The right way to Implement CDC for MySQL and Postgres


There are a number of change information seize strategies obtainable when utilizing a MySQL or Postgres database. A few of these strategies overlap and are very comparable no matter which database expertise you’re utilizing, others are totally different. Finally, we require a technique to specify and detect what has modified and a way of sending these adjustments to a goal system.

This put up assumes you’re conversant in change information seize, if not learn the earlier introductory put up right here “Change Information Seize: What It Is and How To Use It.” On this put up, we’re going to dive deeper into the other ways you may implement CDC in case you have both a MySQL and Postgres database and evaluate the approaches.

CDC with Replace Timestamps and Kafka

One of many easiest methods to implement a CDC resolution in each MySQL and Postgres is through the use of replace timestamps. Any time a file is inserted or modified, the replace timestamp is up to date to the present date and time and allows you to know when that file was final modified.

We will then both construct bespoke options to ballot the database for any new information and write them to a goal system or a CSV file to be processed later. Or we will use a pre-built resolution like Kafka and Kafka Join that has pre-defined connectors that ballot tables and publish rows to a queue when the replace timestamp is bigger than the final processed file. Kafka Join additionally has connectors to focus on techniques that may then write these information for you.

Fetching the Updates and Publishing them to the Goal Database utilizing Kafka

Kafka is an occasion streaming platform that follows a pub-sub mannequin. Publishers ship information to a queue and a number of customers can then learn messages from that queue. If we wished to seize adjustments from a MySQL or Postgres database and ship them to an information warehouse or analytics platform, we first must arrange a writer to ship the adjustments after which a client that would learn the adjustments and apply them to our goal system.

To simplify this course of we will use Kafka Join. Kafka Join works as a center man with pre-built connectors to each publish and eat information that may merely be configured with a config file.

Fig 1. CDC structure with MySQL, Postgres and Kafka

As proven in Fig 1, we will configure a JDBC connector for Kafka Join that specifies which desk we want to eat, the best way to detect adjustments which in our case might be through the use of the replace timestamp and which matter (queue) to publish them to. Utilizing Kafka Connect with deal with this implies all the logic required to detect which rows have modified is finished for us. We solely want to make sure that the replace timestamp area is up to date (lined within the subsequent part) and Kafka Join will deal with:

  • Holding monitor of the utmost replace timestamp of the newest file it has revealed
  • Polling the database for any information with newer replace timestamp fields
  • Writing the information to a queue to be consumed downstream

We will then both configure “sinks” which outline the place to output the information or have the supply system speak to Kafka immediately. Once more, Kafka Join has many pre-defined sink connectors that we will simply configure to output the information to many various goal techniques. Companies like Rockset can speak to Kafka immediately and subsequently don’t require a sink to be configured.

Once more, utilizing Kafka Join implies that out of the field, not solely can we write information to many various areas with little or no coding required, however we additionally get Kafkas throughput and fault tolerance that may assist us scale our resolution sooner or later.

For this to work, we have to be certain that we’ve replace timestamp fields on the tables we wish to seize and that these fields are at all times up to date each time the file is up to date. Within the subsequent part, we cowl the best way to implement this in each MySQL and Postgres.

Utilizing Triggers for Replace Timestamps (MySQL & Postgres)

MySQL and Postgres each help triggers. Triggers will let you carry out actions within the database both instantly earlier than or after one other motion occurs. For this instance, each time an replace command is detected to a row in our supply desk, we wish to set off one other replace on the affected row which units the replace timestamp to the present date and time.

We solely need the set off to run on an replace command as in each MySQL and Postgres you may set the replace timestamp column to routinely use the present date and time when a brand new file is inserted. The desk definition in MySQL would look as follows (the Postgres syntax could be very comparable). Be aware the DEFAULT CURRENTTIMESTAMP key phrases when declaring the replacetimestamp column that ensures when a file is inserted, by default the present date and time are used.

firstname VARCHAR(30) NOT NULL,
lastname VARCHAR(30) NOT NULL,
electronic mail VARCHAR(50),

It will imply our update_timestamp column will get set to the present date and time for any new information, now we have to outline a set off that may replace this area each time a file is up to date within the consumer desk. The MySQL implementation is easy and appears as follows.

