- 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 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:
-- Real-time archiving of stream data: importing stream data from IoT devices and the gateway to GaussDB(DWS) using DIS
- 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, 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
diff --git a/docs/dws/umn/dws_01_00014.html b/docs/dws/umn/dws_01_00014.html index 01a6cdeb3..7e3ef7ef2 100644 --- a/docs/dws/umn/dws_01_00014.html +++ b/docs/dws/umn/dws_01_00014.html @@ -1,4 +1,4 @@ - +Databases Monitoring
@@ -14,8 +14,12 @@- Utilities
+
- Workload Analysis
+- Settings
+
- Checking Task Details
+- Typical Scenarios
diff --git a/docs/dws/umn/dws_01_0002.html b/docs/dws/umn/dws_01_0002.html index 42348252c..f0339865f 100644 --- a/docs/dws/umn/dws_01_0002.html +++ b/docs/dws/umn/dws_01_0002.html @@ -1,73 +1,85 @@ - +
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-
- 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 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+
- 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.
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
Description
+Function
Remarks
+Description
Global Transaction Manager (GTM)
+Cluster Manager (CM)
Generates and maintains the globally unique information, such as the transaction ID, transaction snapshot, and timestamp.
+Cluster Manager. It manages and monitors the running status of functional units and physical resources in the distributed system, ensuring system stability.
The cluster includes only one pair of GTMs: one primary GTM and one standby GTM.
+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).
Workload Manager (WLM)
+Global Transaction Manager (GTM)
WLM controls allocation of system resources to prevent service congestion and system crash resulting from excessive workload.
+Generates and maintains the globally unique information, such as the transaction ID, transaction snapshot, and timestamp.
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.
+The cluster includes only one pair of GTMs: one primary GTM and one standby GTM.
Coordinator (CN)
+Workload Manager (WLM)
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.
+Workload Manager. It controls allocation of system resources to prevent service congestion and system crash resulting from excessive workload.
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.
+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.
Datanode (DN)
+Coordinator (CN)
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 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 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.
- +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.
Storage
+Datanode (DN)
Functions as the server's local storage resources to store data permanently.
+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.
+ +Storage
+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 9cb338375..619c124a3 100644 --- a/docs/dws/umn/dws_01_0003.html +++ b/docs/dws/umn/dws_01_0003.html @@ -1,25 +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.
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.
+-DRS
You can use Data Replication Service (DRS) to synchronize stream data to GaussDB(DWS) in real time.
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.
+-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.
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.
+-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.
DNS
GaussDB(DWS) uses Domain Name Service (DNS) to provide the cluster IP addresses mapped from domain names.
++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.
++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.
diff --git a/docs/dws/umn/dws_01_0004.html b/docs/dws/umn/dws_01_0004.html index da4018c7f..0292b9cf4 100644 --- a/docs/dws/umn/dws_01_0004.html +++ b/docs/dws/umn/dws_01_0004.html @@ -1,15 +1,24 @@ - +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) 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.
+
- -diff --git a/docs/dws/umn/dws_01_0005.html b/docs/dws/umn/dws_01_0005.html deleted file mode 100644 index b12af0469..000000000 --- a/docs/dws/umn/dws_01_0005.html +++ /dev/null @@ -1,20 +0,0 @@ - - -Parent topic: Service OverviewGaussDB(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
Each data warehouse cluster contains at least three nodes, all of which support data storage and analysis.
--Flavor
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_0006.html b/docs/dws/umn/dws_01_0006.html deleted file mode 100644 index c94224b1d..000000000 --- a/docs/dws/umn/dws_01_0006.html +++ /dev/null @@ -1,24 +0,0 @@ - - ---Parent topic: Concepts-GaussDB(DWS) Database Concepts
---Database
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 Connection
You can use a client to connect to a GaussDB(DWS) cluster in the cloud 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.
--- diff --git a/docs/dws/umn/dws_01_0007.html b/docs/dws/umn/dws_01_0007.html index 23b81a210..c5f4fd5b7 100644 --- a/docs/dws/umn/dws_01_0007.html +++ b/docs/dws/umn/dws_01_0007.html @@ -1,26 +1,26 @@ - +--Parent topic: Concepts-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 Oracleand 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 31563c7ea..376f83a5e 100644 --- a/docs/dws/umn/dws_01_0008.html +++ b/docs/dws/umn/dws_01_0008.html @@ -1,27 +1,26 @@ - +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. Since the cluster is charged by hour, 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 the cluster is upgraded, it cannot be rolled back.
+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.
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.
- 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.
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.
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 Basic Information page is displayed.
In the Cluster 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.Figure 2 Configuring the maintenance window-
- Click OK.
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.
diff --git a/docs/dws/umn/dws_01_00081.html b/docs/dws/umn/dws_01_00081.html index 7444c39e8..6f6b4c600 100644 --- a/docs/dws/umn/dws_01_00081.html +++ b/docs/dws/umn/dws_01_00081.html @@ -1,17 +1,15 @@ - +-Parent topic: Clusters+Parent topic: Cluster ManagementDR Overview
-Overview
A homogeneous GaussDB(DWS) 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.
-
-
+ - This feature is supported only in cluster version 8.1.1 or later.
-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.
+
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
- 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
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.
- DR refers to dual-cluster DR of the same tenant.
- The production cluster and DR cluster are in the same VPC and of the same version.
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.
diff --git a/docs/dws/umn/dws_01_00082.html b/docs/dws/umn/dws_01_00082.html index e73e9a9d7..4df097e10 100644 --- a/docs/dws/umn/dws_01_00082.html +++ b/docs/dws/umn/dws_01_00082.html @@ -1,21 +1,23 @@ - +Creating a 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, click 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.
+
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.
- Select a created production cluster from the drop-down list.
- After a production cluster is selected, the system automatically displays its AZ. +
- 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.
+- 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.
- 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.
- 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 2ee4f1689..d3641f600 100644 --- a/docs/dws/umn/dws_01_00083.html +++ b/docs/dws/umn/dws_01_00083.html @@ -1,9 +1,13 @@ - +
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. -
- 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, click 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.
+
- 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.
