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Generate complex streams of test data

Question:

How can you generate realistic test data in Kafka?

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Example use case:

Perhaps you are building an application, or constructing a pipeline, and you would like some mock data to use in testing. Using this connector, you can generate realistic test data that can also be made referentially consistent across topics.

Hands-on code example:

ksqlDB Basic Kafka

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 generate-test-data-streams && cd generate-test-data-streams

Get Confluent Platform

3

Next, create the following docker-compose.yml file to obtain Confluent Platform (for Kafka in the cloud, see Confluent Cloud). Make sure that you create this file in the same place as the cities.sql file that you created above.

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
  connect:
    image: confluentinc/cp-kafka-connect-base:7.3.0
    container_name: connect
    depends_on:
    - broker
    - schema-registry
    ports:
    - 8083:8083
    environment:
      CONNECT_BOOTSTRAP_SERVERS: broker:9092
      CONNECT_REST_ADVERTISED_HOST_NAME: connect
      CONNECT_GROUP_ID: kafka-connect
      CONNECT_CONFIG_STORAGE_TOPIC: _kafka-connect-configs
      CONNECT_OFFSET_STORAGE_TOPIC: _kafka-connect-offsets
      CONNECT_STATUS_STORAGE_TOPIC: _kafka-connect-status
      CONNECT_KEY_CONVERTER: org.apache.kafka.connect.converters.IntegerConverter
      CONNECT_VALUE_CONVERTER: io.confluent.connect.avro.AvroConverter
      CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: http://schema-registry:8081
      CONNECT_LOG4J_ROOT_LOGLEVEL: INFO
      CONNECT_LOG4J_LOGGERS: org.apache.kafka.connect.runtime.rest=WARN,org.reflections=ERROR
      CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: '1'
      CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: '1'
      CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: '1'
      CONNECT_LOG4J_APPENDER_STDOUT_LAYOUT_CONVERSIONPATTERN: '[%d] %p %X{connector.context}%m
        (%c:%L)%n'
      CONNECT_PLUGIN_PATH: /usr/share/java,/usr/share/confluent-hub-components/
    command:
    - bash
    - -c
    - "echo \"Installing connector plugins\"\nmkdir -p /usr/share/confluent-hub-components/\nconfluent-hub
      install --no-prompt --component-dir /usr/share/confluent-hub-components/ confluentinc/kafka-connect-datagen:0.6.0\n#\necho
      \"Launching Kafka Connect worker\"\n/etc/confluent/docker/run & \n#\nsleep infinity\n"
  kcat:
    image: edenhill/kcat:1.7.1
    container_name: kcat
    links:
    - broker
    entrypoint:
    - /bin/sh
    - -c
    - "apk add jq; \nwhile [ 1 -eq 1 ];do sleep 60;done\n"

Now launch Confluent Platform by running:

docker compose up -d

Before continuing, you need to wait for all the containers to fully start up. Run this script which will wait for the final dependencies in the chain to be ready:

#!/bin/bash

# Wait for Schema Registry to become available
while :
  do curl_status=$(curl -s -o /dev/null -w %{http_code} http://localhost:8081)
  echo -e $(date) " Component: Schema Registry\t\tHTTP state: " $curl_status "\t(waiting for 200)"
  if [ $curl_status -eq 200 ]
    then
      echo "✅✅ Schema Registry is ready"
      break
  fi
  sleep 5
done

# Wait for Kafka Connect to become available
while :
  do curl_status=$(curl -s -o /dev/null -w %{http_code} http://localhost:8083/)
  echo -e $(date) " Component: Kafka Connect \t\tHTTP state: " $curl_status "\t(waiting for 200)"
  if [ $curl_status -eq 200 ]
    then
      echo "✅✅ Kafka Connect is ready"
      break
  fi
  sleep 5
done

Once everything is ready you should see this, and get control back at your terminal:

Wed 1 Apr 2020 11:58:49 BST  Component: Schema Registry         HTTP state:  200        (waiting for 200)
✅ Schema Registry is ready
Wed 1 Apr 2020 11:58:49 BST  Component: Kafka Connect           HTTP state:  200        (waiting for 200)
✅ Kafka Connect is ready

Once the stack has started, run the following command to ensure that the connector plugin that we’re going to be using has been loaded.

docker exec -i connect curl -s localhost:8083/connector-plugins|jq '.[].class'|grep Datagen

You should see this output

"io.confluent.kafka.connect.datagen.DatagenConnector"

Create a standalone stream of test data

4

We’re going to use Kafka Connect to run the data generator, which is available as a source connector.

