Change Data Capture from PostgreSQL
Change Data Capture (CDC) refers to the process of observing changes made to a database and extracting them in a form usable by other systems, for the purposes of replication, analysis and many more. Basically anything that requires keeping multiple heterogeneous datastores in sync.
CDC is especially important to Jet, because it allows for the streaming of changes from databases, which can be efficiently processed by Jet. Jet's implementation is based on Debezium, which is an open source distributed platform for change data capture.
Let's see an example, how to process change events in Jet, from a PostgreSQL database.
1. Install Docker
This tutorial uses Docker to simplify the setup of a PostgreSQL database, which you can freely experiment on.
- Follow Docker's Get Started instructions and install it on your system.
- 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.
- Run
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 "0.0.0.0", 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.
3. Start 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.
postgres=#
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:
\dt;
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;
4. Start Hazelcast Jet
- Download Hazelcast Jet
wget https://github.com/hazelcast/hazelcast-jet/releases/download/v4.4/hazelcast-jet-4.4.tar.gz
tar zxvf hazelcast-jet-4.4.tar.gz && cd hazelcast-jet-4.4
If you already have Jet and you skipped the above steps, make sure to follow from here on.
- Make sure the PostgreSQL CDC plugin is in the
lib/
directory.
ls lib/
You should see the following jars:
- hazelcast-jet-cdc-debezium-4.4.jar
- hazelcast-jet-cdc-postgres-4.4.jar
- Start Jet:
bin/jet-start
- When you see output like this, Jet is up:
Members {size:1, ver:1} [
Member [192.168.1.5]:5701 - e7c26f7c-df9e-4994-a41d-203a1c63480e this
]
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:
plugins {
id 'com.github.johnrengelman.shadow' version '5.2.0'
id 'java'
}
group 'org.example'
version '1.0-SNAPSHOT'
repositories.mavenCentral()
dependencies {
implementation 'com.hazelcast.jet:hazelcast-jet:4.4'
implementation 'com.hazelcast.jet:hazelcast-jet-cdc-debezium:4.4'
implementation 'com.hazelcast.jet:hazelcast-jet-cdc-postgres:4.4'
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">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>cdc-tutorial</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.target>1.8</maven.compiler.target>
<maven.compiler.source>1.8</maven.compiler.source>
</properties>
<dependencies>
<dependency>
<groupId>com.hazelcast.jet</groupId>
<artifactId>hazelcast-jet</artifactId>
<version>4.4</version>
</dependency>
<dependency>
<groupId>com.hazelcast.jet</groupId>
<artifactId>hazelcast-jet-cdc-debezium</artifactId>
<version>4.4</version>
</dependency>
<dependency>
<groupId>com.hazelcast.jet</groupId>
<artifactId>hazelcast-jet-cdc-postgres</artifactId>
<version>4.4</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>2.11.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<configuration>
<archive>
<manifest>
<mainClass>org.example.JetJob</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
</plugins>
</build>
</project>
6. Define Jet Job
Let's write the Jet 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.jet.Jet;
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")
.setDatabaseAddress("127.0.0.1")
.setDatabasePort(5432)
.setDatabaseUser("postgres")
.setDatabasePassword("postgres")
.setDatabaseName("postgres")
.setTableWhitelist("inventory.customers")
.build();
Pipeline pipeline = Pipeline.create();
pipeline.readFrom(source)
.withoutTimestamps()
.peek()
.writeTo(CdcSinks.map("customers",
r -> r.key().toMap().get("id"),
r -> r.value().toObject(Customer.class).toString()));
JobConfig cfg = new JobConfig().setName("postgres-monitor");
Jet.bootstrappedInstance().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 {
@JsonProperty("id")
public int id;
@JsonProperty("first_name")
public String firstName;
@JsonProperty("last_name")
public String lastName;
@JsonProperty("email")
public String email;
public Customer() {
}
public Customer(int id, String firstName, String lastName, String email) {
super();
this.id = id;
this.firstName = firstName;
this.lastName = lastName;
this.email = email;
}
@Override
public int hashCode() {
return Objects.hash(email, firstName, id, lastName);
}
@Override
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);
}
@Override
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.jet.Jet;
import com.hazelcast.jet.JetInstance;
public class CacheRead {
public static void main(String[] args) {
JetInstance instance = Jet.newJetClient();
System.out.println("Currently there are following customers in the cache:");
instance.getMap("customers").values().forEach(c -> System.out.println("\t" + c));
instance.shutdown();
}
}
7. Package
Now that we have all the pieces, we need to submit it to Jet for execution. Since Jet 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 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.
8. Submit for Execution
Assuming our cluster is still running and the database is up, all we need to issue is following command:
<path_to_jet>/bin/jet submit build/libs/cdc-tutorial-1.0-SNAPSHOT.jar
<path_to_jet>/bin/jet submit target/cdc-tutorial-1.0-SNAPSHOT.jar
The output in the Jet 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
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 Jet 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}
10. Clean up
Let's clean-up after ourselves. First we cancel our Jet job:
<path_to_jet>/bin/jet cancel postgres-monitor
Then we shut down our Jet member/cluster:
<path_to_jet>/bin/jet-stop
You can 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
Again, 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