+
diff --git a/docs/dws/umn/dws_01_00084.html b/docs/dws/umn/dws_01_00084.html index 2e2f6833f..dd5d5d742 100644 --- a/docs/dws/umn/dws_01_00084.html +++ b/docs/dws/umn/dws_01_00084.html @@ -1,29 +1,53 @@ - +DR Management
-Starting a DR Task
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click 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, and node replacement, on the production cluster and 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) 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.
Stopping the DR Task
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click 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) 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.
Switching to the DR Cluster
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click 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.
- - You can perform a DR switchover when the DR task is in the Running or Abnormal state.
- During a switchover, the original production cluster is not available.
- RPO of DR switchover:
- 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) 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.
Updating DR Configurations
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click 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.
-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.
Deleting DR Tasks
- Log in to the GaussDB(DWS) management console.
- In the navigation pane on the left, click 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.
+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.
+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.
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.
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. - - \ No newline at end of file diff --git a/docs/dws/umn/dws_01_0009.html b/docs/dws/umn/dws_01_0009.html index 7523caa35..3d4edd8c0 100644 --- a/docs/dws/umn/dws_01_0009.html +++ b/docs/dws/umn/dws_01_0009.html @@ -1,24 +1,19 @@ - +
After scale-out, the number of DNs in the production cluster must be the same as that in the DR cluster.
+@@ -14,10 +13,3 @@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.
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.
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 Clusters" in the Data Warehouse Service (DWS) User Guide.
- - Open-source PostgreSQL clientsThe following lists compatible open-source clients:-
- PostgreSQL psql 9.2.4 or later
For more information, see https://www.postgresql.org/.
- - pgadmin
For more information, see https://www.pgadmin.org/.
- - dbeaver
For more information, see https://dbeaver.jkiss.org/download/.
-
- PostgreSQL psql 9.2.4 or later
- 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 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
diff --git a/docs/dws/umn/dws_01_00097.html b/docs/dws/umn/dws_01_00097.html new file mode 100644 index 000000000..88eb30767 --- /dev/null +++ b/docs/dws/umn/dws_01_00097.html @@ -0,0 +1,27 @@ + + +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.
+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.
+
++ + + \ No newline at end of file diff --git a/docs/dws/umn/dws_01_0010.html b/docs/dws/umn/dws_01_0010.html index 7f751b5cc..7fd2a9a58 100644 --- a/docs/dws/umn/dws_01_0010.html +++ b/docs/dws/umn/dws_01_0010.html @@ -1,7 +1,7 @@ - +++Parent topic: Restoration Using a Snapshot+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.
- You cannot change the flavor of an existing cluster. If you need nodes with a higher flavor, create a new one.
- 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.
- 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.
diff --git a/docs/dws/umn/dws_01_00113.html b/docs/dws/umn/dws_01_00113.html deleted file mode 100644 index 19a54de13..000000000 --- a/docs/dws/umn/dws_01_00113.html +++ /dev/null @@ -1,151 +0,0 @@ - - -Technical Specifications
--This section describes the technical specifications of GaussDB(DWS) in different versions.
- --
-Table 1 GaussDB(DWS) technical specifications Technical Specifications
-Maximum Value of 8.0.x
-Maximum Value of 8.1.0
-Maximum Value of 8.1.1
-Data capacity
-10 PB
-10 PB
-20 PB
-Number of cluster nodes
-256
-256
-2048
-Size of a single table
-1 PB
-1 PB
-1 PB
-Size of data in each row
-1 GB
-1 GB
-1 GB
-Size of a single column in each record
-1 GB
-1 GB
-1 GB
-Number of records in each table
-255
-255
-255
-Number of columns in each table
-1600
-1600
-1600
-Number of indexes in each table
-Unlimited
-Unlimited
-Unlimited
-Number of columns in the index of each table
-32
-32
-32
-Number of constraints in each table
-Unlimited
-Unlimited
-Unlimited
-Number of concurrent connections
-60 for analytical long transactions; 5,000 for short transactions
-60 for analytical long transactions; 5,000 for short transactions
-80 for analytical long transactions; 5,000 for short transactions
-Number of partitions in a partitioned table
-32,768
-32768
-32768
-Size of each partition in a partitioned table
-1 PB
-1 PB
-1 PB
-Number of records in each partition in a partitioned table
-255
-255
-255
--- diff --git a/docs/dws/umn/dws_01_0013.html b/docs/dws/umn/dws_01_0013.html index daf79483e..d74ef5d81 100644 --- a/docs/dws/umn/dws_01_0013.html +++ b/docs/dws/umn/dws_01_0013.html @@ -1,48 +1,47 @@ - +--Parent topic: Service Overview-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 together 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.
- Cluster Type: 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.
Figure 1 Configuring node parameters-
- 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 account and its password to connect to the cluster's database.
- Administrator Password: Enter the password.
- Confirm Password: Enter the 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 2 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.
- 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.
-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 3 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.
- Parameter Template: The default database parameter template is associated with the cluster.
- Tag: By default, no tag is added to the cluster.
- Custom: Select this option to configure the following advanced parameters: Automated Snapshot, CNs, Parameter Template, 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 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.
diff --git a/docs/dws/umn/dws_01_00131.html b/docs/dws/umn/dws_01_00131.html index a6e82d7ea..9870ed91c 100644 --- a/docs/dws/umn/dws_01_00131.html +++ b/docs/dws/umn/dws_01_00131.html @@ -1,11 +1,9 @@ - +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.
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.
-+ The database monitoring function is supported by 8.1.1.200 and later versions.
--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 page, locate the target cluster.
- 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 36986d30b..2077203c3 100644 --- a/docs/dws/umn/dws_01_00132.html +++ b/docs/dws/umn/dws_01_00132.html @@ -1,24 +1,33 @@ - +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 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 Status
In the Cluster Status area, you can view the status of the current cluster and the number of available resources, including the numbers of nodes, CNs, and databases.
-
-
-Resource Consumption
In Resource Consumption, you can view the real-time resource consumption of the current cluster, including Memory Usage, Disk Usage, CPU Usage, Disk I/O (KB/s), and Network I/O (KB/s).
