If you have events in a Kafka topic and a table of reference data (also known as a lookup table), how can you join each event in the stream to a piece of data in the table based 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-stream-and-table && cd join-stream-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 (ID INT PRIMARY KEY, title VARCHAR, release_year INT)
WITH (kafka_topic='movies', partitions=1, value_format='avro');
Likewise, you’ll need a Kafka topic and a stream to represent the ratings of those movies:
CREATE STREAM ratings (MOVIE_ID INT KEY, rating DOUBLE)
WITH (kafka_topic='ratings', partitions=1, value_format='avro');
Then insert the following movies:
INSERT INTO movies (id, title, release_year) VALUES (294, 'Die Hard', 1998);
INSERT INTO movies (id, title, release_year) VALUES (354, 'Tree of Life', 2011);
INSERT INTO movies (id, title, release_year) VALUES (782, 'A Walk in the Clouds', 1995);
INSERT INTO movies (id, title, release_year) VALUES (128, 'The Big Lebowski', 1998);
INSERT INTO movies (id, title, release_year) VALUES (780, 'Super Mario Bros.', 1993);
In a similar manner, populate the ratings stream:
INSERT INTO ratings (movie_id, rating) VALUES (294, 8.2);
INSERT INTO ratings (movie_id, rating) VALUES (294, 8.5);
INSERT INTO ratings (movie_id, rating) VALUES (354, 9.9);
INSERT INTO ratings (movie_id, rating) VALUES (354, 9.7);
INSERT INTO ratings (movie_id, rating) VALUES (782, 7.8);
INSERT INTO ratings (movie_id, rating) VALUES (782, 7.7);
INSERT INTO ratings (movie_id, rating) VALUES (128, 8.7);
INSERT INTO ratings (movie_id, rating) VALUES (128, 8.4);
INSERT INTO ratings (movie_id, rating) VALUES (780, 2.1);
|
Populating Data
One fundamental operation for working with tables is populating them with data. There are a number of ways to do this:
This tutorial uses ksqlDB |
Now that you events in both the stream and the table, let’s join them up to obtain a stream of rated movies. 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 ratings stream with more information about the movie that it refers to. The following query does a left join between the ratings stream and the movies table on the movie id. This will block and continue to return results until it’s limit is reached or you tell it to stop.
SELECT ratings.movie_id AS ID, title, release_year, rating
FROM ratings
LEFT JOIN movies ON ratings.movie_id = movies.id
EMIT CHANGES LIMIT 9;
This should yield the following output:
+--------------------+--------------------+--------------------+--------------------+
|ID |TITLE |RELEASE_YEAR |RATING |
+--------------------+--------------------+--------------------+--------------------+
|294 |Die Hard |1998 |8.2 |
|294 |Die Hard |1998 |8.5 |
|354 |Tree of Life |2011 |9.9 |
|354 |Tree of Life |2011 |9.7 |
|782 |A Walk in the Clouds|1995 |7.8 |
|782 |A Walk in the Clouds|1995 |7.7 |
|128 |The Big Lebowski |1998 |8.7 |
|128 |The Big Lebowski |1998 |8.4 |
|780 |Super Mario Bros. |1993 |2.1 |
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 stream that is continuously populated by its query:
CREATE STREAM rated_movies
WITH (kafka_topic='rated_movies',
value_format='avro') AS
SELECT ratings.movie_id as id, title, rating
FROM ratings
LEFT JOIN movies ON ratings.movie_id = movies.id;
To check that it’s working, print out the contents of the output stream’s underlying topic:
PRINT rated_movies FROM BEGINNING LIMIT 9;
This should yield the following output:
Key format: KAFKA_INT
Value format: AVRO
rowtime: 2020/05/04 21:49:12.099 Z, key: 294, value: {"TITLE": "Die Hard", "RATING": 8.2}, partition: 0
rowtime: 2020/05/04 21:49:12.174 Z, key: 294, value: {"TITLE": "Die Hard", "RATING": 8.5}, partition: 0
rowtime: 2020/05/04 21:49:12.245 Z, key: 354, value: {"TITLE": "Tree of Life", "RATING": 9.9}, partition: 0
rowtime: 2020/05/04 21:49:12.307 Z, key: 354, value: {"TITLE": "Tree of Life", "RATING": 9.7}, partition: 0
