How you can Implement CDC for MySQL and Postgres

There are a number of change information seize strategies accessible when utilizing a MySQL or Postgres database. A few of these strategies overlap and are very related no matter which database expertise you might be utilizing, others are totally different. In the end, we require a solution to specify and detect what has modified and a technique of sending these adjustments to a goal system.

This put up assumes you might be aware of 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 alternative ways you’ll be able to implement CDC when you have both a MySQL and Postgres database and examine the approaches.

CDC with Replace Timestamps and Kafka

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

We are able to 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 answer like Kafka and Kafka Join that has pre-defined connectors that ballot tables and publish rows to a queue when the replace timestamp is larger than the final processed document. Kafka Join additionally has connectors to focus on methods 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 shoppers can then learn messages from that queue. If we needed 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 shopper that might 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 wish to eat, how you can detect adjustments which in our case can be through the use of the replace timestamp and which subject (queue) to publish them to. Utilizing Kafka Connect with deal with this implies all the logic required to detect which rows have modified is completed for us. We solely want to make sure that the replace timestamp subject is up to date (lined within the subsequent part) and Kafka Join will maintain:

  • Maintaining observe of the utmost replace timestamp of the most recent document it has printed
  • Polling the database for any information with newer replace timestamp fields
  • Writing the info to a queue to be consumed downstream

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

Once more, utilizing Kafka Join signifies 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 answer sooner or later.

For this to work, we have to make sure that we’ve got replace timestamp fields on the tables we wish to seize and that these fields are all the time up to date every time the document is up to date. Within the subsequent part, we cowl how you can implement this in each MySQL and Postgres.

Utilizing Triggers for Replace Timestamps (MySQL & Postgres)

MySQL and Postgres each assist triggers. Triggers help you carry out actions within the database both instantly earlier than or after one other motion occurs. For this instance, every 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’ll be able to set the replace timestamp column to mechanically use the present date and time when a brand new document is inserted. The desk definition in MySQL would look as follows (the Postgres syntax can be very related). Notice the DEFAULT CURRENTTIMESTAMP key phrases when declaring the replacetimestamp column that ensures when a document is inserted, by default the present date and time are used.

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

This can 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 subject every time a document 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 subject to the present timestamp after which the set off will execute the perform. This can be a delicate distinction however is barely extra overhead as you now have a perform and a set off to take care of within the postgres database.

Utilizing Auto-Replace Syntax in MySQL

In case you are utilizing MySQL there’s one other, a lot less complicated manner of implementing an replace timestamp. When defining the desk in MySQL you’ll be able to outline what worth to set a column to when the document is up to date, which in our case can be to replace it to the present timestamp.

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

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

CDC with Debezium, Kafka and Amazon DMS

An alternative choice for implementing a CDC answer is through the use of the native database logs that each MySQL and Postgres can produce when configured to take action. These database logs document each operation that’s executed towards the database which may 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 jot down any code or add any additional logic to your tables as you do with replace timestamps. Second, it additionally helps deletion of information, one thing that isn’t potential 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 jot down these logs you’ll be able to select a CDC system to assist seize the adjustments. Two well-liked choices are Debezium and Amazon Database Migration Service (DMS). Each of those methods 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 subject. This payload incorporates all of the context required to use these adjustments to our goal system, we simply want to jot down a shopper or use a Kafka Join sink to jot down the info. As Debezium makes use of Kafka, we get all the advantages of Kafka resembling fault tolerance and scalability.


Fig 2. Debezium CDC structure for MySQL and Postgres

AWS DMS works in the same solution to Debezium. It helps many various supply and goal methods and integrates natively with all the well-liked AWS information providers together with Kinesis and Redshift.

The principle advantage of utilizing DMS over Debezium is that it is successfully a “serverless” providing. With Debezium, if you would like the flexibleness and fault tolerance of Kafka, you’ve 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 info to the goal.

Nevertheless, this serverless strategy does have its drawbacks, primarily in its characteristic 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’ll be able to rise up and working with this in a short time, particularly if most tables already embrace some sort of replace timestamp.

In the event you’d somewhat go together with the database log strategy, possibly since you need actual replication then you must look to make use of a service like Debezium or AWS DMS. I’d recommend first checking which system helps the supply and goal methods you require. When you have some extra superior use circumstances resembling masking delicate information or re-routing information to totally different queues based mostly on its content material then Debezium might be your best option. In the event you’re simply in search of 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, it’s possible you’ll 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 info for sub-second analytics at scale. Rockset may learn CDC streams from NoSQL databases, resembling MongoDB and Amazon DynamoDB.

The appropriate reply relies on your particular use case and there are a lot of extra choices than have been mentioned right here, these are simply a number of the extra well-liked 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 abilities inside 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 navy helicopters. He then went on to work for Capgemini the place he helped the UK authorities transfer into the world of Huge Information. He’s at the moment utilizing this expertise to assist rework the info panorama at, an internet charity cashback website, the place he’s serving to to form their information warehousing and reporting functionality from the bottom up.