers circuit breaking.
Abort/Degrade
+Abort/Degrade
120s
+120s
Status
+Status
Circuit breaking handling status of a query statement.
+Circuit breaking handling status of a query statement.
Executing/Completed
+Executing/Completed
120s
+120s
SQL Tuning
- - - - - - - - - - - +SQL Tuning
+ + + + + + + + + + +Query ID
+Query ID
IP address of the current query (query logic ID).
+IP address of the current query (query logic ID).
String
+String
180s
+180s
Database
+Database
Name of the database where the current query is executed.
+Name of the database where the current query is executed.
String
+String
180s
+180s
Schema Name
+Schema Name
Name of the current query schema.
+Name of the current query schema.
String
+String
180s
+180s
User Name
+User Name
Name of the user who performs the query.
+Name of the user who performs the query.
String
+String
180s
+180s
Client
+Client
Name of the client that initiates the current query.
+Name of the client that initiates the current query.
String
+String
180s
+180s
Client IP Address
+Client IP Address
IP address of the client that initiates the current query.
+IP address of the client that initiates the current query.
String
+String
180s
+180s
Running Time
+Running Time
Execution time of the current query, in ms.
+Execution time of the current query, in ms.
≥ 0
+≥ 0
180s
+180s
CPU Time
+CPU Time
CPU time of the current query, in ms.
+CPU time of the current query, in ms.
≥ 0
+≥ 0
180s
+180s
Scale-Out Started
+Scale-Out Started
Start time of the current query.
+Start time of the current query.
Timestamp
+Timestamp
180s
+180s
Completed
+Completed
End time of the current query.
+End time of the current query.
Timestamp
+Timestamp
180s
+180s
Details
+Details
Details about the current query.
+Details about the current query.
String
+String
180s
+180s
INODE
+INODE
Inode Usage
+Inode Usage
Disk inode usage.
+Disk inode usage.
0% to 100%
+0% to 100%
30s
+30s
SCHEMA
+SCHEMA
Schema Usage
+Schema Usage
Database schema usage.
+Database schema usage.
0% to 100%
+0% to 100%
3600s
+3600s
diff --git a/docs/dws/umn/dws_01_00137.html b/docs/dws/umn/dws_01_00137.html index ff0e219b..0d6ab9b8 100644 --- a/docs/dws/umn/dws_01_00137.html +++ b/docs/dws/umn/dws_01_00137.html @@ -1,4 +1,4 @@ - +-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Typical Scenarios
@@ -11,7 +11,7 @@-diff --git a/docs/dws/umn/dws_01_00139.html b/docs/dws/umn/dws_01_00139.html index cbe390fe..71c94e9a 100644 --- a/docs/dws/umn/dws_01_00139.html +++ b/docs/dws/umn/dws_01_00139.html @@ -1,7 +1,7 @@ - +Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Workload Analysis
- +diff --git a/docs/dws/umn/dws_01_00140.html b/docs/dws/umn/dws_01_00140.html index f2f5740f..65c19b54 100644 --- a/docs/dws/umn/dws_01_00140.html +++ b/docs/dws/umn/dws_01_00140.html @@ -1,95 +1,95 @@ - +- Workload Analysis Overview
@@ -13,7 +13,7 @@
-Parent topic: Databases Monitoring+Parent topic: Database Monitoring (DMS)Workload Snapshots
-You can check the basic information about the cluster workload snapshots, manually create a snapshot, and configure snapshot parameters.
-Checking Workload Snapshots
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
+
You can check the basic information about the cluster workload snapshots, manually create a snapshot, and configure snapshot parameters.
+-Checking Workload Snapshots
- Log in to the GaussDB(DWS) management console.
- On the Cluster > Dedicated Cluster page, locate the cluster for which you want to perform workload analysis.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
Creating a Workload Snapshot
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
- Click Create Snapshot. Enter a snapshot name and click OK.
-
-
Before creating a workload snapshot, ensure that the performance view snapshot parameter is enabled. For details, see Configuring Workload Snapshot Parameters.
+-Creating a Workload Snapshot
- Log in to the GaussDB(DWS) management console.
- On the Cluster > Dedicated Cluster page, locate the cluster for which you want to perform workload analysis.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
- Click Create Snapshot. Enter a snapshot name and click OK.
+
+
Before creating a workload snapshot, ensure that the performance view snapshot parameter is enabled. For details, see Configuring Workload Snapshot Parameters.
Configuring Workload Snapshot Parameters
- Log in to the GaussDB(DWS) management console.
- On the Clusters page, locate the target cluster.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
- Click Configure Snapshot in the upper right corner. In the dialog box that is displayed, check or modify GUC parameters. For details, see Table 1.
+
Configuring Workload Snapshot Parameters
- Log in to the GaussDB(DWS) management console.
- On the Cluster > Dedicated Cluster page, locate the cluster for which you want to perform workload analysis.
- In the Operation column of the cluster, choose Monitoring Panel. The database monitoring page is displayed.
- In the navigation pane, choose Workload Analysis > Workload Snapshot. Workload snapshots will be displayed.
- Click Configure Snapshot in the upper right corner. In the dialog box that is displayed, check or modify GUC parameters. For details, see Table 1.
- enable_resource_record
- topsql_retention_time
- resource_track_duration
- resource_track_level
- resource_track_cost
- enable_resource_track
- VPC: You can select an existing VPC from the drop-down list. If no VPC has been configured, click View VPC to enter the VPC management console to create one, for example, vpc-dws. Then, go back to the page for creating a cluster on the GaussDB(DWS) management console, click
- VPC: You can select an existing VPC from the drop-down list. If no VPC has been configured, click View VPC to enter the VPC management console to create one, for example, vpc-dws. Then, go back to the page for creating a cluster on the GaussDB(DWS) management console, click
- GaussDB(DWS) clients
- Configure advanced parameters. Select Default to keep the default values of the advanced parameters. You can also select Custom to modify the values.
- The DR synchronization period indicates the interval for synchronizing incremental data from the production cluster to the DR cluster. Set this parameter based on the actual service data volume.
-
- Select the AZ associated with the region where the DR cluster resides.
- Visualized one-stop management
- Cluster
What Is GaussDB(DWS)?
-GaussDB(DWS) is an online data processing database that runs on the cloud infrastructure to provide scalable, fully-managed, and out-of-the-box analytic database service, freeing you from complex database management and monitoring. It is a native cloud service based on the converged data warehouse GaussDB, and is fully compatible with the standard ANSI SQL 99 and SQL 2003, as well as the PostgreSQL and Oracle ecosystems. GaussDB(DWS) provides competitive solutions for PB-level big data analysis in various industries.
-Architecture
GaussDB(DWS) employs the shared-nothing architecture and the massively parallel processing (MPP) engine, and consists of numerous independent logical nodes that do not share the system resources such as CPUs, memory, and storage. In such a system architecture, service data is separately stored on numerous nodes. Data analysis tasks are executed in parallel on the nodes where data is stored. The massively parallel data processing significantly improves response speed.
-Figure 1 Architecture-