-
-
TOP SQL
In the TOP SQL area, you can query the SQL statements that take the longest time and have the largest amount of data flushed to disks in the current cluster.
- -
-
-Top 5 SQL statement query is implemented by max_ctime, which displays the query duration and the amount of data written to disks in the current collection period. If no query is executed during off-peak hours, the top 5 time-consuming query page will not be refreshed.
+ 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.
Key Metrics
On the Cluster Overview page, you can also view the database metrics of the current cluster, including Sessions and Queries.
-
-
+
+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.
+
+
+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.
+ +
+
+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.
+ +
+
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 c4f33155a..6932ff702 100644 --- a/docs/dws/umn/dws_01_00133.html +++ b/docs/dws/umn/dws_01_00133.html @@ -1,4 +1,4 @@ - +Monitoring
@@ -10,13 +10,13 @@- Database Monitoring
-
- Session Monitoring
+- Real-Time Queries
-
- Query Monitoring
+- Historical Queries
- Instance Monitoring
-
- Load Monitoring
+- Resource Pool Monitoring
diff --git a/docs/dws/umn/dws_01_00134.html b/docs/dws/umn/dws_01_00134.html index ce4f1d534..7c35a0d23 100644 --- a/docs/dws/umn/dws_01_00134.html +++ b/docs/dws/umn/dws_01_00134.html @@ -1,30 +1,30 @@ - +
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 .
+-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 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
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: 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.
- 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 58d15894b..2caf78055 100644 --- a/docs/dws/umn/dws_01_00135.html +++ b/docs/dws/umn/dws_01_00135.html @@ -1,16 +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 management IP address of the cluster is unavailable, the cluster monitoring switch and the button for configuring cluster indicator 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.
-
+
-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.
+
diff --git a/docs/dws/umn/dws_01_00136.html b/docs/dws/umn/dws_01_00136.html index f590578b1..b9d05d297 100644 --- a/docs/dws/umn/dws_01_00136.html +++ b/docs/dws/umn/dws_01_00136.html @@ -1,1836 +1,1755 @@ - +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.
+
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 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.
Character 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.
Character 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 and skew ratio of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
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 and skew ratio of all nodes in the cluster. The formula for calculating the skew is (max-avg)/max.
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 and skew ratio of all disks in the cluster. The formula for calculating the skew is (max-avg)/max.
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 and skew rate of all disks in the cluster. The formula for calculating the skew is (max-avg)/max.
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 and skew rate of all NICs in the cluster. The formula for calculating the skew is (max-avg)/max.
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.
Character 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.
Character 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.
Character string
+String
30s
+30s
Disk Name
+Disk Name
Name of a disk on a host.
+Name of a disk on a host.
Character 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.
Character string
+String
30s
+30s
NIC Name
+NIC Name
Name of the NIC on a host.
+Name of the NIC on a host.
Character 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.
Character 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
Physical Read Times
+Temporary Files
Number of disk blocks read in the current database.
+Number of temporary files created in the current database.
≥ 0
+≥ 0
60s
+60s
Logical Read Times
+Temporary File Capacity
Number of times that disk blocks are found in the cache.
+Size of temporary files written by the current database, in GB.
≥ 0
+≥ 0
60s
+60s
Temporary Files
+Performance Monitoring
+ + + + + + + + + + + + + + + + + + + + + + + +Number of temporary files created in the current database.
+Cluster CPU Usage
≥ 0
+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.
60s
+0% to 100%
Temporary File Capacity
-Size of temporary files written by the current database, in GB.
-≥ 0
-60s
+30s
Performance Monitoring
- - - - - - - - - - - - - - - - - - - - - - - - -Cluster CPU Usage
+Cluster Memory 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.
+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.
0% to 100%
+0% to 100%
30s
+30s
Cluster Memory Usage
+Cluster Disk 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.
+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.
0% to 100%
+0% to 100%
30s
+30s
Cluster Disk Usage
+Cluster Disk I/O
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.
+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.
0% to 100%
+0% to 100%
30s
+30s
Cluster Disk I/O
+Cluster Network 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.
+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.
0% to 100%
+0% to 100%
30s
+30s
Cluster Network I/O
+Cluster Status
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.
+Historical trend of the cluster status.
0% to 100%
+Normal/Abnormal/Degraded
30s
+30s
Cluster Status
+Cluster Read-only
Historical trend of the cluster status.
+Historical trend of the cluster read-only status change trend.
Normal/Abnormal/Degraded
+Yes/No
30s
+30s
Cluster Read-only
+Cluster Abnormal CNs
Historical trend of the cluster read-only status change trend.
+Historical trend of the number of abnormal CNs in the cluster.
Yes/No
+≥ 0
30s
+60s
Cluster Abnormal CNs
+Cluster Abnormal DNs
Historical trend of the number of abnormal CNs in the cluster.
+Historical trend of the number of abnormal DNs in the cluster.
≥ 0
+≥ 0
60s
+60s
Cluster Abnormal DNs
+Cluster CPU Usage of DNs
Historical trend of the number of abnormal DNs in the cluster.
+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.
≥ 0
+0% to 100%
60s
+60s
Cluster CPU Usage of DNs
+Cluster Sessions
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.
+Historical trend of the number of sessions in a cluster.
0% to 100%
+≥ 0
60s
+30s
Cluster Sessions
+Cluster Queries
Historical trend of the number of sessions in a cluster.
+Historical change trend of the number of queries in the cluster.
≥ 0
+≥ 0
30s
+30s
Cluster Queries
+Cluster Deadlocks
Historical change trend of the number of queries in the cluster.
+Historical trend of the number of deadlocks in a cluster.
≥ 0
+≥ 0
30s
+60s
Cluster Deadlocks
+Cluster TPS
Historical trend of the number of deadlocks in a cluster.
+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 TPS
+Cluster QPS
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 concurrent requests per second of all databases in a cluster. Formula: delta_query_count/current_collect_rate
≥0
+≥ 0
60s
+60s
Cluster QPS
+Database Sessions
Average number of concurrent requests per second of all databases in a cluster. Formula: delta_query_count/current_collect_rate
+Historical trend of the number of sessions on a single database in a cluster.
