This is a prerelease version.

Capture Changes from MySQL

In this tutorial, you will learn how to process change events from a MySQL database.

Step 1. Install Docker

This tutorial uses Docker to simplify the setup of a MySQL database, which you can freely experiment on.

  1. Follow Docker’s Get Started instructions and install it on your system.

  2. Test that it works:

    • Run docker version to check that you have the latest release installed.

    • Run docker run hello-world to verify that Docker is pulling images and running as expected.

Step 2. Start MySQL Database

Open a terminal, and run following command. It will start a new container that runs a MySQL database server preconfigured with an inventory database:

docker run -it --rm --name mysql -p 3306:3306 \
    -e MYSQL_ROOT_PASSWORD=debezium -e MYSQL_USER=mysqluser \
    -e MYSQL_PASSWORD=mysqlpw debezium/example-mysql:1.2

This runs a new container using version 1.2 of the debezium/example-mysql image (based on mysql:5.7. It defines and populates a sample "inventory" database and creates a debezium user with password dbz that has the minimum privileges required by Debezium’s MySQL connector.

The command assigns the name mysql to the container so that it can be easily referenced later. The -it flag makes the container interactive, meaning it attaches the terminal’s standard input and output to the container so that you can see what is going on in the container. The --rm flag instructs Docker to remove the container when it is stopped.

The command maps port 3306 (the default MySQL port) in the container to the same port on the Docker host so that software outside of the container can connect to the database server.

Finally, it also uses the -e option three times to set the MYSQL_ROOT_PASSWORD, MYSQL_USER, and MYSQL_PASSWORD environment variables to specific values.

You should see in your terminal something like the following:

2020-03-09T09:48:24.579480Z 0 [Note] mysqld: ready for connections.
Version: '5.7.29-log'  socket: '/var/run/mysqld/mysqld.sock'  port: 3306  MySQL Community Server (GPL)

Notice that the MySQL server starts and stops a few times as the configuration is modified. The last line listed above reports that the MySQL server is running and ready for use.

Step 3. Start the MySQL Command Line Client

Open a new terminal, and use it to start a new container for the MySQL command line client and connect it to the MySQL server running in the mysql container:

docker run -it --rm --name mysqlterm --link mysql --rm mysql:5.7 sh \
    -c 'exec mysql -h"$MYSQL_PORT_3306_TCP_ADDR" -P"$MYSQL_PORT_3306_TCP_PORT" -uroot -p"$MYSQL_ENV_MYSQL_ROOT_PASSWORD"'

Here we start the container using the mysql:5.7 image, name the container mysqlterm and link it to the mysql container where the database server is running.

The --rm option tells Docker to remove the container when it stops, and the rest of the command defines the shell command that the container should run. This shell command runs the MySQL command line client and specifies the correct options so that it can connect properly.

The container should output lines similar to the following:

mysql: [Warning] Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 4
Server version: 5.7.29-log MySQL Community Server (GPL)

Copyright (c) 2000, 2020, Oracle and/or its affiliates. All rights reserved.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.


Unlike the other containers, this container runs a process that produces a prompt. We’ll use the prompt to interact with the database. First, switch to the "inventory" database:

mysql> use inventory;

and then list the tables in the database:

mysql> show tables;

which should then display:

| Tables_in_inventory |
| addresses           |
| customers           |
| geom                |
| orders              |
| products            |
| products_on_hand    |
6 rows in set (0.01 sec)

Use the MySQL command line client to explore the database and view the pre-loaded data. For example:

mysql> SELECT * FROM customers;

Step 4. Start Hazelcast

  1. Download Hazelcast.

    wget '' | tar zxvf -
    cd hazelcast-5.0-BETA-1

    If you already have Jet and you skipped the above steps, make sure to follow from here on.

