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Capture Changes from PostgreSQL

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

Step 1. Install Docker

This tutorial uses Docker to simplify the setup of a PostgreSQL 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 PostgreSQL Database

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

docker run -it --rm --name postgres -p 5432:5432 \
    -e POSTGRES_DB=postgres -e POSTGRES_USER=postgres \
    -e POSTGRES_PASSWORD=postgres debezium/example-postgres:1.2

This runs a new container using version 1.2 of the debezium/example-postgres image (based on postgres:11). It defines and populates a sample "postgres" database and creates the postgres user with password postgres that has superuser privileges. The debezium/example-postgres image also initiates a schema called inventory with some sample tables and data in it.

The command assigns the name postgres 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 5432 (the default PostgreSQL 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.

In your terminal you should see something like the following:

PostgreSQL init process complete; ready for start up.

2020-06-02 11:36:19.581 GMT [1] LOG:  listening on IPv4 address "", port 5432
2020-06-02 11:36:19.581 GMT [1] LOG:  listening on IPv6 address "::", port 5432
2020-06-02 11:36:19.585 GMT [1] LOG:  listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432"
2020-06-02 11:36:19.618 GMT [1] LOG:  database system is ready to accept connections

The PostgreSQL server is running and ready for use.

Step 3. Start the PostgreSQL Command Line Client

Open a new terminal, and use it to run psql (PostgreSQL interactive terminal) inside the already running postgres container:

docker exec -it postgres psql -U postgres

You should end up with a prompt similar to this:

psql (11.8 (Debian 11.8-1.pgdg90+1))
Type "help" for help.


We’ll use the prompt to interact with the database. First, switch to the "inventory" schema:

SET search_path TO inventory;

and then list the tables in the database:


This should display the following:

                List of relations
  Schema   |       Name       | Type  |  Owner
 inventory | customers        | table | postgres
 inventory | geom             | table | postgres
 inventory | orders           | table | postgres
 inventory | products         | table | postgres
 inventory | products_on_hand | table | postgres
 inventory | spatial_ref_sys  | table | postgres
(6 rows)

Feel free to explore the database and view the pre-loaded data. For example:

SELECT * FROM customers;

Step 4. Start Hazelcast

  1. Download Hazelcast.

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

  2. Make sure the PostgreSQL CDC plugin is in the lib/ directory.

    ls lib/

    You should see the following jars:

    • hazelcast-jet-cdc-debezium-5.3.7.jar

    • hazelcast-jet-cdc-postgres-5.3.7.jar

  3. Start Hazelcast

  4. 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:5.3.7'
    implementation 'com.hazelcast.jet:hazelcast-jet-cdc-debezium:5.3.7'
    implementation 'com.hazelcast.jet:hazelcast-jet-cdc-postgres:5.3.7'
    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="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">





Step 6. Define 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.postgres.PostgresCdcSources;
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 = PostgresCdcSources.postgres("source")

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

        JobConfig cfg = new JobConfig().setName("postgres-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.io.Serializable;
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) {
        this.id = id;
        this.firstName = firstName;
        this.lastName = lastName;
        this.email = 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 == other.id
                && Objects.equals(firstName, other.firstName)
                && Objects.equals(lastName, other.lastName)
                && Objects.equals(email, other.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.client.HazelcastClient;
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 directory of our project.

mvn package

This will produce a JAR file called cdc-tutorial-1.0-SNAPSHOT.jar in the target directory 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):

... Snapshot ended with SnapshotResult [...]
... Obtained valid replication slot ReplicationSlot [...]
... REPLICA IDENTITY for 'inventory.customers' is 'FULL'; UPDATE AND DELETE events will contain the previous values of all the columns
... 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, email=gbailey@foobar.com}
    Customer {id=1003, firstName=Edward, lastName=Walker, email=ed@walker.com}
    Customer {id=1004, firstName=Anne, lastName=Kretchmar, email=annek@noanswer.org}
    Customer {id=1001, firstName=Sally, lastName=Thomas, email=sally.thomas@acme.com}

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

UPDATE customers SET first_name='Anne Marie' WHERE id=1004;

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, email=gbailey@foobar.com}
    Customer {id=1003, firstName=Edward, lastName=Walker, email=ed@walker.com}
    Customer {id=1004, firstName=Anne Marie, lastName=Kretchmar, email=annek@noanswer.org}
    Customer {id=1001, firstName=Sally, lastName=Thomas, email=sally.thomas@acme.com}

One more:

UPDATE customers SET email='edward.walker@walker.com' WHERE id=1003;
Currently there are following customers in the cache:
    Customer {id=1002, firstName=George, lastName=Bailey, email=gbailey@foobar.com}
    Customer {id=1003, firstName=Edward, lastName=Walker, email=edward.walker@walker.com}
    Customer {id=1004, firstName=Anne Marie, lastName=Kretchmar, email=annek@noanswer.org}
    Customer {id=1001, firstName=Sally, lastName=Thomas, email=sally.thomas@acme.com}

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