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How to flatten deeply nested events

Question:

How can you transform a stream of events with nested data into a flattened dataset that is simpler to handle?

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Example use case:

Consider a topic containing product orders. Each order contains data about the customer and the product, specified as nested data. In this tutorial, we'll write a program that transforms each order into a new version that contains all of the data as flat fields.

Hands-on code example:

ksqlDB

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 flatten-nested-data && cd flatten-nested-data

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
    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

Create the input topic with a stream

4

The first thing that we’re going to do is create a input topic that will contain the orders. For this, we are going to create a stream with the definition of the order and the fields that contain nested data. To create the stream, open a session with KSQL using the following command:

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

Create the following stream with the representation of the orders. We use the STRUCT keyword to define the fields that contain nested data.

CREATE STREAM ORDERS (
    id VARCHAR,
    timestamp VARCHAR,
    amount DOUBLE,
    customer STRUCT<firstName VARCHAR,
                    lastName VARCHAR,
                    phoneNumber VARCHAR,
                    address STRUCT<street VARCHAR,
                                   number VARCHAR,
                                   zipcode VARCHAR,
                                   city VARCHAR,
                                   state VARCHAR>>,
    product STRUCT<sku VARCHAR,
                   name VARCHAR,
                   vendor STRUCT<vendorName VARCHAR,
                                 country VARCHAR>>)
    WITH (KAFKA_TOPIC = 'ORDERS',
          VALUE_FORMAT = 'JSON',
          TIMESTAMP = 'TIMESTAMP',
          TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
          PARTITIONS = 1);

Produce events to the input topic

5

Before we move foward with the implementation, we need to produce records to the ORDERS stream. Let’s use the console producer to create some records.

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

When the console producer starts, it will log some messages and hang, waiting for your input. Type in one line at a time and press enter to send it. Each line represents an order of one 'Highly Durable Glue' bought by each member of Confluent’s developer advocacy team. Note that each order contains the fields customer and product that in turn contains nested data. To send all orders below, click on the clipboard icon on the right, then paste the following into the prompt and press enter:

{"id": "1", "timestamp": "2020-01-18 01:12:05", "amount": 84.02, "customer": {"firstName": "Ricardo", "lastName": "Ferreira", "phoneNumber": "1234567899", "address": {"street": "Street SDF", "number": "8602", "zipcode": "27640", "city": "Raleigh", "state": "NC"}}, "product": {"sku": "P12345", "name": "Highly Durable Glue", "vendor": {"vendorName": "Acme Corp", "country": "US"}}}
{"id": "2", "timestamp": "2020-01-18 01:35:12", "amount": 84.02, "customer": {"firstName": "Tim", "lastName": "Berglund", "phoneNumber": "9987654321", "address": {"street": "Street UOI", "number": "1124", "zipcode": "85756", "city": "Littletown", "state": "CO"}}, "product": {"sku": "P12345", "name": "Highly Durable Glue", "vendor": {"vendorName": "Acme Corp", "country": "US"}}}
{"id": "3", "timestamp": "2020-01-18 01:58:55", "amount": 84.02, "customer": {"firstName": "Robin", "lastName": "Moffatt", "phoneNumber": "4412356789", "address": {"street": "Street YUP", "number": "9066", "zipcode": "BD111NE", "city": "Leeds", "state": "YS"}}, "product": {"sku": "P12345", "name": "Highly Durable Glue", "vendor": {"vendorName": "Acme Corp", "country": "US"}}}
{"id": "4", "timestamp": "2020-01-18 02:31:43", "amount": 84.02, "customer": {"firstName": "Viktor", "lastName": "Gamov", "phoneNumber": "9874563210", "address": {"street": "Street SHT", "number": "12450", "zipcode": "07003", "city": "New Jersey", "state": "NJ"}}, "product": {"sku": "P12345", "name": "Highly Durable Glue", "vendor": {"vendorName": "Acme Corp", "country": "US"}}}

Write the program interactively using the CLI

6

To begin developing interactively, open up the KSQL CLI again:

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

Now that you have a stream with some events in it, let’s start to leverage them. 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';

We need to create a query capable of flattening the records sent to the ORDER stream. Since we modeled each field containing a nested data using a struct, we can write the query using the operator → operator to retrieve the data from specific nested fields.

