The way to Implement CDC for MySQL and Postgres
12 mins read

The way to Implement CDC for MySQL and Postgres

There are a number of change knowledge seize strategies out there when utilizing a MySQL or Postgres database. A few of these strategies overlap and are very related no matter which database know-how you’re utilizing, others are completely different. Finally, we require a approach to specify and detect what has modified and a technique of sending these modifications to a goal system.

This publish assumes you’re aware of change knowledge seize, if not learn the earlier introductory publish right here “Change Information Seize: What It Is and How To Use It.” On this publish, we’re going to dive deeper into the alternative ways you may implement CDC if 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 answer in each MySQL and Postgres is by utilizing 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 will then both construct bespoke options to ballot the database for any new data 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 bigger than the final processed document. Kafka Join additionally has connectors to focus on programs that may then write these data 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 knowledge to a queue and a number of customers can then learn messages from that queue. If we wished to seize modifications from a MySQL or Postgres database and ship them to a knowledge warehouse or analytics platform, we first have to arrange a writer to ship the modifications after which a client that would learn the modifications 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 knowledge 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, easy methods to detect modifications which in our case will probably be by utilizing the replace timestamp and which matter (queue) to publish them to. Utilizing Kafka Connect with deal with this implies the entire logic required to detect which rows have modified is finished 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:

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

We will 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 alternative goal programs. Companies like Rockset can speak to Kafka straight 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 knowledge to many alternative 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 be certain that now we have replace timestamp fields on the tables we wish to seize and that these fields are at all times up to date each time the document is up to date. Within the subsequent part, we cowl easy methods to implement this in each MySQL and Postgres.

Utilizing Triggers for Replace Timestamps (MySQL & Postgres)

MySQL and Postgres each assist triggers. Triggers permit you to 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 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,
electronic mail VARCHAR(50),

This can imply our update_timestamp column will get set to the present date and time for any new data, now we have to outline a set off that may replace this subject each time a document is up to date within the person desk. The MySQL implementation is straightforward and appears as follows.

CREATE TRIGGER user_update_timestamp 
      SET NEW.update_timestamp = CURRENT_TIMESTAMP; 

For Postgres, you first must outline a perform that may set the update_timestamp subject to the present timestamp after which the set off will execute the perform. It is 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 may be one other, a lot less complicated approach of implementing an replace timestamp. When defining the desk in MySQL you may 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,
electronic mail VARCHAR(50),

The advantage of that is that we now 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

Another choice for implementing a CDC answer is by utilizing 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 in opposition to the database which may then be used to copy these modifications 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 data, 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 may select a CDC system to assist seize the modifications. Two common choices are Debezium and Amazon Database Migration Service (DMS). Each of those programs utilise the binlog for MySQL and WAL for Postgres.

Debezium works natively with Kafka. It picks up the related modifications, converts them right into a JSON object that comprises a payload describing what has modified and the schema of the desk and places it on a Kafka matter. This payload comprises all of the context required to use these modifications to our goal system, we simply want to jot down a client or use a Kafka Join sink to jot down the info. 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 an identical approach to Debezium. It helps many alternative supply and goal programs and integrates natively with the entire common AWS knowledge companies together with Kinesis and Redshift.

The principle advantage of utilizing DMS over Debezium is that it is successfully a “serverless” providing. With Debezium, in order for you the pliability and fault tolerance of Kafka, you’ve the overhead of deploying a Kafka cluster. DMS as its title 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.

Nonetheless, 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 vital 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 stand up and operating with this in a short time, particularly if most tables already embrace some type of replace timestamp.

Should you’d reasonably go along with the database log method, possibly since you need actual replication then it’s best to look to make use of a service like Debezium or AWS DMS. I’d recommend first checking which system helps the supply and goal programs you require. If in case you have some extra superior use circumstances equivalent to masking delicate knowledge or re-routing knowledge to completely different queues based mostly on its content material then Debezium might be your best option. Should you’re simply searching for easy replication with little overhead then DMS will give you the results you want if it helps your supply and goal system.

If in case 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 info for sub-second analytics at scale. Rockset may also learn CDC streams from NoSQL databases, equivalent to MongoDB and Amazon DynamoDB.

The proper reply is determined by your particular use case and there are lots of extra choices than have been mentioned right here, these are simply among the extra common 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 throughout the Information neighborhood for 4 years on a private weblog and Medium. Throughout his pc science diploma, he labored for the Airbus Helicopter crew 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 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 knowledge warehousing and reporting functionality from the bottom up.

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