≥ 0
+≥ 0
60s
+30s
Database Sessions
+Database Queries
Historical trend of the number of sessions 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 Queries
+Database Inserted Rows
Historical trend of the number of queries on a single database in a cluster.
+Historical trend of the number of rows inserted into a single database in a cluster.
≥ 0
+≥ 0
30s
+60s
Database Submitted Transactions
+Database Updated Rows
Historical trend of the number of transactions submitted on 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 Rollback Transactions
+Database Deleted Rows
Historical trend of the number of rollback transactions on 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
Cluster Scanning Rows
+Database Capacity
Historical trend of the number of rows returned by a full table scan on a single database in a cluster.
+Historical trend of the capacity in a single database in a cluster.
≥ 0
+≥ 0
60s
+86400s
Database Index Query Rows
+Live Session
+ + + + + + + +Historical trend of the number of rows returned by an index query in a single database of a cluster.
+Session ID
≥ 0
+ID of the current session (query thread ID).
60s
+String
Database Inserted Rows
-Historical trend of the number of rows inserted into a single database in a cluster.
-≥ 0
-60s
+30s
Database Updated Rows
+User Name
Historical trend of the number of updated rows in a single database in a cluster.
+Name of the user who executes the current session.
≥ 0
+String
60s
+30s
Database Deleted Rows
+Database Name
Historical trend of the number of deleted rows in a single database in a cluster.
+Name of the database connected to the current session.
≥ 0
+String
60s
+30s
Database Capacity
+Session Duration
Historical trend of the capacity in a single database in a cluster.
+Duration of the current session (unit: ms).
≥ 0
+≥ 0 ms
86400s
+30s
Database Length of the Request Waiting Queue
+Application Name
Historical trend of the waiting queue length on a single database in a cluster.
+Name of the application that creates the current session.
≥ 0
+String
30s
+30s
Database TPS
+Queries
Number of transactions per second of each database in a cluster. Formula: (delta_xact_commit + delta_xact_rollback)/current_collect_rate.
+Number of SQL statements executed in the current session.
≥0
+≥ 0
60s
+30s
Session Monitoring
- - - - - - - - -Session ID
+Latest Query Duration
ID of the current session (query thread ID).
+Duration for executing the previous SQL statement in the current session.
Character string
+≥ 0 ms
30s
+30s
User Name
+Client IP Address
Name of the user who executes the current session.
+IP address of the client that initiates the current session.
Character string
+String
30s
+30s
Database Name
+Connected CN
Name of the database connected to the current session.
+Connected CN of the current session.
Character string
+String
30s
+30s
Session Duration
+Session Status
Duration of the current session (unit: ms).
+Execution status of the current session.
≥ 0 ms
+Running/Idle/Retry
30s
+30s
Application Name
+Real-Time Query
+ + + + + + + + + + + + + + + + +Name of the application that creates the current session.
+Query ID
Character string
+Query ID of a current query statement, which is a unique identifier allocated by the kernel to each query statement.
30s
+String
Queries
-Number of SQL statements executed in the current session.
-≥ 0
-30s
+30s
Latest Query Duration
+User Name
Duration for executing the previous SQL statement in the current session.
+Name of the user who submits the current query statement.
≥ 0 ms
+String
30s
+30s
Client IP Address
+Database Name
IP address of the client that initiates the current session.
+Name of the database corresponding to the current query statement.
Character string
+String
30s
+30s
Connected CN
+Application Name
Connected CN of the current session.
+Name of the application corresponding to the current query statement.
Character string
+String
30s
+30s
Session Status
+Resource Pool
Execution status of the current session.
+Name of the resource pool for the current query statement.
Running/Idle/Retry
+String
30s
+30s
Query Monitoring-Real-Time
- - - - - - - - - - - - - - - - - -Query ID
+Submitted
Query ID of a current query statement, which is a unique identifier allocated by the kernel to each query statement.
+Timestamp when the current query statement is submitted.
Character string
+String
30s
+30s
User Name
+Blocking Time
Name of the user who submits the current query statement.
+Waiting time before the current query statement is executed, in ms.
Character string
+≥ 0
30s
+30s
Database Name
+Execution Time
Name of the database corresponding to the current query statement.
+Execution time of the current query statement, in ms.
Character string
+≥ 0
30s
+30s
Application Name
+CPU Time
Name of the application corresponding to the current query statement.
+Total CPU time spent by the current query statement on all DNs, in ms.
Character string
+≥ 0
30s
+30s
Workload Queue
+CPU Time Skew
Name of the workload queue that carries the current query statement.
+CPU time skew of the current query statement among all DNs.
Character string
+0% to 100%
30s
+30s
Submitted
+Statement
Timestamp when the current query statement is submitted.
+Query statement that is being executed.
Character string
+String
30s
+30s
Blocking Time
+Connected CN
Waiting time before the current query statement is executed, in ms.
+Name of the CN that submits the current query statement.
≥ 0
+String
30s
+30s
Execution Time
+Client IP Address
Execution time of the current query statement, in ms.
+IP address of the client that submits the current query statement.
≥ 0
+String
30s
+30s
CPU Time
+Lane
Total CPU time spent by the current query statement on all DNs, in ms.
+Lane where the current query statement is located.
≥ 0
+Fast Lane/Slow Lane
30s
+30s
CPU Time Skew
+Query Status
CPU time skew of the current query statement among all DNs.
+Query status of the statement that is being executed.
0% to 100%
+String
30s
+30s
Average Written Data
+Session ID
Average data size of the current query statement flushed to disks on all DNs, in MB.
+Session ID of the current query statement, which is a unique identifier allocated by the kernel to each client connection.
≥ 0
+String
30s
+30s
Statement
+Queuing Status
Query statement that is being executed.
+Status of the current query execution in the database, indicating whether the query is queued in the resource pool.
Character string
+Yes/No
30s
+30s
Connected CN
+Historical Query
+ + + + + + + + + + +Name of the CN that submits the current query statement.
+Query ID
Character string
+Query ID of a query statement, which is a unique identifier allocated by the kernel to each query statement.
30s
+String
Client IP Address
-IP address of the client that submits the current query statement.
