If you have two tables of reference data in Kafka topics, how can you join the tables on a common key?
This tutorial installs Confluent Platform using Docker. Before proceeding:
• Install Docker Desktop (version 4.0.0
or later) or Docker Engine (version 19.03.0
or later) if you don’t already have it
• Install the Docker Compose plugin if you don’t already have it. This isn’t necessary if you have Docker Desktop since it includes Docker Compose.
• Start Docker if it’s not already running, either by starting Docker Desktop or, if you manage Docker Engine with systemd
, via systemctl
• Verify that Docker is set up properly by ensuring no errors are output when you run docker info
and docker compose version
on the command line
To get started, make a new directory anywhere you’d like for this project:
mkdir join-table-and-table && cd join-table-and-table
Then make the following directories to set up its structure:
mkdir src test
Next, create the following docker-compose.yml
file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud):
version: '2'
services:
broker:
image: confluentinc/cp-kafka:7.4.1
hostname: broker
container_name: broker
ports:
- 29092:29092
environment:
KAFKA_BROKER_ID: 1
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT,CONTROLLER:PLAINTEXT
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_PROCESS_ROLES: broker,controller
KAFKA_NODE_ID: 1
KAFKA_CONTROLLER_QUORUM_VOTERS: 1@broker:29093
KAFKA_LISTENERS: PLAINTEXT://broker:9092,CONTROLLER://broker:29093,PLAINTEXT_HOST://0.0.0.0:29092
KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER
KAFKA_LOG_DIRS: /tmp/kraft-combined-logs
CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk
schema-registry:
image: confluentinc/cp-schema-registry:7.3.0
hostname: schema-registry
container_name: schema-registry
depends_on:
- broker
ports:
- 8081:8081
environment:
SCHEMA_REGISTRY_HOST_NAME: schema-registry
SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
ksqldb-server:
image: confluentinc/ksqldb-server:0.28.2
hostname: ksqldb-server
container_name: ksqldb-server
depends_on:
- broker
- schema-registry
ports:
- 8088:8088
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
KSQL_LOG4J_OPTS: -Dlog4j.configuration=file:/etc/ksqldb/log4j.properties
KSQL_BOOTSTRAP_SERVERS: broker:9092
KSQL_HOST_NAME: ksqldb-server
KSQL_LISTENERS: http://0.0.0.0:8088
KSQL_CACHE_MAX_BYTES_BUFFERING: 0
KSQL_KSQL_SCHEMA_REGISTRY_URL: http://schema-registry:8081
ksqldb-cli:
image: confluentinc/ksqldb-cli:0.28.2
container_name: ksqldb-cli
depends_on:
- broker
- ksqldb-server
entrypoint: /bin/sh
tty: true
environment:
KSQL_CONFIG_DIR: /etc/ksqldb
volumes:
- ./src:/opt/app/src
- ./test:/opt/app/test
And launch it by running:
docker compose up -d
To begin developing interactively, open up the ksqlDB CLI:
docker exec -it ksqldb-cli ksql http://ksqldb-server:8088
First, you’ll need to create a Kafka topic and table to represent the movie reference data. The following creates both in one shot:
CREATE TABLE movies (
title VARCHAR PRIMARY KEY,
id INT,
release_year INT
) WITH (
KAFKA_TOPIC='movies',
PARTITIONS=1,
VALUE_FORMAT='avro'
);
Likewise, you’ll need a Kafka topic and a second table to represent the additional movie data about leading actor:
CREATE TABLE lead_actor (
title VARCHAR PRIMARY KEY,
actor_name VARCHAR
) WITH (
KAFKA_TOPIC='lead_actors',
PARTITIONS=1,
VALUE_FORMAT='avro'
);
Then insert the following movies:
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (48, 'Aliens', 1986);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (294, 'Die Hard', 1998);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (128, 'The Big Lebowski', 1998);
INSERT INTO MOVIES (ID, TITLE, RELEASE_YEAR) VALUES (42, 'The Godfather', 1998);
In a similar manner, populate the lead actor information:
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('Aliens','Sigourney Weaver');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('Die Hard','Bruce Willis');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('The Big Lebowski','Jeff Bridges');
INSERT INTO LEAD_ACTOR (TITLE, ACTOR_NAME) VALUES ('The Godfather','Al Pacino');
Now that you have events in both tables, let’s join them up to obtain an enriched table of movie information. The first thing to do is set the following properties to ensure that you’re reading from the beginning of the stream:
SET 'auto.offset.reset' = 'earliest';
Let’s enrich the movie data with more information about who the lead actor in the movie is. The following query does a left join between the movie table and the lead actor table. This will block and continue to return results until it’s limit is reached or you tell it to stop.
SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
FROM MOVIES M
INNER JOIN LEAD_ACTOR L
ON M.TITLE = L.TITLE
EMIT CHANGES
LIMIT 3;
This should yield the following output:
+--------------------+--------------------+--------------------+--------------------+
|ID |M_TITLE |RELEASE_YEAR |ACTOR_NAME |
+--------------------+--------------------+--------------------+--------------------+
|48 |Aliens |1986 |Sigourney Weaver |
|294 |Die Hard |1998 |Bruce Willis |
|128 |The Big Lebowski |1998 |Jeff Bridges |
Limit Reached
Query terminated
Since the output looks right, the next step is to make the query continuous. Issue the following to create a new table that is continuously populated by its query:
CREATE TABLE MOVIES_ENRICHED AS
SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
FROM MOVIES M
INNER JOIN LEAD_ACTOR L
ON M.TITLE = L.TITLE
EMIT CHANGES;
To check that it’s working, print out the contents of the output stream’s underlying topic:
PRINT MOVIES_ENRICHED FROM BEGINNING LIMIT 3;
This should yield the following output:
Key format: KAFKA_STRING
Value format: AVRO
rowtime: 2020/05/04 22:03:47.754 Z, key: Aliens, value: {"ID": 48, "RELEASE_YEAR": 1986, "ACTOR_NAME": "Sigourney Weaver"}, partition: 0
rowtime: 2020/05/04 22:03:47.879 Z, key: Die Hard, value: {"ID": 294, "RELEASE_YEAR": 1998, "ACTOR_NAME": "Bruce Willis"}, partition: 0
rowtime: 2020/05/04 22:03:47.997 Z, key: The Big Lebowski, value: {"ID": 128, "RELEASE_YEAR": 1998, "ACTOR_NAME": "Jeff Bridges"}, partition: 0
Topic printing ceased
Type 'exit' and hit enter to exit the ksqlDB CLI.
Now that you have a series of statements that’s doing the right thing, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql
with the following content:
CREATE TABLE movies (TITLE VARCHAR PRIMARY KEY, id INT, release_year INT)
WITH (KAFKA_TOPIC='movies',
PARTITIONS=1,
VALUE_FORMAT='avro');
CREATE TABLE lead_actor (TITLE VARCHAR PRIMARY KEY, actor_name VARCHAR)
WITH (KAFKA_TOPIC='lead_actors',
PARTITIONS=1,
VALUE_FORMAT='avro');
CREATE TABLE MOVIES_ENRICHED AS
SELECT M.ID, M.TITLE, M.RELEASE_YEAR, L.ACTOR_NAME
FROM MOVIES M
INNER JOIN LEAD_ACTOR L
ON M.TITLE = L.TITLE;
Create a file at test/input.json
with the inputs for testing:
{
"inputs": [
{
"topic": "movies",
"key": "Aliens",
"value": {
"ID": 48,
"RELEASE_YEAR": 1986
}
},
{
"topic": "movies",
"key": "Die Hard",
"value": {
"ID": 294,
"RELEASE_YEAR": 1998
}
},
{
"topic": "movies",
"key": "The Big Lebowski",
"value": {
"ID": 128,
"RELEASE_YEAR": 1998
}
},
{
"topic": "movies",
"key": "The Godfather",
"value": {
"ID": 128,
"RELEASE_YEAR": 1998
}
},
{
"topic": "lead_actors",
"key": "Aliens",
"value": {
"ACTOR_NAME": "Sigourney Weaver"
}
},
{
"topic": "lead_actors",
"key": "Die Hard",
"value": {
"ACTOR_NAME": "Bruce Willis"
}
},
{
"topic": "lead_actors",
"key": "The Big Lebowski",
"value": {
"ACTOR_NAME": "Jeff Bridges"
}
},
{
"topic": "lead_actors",
"key": "The Godfather",
"value": {
"ACTOR_NAME": "Al Pacino"
}
}
]
}
Similarly, create a file at test/output.json
with the expected outputs:
{
"outputs": [
{
"topic": "MOVIES_ENRICHED",
"key": "Aliens",
"value": {
"ID": 48,
"RELEASE_YEAR": 1986,
"ACTOR_NAME": "Sigourney Weaver"
}
},
{
"topic": "MOVIES_ENRICHED",
"key": "Die Hard",
"value": {
"ID": 294,
"RELEASE_YEAR": 1998,
"ACTOR_NAME": "Bruce Willis"
}
},
{
"topic": "MOVIES_ENRICHED",
"key": "The Big Lebowski",
"value": {
"ID": 128,
"RELEASE_YEAR": 1998,
"ACTOR_NAME": "Jeff Bridges"
}
},
{
"topic": "MOVIES_ENRICHED",
"key": "The Godfather",
"value": {
"ID": 128,
"RELEASE_YEAR": 1998,
"ACTOR_NAME": "Al Pacino"
}
}
]
}
Lastly, invoke the tests using the test runner and the statements file that you created earlier:
docker exec ksqldb-cli ksql-test-runner -i /opt/app/test/input.json -s /opt/app/src/statements.sql -o /opt/app/test/output.json
Which should pass:
>>> Test passed!
Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.
Sign up for Confluent Cloud, a fully managed Apache Kafka service.
After you log in to Confluent Cloud Console, click on Add cloud environment
and name the environment learn-kafka
. Using a new environment keeps your learning resources separate from your other Confluent Cloud resources.
From the Billing & payment
section in the Menu, apply the promo code CC100KTS
to receive an additional $100 free usage on Confluent Cloud (details).
Click on LEARN and follow the instructions to launch a Kafka cluster and to enable Schema Registry.
Next, from the Confluent Cloud Console, click on Clients
to get the cluster-specific configurations, e.g. Kafka cluster bootstrap servers and credentials, Confluent Cloud Schema Registry and credentials, etc., and set the appropriate parameters in your client application.
Now you’re all set to run your streaming application locally, backed by a Kafka cluster fully managed by Confluent Cloud.