How to filter a stream of events

Problem:

you have events in a Kafka topic, and you want to filter some of them out so that only those you're interested in appear in another topic.

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Example use case:

Consider a topic with events that represent book publications. In this tutorial, we'll write a program that creates a new topic which only contains the events for a particular author.

Code example:

Try it

1
Initialize the project

To get started, make a new directory anywhere you’d like for this project:

mkdir filter-events && cd filter-events

Then make the following directories to set up its structure:

mkdir src test

2
Get Confluent Platform

Next, create the following docker-compose.yml file to obtain Confluent Platform:

---
version: '2'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:5.3.0
    hostname: zookeeper
    container_name: zookeeper
    ports:
      - "2181:2181"
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

  broker:
    image: confluentinc/cp-enterprise-kafka:5.3.0
    hostname: broker
    container_name: broker
    depends_on:
      - zookeeper
    ports:
      - "29092:29092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
      KAFKA_METRIC_REPORTERS: io.confluent.metrics.reporter.ConfluentMetricsReporter
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
      KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
      CONFLUENT_METRICS_REPORTER_BOOTSTRAP_SERVERS: broker:9092
      CONFLUENT_METRICS_REPORTER_ZOOKEEPER_CONNECT: zookeeper:2181
      CONFLUENT_METRICS_REPORTER_TOPIC_REPLICAS: 1
      CONFLUENT_METRICS_ENABLE: 'true'
      CONFLUENT_SUPPORT_CUSTOMER_ID: 'anonymous'

  schema-registry:
    image: confluentinc/cp-schema-registry:5.3.0
    hostname: schema-registry
    container_name: schema-registry
    depends_on:
      - zookeeper
      - broker
    ports:
      - "8081:8081"
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_CONNECTION_URL: 'zookeeper:2181'

  ksql-server:
    image: confluentinc/cp-ksql-server:5.3.0
    hostname: ksql-server
    container_name: ksql-server
    depends_on:
      - broker
      - schema-registry
    ports:
      - "8088:8088"
    environment:
      KSQL_CONFIG_DIR: "/etc/ksql"
      KSQL_LOG4J_OPTS: "-Dlog4j.configuration=file:/etc/ksql/log4j-rolling.properties"
      KSQL_BOOTSTRAP_SERVERS: "broker:9092"
      KSQL_HOST_NAME: ksql-server
      KSQL_APPLICATION_ID: "cp-all-in-one"
      KSQL_LISTENERS: "http://0.0.0.0:8088"
      KSQL_CACHE_MAX_BYTES_BUFFERING: 0
      KSQL_KSQL_SCHEMA_REGISTRY_URL: "http://schema-registry:8081"
      KSQL_PRODUCER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringProducerInterceptor"
      KSQL_CONSUMER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringConsumerInterceptor"

  ksql-cli:
    image: confluentinc/cp-ksql-cli:5.3.0
    container_name: ksql-cli
    depends_on:
      - broker
      - ksql-server
    entrypoint: /bin/sh
    tty: true
    volumes:
      - ./src:/opt/app/src
      - ./test:/opt/app/test

And launch it by running:

docker-compose up

3
Write the program interactively using the CLI

To begin developing interactively, open up the KSQL CLI:

docker exec -it ksql-cli ksql http://ksql-server:8088

First, you’ll need to create a Kafka topic and stream to represent the publications. The following creates both in one shot:

CREATE STREAM all_publications (author VARCHAR, title VARCHAR)
    WITH (kafka_topic = 'publication_events', partitions = 1, key = 'author', value_format = 'avro');

Then produce the following events to the stream:

INSERT INTO all_publications (author, title) VALUES ('C.S. Lewis', 'The Silver Chair');
INSERT INTO all_publications (author, title) VALUES ('George R. R. Martin', 'A Song of Ice and Fire');
INSERT INTO all_publications (author, title) VALUES ('C.S. Lewis', 'Perelandra');
INSERT INTO all_publications (author, title) VALUES ('George R. R. Martin', 'Fire & Blood');
INSERT INTO all_publications (author, title) VALUES ('J. R. R. Tolkien', 'The Hobbit');
INSERT INTO all_publications (author, title) VALUES ('J. R. R. Tolkien', 'The Lord of the Rings');
INSERT INTO all_publications (author, title) VALUES ('George R. R. Martin', 'A Dream of Spring');
INSERT INTO all_publications (author, title) VALUES ('J. R. R. Tolkien', 'The Fellowship of the Ring');
INSERT INTO all_publications (author, title) VALUES ('George R. R. Martin', 'The Ice Dragon');

Now that you have stream with some events in it, let’s read them out. 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 find all of the books written by George R. R. Martin. Issue the following transient query. This will block and continue to return results until its limit is reached or you tell it to stop.