-Character string
-30s
+180s
Lane
+User Name
Lane where the current query statement is located.
+Name of the user who submits a query statement.
Fast Lane/Slow Lane
+String
30s
+180s
Query Status
+Application Name
Query status of the statement that is being executed.
+Application name corresponding to a query statement.
Character string
+String
30s
+180s
Session ID
+Database Name
Session ID of the current query statement, which is a unique identifier allocated by the kernel to each client connection.
+Name of the database corresponding to a query statement.
Character string
+String
30s
+180s
Queuing Status
+Resource Pool
Status of the current query execution in the database, indicating whether the query is queued in the workload queue.
+Name of the resource pool for the current query statement.
Yes/No
+String
30s
+180s
Query Monitoring-History
- - - - - - - - - - - -Query ID
+Submitted
Query ID of a query statement, which is a unique identifier allocated by the kernel to each query statement.
+Timestamp when a query statement is submitted.
Character string
+String
180s
+180s
User Name
+Blocking Time
Name of the user who submits a query statement.
+Waiting time before the query statement is executed, in ms.
Character string
+≥ 0
180s
+180s
Application Name
+Execution Time
Application name corresponding to a query statement.
+Execution time of the query statement, in ms.
Character string
+≥ 0
180s
+180s
Database Name
+CPU Time
Name of the database corresponding to a query statement.
+Total CPU time spent by the query statement on all DNs, in ms.
Character string
+≥ 0
180s
+180s
Workload Queue
+CPU Time Skew
Name of the workload queue that carries the current query statement.
+CPU time skew of a query statement executed on all DNs.
Character string
+0% to 100%
180s
+180s
Submitted
+Statement
Timestamp when a query statement is submitted.
+Query statements to be parsed
Character string
+String
180s
+180s
Blocking Time
+Slow Instance Monitoring
+ + + +Waiting time before the query statement is executed, in ms.
+Slow Instance
≥ 0
+Number of slow instances detected at the current time point.
180s
+≥ 0
Execution Time
-Execution time of the query statement, in ms.
-≥ 0
-180s
+240s
CPU Time
+Detected
Total CPU time spent by the query statement on all DNs, in ms.
+Time when a slow instance is detected for the first time.
≥ 0
+String
180s
+240s
CPU Time Skew
+Node Name
CPU time skew of a query statement executed on all DNs.
+Name of the node where the slow instance is deployed.
0% to 100%
+String
180s
+240s
Average Written Data
+Instance
Average data size of the query statement flushed to disks on all DNs, in MB.
+Name of an instance.
≥ 0
+String
180s
+240s
Statement
+Slow Node Detections (within 24 hours)
Query statements to be parsed
+Number of times that a slow instance is detected within 24 hours.
Character string
+≥ 0
180s
+240s
Slow Instance Monitoring
- - - - -Slow Instance
+Resource Pool Monitoring
+ + + + + + + + + + + + + + + +Number of slow instances detected at the current time point.
+Resource Pool
≥ 0
+Name of a resource pool in a cluster.
240s
+String
Detected
-Time when a slow instance is detected for the first time.
-Character string
-240s
+120s
Node Name
+CPU Usage
Name of the node where the slow instance is deployed.
+Real-time CPU usage of a resource pool.
Character string
+0% to 100%
240s
+120s
Instance
+CPU Resource
Name of an instance.
+CPU usage quota of a resource pool.
Character string
+0% to 100%
240s
+120s
Slow Node Detections (within 24 hours)
+Real-Time Concurrent Short Queries
Number of times that a slow instance is detected within 24 hours.
+Simple concurrency in a resource pool.
≥ 0
+≥ 0
240s
+120s
Workload Queue Monitoring
- - - - - - - - - - - - -Workload Queue
+Concurrent Short Queries
Name of the workload queue in the cluster.
+Quota for simple concurrency in a resource pool.
Character string
+≥ 0
120s
+120s
CPU Usage
+Real-Time Concurrent Queries
Real-time CPU usage of the workload queue.
+Real-time complex concurrency in a resource pool.
0% to 100%
+≥ 0
120s
+120s
CPU Resource
+Query Concurrency
CPU usage quotas of the workload queue.
+Quota for complex concurrency in a resource pool.
0% to 100%
+≥ 0
120s
+120s
Real-Time Concurrent Short Queries
+Storage
Number of real-time concurrent simple queries in a workload queue.
+Storage resource quota of a resource pool.
≥ 0
+≥ 0
120s
+120s
Concurrent Short Queries
+Disk Usage
Concurrent simple query quotas of a workload queue.
+Disk usage of a resource pool.
≥ 0
+0% to 100%
120s
+120s
Real-Time Concurrent Queries
+Memory
Number of real-time concurrent complex queries in a workload queue.
+Memory quota of a resource pool.
≥ 0
+≥ 0
120s
+120s
Query Concurrency
+Memory Usage
Concurrent complex query quotas of a workload queue.
+Memory usage of a resource pool.
≥ 0
+0% to 100%
120s
+120s
Waiting Queries
- - - - - - +Queries Waiting in a Resource Pool
+ + + + + +User
+User
Name of the user of waiting queries
+Name of the user of waiting queries
Character string
+String
120s
+120s
Application
+Application
Name of the application to be queried.
+Name of the application to be queried.
Character string
+String
120s
+120s
Database
+Database
Name of the database to be queried.
+Name of the database to be queried.
Character 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).
Character 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
Workload Queue
+Resource Pool
Workload queue to which the waiting query belongs.
+Resource pool of the waiting query.
Character string
+String
120s
+120s
Statement
+Statement
Query statement for the waiting status.
+Query statement for the waiting status.
Character 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.
Character string
+String
120s
+120s
Query Statement
+Query Statement
Query statement for the circuit breaking status.
+Query statement for the circuit breaking status.
Character 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).
Character 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.
Character string
+String
180s
+180s
Schema Name
+Schema Name
Name of the current query schema.
+Name of the current query schema.
Character string
+String
180s
+180s
User Name
+User Name
Name of the user who performs the query.
+Name of the user who performs the query.
Character string
+String
180s
+180s
Client
+Client
Name of the client that initiates the current query.
