Data Transmission Service (DTS) supports data transmission between different data sources, such as relational database management system (RDBMS), NoSQL, and online analytical processing (OLAP) databases. Multiple data transmission features are available in DTS, including data migration, real-time change tracking, and real-time data synchronization. Compared with third-party data streaming tools, DTS provides multiple types of instances with high performance, security, and reliability. In addition, it is simple to create and manage instances.
Features
DTS supports data migration between homogeneous and heterogeneous data sources. For example, you can migrate data between MySQL databases or from Oracle databases to PolarDB for Oracle clusters. For migration between heterogeneous data sources, DTS supports schema conversion. For example, you can convert a synonym in Oracle to a synonym in PolarDB for Oracle.
DTS supports multiple data transmission modes, including data migration, real-time change tracking, and real-time data synchronization.
Real-time data synchronization supports one-way and two-way synchronization between two data sources. This feature is ideal for the following scenarios: geo-disaster recovery, active geo-redundancy, nearby application access, query load balancing, and real-time data warehousing.
DTS supports data migration with minimized downtime to ensure your application availability. The application downtime during data migration is reduced to minutes.
High performance
DTS uses servers with high specifications to ensure the performance of each data synchronization or migration instance.
The infrastructure of DTS has been optimized to ensure high-speed and reliable data migration. The peak rate of full data migration can reach 70 MB per second or 200,000 transactions per second (TPS).
Compared with traditional data synchronization tools, DTS provides better synchronization performance. You can use DTS to concurrently synchronize transactions and the incremental data of a single table. During peak hours, the data synchronization performance can reach 30,000 records per second (RPS).
DTS supports concurrent compressed data transmission that minimizes the bandwidth utilization.
High reliability
DTS is implemented based on clusters. If a node in a cluster is down or faulty, the control center moves all tasks from this node to another healthy node in the cluster within seconds.
DTS provides a 24 x 7 mechanism for validating data accuracy in some instances to discover and rectify inaccurate data. This helps ensure data integrity.
Secure transmission protocols and tokens are used for authentication across DTS modules to ensure reliable data transmission. DTS also supports resumable transmission.
Ease of use
The DTS console is a visual management interface that can guide you through creating instances in a simple manner.
The DTS console provides task information for you to manage your tasks, such as task state, progress, and performance.
DTS supports resumable transmission and regularly monitors task states. If DTS detects an error such as network failure or system exception, it automatically fixes the error and restarts the task. If the error persists, you must manually check and restart the task in the DTS console.
Replication modes
DTS provides multiple data replication modes, including data migration, data synchronization, and change tracking. You can choose an ideal replication mode for your actual scenario.
The data synchronization mode can be used to synchronize data between data sources. You have the option to choose one-way or two-way data synchronization.
Two-way data synchronization is available only for scenarios between MySQL databases, PolarDB for MySQL clusters, or ApsaraDB for Redis Enhanced Edition (Tair) instances.
The data synchronization mode can be used to distribute workloads among nodes in real time. This delivers high availability and load balancing and can be used as real-time data warehousing.
Data migration with minimized downtime
The data migration mode can be used to migrate data with minimized downtime. Source databases can remain operational during data migration. Your service downtime during data migration is reduced to minutes.