Functions

MPP computing framework, hybrid row-column storage, and vectorized execution, enabling response to billion-level data correlation analysis within seconds
Optimized in-memory computing based on Hash Join of Bloom Filter, improving the performance by 2 to 10 times

Optimized communication between large-scale clusters based on telecommunication technologies, improving data transmission efficiency between compute nodes
Cost-based intelligent optimizers, helping generate the optimal plan based on the cluster scale and data volume to improve execution efficiency

GaussDB(DWS) has comprehensive SQL capabilities:

Supports SQL 92 and SQL 2003 standards, stored procedures, GBK and UTF-8 character sets, and SQL standard functions and OLAP analysis functions.
Compatible with the PostgreSQL ecosystem and supports interconnection with mainstream database ETL and BI tools provided by third-party vendors.

For details about the SQL syntax and database operation guidance, see the Data Warehouse Service Database Development Guide.

Cluster Management

A data warehouse cluster contains nodes with the same flavor in the same subnet. These nodes jointly provide services. GaussDB(DWS) provides a professional, efficient, and centralized management console, allowing you to quickly apply for clusters, easily manage data warehouses, and focus on data and services.

Main functions of cluster management are described as follows:

Creating a cluster
To use data warehouse services on the cloud, create a GaussDB(DWS) cluster first. You can select product and node specifications to quickly create a cluster.
Managing snapshots
A snapshot is a complete backup that records point-in-time configuration data and service data of a GaussDB(DWS) cluster. A snapshot can be used to restore a cluster at a certain time. You can manually create snapshots for a cluster or enable automated snapshot creation (periodic). Automated snapshots have a limited retention period. You can copy automatic snapshots for long-term retention.

When you restore a cluster from a snapshot, the system creates a new cluster with the same flavor and node quantity as the original one, and imports the snapshot data.

You can delete snapshots that are no longer needed to release the storage space.
Managing nodes
You can check the nodes in a cluster, including the status, specifications, and usage of each node. To prepare for a large scale-out, you can add nodes in batches. For example, if 180 more BMS nodes are needed for a scale-out, add them in three batches (60 in each batch). If some nodes fail to be added, add them again. After all the 180 nodes are successfully added, use the nodes for cluster scale-out. Adding nodes does not affect cluster services.
Scaling out a cluster
As the service volume increases, the current scale of a cluster may not meet service requirements. In this case, you can scale out the cluster by adding compute nodes to it. Services are not interrupted during the scale-out. You can enable online scale-out and automatic redistribution if necessary.
Managing redistribution
By default, redistribution is automatically started after cluster scale-out. For enhanced reliability, disable the automatic redistribution function and manually start a redistribution task after the scale-out is successful. Data redistribution can accelerate service response. Currently, offline redistribution, online redistribution, and offline scheduling are supported. The default mode is offline redistribution.
Managing workloads
When multiple database users query jobs at the same time, some complex queries may occupy cluster resources for a long time, affecting the performance of other queries. For example, a group of database users continuously submit complex and time-consuming queries, while another group of users frequently submit short queries. In this case, short queries may have to wait in the queue for the time-consuming queries to complete. To improve efficiency, you can use the GaussDB(DWS) workload management function to handle such problems. GaussDB(DWS) workload management uses workload queues as resource bearers. You can create different workload queues for different service types and configure different resource ratios for these queues. Then, add database users to the corresponding queues to restrict their resource usages.
Logical cluster
A physical cluster can be divided into logical clusters that use the node-group mechanism. Tables in a database can be allocated to different physical nodes by logical cluster. A logical cluster can contain tables from multiple databases.
Restarting a cluster
Restarting a cluster may cause data loss in running services. If you have to restart a cluster, ensure that there is no running service and all data has been saved.
Deleting a cluster
You can delete a cluster when you do not need it. Deleting a cluster is risky and may cause data loss. Therefore, exercise caution when performing this operation.

GaussDB(DWS) allows you to manage clusters and snapshots in either of the following ways:

Management console
Use the management console to access GaussDB(DWS) clusters. When you have registered an account, log in to the management console and choose Data Warehouse Service.

For more information about cluster management, see "Cluster Management".
REST APIs
Use REST APIs provided by GaussDB(DWS) to manage clusters. In addition, if you need to integrate GaussDB(DWS) into a third-party system for secondary development, use APIs to access the service.

For details, see the Data Warehouse Service API Reference.

Diverse Data Import Modes

GaussDB(DWS) supports efficient data import from multiple data sources. The following lists typical data import modes. For details, see "Data Import" in the Data Warehouse Service (DWS) Developer Guide.

