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How to count a stream of events

Question:

How can you count the number of events in a Kafka topic based on some criteria?

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Example use case:

Suppose you have a topic with events that represent ticket sales for movies. In this tutorial, you'll see an example of 'groupby count' in Kafka Streams, ksqlDB, and Flink SQL. We'll write a program that calculates the total number of tickets sold per movie.

Hands-on code example:

Confluent Cloud ksqlDB Flink SQL Kafka Streams

Short Answer

Persistent query using COUNT.

CREATE TABLE MOVIE_TICKETS_SOLD AS
    SELECT TITLE,
           COUNT(TICKET_TOTAL_VALUE) AS TICKETS_SOLD
    FROM MOVIE_TICKET_SALES
    GROUP BY TITLE
    EMIT CHANGES;

Run it

Prerequisites

1

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

Initialize the project

2

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

mkdir aggregate-count && cd aggregate-count

Then make the following directories to set up its structure:

mkdir src test

Get Confluent Platform

3

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
    environment:
      KSQL_CONFIG_DIR: /etc/ksqldb
    tty: true
    volumes:
    - ./src:/opt/app/src
    - ./test:/opt/app/test

And launch it by running:

docker compose up -d

Write the program interactively using the CLI

4

The best way to interact with ksqlDB when you’re learning how things work is with the ksqlDB CLI. Fire it up as follows:

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

This tutorial takes a stream of individual movie ticket sales events and counts the total number of tickets sold per movie. Not all ticket prices are the same (apparently some of these theaters are fancier than others), but the task of the ksqlDB query is just to group and count regardless of ticket price.

This line of ksqlDB DDL creates a stream and its underlying Kafka topic to represent the annual sales totals. If the topic already exists, then ksqlDB simply registers is as the source of data underlying the new stream. The stream has three fields: title, the name of the movie; sale_ts, the time at which the ticket was sold; and ticket_total_value, the price paid for the ticket. The statement also specifies the underlying Kafka topic as movie-ticket-sales, that it should have a single partition, and defines Avro as its data format.

CREATE STREAM MOVIE_TICKET_SALES (title VARCHAR, sale_ts VARCHAR, ticket_total_value INT)
    WITH (KAFKA_TOPIC='movie-ticket-sales',
          PARTITIONS=1,
          VALUE_FORMAT='avro');

We’ll need some simulated ticket sales for this tutorial to be interesting. You can copy and paste all these lines into the CLI at once, or if you prefer, open up a second ksqlDB CLI and copy them one at a time after you have all the subsequent steps complete, so you can see the results produced in real time.

INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Aliens', '2019-07-18T10:00:00Z', 10);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard', '2019-07-18T10:00:00Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard', '2019-07-18T10:01:00Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather', '2019-07-18T10:01:31Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('Die Hard', '2019-07-18T10:01:36Z', 24);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather', '2019-07-18T10:02:00Z', 18);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Big Lebowski', '2019-07-18T11:03:21Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Big Lebowski', '2019-07-18T11:03:50Z', 12);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather', '2019-07-18T11:40:00Z', 36);
INSERT INTO MOVIE_TICKET_SALES (title, sale_ts, ticket_total_value) VALUES ('The Godfather', '2019-07-18T11:40:09Z', 18);

Before we get too far, let’s set the auto.offset.reset configuration parameter to earliest. This means all new ksqlDB queries will automatically compute their results from the beginning of a stream, rather than the end. This isn’t always what you’ll want to do in production, but it makes query results much easier to see in examples like this.

SET 'auto.offset.reset' = 'earliest';

For the purposes of this tutorial only, we are also going to configure ksqlDB to buffer the aggregates as it builds them. This makes the query feel like it responds more slowly, but means that you get just one row per movie from which it is simpler to understand the concept:

SET 'ksql.streams.cache.max.bytes.buffering' = '10000000';

With our test data and configuration parameters in place, let’s try a query to compute our ticket totals. A SELECT statement with an EMIT CHANGES in ksqlDB is called a transient push query.

Unlike a traditional database query, which returns a single result set, the query continuously streams back results. Once we stop it, it is gone and will not keep processing the input stream. That’s what we’re doing in this step. The counterpart to a transient query is a persistent query, which we’ll create a few steps from now.

If you’re familiar with SQL, the text of the query itself is fairly self-explanatory. We are calculating the total number of records in each group, grouped by movie title. Note that COUNT(TICKET_TOTAL_VALUE) is still just counting the number of rows in the group, and is not doing any calculation based on ticket value itself. This is a standard SQL idiom that applies in ksqlDB as well.

SELECT TITLE,
       COUNT(TICKET_TOTAL_VALUE) AS TICKETS_SOLD
FROM MOVIE_TICKET_SALES
GROUP BY TITLE
EMIT CHANGES
LIMIT 3;

This should yield the following output:

+--------------------+--------------------+
|TITLE               |TICKETS_SOLD        |
+--------------------+--------------------+
|Aliens              |1                   |
|Die Hard            |3                   |
|The Big Lebowski    |2                   |
Limit Reached
Query terminated

Since the output looks right, the next step is to make the query persistent. This looks exactly like the push query, except we have added a CREATE TABLE AS statement to the beginning of it. This statement returns to the CLI prompt right away, having created a persistent stream processing program running in the ksqlDB engine, continuously processing input records and updating the resulting MOVIE_TICKETS_SOLD table. Moreover, we don’t see the results of the query displayed in the CLI, because they are updating the newly-created table itself. That table is available to other ksqlDB queries for further processing, and by default all its records are produced to a topic having the same name (MOVIE_TICKETS_SOLD).