SELECT author, title FROM all_publications WHERE author = 'George R. R. Martin' LIMIT 4;

Since the output looks right, the next step is to make the query continuous. Issue the following to create a new stream that is continously populated by its query:

CREATE STREAM george_martin WITH (kafka_topic = 'george_martin_books', partitions = 1) AS
    SELECT author, title
    FROM all_publications
    WHERE author = 'George R. R. Martin';

To check that it’s working, print out the contents of the output stream’s underlying topic:

PRINT 'george_martin_books' FROM BEGINNING LIMIT 4;

4
Write your statements to a file

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 STREAM all_publications (author VARCHAR, title VARCHAR)
    WITH (kafka_topic = 'publication_events',
          partitions = 1,
          key = 'author',
          value_format = 'avro');

CREATE STREAM george_martin
    WITH (kafka_topic = 'george_martin_books',
          partitions = 1) AS
    SELECT author, title
    FROM all_publications
    WHERE author = 'George R. R. Martin';

Test it

1
Create the test data

Create a file at test/input.json with the inputs for testing:

{
  "inputs": [
    {
      "topic": "publication_events",
      "key": "C.S. Lewis",
      "value": {
        "author": "C.S. Lewis",
        "title": "The Silver Chair"
      }
    },
    {
      "topic": "publication_events",
      "key": "George R. R. Martin",
      "value": {
        "author": "George R. R. Martin",
        "title": "A Song of Ice and Fire"
      }
    },
    {
      "topic": "publication_events",
      "key": "C.S. Lewis",
      "value": {
        "author": "C.S. Lewis",
        "title": "Perelandra"
      }
    },
    {
      "topic": "publication_events",
      "key": "George R. R. Martin",
      "value": {
        "author": "George R. R. Martin",
        "title": "Fire & Blood"
      }
    },
    {
      "topic": "publication_events",
      "key": "J. R. R. Tolkien",
      "value": {
        "author": "J. R. R. Tolkien",
        "title": "The Hobbit"
      }
    },
    {
      "topic": "publication_events",
      "key": "J. R. R. Tolkien",
      "value": {
        "author": "J. R. R. Tolkien",
        "title": "The Lord of the Rings"
      }
    },
    {
      "topic": "publication_events",
      "key": "George R. R. Martin",
      "value": {
        "author": "George R. R. Martin",
        "title": "A Dream of Spring"
      }
    },
    {
      "topic": "publication_events",
      "key": "J. R. R. Tolkien",
      "value": {
        "author": "J. R. R. Tolkien",
        "title": "The Fellowship of the Ring"
      }
    },
    {
      "topic": "publication_events",
      "key": "George R. R. Martin",
      "value": {
        "author": "George R. R. Martin",
        "title": "The Ice Dragon"
      }
    }
  ]
}

Similarly, create a file at test/output.json with the expected outputs:

{
  "outputs": [
    {
      "topic": "george_martin_books",
      "key": "George R. R. Martin",
      "value": {
        "AUTHOR": "George R. R. Martin",
        "TITLE": "A Song of Ice and Fire"
      }
    },
    {
      "topic": "george_martin_books",
      "key": "George R. R. Martin",
      "value": {
        "AUTHOR": "George R. R. Martin",
        "TITLE": "Fire & Blood"
      }
    },
    {
      "topic": "george_martin_books",
      "key": "George R. R. Martin",
      "value": {
        "AUTHOR": "George R. R. Martin",
        "TITLE": "A Dream of Spring"
      }
    },
    {
      "topic": "george_martin_books",
      "key": "George R. R. Martin",
      "value": {
        "AUTHOR": "George R. R. Martin",
        "TITLE": "The Ice Dragon"
      }
    }
  ]
}

2
Invoke the tests

Lastly, invoke the tests using the test runner and the statements file that you created earlier:

docker exec ksql-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!

Take it to production

1
Send the statements to the REST API

Launch your statements into production by sending them to the REST API with the following command:

statements=$(< src/statements.sql) && \
    echo '{"ksql":"'$statements'", "streamsProperties": {}}' | \
        curl -X "POST" "http://localhost:8088/ksql" \
             -H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" \
             -d @- | \
        jq