Concurrently Importing Data from OBS
Using GDS to Import Data from a Remote Server
Running the INSERT Statement to Insert Data
Running the COPY FROM STDIN Statement to Import Data
Using a gsql Meta-Command to Import Data
Importing Data from MRS to a Data Warehouse Cluster
Using Database Schema Convertor (DSC) to Migrate SQL Scripts

In addition, GaussDB(DWS)supports data import using mainstream third-party ETL tools.

APIs

You can call standard APIs, such as JDBC and ODBC, to access databases in GaussDB(DWS) clusters.

For details, see "Using the JDBC and ODBC Drivers to Connect to a Cluster" in the Data Warehouse Service (DWS) User Guide.

High Reliability

Supports instance and data redundancy, ensuring zero single points of failure (SPOF) in the entire system.
Supports multiple data backups, and all data can be manually backed up to OBS.
Automatically isolates the faulty node, uses the backup to restore data, and replaces the faulty node when necessary.
Automatic snapshots work with OBS to implement cross-AZ disaster recovery (DR). 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.
In the Unbalanced state, the number of primary instances on some nodes increases. As a result, the load pressure is high. In this case, you can perform a primary/standby switchback for the cluster during off-peak hours to improve performance.
If the internal IP address or EIP of a CN is used to connect to a cluster, the failure of this CN will lead to cluster connection failure. To avoid single-CN failures, GaussDB(DWS) uses Elastic Load Balance (ELB). An ELB distributes access traffic to multiple ECSs for traffic control based on forwarding policies. It improves the fault tolerance capability of application programs.
After a cluster is created, the number of required CNs varies with service requirements. GaussDB(DWS) allows you to add or delete CNs as needed.

Security Management

Isolates tenants and controls access permissions to protect the privacy and data security of systems and users based on the network isolation and security group rules, as well as security hardening measures.
Supports SSL network connections, user permission management, and password management, ensuring data security at the network, management, application, and system layers.

Monitoring and Auditing

Monitoring Clusters
GaussDB(DWS) integrates with Cloud Eye, allowing you to monitor compute nodes and databases in the cluster in real time. For details, see "Cluster Monitoring" in the Data Warehouse Service (DWS) User Guide.
Monitoring databases
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. For details, see "Database Monitoring" in Data Warehouse Service User Guide.
Managing alarms
Alarm management includes viewing and configuring alarm rules and subscribing to alarm information. Alarm rules display alarm statistics and details of the past week for users to view tenant alarms. In addition to providing a set of default GaussDB(DWS) alarm rules, this feature allows you to modify alarm thresholds based on your own services. For details, see "Alarms" in Data Warehouse Service User Guide.
Event notification
GaussDB(DWS) interconnects with Simple Message Notification (SMN) so that you can subscribe to events and view events that are triggered. For details, see "Event Notifications" in the Data Warehouse Service (DWS) User Guide.
Audit Logs
- GaussDB(DWS) integrates with Cloud Trace Service (CTS), allowing you to audit operations performed on the management console and API invocation operations. For details, see "Viewing Audit Logs of Key Operations on the Management Console".
- GaussDB(DWS) records all SQL operations, including connection attempts, query attempts, and database changes. For details, see "Configuring the Database Audit Logs" in the Data Warehouse Service (DWS) User Guide.

Multiple Database Tools

GaussDB(DWS) provides the following self-developed tools. You can download the tool packages on the GaussDB(DWS) management console. For details about the tools, see the Data Warehouse Service (DWS) Tool Guide.

gsql
gsql is a command line SQL client tool running on the Linux operating system. It helps connect to, operate, and maintain the database in a data warehouse cluster.
Data Studio
Data Studio is a Graphical User Interface (GUI) SQL client tool running on the Windows operating system. It is used to connect to the database in a data warehouse cluster, manage the database and database objects, edit, run, and debug SQL scripts, and view the execution plans.
GDS
GDS is a data service tool provided by GaussDB(DWS). It works with the foreign table mechanism to implement high-speed data import and export.

The GDS tool package needs to be installed on the server where the data source file is located. This server is called the data server or the GDS server.
DSC SQL syntax migration tool
The DSC is a command-line tool running on the Linux or Windows OS. It is dedicated to providing customers with simple, fast, reliable application SQL script migration services. It parses SQL scripts of source database applications by using the built-in syntax migration logic, and migrates them to be applicable to GaussDB(DWS) databases.

The DSC can migrate SQL scripts of Teradata, Oracle, Netezza, MySQL, and DB2 databases.