SELECT
    ID AS ORDER_ID,
    TIMESTAMP AS ORDER_TS,
    AMOUNT AS ORDER_AMOUNT,
    CUSTOMER->FIRSTNAME AS CUST_FIRST_NAME,
    CUSTOMER->LASTNAME AS CUST_LAST_NAME,
    CUSTOMER->PHONENUMBER AS CUST_PHONE_NUMBER,
    CUSTOMER->ADDRESS->STREET AS CUST_ADDR_STREET,
    CUSTOMER->ADDRESS->NUMBER AS CUST_ADDR_NUMBER,
    CUSTOMER->ADDRESS->ZIPCODE AS CUST_ADDR_ZIPCODE,
    CUSTOMER->ADDRESS->CITY AS CUST_ADDR_CITY,
    CUSTOMER->ADDRESS->STATE AS CUST_ADDR_STATE,
    PRODUCT->SKU AS PROD_SKU,
    PRODUCT->NAME AS PROD_NAME,
    PRODUCT->VENDOR->VENDORNAME AS PROD_VENDOR_NAME,
    PRODUCT->VENDOR->COUNTRY AS PROD_VENDOR_COUNTRY
FROM
    ORDERS
EMIT CHANGES
LIMIT 4;

This query should produce the following output:

+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|ORDER_ID            |ORDER_TS            |ORDER_AMOUNT        |CUST_FIRST_NAME     |CUST_LAST_NAME      |CUST_PHONE_NUMBER   |CUST_ADDR_STREET    |CUST_ADDR_NUMBER    |CUST_ADDR_ZIPCODE   |CUST_ADDR_CITY      |CUST_ADDR_STATE     |PROD_SKU            |PROD_NAME           |PROD_VENDOR_NAME    |PROD_VENDOR_COUNTRY |
+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|1                   |2020-01-18 01:12:05 |84.02               |Ricardo             |Ferreira            |1234567899          |Street SDF          |8602                |27640               |Raleigh             |NC                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|2                   |2020-01-18 01:35:12 |84.02               |Tim                 |Berglund            |9987654321          |Street UOI          |1124                |85756               |Littletown          |CO                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|3                   |2020-01-18 01:58:55 |84.02               |Robin               |Moffatt             |4412356789          |Street YUP          |9066                |BD111NE             |Leeds               |YS                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|4                   |2020-01-18 02:31:43 |84.02               |Viktor              |Gamov               |9874563210          |Street SHT          |12450               |07003               |New Jersey          |NJ                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
Limit Reached
Query terminated

Note that now each field is being shown in a flat structure. This means that our query is working properly. Now let’s create a continuous query to implement this scenario.

CREATE STREAM FLATTENED_ORDERS AS
    SELECT
        ID AS ORDER_ID,
        TIMESTAMP AS ORDER_TS,
        AMOUNT AS ORDER_AMOUNT,
        CUSTOMER->FIRSTNAME AS CUST_FIRST_NAME,
        CUSTOMER->LASTNAME AS CUST_LAST_NAME,
        CUSTOMER->PHONENUMBER AS CUST_PHONE_NUMBER,
        CUSTOMER->ADDRESS->STREET AS CUST_ADDR_STREET,
        CUSTOMER->ADDRESS->NUMBER AS CUST_ADDR_NUMBER,
        CUSTOMER->ADDRESS->ZIPCODE AS CUST_ADDR_ZIPCODE,
        CUSTOMER->ADDRESS->CITY AS CUST_ADDR_CITY,
        CUSTOMER->ADDRESS->STATE AS CUST_ADDR_STATE,
        PRODUCT->SKU AS PROD_SKU,
        PRODUCT->NAME AS PROD_NAME,
        PRODUCT->VENDOR->VENDORNAME AS PROD_VENDOR_NAME,
        PRODUCT->VENDOR->COUNTRY AS PROD_VENDOR_COUNTRY
    FROM
        ORDERS;

We can query the new result stream called FLATTENED_ORDERS with a much simpler query that doesn’t need to handle nested data.