To start with, let’s create a connector populating a single topic with some random purchase transactions

curl -X PUT http://localhost:8083/connectors/pizza_orders/config \
     -i -H "Content-Type: application/json" -d '{
    "connector.class"                             : "io.confluent.kafka.connect.datagen.DatagenConnector",
    "quickstart"                                  : "pizza_orders",
    "schema.keyfield"                             : "store_id",
    "kafka.topic"                                 : "pizza_orders",
    "key.converter"                               : "org.apache.kafka.connect.converters.IntegerConverter",
    "value.converter"                             : "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url"         : "http://schema-registry:8081",
    "value.converter.schemas.enable"              : "false",
    "max.interval"                                : 1000,
    "tasks.max"                                   : "1"

}'

Check that the connector is running:

curl -s http://localhost:8083/connectors/pizza_orders/status

You should see that the state is RUNNING:

{"name":"pizza_orders","connector":{"state":"RUNNING","worker_id":"connect:8083"},"tasks":[{"id":0,"state":"RUNNING","worker_id":"connect:8083"}],"type":"source"}

Consume events from the test topic

5

With the connector running let’s now inspect the data that’s being generated to the Kafka topic. Here we’ll use kcat, but you could use any Kafka consumer if you wanted.

docker exec -i kcat kcat -b broker:9092 -t pizza_orders \
            -s value=avro -r http://schema-registry:8081        \
            -u -f 'Key     (%K bytes):\t%k\nPayload (%S bytes):\t%s\n--\n' \
            -C -c5 -o end

You should see 5 messages and then the command will exit. Observe that the one message is being generated per second, which is what was specified in the 'max.interval' = 1000 setting.

Key     (4 bytes):
Payload (122 bytes):	{"store_id": 5, "store_order_id": 1179, "coupon_code": 1046, "date": 18486, "status": "accepted", "order_lines": [{"product_id": 79, "category": "pizza", "quantity": 3, "unit_price": 19.609999999999999, "net_price": 58.829999999999998}, {"product_id": 6, "category": "pizza", "quantity": 3, "unit_price": 13.640000000000001, "net_price": 40.920000000000002}, {"product_id": 95, "category": "wings", "quantity": 1, "unit_price": 24.899999999999999, "net_price": 24.899999999999999}, {"product_id": 37, "category": "pizza", "quantity": 3, "unit_price": 14.619999999999999, "net_price": 43.859999999999999}]}
--
% Reached end of topic pizza_orders [0] at offset 180
Key     (4 bytes):
Payload (50 bytes):	{"store_id": 3, "store_order_id": 1180, "coupon_code": 1898, "date": 18811, "status": "accepted", "order_lines": [{"product_id": 22, "category": "calzone", "quantity": 5, "unit_price": 2.79, "net_price": 13.949999999999999}]}
--
% Reached end of topic pizza_orders [0] at offset 181
Key     (4 bytes):
Payload (99 bytes):	{"store_id": 1, "store_order_id": 1181, "coupon_code": 1991, "date": 18840, "status": "accepted", "order_lines": [{"product_id": 6, "category": "pizza", "quantity": 3, "unit_price": 13.640000000000001, "net_price": 40.920000000000002}, {"product_id": 80, "category": "dessert", "quantity": 5, "unit_price": 12.69, "net_price": 63.450000000000003}, {"product_id": 27, "category": "salad", "quantity": 4, "unit_price": 9.2200000000000006, "net_price": 36.880000000000003}]}
--
Key     (4 bytes):
Payload (98 bytes):	{"store_id": 3, "store_order_id": 1182, "coupon_code": 1268, "date": 18075, "status": "accepted", "order_lines": [{"product_id": 52, "category": "wings", "quantity": 1, "unit_price": 5.6799999999999997, "net_price": 5.6799999999999997}, {"product_id": 33, "category": "salad", "quantity": 3, "unit_price": 10.880000000000001, "net_price": 32.640000000000001}, {"product_id": 4, "category": "calzone", "quantity": 1, "unit_price": 19.039999999999999, "net_price": 19.039999999999999}]}
--
Key     (4 bytes):
Payload (126 bytes):	{"store_id": 8, "store_order_id": 1183, "coupon_code": 1125, "date": 18781, "status": "accepted", "order_lines": [{"product_id": 59, "category": "calzone", "quantity": 3, "unit_price": 5.7400000000000002, "net_price": 17.219999999999999}, {"product_id": 14, "category": "dessert", "quantity": 2, "unit_price": 18.32, "net_price": 36.640000000000001}, {"product_id": 42, "category": "dessert", "quantity": 1, "unit_price": 6.6100000000000003, "net_price": 6.6100000000000003}, {"product_id": 7, "category": "salad", "quantity": 2, "unit_price": 18.25, "net_price": 36.5}]}

You can also run this without the -c5 argument to see a continuous stream of messages. If you do this, press Ctrl-C to return to the command prompt.

6

Now let’s see how you can generate streams of data that are related and consistent. You are going to use bundled schemas available in the Kafka Connect DatagenConnector.

Consider an example where we want to generate some dummy data for tracking sensors on a fleet of delivery trucks. There will be two streams of data: (1) the current location of each truck, and (2) truck sensor information from each truck. The first stream representing the location of the trucks would contain the following fields:

→ Vehicle id (primary key / unique identifier)

→ Current location of the truck expressed in longitude and latitude

The second stream will contain sensor readings attached to the truck. Now instead of generating lots of random sensor data, we want a limited set so that it’s more realistic, tying it back to a fixed set of imaginary trucks. Each sensor reading will provide the following information:

→ Vehicle id (primary key / unique identifier)

→ Engine Temerature

→ Average RPM

For building the two related streams you’ll need to create two datagen connectors.