CREATE TRIGGER user_update_timestamp 
      SET NEW.update_timestamp = CURRENT_TIMESTAMP; 

For Postgres, you first need to outline a perform that may set the update_timestamp area to the present timestamp after which the set off will execute the perform. This can be a refined distinction however is barely extra overhead as you now have a perform and a set off to keep up within the postgres database.

Utilizing Auto-Replace Syntax in MySQL

In case you are utilizing MySQL there’s one other, a lot easier method of implementing an replace timestamp. When defining the desk in MySQL you may outline what worth to set a column to when the file is up to date, which in our case could be to replace it to the present timestamp.

firstname VARCHAR(30) NOT NULL,
lastname VARCHAR(30) NOT NULL,
electronic mail VARCHAR(50),

The good thing about that is that we not have to keep up the set off code (or the perform code within the case of Postgres).

CDC with Debezium, Kafka and Amazon DMS

Another choice for implementing a CDC resolution is through the use of the native database logs that each MySQL and Postgres can produce when configured to take action. These database logs file each operation that’s executed towards the database which might then be used to copy these adjustments in a goal system.

The benefit of utilizing database logs is that firstly, you don’t want to write down any code or add any further logic to your tables as you do with replace timestamps. Second, it additionally helps deletion of information, one thing that isn’t attainable with replace timestamps.

In MySQL you do that by turning on the binlog and in Postgres, you configure the Write Forward Log (WAL) for replication. As soon as the database is configured to write down these logs you may select a CDC system to assist seize the adjustments. Two standard choices are Debezium and Amazon Database Migration Service (DMS). Each of those techniques utilise the binlog for MySQL and WAL for Postgres.

Debezium works natively with Kafka. It picks up the related adjustments, converts them right into a JSON object that incorporates a payload describing what has modified and the schema of the desk and places it on a Kafka matter. This payload incorporates all of the context required to use these adjustments to our goal system, we simply want to write down a client or use a Kafka Join sink to write down the information. As Debezium makes use of Kafka, we get all the advantages of Kafka equivalent to fault tolerance and scalability.


Fig 2. Debezium CDC structure for MySQL and Postgres

AWS DMS works in the same technique to Debezium. It helps many various supply and goal techniques and integrates natively with all the standard AWS information providers together with Kinesis and Redshift.

The primary good thing about utilizing DMS over Debezium is that it is successfully a “serverless” providing. With Debezium, if you would like the pliability and fault tolerance of Kafka, you will have the overhead of deploying a Kafka cluster. DMS as its identify states is a service. You configure the supply and goal endpoints and AWS takes care of dealing with the infrastructure to take care of monitoring the database logs and copying the information to the goal.

Nevertheless, this serverless method does have its drawbacks, primarily in its function set.

Which Possibility for CDC?

When weighing up which sample to comply with it’s essential to evaluate your particular use case. Utilizing replace timestamps works while you solely wish to seize inserts and updates, if you have already got a Kafka cluster you may rise up and operating with this in a short time, particularly if most tables already embody some type of replace timestamp.

If you happen to’d slightly go together with the database log method, possibly since you need actual replication then it is best to look to make use of a service like Debezium or AWS DMS. I’d counsel first checking which system helps the supply and goal techniques you require. When you have some extra superior use instances equivalent to masking delicate information or re-routing information to totally different queues based mostly on its content material then Debezium might be the only option. If you happen to’re simply on the lookout for easy replication with little overhead then DMS will give you the results you want if it helps your supply and goal system.

When you have real-time analytics wants, you might think about using a goal database like Rockset as an analytics serving layer. Rockset integrates with MySQL and Postgres, utilizing AWS DMS, to ingest CDC streams and index the information for sub-second analytics at scale. Rockset can even learn CDC streams from NoSQL databases, equivalent to MongoDB and Amazon DynamoDB.

The best reply depends upon your particular use case and there are lots of extra choices than have been mentioned right here, these are simply a number of the extra standard methods to implement a contemporary CDC system.

Lewis Gavin has been a knowledge engineer for 5 years and has additionally been running a blog about expertise throughout the Information neighborhood for 4 years on a private weblog and Medium. Throughout his pc science diploma, he labored for the Airbus Helicopter staff in Munich enhancing simulator software program for army helicopters. He then went on to work for Capgemini the place he helped the UK authorities transfer into the world of Massive Information. He’s presently utilizing this expertise to assist remodel the information panorama at, a web-based charity cashback web site, the place he’s serving to to form their information warehousing and reporting functionality from the bottom up.