  2. Activate the MySQL CDC plugin:

  3. Make sure the MySQL CDC plugin is in lib/ directory.

    ls lib/

    You should see the following jars:

    • hazelcast-jet-cdc-debezium-5.0-BETA-1.jar

    • hazelcast-jet-cdc-mysql-5.0-BETA-1.jar

    • hazelcast-jet-cdc-postgres-5.0-BETA-1.jar

  4. Start Hazelcast.

  5. When you see output like this, Hazelcast is up:

    Members {size:1, ver:1} [
        Member []:5701 - e7c26f7c-df9e-4994-a41d-203a1c63480e this

Step 5. Create a New Java Project

We’ll assume you’re using an IDE. Create a blank Java project named cdc-tutorial and copy the Gradle or Maven file into it:

  • Gradle

  • Maven

plugins {
    id 'com.github.johnrengelman.shadow' version '5.2.0'
    id 'java'

group 'org.example'
version '1.0-SNAPSHOT'


dependencies {
    implementation 'com.hazelcast:hazelcast:5.0-BETA-1'
    implementation 'com.hazelcast.jet:hazelcast-jet-cdc-debezium:5.0-BETA-1'
    implementation 'com.hazelcast.jet:hazelcast-jet-cdc-mysql:5.0-BETA-1'
    implementation 'com.fasterxml.jackson.core:jackson-annotations:2.11.0'

jar.manifest.attributes 'Main-Class': 'org.example.JetJob'
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="" xmlns:xsi=""





Step 6. Define a Data Pipeline

Let’s write the code that will monitor the database and do something useful with the data it sees. We will only monitor the customers table and use the change events coming from it to maintain an up-to-date view of all current customers.

By up-to-date view we mean an IMap keyed by customer ID and who’s values are Customer data objects containing all information for a customer with a specific ID.

This is how the code doing this looks like:

package org.example;

import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.jet.cdc.CdcSinks;
import com.hazelcast.jet.cdc.ChangeRecord;
import com.hazelcast.jet.cdc.mysql.MySqlCdcSources;
import com.hazelcast.jet.config.JobConfig;
import com.hazelcast.jet.pipeline.Pipeline;
import com.hazelcast.jet.pipeline.StreamSource;

public class JetJob {

    public static void main(String[] args) {
        StreamSource<ChangeRecord> source = MySqlCdcSources.mysql("source")

        Pipeline pipeline = Pipeline.create();
                        r -> r.key().toMap().get("id"),
                        r -> r.value().toObject(Customer.class).toString()));

        JobConfig cfg = new JobConfig().setName("mysql-monitor");
        HazelcastInstance hz = Hazelcast.bootstrappedInstance();
        hz.getJet().newJob(pipeline, cfg);


The Customer class we map change events to is quite simple too:

package org.example;

import com.fasterxml.jackson.annotation.JsonProperty;

import java.util.Objects;

public class Customer implements Serializable {

    public int id;

    public String firstName;

    public String lastName;

    public String email;

    public Customer() {

    public Customer(int id, String firstName, String lastName, String email) {
        super(); = id;
        this.firstName = firstName;
        this.lastName = lastName; = email;

    public int hashCode() {
        return Objects.hash(email, firstName, id, lastName);

    public boolean equals(Object obj) {
        if (this == obj) {
            return true;
        if (obj == null || getClass() != obj.getClass()) {
            return false;
        Customer other = (Customer) obj;
        return id ==
                && Objects.equals(firstName, other.firstName)
                && Objects.equals(lastName, other.lastName)
                && Objects.equals(email,;

    public String toString() {
        return "Customer {id=" + id + ", firstName=" + firstName + ", lastName=" + lastName + ", email=" + email + '}';

To make it evident that our pipeline serves the purpose of building an up-to-date cache of customers, which can be interrogated at any time let’s add one more class. This code can be executed at any time in your IDE and will print the current content of the cache.

package org.example;

import com.hazelcast.core.Hazelcast;
import com.hazelcast.core.HazelcastInstance;

public class CacheRead {

    public static void main(String[] args) {
        HazelcastInstance instance = HazelcastClient.newHazelcastClient();

        System.out.println("Currently there are following customers in the cache:");
        instance.getMap("customers").values().forEach(c -> System.out.println("\t" + c));



Step 7. Package the Pipeline into a JAR

Now that we have all the pieces, we need to submit it to Hazelcast for execution. Since Hazelcast runs on our machine as a standalone cluster in a standalone process we need to give it all the code that we have written.