+Name of the client that initiates the current query.
Character 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.
Character 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.
Character 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.
-0% to 100%
-3600s
-Table 2 Restrictions on monitoring metrics Type
-Metric
-ECS Cluster
-BMS Cluster
-Network
- -NIC nominal speed
-None
-100/1000/10000/50000M
-NIC working mode (duplex)
+Database schema usage.
None
+0% to 100%
full/half
+3600s
Typical Scenarios
diff --git a/docs/dws/umn/dws_01_00139.html b/docs/dws/umn/dws_01_00139.html new file mode 100644 index 000000000..cbe390fe1 --- /dev/null +++ b/docs/dws/umn/dws_01_00139.html @@ -0,0 +1,19 @@ + + +Workload Analysis
+ ++ + ++ diff --git a/docs/dws/umn/dws_01_00140.html b/docs/dws/umn/dws_01_00140.html new file mode 100644 index 000000000..f2f5740f5 --- /dev/null +++ b/docs/dws/umn/dws_01_00140.html @@ -0,0 +1,112 @@ + + +++Parent topic: Databases Monitoring+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.
+
+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.
+
+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.
+
+
+Table 1 Workload snapshot parameters Name
+Default Value
+Description
+Performance view snapshot (enable_wdr_snapshot)
+off
+Whether to enable the performance view snapshot function. If this function is enabled, GaussDB(DWS) will periodically create snapshots for certain system performance views and save them to disk. You can also manually create snapshots.
+Resource monitoring (enable_resource_track)
+on
+Whether to enable the resource monitoring function. Resource statistics parameters are valid only if this parameter is enabled.
+Logical memory management module (enable_memory_limit)
+on
+Whether to enable the logical memory management module.
+Wait event statistics (enable_track_wait_event)
+off
+Whether to collect statistics on wait events, including the number of occurrences, number of failures, duration, maximum waiting time, minimum waiting time, and average waiting time.
+I/O call time series statistics (track_io_timing)
+off
+Whether to collect time series statistics on database I/O calls. If this function is enabled, the collector will periodically query the OS time, which may cause heavy overhead on certain platforms.
+SQL count (track_sql_count)
+The default value is off for versions earlier than 8.1.3 and on for 8.1.3 and later versions.
+Whether to collect statistics on the number of the SELECT, INSERT, UPDATE, DELETE, and MERGE INTO statements that are being executed in each session, the response time of the SELECT, INSERT, UPDATE, and DELETE statements, and the number of DDL, DML, and DCL statements. This parameter takes effect only if track_activities is set to on.
+Session command statistics (track_activities)
+on
+Whether to collect statistics on the commands that are being executed in each session.
+Unique SQL statistics (instr_unique_sql_count)
+0
+Whether to collect unique SQL statements and how many statements can be collected.
+Snapshot creation interval (wdr_snapshot_interval)
+60
+Interval for automatically creating performance view snapshots. It must be longer than the time needed to create a snapshot. The unit is minute.
+Maximum snapshot retention period (wdr_snapshot_retention_days)
+8
+Maximum retention period of performance snapshots. A large value will require a lot of disk space. The unit is day.
+++ + + \ No newline at end of file diff --git a/docs/dws/umn/dws_01_00141.html b/docs/dws/umn/dws_01_00141.html new file mode 100644 index 000000000..eda8c00be --- /dev/null +++ b/docs/dws/umn/dws_01_00141.html @@ -0,0 +1,47 @@ + + +++Parent topic: Workload Analysis+Workload Reports
++You can create, download, and delete work diagnosis reports, and check historical workload diagnosis reports.
++ To create a workload report, obtain the required OBS bucket permissions first.
++Checking Workload Reports
- 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 Reports. Workload reports will be displayed.
+
+Generating a Workload Report
- 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 Reports.
- Click Generate Report. In the displayed dialog box, configure the following parameters and click OK:
- Report Name: Enter a unique report name. The name can contain a maximum of 100 characters, including digits, letters, and underscores (_).)
- Object Type. Its value can be:
- node: The performance data of a specified node will be provided.
- cluster: The performance data of the entire cluster will be provided.
- Node Name: Select a node.
- Content Type. Its value can be:
- summary: A report contains only brief analysis and calculation results.
- detail: A report contains only detailed metric data.
- all: A report contains content of both the summary and detail reports.
- Starting Snapshot: Select a snapshot.
- Ending Snapshot: Select a snapshot.
- OBS Bucket: Select a bucket to store reports.
- OBS Path: A storage directory. Multiple levels of directories can be separated by slashes (/). The value cannot start with a slash (/). Up to 50 characters are allowed.
+
+
+
+ The time of the starting snapshot start must be earlier than that of the ending snapshot.
+
+Downloading Workload Reports in Batches
- 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 Reports.
- Select reports and click Download.
+
+ Up to 10 report records can be downloaded at a time.
+
+Deleting Workload Reports in Batches
- 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 Reports.
- Select reports and click Delete.
+
+Deleting a Workload Diagnosis Report
- 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 Reports.
- Click Delete in the Operation column of a report to delete the report record and file.
+
+Configuring Workload Report 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 Reports.
- Click Configure Report in the upper right corner. In the displayed dialog box, set the report retention period and OBS parameters.
+
++ + + \ No newline at end of file diff --git a/docs/dws/umn/dws_01_00142.html b/docs/dws/umn/dws_01_00142.html new file mode 100644 index 000000000..b43a9e20f --- /dev/null +++ b/docs/dws/umn/dws_01_00142.html @@ -0,0 +1,13 @@ + + +++Parent topic: Workload Analysis+Workload Analysis Overview
++The workload analysis tool of GaussDB(DWS) collects and analyzes database performance data. You can create workload snapshots to record cluster workload data in a specified period. A workload diagnosis report can be generated based on two workload information snapshots within a certain time segment. Workload Diagnosis Report (WDR) provides performance data in a specified period and presents the data on HTML web pages. It helps you detect exceptions, diagnose problems, and optimize performance. It is a powerful tool for database performance tuning.
++ - The WDR function is available only in 8.1.1.300 and later versions.
- Workload diagnosis reports can be stored only in OBS.
