Console Producer and Consumer with (de)serializers using Kafka

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 console-consumer-producer-avro && cd console-consumer-producer-avro

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
    volumes:
    - ${PWD}/:/etc/tutorial/
    environment:
      SCHEMA_REGISTRY_HOST_NAME: schema-registry
      SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: broker:9092
      SCHEMA_REGISTRY_LOG4J_ROOT_LOGLEVEL: WARN

Now launch Confluent Platform by running:

docker compose up -d

Create the Kafka topic

4

Your first step is to create a topic to produce to and consume from. Use the following command to create the topic:

docker exec -t broker kafka-topics --create --topic orders-avro --bootstrap-server broker:9092

Create a schema for your records

5

We are going to use Schema Registry to control our record format. The first step is creating a schema definition which we will use when producing new records.

Create the following orders-avro-schema.json file:

{
"type": "record",
"namespace": "io.confluent.tutorial",
"name": "OrderDetail",
"fields": [
    {"name": "number", "type": "long", "doc": "The order number."},
    {"name": "shipping_address", "type": "string", "doc": "The shipping address."},
    {"name": "subtotal", "type": "double", "doc": "The amount without shipping cost and tax."},
    {"name": "shipping_cost", "type": "double", "doc": "The shipping cost."},
    {"name": "tax", "type": "double", "doc": "The applicable tax."},
    {"name": "grand_total", "type": "double", "doc": "The order grand total ."}
    ]
}

Start a console consumer

6

Next let’s open up a console consumer to read records sent to the topic you created in the previous step.

From the same terminal you used to create the topic above, run the following command to open a terminal on the broker container:

docker exec schema-registry bash

From within the terminal on the broker container, run this command to start a console consumer:

kafka-avro-console-consumer \
  --topic orders-avro \
  --bootstrap-server broker:9092 \
  --property schema.registry.url=http://localhost:8081

The consumer will start up and block waiting for records, you won’t see any output until after the next step.

Produce your first records

7

To produce your first record into Kafka, open another terminal window and run the following command to open a second shell on the broker container:

docker exec schema-registry bash

From inside the second terminal on the broker container, run the following command to start a console producer:

kafka-avro-console-producer \
  --topic orders-avro \
  --bootstrap-server broker:9092 \
  --property schema.registry.url=http://localhost:8081 \
  --property value.schema="$(< /etc/tutorial/orders-avro-schema.json)"

The producer will start and wait for you to enter input. Each line represents one record and to send it you’ll hit the enter key. If you type multiple words and then hit enter, the entire line is considered one record.

Try typing one line at a time, hit enter and go back to the console consumer window and look for the output. Or, you can select all the records and send at one time.

{"number":1,"shipping_address":"ABC Sesame Street,Wichita, KS. 12345","subtotal":110.00,"tax":10.00,"grand_total":120.00,"shipping_cost":0.00}
{"number":2,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":5.00,"tax":0.53,"grand_total":6.53,"shipping_cost":1.00}
{"number":3,"shipping_address":"5014  Pinnickinick Street, Portland, WA. 97205","subtotal":93.45,"tax":9.34,"grand_total":102.79,"shipping_cost":0.00}
{"number":4,"shipping_address":"4082 Elmwood Avenue, Tempe, AX. 85281","subtotal":50.00,"tax":1.00,"grand_total":51.00,"shipping_cost":0.00}
{"number":5,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":33.00,"tax":3.33,"grand_total":38.33,"shipping_cost":2.00}

Once you’ve sent all the records you should see the same output in your console consumer window. After you’ve confirmed receiving all records, go ahead and close the consumer with Ctrl-C.

Produce records with full key-value pairs

8

Kafka works with key-value pairs, but so far you’ve only sent records with values only. Well to be fair you’ve sent key-value pairs, but the keys are null. Sometimes you’ll need to send a valid key in addition to the value from the command line.

To enable sending full key-value pairs from the command line you add two properties to your console producer, parse.key and key.separator. Since we want the key to use String and not a schema, also set the configuration parameter for key.serializer (by default, kafka-avro-console-producer expects the key to also be serialized as Avro and you would have to pass in key.schema).

Let’s try to send some full key-value records now. If your previous console producer is still running close it with Ctrl-C and run the following command to start a new console producer:

kafka-avro-console-producer \
  --topic orders-avro \
  --bootstrap-server broker:9092 \
  --property schema.registry.url=http://localhost:8081 \
  --property value.schema="$(< /etc/tutorial/orders-avro-schema.json)" \
  --property key.serializer=org.apache.kafka.common.serialization.StringSerializer \
  --property parse.key=true \
  --property key.separator=":"

Then enter these records either one at time or copy-paste all of them into the terminal and hit enter:

6:{"number":6,"shipping_address":"9182 Shipyard Drive, Raleigh, NC. 27609","subtotal":72.00,"tax":3.00,"grand_total":75.00,"shipping_cost":0.00}
7:{"number":7,"shipping_address":"644 Lagon Street, Chicago, IL. 07712","subtotal":11.00,"tax":1.00,"grand_total":14.00,"shipping_cost":2.00}

Start a consumer to show full key-value pairs

9

Next, let’s run the consumer to read records from the topic. Since the key was serialized as just a String and not a schema, also set the configuration parameter for key.deserializer (by default, kafka-avro-console-consumer expects the key to also be deserialized as Avro).

kafka-avro-console-consumer \
  --topic orders-avro \
  --property schema.registry.url=http://localhost:8081 \
  --bootstrap-server broker:9092 \
  --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \
  --property print.key=true \
  --property key.separator="-" \
  --from-beginning

After the consumer starts you should see the following output in a few seconds:

null-{"number":1,"shipping_address":"ABC Sesame Street,Wichita, KS. 12345","subtotal":110.0,"shipping_cost":0.0,"tax":10.0,"grand_total":120.0}
null-{"number":2,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":5.0,"shipping_cost":1.0,"tax":0.53,"grand_total":6.53}
null-{"number":3,"shipping_address":"5014  Pinnickinick Street, Portland, WA. 97205","subtotal":93.45,"shipping_cost":0.0,"tax":9.34,"grand_total":102.79}
null-{"number":4,"shipping_address":"4082 Elmwood Avenue, Tempe, AX. 85281","subtotal":50.0,"shipping_cost":0.0,"tax":1.0,"grand_total":51.0}
null-{"number":5,"shipping_address":"123 Cross Street,Irving, CA. 12345","subtotal":33.0,"shipping_cost":2.0,"tax":3.33,"grand_total":38.33}
6-{"number":6,"shipping_address":"9182 Shipyard Drive, Raleigh, NC. 27609","subtotal":72.0,"shipping_cost":0.0,"tax":3.0,"grand_total":75.0}
7-{"number":7,"shipping_address":"644 Lagon Street, Chicago, IL. 07712","subtotal":11.0,"shipping_cost":2.0,"tax":1.0,"grand_total":14.0}

Since we kept the --from-beginning property, you’ll see all the records sent to the topic. You’ll notice the results before you sent keys are formatted as null-<value>.

Clean Up

10

You’re all done now!

Go back to your open windows and stop any console producers and consumers with Ctrl-C then close the container shells with Ctrl-D.

Then you can shut down the stack by running:

docker compose down

Console Producer and Consumer with (de)serializers

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