For this reason we create a jar containing everything we need. All we need to do is to run the build command:

  • Gradle

  • Maven

gradle build

This will produce a JAR file called cdc-tutorial-1.0-SNAPSHOT.jar in the build/libs folder of our project.

mvn package

This will produce a JAR file called cdc-tutorial-1.0-SNAPSHOT.jar in the target folder or our project.

Step 8. Submit the Job for Execution

Assuming our cluster is still running and the database is up, all we need to issue is following command:

  • Gradle

  • Maven

bin/hz-cli submit build/libs/cdc-tutorial-1.0-SNAPSHOT.jar
bin/hz-cli submit target/cdc-tutorial-1.0-SNAPSHOT.jar

The output in the Hazelcast member’s log should look something like this (we also log what we put in the IMap sink thanks to the peek() stage we inserted):

... Completed snapshot in 00:00:01.519
... Output to ordinal 0: key:{{"id":1001}}, value:{{"id":1001,"first_name":"Sally","last_name":"Thomas",...
... Output to ordinal 0: key:{{"id":1002}}, value:{{"id":1002,"first_name":"George","last_name":"Bailey",...
... Output to ordinal 0: key:{{"id":1003}}, value:{{"id":1003,"first_name":"Edward","last_name":"Walker",...
... Output to ordinal 0: key:{{"id":1004}}, value:{{"id":1004,"first_name":"Anne","last_name":"Kretchmar",...
... Transitioning from the snapshot reader to the binlog reader

Step 9. Track Updates

Let’s see how our cache looks like at this time. If we execute the CacheRead code defined above, we’ll get:

Currently there are following customers in the cache:
    Customer {id=1002, firstName=George, lastName=Bailey,}
    Customer {id=1003, firstName=Edward, lastName=Walker,}
    Customer {id=1004, firstName=Anne, lastName=Kretchmar,}
    Customer {id=1001, firstName=Sally, lastName=Thomas,}

Let’s do some updates in our database. Go to the MySQL CLI we’ve started earlier and run following update statement:

mysql> UPDATE customers SET first_name='Anne Marie' WHERE id=1004;
Query OK, 1 row affected (0.00 sec)
Rows matched: 1  Changed: 1  Warnings: 0

In the log of the Hazelcast member we should immediately see the effect:

... Output to ordinal 0: key:{{"id":1004}}, value:{{"id":1004,"first_name":"Anne Marie","last_name":"Kretchmar",...

If we check the cache with CacheRead we get:

Currently there are following customers in the cache:
    Customer {id=1002, firstName=George, lastName=Bailey,}
    Customer {id=1003, firstName=Edward, lastName=Walker,}
    Customer {id=1004, firstName=Anne Marie, lastName=Kretchmar,}
    Customer {id=1001, firstName=Sally, lastName=Thomas,}

One more:

mysql> UPDATE customers SET email='' WHERE id=1003;
Query OK, 1 row affected (0.00 sec)
Rows matched: 1  Changed: 1  Warnings: 0
Currently there are following customers in the cache:
    Customer {id=1002, firstName=George, lastName=Bailey,}
    Customer {id=1003, firstName=Edward, lastName=Walker,}
    Customer {id=1004, firstName=Anne Marie, lastName=Kretchmar,}
    Customer {id=1001, firstName=Sally, lastName=Thomas,}

Step 10. Clean up

  1. Cancel the job.

    bin/hz-cli cancel postgres-monitor

Shut down the Hazelcast cluster.


  1. Use Docker to stop the running container (this will kill the command-line client too, since it’s running in the same container):

    docker stop postgres

    Since we’ve used the --rm flag when starting the connectors, Docker should remove them right after we stop them. We can verify that all processes are stopped and removed with following command:

docker ps -a