++ diff --git a/docs/dws/umn/dws_01_00175.html b/docs/dws/umn/dws_01_00175.html index 8cbb273e0..eb1079695 100644 --- a/docs/dws/umn/dws_01_00175.html +++ b/docs/dws/umn/dws_01_00175.html @@ -1,13 +1,15 @@ - +++Parent topic: Workload Analysis+Utilities
-+diff --git a/docs/dws/umn/dws_01_0018.html b/docs/dws/umn/dws_01_0018.html index 4450dbd1c..ad8580259 100644 --- a/docs/dws/umn/dws_01_0018.html +++ b/docs/dws/umn/dws_01_0018.html @@ -1,79 +1,79 @@ - +Process for Using GaussDB(DWS)
-GaussDB(DWS) is an online data processing database that uses 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.
-GaussDB(DWS) provides an easy-to-use management console, allowing you to quickly create clusters and easily manage data warehouses.
-Process Description
Figure 1 Process for using GaussDB(DWS)+
GaussDB(DWS) is an online data processing database that uses 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.
+GaussDB(DWS) provides an easy-to-use management console, allowing you to quickly create clusters and easily manage data warehouses.
+Process Description
Figure 1 Process for using GaussDB(DWS)-
Table 1 Process description Process
+Table 1 Process description Process
Task
+Task
Description
+Description
Operation Instruction
+Operation Instruction
Make preparations.
+Make preparations.
-
+-
Before using GaussDB(DWS), select an open port on your firewall as the database port of your data warehouse cluster.
+Before using GaussDB(DWS), select an open port on your firewall as the database port of your data warehouse cluster.
+ Create a cluster.
+Create a cluster.
-
+-
Create a cluster before using GaussDB(DWS) to execute data analysis tasks. A GaussDB(DWS) cluster contains nodes in the same subnet. These nodes jointly provide services. During cluster creation, the system creates a default database.
+Create a cluster before using GaussDB(DWS) to execute data analysis tasks. A GaussDB(DWS) cluster contains nodes in the same subnet. These nodes jointly provide services. During cluster creation, the system creates a default database.
+ Connect to the cluster.
+Connect to the cluster.
-
+-
After the data warehouse cluster is successfully created, use the SQL client tool or a third-party driver such as JDBC or ODBC to connect to the database in the cluster. You can download the SQL client tool and JDBC/ODBC driver on the Connection Management page of the GaussDB(DWS) management console.
+After the data warehouse cluster is successfully created, use the SQL client tool or a third-party driver such as JDBC or ODBC to connect to the database in the cluster. You can download the SQL client tool and JDBC/ODBC driver on the Connection Management page of the GaussDB(DWS) management console.
+ Access the database.
+Access the database.
-
+-
After connecting to the cluster, you can create and manage databases, manage users and permissions, import and export data, and query and analyze data.
+After connecting to the cluster, you can create and manage databases, manage users and permissions, import and export data, and query and analyze data.
Data Warehouse Service (DWS) Developer Guide
+Data Warehouse Service (DWS) Developer Guide
Manage and monitor the cluster.
- - +Manage and monitor the cluster.
+ +Manage the cluster.
+Manage the cluster.
View the cluster status, modify cluster configurations, add cluster tags, and scale out, restart, and delete the cluster.
+View the cluster status, modify cluster configurations, add cluster tags, and scale out, restart, and delete the cluster.
+ Manage the snapshot.
+Manage the snapshot.
Create snapshots to back up and restore the cluster.
+Create snapshots to back up and restore the cluster.
+ Perform O&M and monitoring.
+Perform O&M and monitoring.
View the running status and performance of the cluster through monitoring, log auditing, event notification, and resource load management.
+View the running status and performance of the cluster through monitoring, log auditing, event notification, and resource load management.
- Monitoring Clusters Using Cloud Eye
- Audit Logs
- "Resource Load Management"
For details, see "Resource Load Management" in the Data Warehouse Service (DWS) Developer Guide.
+
Creating a Cluster
-To use cloud GaussDB(DWS), create a cluster first.
-This section describes how to create a data warehouse cluster on the GaussDB(DWS) management console.
-Preparations Before Creating a Cluster
- You have evaluated the flavor of cluster nodes.
You can select the number of nodes by data volume, service load, and performance. More nodes bring you stronger storage and compute capabilities. In a GaussDB(DWS) cluster, the number of DNs on each node varies depending on the cluster flavor. Generally, the number ranges from 1 to 4. A DN stores service data by column or row or in hybrid mode, executes data query tasks, and returns execution results.
-When first using GaussDB(DWS), you can create a cluster with a smaller flavor. Then, you can adjust the cluster scale and node flavor based on the data volume and service load changes without interrupting services. For details, see Cluster Scale-out.
- - A network access topology has been designed.
Plan an appropriate AZ and configure the network to isolate the GaussDB(DWS) cluster from other cloud services.
- - Ensure that the number of available nodes meets the following conditions. Otherwise, the cluster cannot be created.
- At least three nodes are required for creating a cluster. You can view the number of available nodes on the Clusters page.
- The number of nodes in the cluster to be created must be less than or equal to the number of available nodes.
To use cloud GaussDB(DWS), create a data warehouse cluster first.
+This section describes how to create a data warehouse cluster on the GaussDB(DWS) management console.
+-Preparations Before Creating a Cluster
- You have evaluated the flavor of cluster nodes.
You can select the number of nodes by data volume, service load, and performance. More nodes bring you stronger storage and compute capabilities.
+When first using GaussDB(DWS), you can create a cluster with a smaller flavor. Then, you can adjust the cluster scale and node flavor based on the data volume and service load changes without interrupting services. For details, see Scaling Out a Cluster.
+ - Ensure that the number of available nodes meets the following conditions. Otherwise, the cluster cannot be created.
- The number of nodes that can be used by a user depends on the product type you select.A hybrid data warehouse cluster (standalone mode) has only one node. For other types of clusters, the number of nodes can be greater than or equal to 3. You can view the number of available nodes on the Clusters page.
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.
- Select Region.
-
Table 1 Region parameters Parameter
+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.
- Select Region.