CREATE TABLE MOVIE_TICKETS_SOLD AS
    SELECT TITLE,
           COUNT(TICKET_TOTAL_VALUE) AS TICKETS_SOLD
    FROM MOVIE_TICKET_SALES
    GROUP BY TITLE
    EMIT CHANGES;

Now let’s directly inspect that output topic using the print ksqlDB CLI command. We could also SELECT * FROM MOVIE_TICKETS_SOLD, but here we opt for a more direct approach.

PRINT MOVIE_TICKETS_SOLD FROM BEGINNING LIMIT 3;

This should yield the following output:

Key format: KAFKA_STRING
Value format: AVRO or KAFKA_STRING
rowtime: 2020/05/04 21:19:17.935 Z, key: Aliens, value: {"TICKETS_SOLD": 1}, partition: 0
rowtime: 2020/05/04 21:19:18.365 Z, key: Die Hard, value: {"TICKETS_SOLD": 3}, partition: 0
rowtime: 2020/05/04 21:19:18.586 Z, key: The Big Lebowski, value: {"TICKETS_SOLD": 2}, partition: 0
Topic printing ceased

Notice that ksqlDB is storing the TITLE in the key of the Kafka message. It does this because TITLE is the primary key of the MOVIE_TICKETS_SOLD table. If needed, a copy of TITLE can also be stored in the value by adding AsValue(TITLE) in the projection.

Write your statements to a file

5

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 MOVIE_TICKET_SALES (title VARCHAR, sale_ts VARCHAR, ticket_total_value INT)
    WITH (KAFKA_TOPIC='movie-ticket-sales',
          PARTITIONS=1,
          VALUE_FORMAT='avro',
          TIMESTAMP='sale_ts',
          TIMESTAMP_FORMAT='yyyy-MM-dd''T''HH:mm:ssX');

CREATE TABLE MOVIE_TICKETS_SOLD AS
    SELECT TITLE,
           COUNT(TICKET_TOTAL_VALUE) AS TICKETS_SOLD
    FROM MOVIE_TICKET_SALES
    GROUP BY TITLE
    EMIT CHANGES;

Test it

Create the test data

1

The Confluent ksqlDB CLI Docker image contains a program called the ksql-test-runner. We can pass this program a JSON file describing our desired input data, a JSON file containing the intended output results, and a file of ksqlDB queries to run, and it will tell us whether our queries successfully turn the input into the output. To get started, create a file at test/input.json with the inputs for testing

{
  "inputs": [
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "Aliens", "SALE_TS": "2019-07-18T10:00:00Z", "TICKET_TOTAL_VALUE": 10}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:00:00Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:01:00Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T10:01:31Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "Die Hard", "SALE_TS": "2019-07-18T10:01:36Z", "TICKET_TOTAL_VALUE": 24}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T10:02:00Z", "TICKET_TOTAL_VALUE": 18}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Big Lebowski", "SALE_TS": "2019-07-18T11:03:21Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Big Lebowski", "SALE_TS": "2019-07-18T11:03:50Z", "TICKET_TOTAL_VALUE": 12}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T11:40:00Z", "TICKET_TOTAL_VALUE": 36}},
    {"topic": "movie-ticket-sales", "key": null, "value": {"TITLE": "The Godfather", "SALE_TS": "2019-07-18T11:40:09Z", "TICKET_TOTAL_VALUE": 18}}
  ]
}

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

{
  "outputs": [
    {"topic": "MOVIE_TICKETS_SOLD", "key": "Aliens", "value": {"TICKETS_SOLD": 1}, "timestamp": 1563444000000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "Die Hard", "value": {"TICKETS_SOLD": 1}, "timestamp": 1563444000000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "Die Hard", "value": {"TICKETS_SOLD": 2}, "timestamp": 1563444060000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Godfather", "value": {"TICKETS_SOLD": 1}, "timestamp": 1563444091000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "Die Hard", "value": {"TICKETS_SOLD": 3}, "timestamp": 1563444096000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Godfather", "value": {"TICKETS_SOLD": 2}, "timestamp": 1563444120000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Big Lebowski", "value": {"TICKETS_SOLD": 1}, "timestamp": 1563447801000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Big Lebowski", "value": {"TICKETS_SOLD": 2}, "timestamp": 1563447830000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Godfather", "value": {"TICKETS_SOLD": 3}, "timestamp": 1563450000000},
    {"topic": "MOVIE_TICKETS_SOLD", "key": "The Godfather", "value": {"TICKETS_SOLD": 4}, "timestamp": 1563450009000}
  ]
}

Invoke the tests

2

Finally, 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

When it passes (how’s that for confidence?), you will see this output:

	 >>> Test passed!

Deploy on Confluent Cloud

Run your app with Confluent Cloud

1

Instead of running a local Kafka cluster, you may use Confluent Cloud, a fully managed Apache Kafka service.

  1. Sign up for Confluent Cloud, a fully managed Apache Kafka service.

  2. 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.

  3. 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.

  4. Click on LEARN and follow the instructions to launch a Kafka cluster and enable Schema Registry.

Confluent Cloud

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.