SELECT
    ORDER_ID,
    ORDER_TS,
    ORDER_AMOUNT,
    CUST_FIRST_NAME,
    CUST_LAST_NAME,
    CUST_PHONE_NUMBER,
    CUST_ADDR_STREET,
    CUST_ADDR_NUMBER,
    CUST_ADDR_ZIPCODE,
    CUST_ADDR_CITY,
    CUST_ADDR_STATE,
    PROD_SKU,
    PROD_NAME,
    PROD_VENDOR_NAME,
    PROD_VENDOR_COUNTRY
FROM FLATTENED_ORDERS
EMIT CHANGES
LIMIT 4;

The output should look similar to:

+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|ORDER_ID            |ORDER_TS            |ORDER_AMOUNT        |CUST_FIRST_NAME     |CUST_LAST_NAME      |CUST_PHONE_NUMBER   |CUST_ADDR_STREET    |CUST_ADDR_NUMBER    |CUST_ADDR_ZIPCODE   |CUST_ADDR_CITY      |CUST_ADDR_STATE     |PROD_SKU            |PROD_NAME           |PROD_VENDOR_NAME    |PROD_VENDOR_COUNTRY |
+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+
|1                   |2020-01-18 01:12:05 |84.02               |Ricardo             |Ferreira            |1234567899          |Street SDF          |8602                |27640               |Raleigh             |NC                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|2                   |2020-01-18 01:35:12 |84.02               |Tim                 |Berglund            |9987654321          |Street UOI          |1124                |85756               |Littletown          |CO                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|3                   |2020-01-18 01:58:55 |84.02               |Robin               |Moffatt             |4412356789          |Street YUP          |9066                |BD111NE             |Leeds               |YS                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
|4                   |2020-01-18 02:31:43 |84.02               |Viktor              |Gamov               |9874563210          |Street SHT          |12450               |07003               |New Jersey          |NJ                  |P12345              |Highly Durable Glue |Acme Corp           |US                  |
Limit Reached
Query terminated

Finally, let’s see what’s available on the underlying Kafka topic for the table. We can print that out easily.

PRINT FLATTENED_ORDERS FROM BEGINNING LIMIT 4;

The output should look similar to:

