![]() ![]() With some CDC software, you must configure each table separately. For many users, though, there are some disadvantages to the existing solutions. Pain pointsĬDC isn't a new concept and various existing projects implement it. ![]() In some CQRS systems, a similar method can be used to update the query view. This event is consumed by the ElasticSearch system as it monitors the database, and the event prompts an update within ElasticSearch. Instead, you modify the upstream MySQL data, which generates a data update event. In this architecture, ElasticSearch searches all queries, but when you want to modify data, you don't directly change ElasticSearch. You can also use CDC to ensure final consistency of data, which has better performance and scalability, but at the cost of data latency, which can currently be kept in the range of millisecond in the industry.įor example, you could use CDC to synchronize MySQL data to your full-text search engine, such as ElasticSearch. Because of the use of two stores, in order to ensure data consistency, we can use distributed transactions to ensure strong data consistency, at the cost of availability, performance, and scalability. You can use CDC to obtain data update events from a database and let that control the refresh or invalidation of the cache.ĬQRS design usually uses two different storage instances to support business query and change operations. The problem is, when does a read event happen in relation to when a write event happened, and what bears the burden of making those events occur? A command writes data to a data source.The concept of Command Query Responsibility Segregation (CQRS) is the separation of command activity from query activity. Microservices can use CDC to obtain changes in other microservice databases, acquire data status updates, and execute the corresponding logic. Microservices are popular, but sharing information between them is often complicated. There's no need to embed the analysis process into the existing system. When designed well, the data analysis system obtains data for processing by subscribing to changes in the target data tables. ![]() With CDC, you can create backups of data based on what CDC has captured. You could synchronize the data to a full-text search engine in a different example. ![]() You may synchronize data between MySQL, PostgreSQL, MariaDB, and similar databases in one scenario. CDC usage scenariosĬlassic use cases for CDC is data synchronization or backups between heterogeneous databases. However, the open source Apache SeaTunnel project i is a data integration platform provides CDC function with a design philosophy and feature set that makes these captures possible, with features above and beyond existing solutions. A changing table that mirrors the column structure of the tracked source table stores this information.Ĭapturing change data is no easy feat. It captures column information and metadata needed to apply the changes to the target environment for modified rows. CDC provides details on changes in an easy-to-use relational format.
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