Run the following to create the fleet managment location stream:

curl -X PUT http://localhost:8083/connectors/fleet_location/config \
     -i -H "Content-Type: application/json" -d '{
    "connector.class"                             : "io.confluent.kafka.connect.datagen.DatagenConnector",
    "quickstart"                                  : "fleet_mgmt_location",
    "schema.keyfield"                             : "vehicle_id",
    "kafka.topic"                                 : "fleet_mgmt_location",
    "key.converter"                               : "org.apache.kafka.connect.converters.IntegerConverter",
    "value.converter"                             : "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url"         : "http://schema-registry:8081",
    "value.converter.schemas.enable"              : "false",
    "max.interval"                                : 500,
    "tasks.max"                                   : "1"
}'

Check that the fleet connector is running:

curl -s http://localhost:8083/connectors/fleet_location/status

You should see that the state is RUNNING:

{"name":"fleet_location","connector":{"state":"RUNNING","worker_id":"connect:8083"},"tasks":[{"id":0,"state":"RUNNING","worker_id":"connect:8083"}],"type":"source"}

Then create the fleet management sensor reading stream you’ll execute the following:

curl -X PUT http://localhost:8083/connectors/fleet_sensor/config \
     -i -H "Content-Type: application/json" -d '{
    "connector.class"                             : "io.confluent.kafka.connect.datagen.DatagenConnector",
    "quickstart"                                  : "fleet_mgmt_sensors",
    "schema.keyfield"                             : "vehicle_id",
    "kafka.topic"                                 : "fleet_mgmt_sensors",
    "key.converter"                               : "org.apache.kafka.connect.converters.IntegerConverter",
    "value.converter"                             : "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url"         : "http://schema-registry:8081",
    "value.converter.schemas.enable"              : "false",
    "max.interval"                                : 500,
    "tasks.max"                                   : "1"
}'

When looking at the connector setting notice the quickstart entry in the JSON, this is where you specify a bundled schema the datagen connector provides. By generating two streams of data with related keys you’ll be able to join records or perform aggregations with the related keys.

Now let’s confirm the sensor connector is running:

curl -s http://localhost:8083/connectors/fleet_sensor/status

Again the state should be RUNNING:

{"name":"fleet_sensor","connector":{"state":"RUNNING","worker_id":"connect:8083"},"tasks":[{"id":0,"state":"RUNNING","worker_id":"connect:8083"}],"type":"source"}

Consume events from the test topics

7

We now have two Kafka topics being written to. The first (devices) is keyed on the MAC address, as can be seen from the data:

docker exec -i kcat kcat -b broker:9092 -t fleet_mgmt_location \
            -s key=s -s value=avro -r http://schema-registry:8081        \
            -f 'Key     (%K bytes):\t%k\nPayload (%S bytes):\t%s\n--\n' \
            -C -c3 -o beginning 2>/dev/null

Key     (4 bytes):
Payload (29 bytes):	{"vehicle_id": 6242, "location": {"latitude": 0.6188530445835535, "longitude": 0.53424204571321932}, "ts": 1609459200000}
--
Key     (4 bytes):
Payload (29 bytes):	{"vehicle_id": 2956, "location": {"latitude": 0.38269701406096923, "longitude": 0.92006741132971748}, "ts": 1609459300000}
--
Key     (4 bytes):
Payload (29 bytes):	{"vehicle_id": 7982, "location": {"latitude": 0.35200846940237418, "longitude": 0.90592937818464314}, "ts": 1609459400000}
--

The second is a stream of information about network traffic for the devices created in the topic above.

docker exec -i kcat kcat -b broker:9092 -t fleet_mgmt_sensors \
            -s key=s -s value=avro -r http://schema-registry:8081        \
            -f 'Key     (%K bytes):\t%k\nPayload (%S bytes):\t%s\n--\n' \
            -C -c5 -o end -u 2>/dev/null

Key     (4 bytes):
Payload (11 bytes):	{"vehicle_id": 5712, "engine_temperature": 172, "average_rpm": 2359}
--
Key     (4 bytes):
Payload (11 bytes):	{"vehicle_id": 2255, "engine_temperature": 195, "average_rpm": 2444}
--
Key     (4 bytes):
Payload (11 bytes):	{"vehicle_id": 5973, "engine_temperature": 233, "average_rpm": 2194}
--
Key     (4 bytes):
Payload (12 bytes):	{"vehicle_id": 8429, "engine_temperature": 194, "average_rpm": 4729}
--
Key     (4 bytes):
Payload (11 bytes):	{"vehicle_id": 2816, "engine_temperature": 180, "average_rpm": 3598}
--

Clean up

8

Shut down the stack by running:

docker compose down