rowtime: 2020/05/04 21:49:12.371 Z, key: 782, value: {"TITLE": "A Walk in the Clouds", "RATING": 7.8}, partition: 0
rowtime: 2020/05/04 21:49:12.446 Z, key: 782, value: {"TITLE": "A Walk in the Clouds", "RATING": 7.7}, partition: 0
rowtime: 2020/05/04 21:49:12.503 Z, key: 128, value: {"TITLE": "The Big Lebowski", "RATING": 8.7}, partition: 0
rowtime: 2020/05/04 21:49:12.576 Z, key: 128, value: {"TITLE": "The Big Lebowski", "RATING": 8.4}, partition: 0
rowtime: 2020/05/04 21:49:12.634 Z, key: 780, value: {"TITLE": "Super Mario Bros.", "RATING": 2.1}, 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 (ID INT PRIMARY KEY, title VARCHAR, release_year INT)
WITH (kafka_topic='movies', partitions=1, value_format='avro');
CREATE STREAM ratings (MOVIE_ID INT KEY, rating DOUBLE)
WITH (kafka_topic='ratings', partitions=1, value_format='avro');
CREATE STREAM rated_movies
WITH (kafka_topic='rated_movies',
value_format='avro') AS
SELECT ratings.movie_id AS id, title, release_year, rating
FROM ratings
LEFT JOIN movies ON ratings.movie_id = movies.id;
Create a file at test/input.json with the inputs for testing:
{
"inputs": [
{
"topic": "movies",
"key": 294,
"value": {
"title": "Die Hard",
"release_year": 1998
}
},
{
"topic": "movies",
"key": 354,
"value": {
"title": "Tree of Life",
"release_year": 2011
}
},
{
"topic": "movies",
"key": 782,
"value": {
"title": "A Walk in the Clouds",
"release_year": 1995
}
},
{
"topic": "movies",
"key": 128,
"value": {
"title": "The Big Lebowski",
"release_year": 1998
}
},
{
"topic": "movies",
"key": 780,
"value": {
"title": "Super Mario Bros.",
"release_year": 1993
}
},
{
"topic": "ratings",
"key": 294,
"value": {
"rating": 8.2
}
},
{
"topic": "ratings",
"key": 294,
"value": {
"rating": 8.5
}
},
{
"topic": "ratings",
"key": 354,
"value": {
"rating": 9.9
}
},
{
"topic": "ratings",
"key": 354,
"value": {
"rating": 9.7
}
},
{
"topic": "ratings",
"key": 782,
"value": {
"rating": 7.8
}
},
{
"topic": "ratings",
"key": 782,
"value": {
"rating": 7.7
}
},
{
"topic": "ratings",
"key": 128,
"value": {
"rating": 8.7
}
},
{
"topic": "ratings",
"key": 128,
"value": {
"rating": 8.4
}
},
{
"topic": "ratings",
"key": 780,
"value": {
"rating": 2.1
}
}
]
}
Similarly, create a file at test/output.json with the expected outputs:
{
"outputs": [
{
"topic": "rated_movies",
"key": 294,
"value": {
"TITLE": "Die Hard",
"RELEASE_YEAR": 1998,
"RATING": 8.2
}
},
{
"topic": "rated_movies",
"key": 294,
"value": {
"TITLE": "Die Hard",
"RELEASE_YEAR": 1998,
"RATING": 8.5
}
},
{
"topic": "rated_movies",
"key": 354,
"value": {
"TITLE": "Tree of Life",
"RELEASE_YEAR": 2011,
"RATING": 9.9
}
},
{
"topic": "rated_movies",
"key": 354,
"value": {
"TITLE": "Tree of Life",
"RELEASE_YEAR": 2011,
"RATING": 9.7
}
},
{
"topic": "rated_movies",
"key": 782,
"value": {
"TITLE": "A Walk in the Clouds",
"RELEASE_YEAR": 1995,
"RATING": 7.8
}
},
{
"topic": "rated_movies",
"key": 782,
"value": {
"TITLE": "A Walk in the Clouds",
"RELEASE_YEAR": 1995,
"RATING": 7.7
}
},
{
"topic": "rated_movies",
"key": 128,
"value": {
"TITLE": "The Big Lebowski",
"RELEASE_YEAR": 1998,
"RATING": 8.7
}
},
{
"topic": "rated_movies",
"key": 128,
"value": {
"TITLE": "The Big Lebowski",
"RELEASE_YEAR": 1998,
"RATING": 8.4
}
},
{
"topic": "rated_movies",
"key": 780,
"value": {
"TITLE": "Super Mario Bros.",
"RELEASE_YEAR": 1993,
"RATING": 2.1
}
}
]
}
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 Environments in the lefthand navigation, 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). To avoid having to enter a credit card, add an additional promo code CONFLUENTDEV1. With this promo code, you will not have to enter a credit card for 30 days or until your credits run out.
Click on LEARN and follow the instructions to launch a Kafka cluster and 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.