Working with heterogenous JSON records

Question:

How do you select fields from a stream of records with different structures and possibly different values?

Example use case:

Suppose you have a topic with records formatted in JSON, but not all of the records have the same structure and value types. In this tutorial, we'll write a query that handles the different structures and pulls out specific fields.

Hands-on code example:

ksqlDB

Short Answer

Create a stream and define the outer-most element of the JSON structures as VARCHAR

CREATE STREAM DATA_STREAM (
  JSONType1 VARCHAR,
  JSONType2 VARCHAR,
  JSONType3 VARCHAR
  )

 WITH (KAFKA_TOPIC='source_data',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);

Then you can access fields in the JSON structure using the EXTRACTJSONFIELD keyword

CREATE STREAM SUMMARY_REPORTS AS
   SELECT
    EXTRACTJSONFIELD (JSONType1, '$.oneOnlyField') AS SPECIAL_INFO,
    CAST(EXTRACTJSONFIELD (JSONType2, '$.numberField') AS DOUBLE) AS RUNFLD,
    EXTRACTJSONFIELD (JSONType3, '$.fieldD') AS DESCRIPTION
FROM
    DATA_STREAM;

Run it

Prerequisites

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

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

mkdir ksql-heterogeneous-json && cd ksql-heterogeneous-json

Then make the following directories to set up its structure:

mkdir src test

Get Confluent Platform

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
      KSQL_KSQL_STREAMS_AUTO_OFFSET_RESET: earliest
  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

Problem description

Let’s say you have a Kafka topic source_data that contains JSON-formatted data. But each nested JSON object has a different structure. Additionally, within each object the values have a mix of types.

They each have a field that you want to pull out in a query and you don’t care about the structure of the individual JSON objects

  "JSONType1": {
    "fieldA": "some data",
    "numberField": 1.001,
    "oneOnlyField": "more data", (1)
    "randomField": "random data"
  }

  "JSONType2": {
    "fieldA": "data",
    "fieldB": "b-data",
    "numberField": 98.6   (2)
  }

  "JSONType3": {
    "fieldA": "data",
    "fieldB": "b-data",
    "numberField": 98.6,
    "fieldC": "data",
    "fieldD": "D-data"    (3)
  }

1	The field you want from JSONType1
2	The field you want from JSONType2
3	The field you want from JSONType3

The key to approaching this problem is having some way to generically model each structure, without having to know details beyond the name of the field you want to extract. Since there is varying number of fields you can’t use the ksqlDB STRUCT and because there is a mix of types in the values using the ksqlDB map function isn’t an option either.

Create the ksqlDB stream interactively using the CLI

To begin developing interactively, open up the ksqlDB CLI:

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

The first thing we do is to create a stream DATA_STREAM based off the topic source_data. Within the CREATE STREAM statement, you’ll use a VARCHAR keyword to define each of outer most element of the JSON types.

CREATE STREAM DATA_STREAM (
  JSONType1 VARCHAR,          (1)
  JSONType2 VARCHAR,          (2)
  JSONType3 VARCHAR         (3)
  )

 WITH (KAFKA_TOPIC='source_data',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);

1	Defining outer JSON element of type one as VARCHAR
2	Defining outer JSON element of type two as VARCHAR
3	Defining outer JSON element of type three as VARCHAR

Go ahead and create the stream now by pasting this statement into the ksqlDB window you opened at the beginning of this step. After you’ve created the stream, quit the ksqlDB CLI for now by typing exit.

By defining outer most element of the different JSON objects as VARCHAR, we’re setting ourselves up with the ability to extract arbitrary fields on the different JSON records as we’ll see in an upcoming section. But first we need to add some records to the source_data topic which we’ll do in the next step.

Produce events to the input topic

Now let’s produce some records for the DATA_STREAM stream

docker exec -i broker /usr/bin/kafka-console-producer --bootstrap-server broker:9092 --topic source_data

After starting the console producer it will wait for your input. To send all send all the stock transactions click on the clipboard icon on the right, then paste the following into the terminal and press enter:

{ "JSONType1": { "fieldA": "some data", "numberField": 1.001, "oneOnlyField": "more data", "randomField": "random data" }, "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6 }, "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data" }}
{ "JSONType1": { "fieldA": "some data", "numberField": 2.001, "oneOnlyField": "more data", "randomField": "random data" }, "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 99.6 }, "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-2" }}
{ "JSONType1": { "fieldA": "some data", "numberField": 3.001, "oneOnlyField": "more data", "randomField": "random data" }, "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 100.6 }, "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-3" }}
{ "JSONType1": { "fieldA": "some data", "numberField": 4.001, "oneOnlyField": "more data", "randomField": "random data" }, "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 101.6 }, "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-4" }}

After you’ve sent the records above, you can close the console producer with Ctrl-C.

Run the streaming report interactively with the ksqldb-cli

To begin developing interactively, open up the ksqlDB CLI:

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

Set ksqlDB to process data from the beginning of each Kafka topic.