+
-Table 1 Region parameters Parameter
Description
+Description
Example Value
+Example Value
Region
+Region
Select the actual region where the cluster nodes run.
+Select the actual region where the cluster nodes run.
eu-de
+eu-de
AZ
+AZ
Select an AZ associated with the cluster region.
+Select an AZ associated with the cluster region.
+For more information, see Regions and AZs.
-
+-
- Configure Resource, CPU Architecture, and Node Flavor.
The number of nodes in a new cluster cannot exceed the quota that can be used by a user or 32. If the node quota is insufficient, click Increase quota to submit a service ticket and apply for higher node quota.
+- Configure Resource, CPU Architecture, and Node Flavor.
- - The number of nodes in a new cluster cannot exceed the quota that can be used by a user or 256. If the node quota is insufficient, click Increase quota to submit a service ticket and apply for higher node quota.
Figure 1 Configuring node parameters-
-Table 2 Node configuration parameters Parameter
+Figure 1 Configuring node parameters+
++-
-Table 2 Node configuration parameters Parameter
Description
+Description
Example Value
+Example Value
Cluster Type
+Resource
GaussDB(DWS) cluster types include:
-- Standard: The OLAP database engine is used, supporting online query and offline analysis of 10 PB data. A Standard cluster can be scaled out to 1024 nodes.
Standard
-CPU Architecture
-The CPU architecture includes:
-- x86
NOTE:-The only difference between the x86 and Kunpeng architectures lies in the underlying architecture, of which the application layer is unaware. The same SQL syntax is used. If x86 servers are sold out when you create a cluster, select the Kunpeng architecture.
-x86
-Node Flavor
-Select the desired node flavor based on service requirements. Each node flavor displays the vCPU, memory, and recommended application scenario.
- -Table 3 describes the node flavors supported by GaussDB(DWS).
-dws.m3.xlarge
-Nodes
-Specify the number of nodes in the cluster.
-The number of nodes ranges from 3 to 256.
-3
-Total Capacity (GB)
-Displays the total capacity of a cluster.
-The storage capacity of each flavor is the actual database space used for storing data. The displayed storage capacity has deducted the disk space consumed by backups and RAIDs.
--
-Product type. It can be:
+- Standard data warehouse: It can analyze hot and cold data and is highly cost-effective. Its storage and computing resources are not limited, and can be elastically scaled and billed per use. It is suitable for the converged analysis that requires integrated databases, warehouses, marts, and lakes. It is most suitable for OLAP workloads.
-
-Table 3 Flavor description Type
-vCPU Cores
-Memory
-Disk Size
-Disk Type
-dws.m1.xlarge.ultrahigh
4
-32 GB
-256 GB
-General-purpose generation I, SSD
+Standard
dws.d1.xlarge
+Compute Resource
4
+It can be:
+- ESC: Scalable, reliable, and high-throughput virtual block storage is provided in a distributed architecture. This ensures that data can be quickly migrated and restored if any data replica is unavailable, preventing data from being lost because of a single hardware fault. Backup and restoration can be performed on ECSs and EVS disks. You can configure automatic backup policies for them.
32 GB
-1.68 TB
-Local disk (HDD)
+-
dws.d2.15xlarge
+Storage Type
60
+It can be:
+- Cloud SSD
540 GB
-13.41 TB
-Disk-intensive generation II (KVM), local disk (HDD)
+-
dws.d2.xlarge
+CPU Architecture
4
+The CPU architecture includes:
+- x86
32 GB
-1.68 TB
-Disk-intensive generation II (KVM), local disk (HDD)
+x86
dws.m3.xlarge
+Node Flavor
4
+Select the desired node flavor based on service requirements. Each node flavor displays the vCPU, memory, and recommended application scenario.
+32 GB
-160 GB
-General-purpose generation III (KVM), SSD
+dws.m3.xlarge
- Configure cluster parameters.
Figure 2 Cluster parameters-
--Table 4 Cluster parameters Parameter
-Description
-Example Value
-Cluster Name
+Nodes
Set the name of the data warehouse cluster.
-Enter 4 to 64 characters. Only case-insensitive letters, digits, hyphens (-), and underscores (_) are allowed. The value must start with a letter.
-NOTE:+After a cluster is created, its name cannot be changed.
-Specify the number of nodes in the cluster.
+The number of nodes ranges from 3 to 256.
dws-demo
+3
Cluster Version
+Total
Displays the version of the database instance installed in the cluster. The figure is for reference only.
+Displays the total capacity of a cluster.
+The storage capacity of each flavor is the actual database space used for storing data. The displayed storage capacity has deducted the disk space consumed by backups and RAIDs.
-
-Default Database
-The default database name of the cluster is gaussdb.
-NOTE:-This name cannot be changed.
-gaussdb
-Administrator Account
-Set the database administrator name.
-The administrator username must:
-- Consist of lowercase letters, digits, or underscores.
- Start with a lowercase letter or an underscore.
- Contain 6 to 64 characters.
- Cannot be a keyword of the GaussDB(DWS) database. For details about the keywords of the GaussDB(DWS) database, see "SQL Reference > Keyword" in the Data Warehouse Service (DWS) Developer Guide.
dbadmin
-Administrator Password
-Set the password of the database administrator account.
-The password complexity requirements are as follows:-- Consists of 8 to 32 characters.
- Cannot be the username or the username spelled backwards.
- Must contain at least three of the following character types: uppercase letters, lowercase letters, digits, and special characters (~!`?,.:;-_'"(){}[]/<>@#%^&*+|\=)
- Passes the weak password check.
NOTE:-Change the password regularly and keep it secure.
--
-Confirm Password
-Enter the database administrator password again.
--
-Database Port
-Specify the port used when the client or application connects to the database in the cluster.
-The port number ranges from 8000 to 30000.
-8000
+-
- Configure network parameters.
Figure 3 Configuring the network-
-Table 5 Network parameters Parameter
+- Configure cluster parameters.
Figure 2 Cluster parameters+
+Table 3 Cluster parameters - Configure Resource, CPU Architecture, and Node Flavor.
- 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 the DR cluster.
- Select the AZ associated with the region where the DR cluster resides.
- Visualized one-stop management
- Application layer
