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    Data Transmission Service:Supported databases

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    Data Transmission Service:Supported databases

    Last Updated:Nov 20, 2024

    Data Transmission Service (DTS) supports data transmission between various data sources, such as relational database management systems (RDBMSs), NoSQL databases, and online analytical processing (OLAP) databases. This topic describes the database types, migration types, synchronization types, synchronization topologies, and data change types that are supported by DTS.

    Note

    For information about terms related to migration types, synchronization types, and change tracking, see Terms.

    Data synchronization

    You can use DTS to synchronize data between data sources in real time. This feature is suitable for the following scenarios: active geo-redundancy, geo-disaster recovery, zone-disaster recovery, cross-border data synchronization, query load balancing, cloud business intelligence (BI) systems, and real-time data warehousing.

    For information about the supported database types, database engine versions, synchronization types, and how to configure a data synchronization task, see the following tables or Data synchronization scenarios.

    Note

    If your source database type is not supported by DTS, you can use the data shipping feature.Database Type For more information, see Data shipping.

    Data migration

    You can use DTS to migrate data between homogeneous and heterogeneous data sources. Typical scenarios include data migration to Alibaba Cloud, data migration between instances within Alibaba Cloud, and database splitting and scale-out.

    For information about the supported databases, database engine versions, migration types, and how to configure a data migration task, see the following tables or Data migration scenarios.

    Note

    • Data migration can partly achieve data synchronization in specific scenarios. However, data migration and data synchronization differ in scenarios, supported databases, features, and billing. For more information about the differences between data migration and data synchronization, see What are the differences between data migration and data synchronization?

    • If you need to migrate incremental data for a long period of time, we recommend that you use data synchronization, which can achieve better performance and network stability.

    Change tracking

    You can use DTS to track data changes from databases in real time. Then, you can consume the tracked data in the following scenarios: cache updates, asynchronous business decoupling, data synchronization between heterogeneous data sources, and data synchronization with extract, transform, and load (ETL) operations. Change tracking allows you to track incremental data of a variety of databases in real-time, such as a self-managed MySQL database and an ApsaraDB RDS for MySQL instance. You can use different clients to consume tracked data, such as SDK clients and Flink clients. For information about the supported change tracking solutions and how to configure a change tracking task, see the following table or Change tracking scenarios.

    Source database

    Data change type

    Reference

    • Self-managed MySQL database

      Supported versions: 5.1, 5.5, 5.6, 5.7, and 8.0

    • ApsaraDB RDS for MySQL

      All versions

    • Data update

    • Schema update

    Track data changes from an ApsaraDB RDS for MySQL instance

    PolarDB for MySQL

    All versions

    • Data update

    • Schema update

    Track data changes from a PolarDB for MySQL cluster

    PolarDB-X 1.0

    Important

    A database in a PolarDB-X instance must be created based on ApsaraDB RDS for MySQL instances. DTS does not support PolarDB-X databases that are created based on PolarDB for MySQL clusters.

    • Data update

    • Schema update

    Track data changes from a PolarDB-X 1.0 instance

    PolarDB-X 2.0

    Important

    A database in a PolarDB-X instance must be created based on ApsaraDB RDS for MySQL instances. DTS does not support PolarDB-X databases that are created based on PolarDB for MySQL clusters.

    • Data update

    • Schema update

    Track data changes from a PolarDB-X instance

    PolarDB for PostgreSQL (Compatible with Oracle)

    All versions

    Data update

    Track data changes from a PolarDB for Oracle cluster

    Self-managed Oracle database (non-RAC architecture)

    Supported versions: 9i, 10g, and 11g

    • Data update

    • Schema update

    Track data changes from a self-managed Oracle database

    • ApsaraDB RDS for PostgreSQL

      Supported versions: 9.4, 10, 11, 12, 13, 14, 15, and 16

    • Self-managed PostgreSQL database

      Supported versions 9.4.8 and later, 9.5, 9.6, 10.x, 11.x, 12.x, 13.x, 14.x, 15.x, and 16.x

    Data update

    Track data changes from an ApsaraDB RDS for PostgreSQL instance

    Data Management (DMS) logical database

    Important

    A logical database in DMS must be created based on the database shards of multiple PolarDB for MySQL clusters.

    Data update

    Track data changes from a DMS logical database

    PolarDB for PostgreSQL

    Supported versions: 11 and 14

    • Data update

    • Schema update

    Track data changes from a PolarDB for PostgreSQL cluster