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

Then let’s adjust the column width so we can easily see the results of the query

SET CLI COLUMN-WIDTH 15

We need to create a query that extracts the fields we want from input sources. Since we have defined the top element of the JSON as a String using the VARCHAR keyword, we can use the ksqlDB EXTRACTJSONFIELD function to extract the different values at a specified JSONPath. If the requested JSONpath doesn’t exist, the EXTRACTJSONFIELD function returns NULL.

The result of EXTRACTJSONFIELD function is always a STRING type. To convert the result to another type you’ll need to use the CAST operator. We’ve done that with our queries in this tutorial. If

SELECT
    EXTRACTJSONFIELD (JSONType1, '$.oneOnlyField') AS SPECIAL_INFO,
    CAST(EXTRACTJSONFIELD (JSONType2, '$.numberField') AS DOUBLE) AS RUNFLD,
    EXTRACTJSONFIELD (JSONType3, '$.fieldD') AS DESCRIPTION
FROM
    DATA_STREAM
EMIT CHANGES
LIMIT 4;

This query should produce the following output:

+---------------+---------------+---------------+
|SPECIAL_INFO   |RUNFLD         |DESCRIPTION    |
+---------------+---------------+---------------+
|more data      |98.6           |D-data         |
|more data      |99.6           |D-data-2       |
|more data      |100.6          |D-data-3       |
|more data      |101.6          |D-data-4       |
Limit Reached
Query terminated

Now that the reporting query works, let’s update it to create a continous query for your reporting scenario

CREATE STREAM SUMMARY_REPORTS AS
   SELECT
    EXTRACTJSONFIELD (JSONType1, '$.oneOnlyField') AS SPECIAL_INFO,
    CAST(EXTRACTJSONFIELD (JSONType2, '$.numberField') AS DOUBLE) AS RUNFLD,
    EXTRACTJSONFIELD (JSONType3, '$.fieldD') AS DESCRIPTION
FROM
    DATA_STREAM;

We’re done with the ksqlDB CLI for now so go ahead and type exit to quit.

Write your statements to a file

Now that you have a series of statements that’s extracting the fields you care about, 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 DATA_STREAM (
  JSONType1 VARCHAR,
  JSONType2 VARCHAR,
  JSONType3 VARCHAR
  )

 WITH (KAFKA_TOPIC='source_data',
       VALUE_FORMAT='JSON',
       PARTITIONS=1);


CREATE STREAM SUMMARY_REPORTS AS
   SELECT
    EXTRACTJSONFIELD (JSONType1, '$.oneOnlyField') AS SPECIAL_INFO,
    CAST(EXTRACTJSONFIELD (JSONType2, '$.numberField') AS DOUBLE) AS RUNFLD,
    EXTRACTJSONFIELD (JSONType3, '$.fieldD') AS DESCRIPTION
FROM
    DATA_STREAM;

Test it

Create the test data

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

{
  "inputs": [
    {
      "topic" : "source_data",
      "value" :
        { "JSONType1": { "fieldA": "some data", "numberField": 1.001, "oneOnlyField": "more data", "randomField": "random data" },
          "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6 },
          "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data" }
        }
    },

    {
      "topic" : "source_data",
      "value" :
        { "JSONType1": { "fieldA": "some data", "numberField": 2.001, "oneOnlyField": "more data", "randomField": "random data" },
          "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 99.6 },
          "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-2" }
        }
    },

    {
      "topic" : "source_data",
      "value" :
        { "JSONType1": { "fieldA": "some data", "numberField": 3.001, "oneOnlyField": "more data", "randomField": "random data" },
          "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 100.6 },
          "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-3" }
        }
    },

    {
      "topic" : "source_data",
      "value" :
        { "JSONType1": { "fieldA": "some data", "numberField": 4.001, "oneOnlyField": "more data", "randomField": "random data" },
          "JSONType2": { "fieldA": "data", "fieldB": "b-data", "numberField": 101.6 },
          "JSONType3": { "fieldA": "data", "fieldB": "b-data", "numberField": 98.6, "fieldC": "data", "fieldD": "D-data-4" }
        }
    }
  ]
}

Create a file at test/output.json with the expected outputs:

{
  "outputs": [
    {
      "topic": "SUMMARY_REPORTS",
      "value": {
        "SPECIAL_INFO" : "more data",
        "RUNFLD": 98.6,
        "DESCRIPTION" : "D-data"
      }
    },
    {
      "topic": "SUMMARY_REPORTS",
      "value": {
         "SPECIAL_INFO" : "more data" ,
         "RUNFLD": 99.6,
         "DESCRIPTION" : "D-data-2"
      }
    },
    {
      "topic": "SUMMARY_REPORTS",
      "value": {
         "SPECIAL_INFO" : "more data" ,
         "RUNFLD": 100.6,
         "DESCRIPTION" : "D-data-3"
      }
    },
    {
      "topic": "SUMMARY_REPORTS",
      "value": {
        "SPECIAL_INFO" : "more data" ,
        "RUNFLD": 101.6,
        "DESCRIPTION" : "D-data-4"
      }
    }
  ]
}

Invoke the tests

Invoke the tests using the ksqlDB 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!

Deploy on Confluent Cloud

Run your app with Confluent Cloud

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.