Key format: JSON or KAFKA_STRING
Value format: JSON or KAFKA_STRING
rowtime: 2020/01/18 01:12:05.000 Z, key: <null>, value: {"ORDER_ID":"1","ORDER_TS":"2020-01-18 01:12:05","ORDER_AMOUNT":84.02,"CUST_FIRST_NAME":"Ricardo","CUST_LAST_NAME":"Ferreira","CUST_PHONE_NUMBER":"1234567899","CUST_ADDR_STREET":"Street SDF","CUST_ADDR_NUMBER":"8602","CUST_ADDR_ZIPCODE":"27640","CUST_ADDR_CITY":"Raleigh","CUST_ADDR_STATE":"NC","PROD_SKU":"P12345","PROD_NAME":"Highly Durable Glue","PROD_VENDOR_NAME":"Acme Corp","PROD_VENDOR_COUNTRY":"US"}
rowtime: 2020/01/18 01:35:12.000 Z, key: <null>, value: {"ORDER_ID":"2","ORDER_TS":"2020-01-18 01:35:12","ORDER_AMOUNT":84.02,"CUST_FIRST_NAME":"Tim","CUST_LAST_NAME":"Berglund","CUST_PHONE_NUMBER":"9987654321","CUST_ADDR_STREET":"Street UOI","CUST_ADDR_NUMBER":"1124","CUST_ADDR_ZIPCODE":"85756","CUST_ADDR_CITY":"Littletown","CUST_ADDR_STATE":"CO","PROD_SKU":"P12345","PROD_NAME":"Highly Durable Glue","PROD_VENDOR_NAME":"Acme Corp","PROD_VENDOR_COUNTRY":"US"}
rowtime: 2020/01/18 01:58:55.000 Z, key: <null>, value: {"ORDER_ID":"3","ORDER_TS":"2020-01-18 01:58:55","ORDER_AMOUNT":84.02,"CUST_FIRST_NAME":"Robin","CUST_LAST_NAME":"Moffatt","CUST_PHONE_NUMBER":"4412356789","CUST_ADDR_STREET":"Street YUP","CUST_ADDR_NUMBER":"9066","CUST_ADDR_ZIPCODE":"BD111NE","CUST_ADDR_CITY":"Leeds","CUST_ADDR_STATE":"YS","PROD_SKU":"P12345","PROD_NAME":"Highly Durable Glue","PROD_VENDOR_NAME":"Acme Corp","PROD_VENDOR_COUNTRY":"US"}
rowtime: 2020/01/18 02:31:43.000 Z, key: <null>, value: {"ORDER_ID":"4","ORDER_TS":"2020-01-18 02:31:43","ORDER_AMOUNT":84.02,"CUST_FIRST_NAME":"Viktor","CUST_LAST_NAME":"Gamov","CUST_PHONE_NUMBER":"9874563210","CUST_ADDR_STREET":"Street SHT","CUST_ADDR_NUMBER":"12450","CUST_ADDR_ZIPCODE":"07003","CUST_ADDR_CITY":"New Jersey","CUST_ADDR_STATE":"NJ","PROD_SKU":"P12345","PROD_NAME":"Highly Durable Glue","PROD_VENDOR_NAME":"Acme Corp","PROD_VENDOR_COUNTRY":"US"}
Topic printing ceased

Write your statements to a file

7

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 ORDERS (
    id VARCHAR,
    timestamp VARCHAR,
    amount DOUBLE,
    customer STRUCT<firstName VARCHAR,
                    lastName VARCHAR,
                    phoneNumber VARCHAR,
                    address STRUCT<street VARCHAR,
                                   number VARCHAR,
                                   zipcode VARCHAR,
                                   city VARCHAR,
                                   state VARCHAR>>,
    product STRUCT<sku VARCHAR,
                   name VARCHAR,
                   vendor STRUCT<vendorName VARCHAR,
                                 country VARCHAR>>)
    WITH (KAFKA_TOPIC = 'ORDERS',
          VALUE_FORMAT = 'JSON',
          TIMESTAMP = 'TIMESTAMP',
          TIMESTAMP_FORMAT = 'yyyy-MM-dd HH:mm:ss',
          PARTITIONS = 1);

CREATE STREAM FLATTENED_ORDERS AS
    SELECT
        ID AS ORDER_ID,
        TIMESTAMP AS ORDER_TS,
        AMOUNT AS ORDER_AMOUNT,
        CUSTOMER->FIRSTNAME AS CUST_FIRST_NAME,
        CUSTOMER->LASTNAME AS CUST_LAST_NAME,
        CUSTOMER->PHONENUMBER AS CUST_PHONE_NUMBER,
        CUSTOMER->ADDRESS->STREET AS CUST_ADDR_STREET,
        CUSTOMER->ADDRESS->NUMBER AS CUST_ADDR_NUMBER,
        CUSTOMER->ADDRESS->ZIPCODE AS CUST_ADDR_ZIPCODE,
        CUSTOMER->ADDRESS->CITY AS CUST_ADDR_CITY,
        CUSTOMER->ADDRESS->STATE AS CUST_ADDR_STATE,
        PRODUCT->SKU AS PROD_SKU,
        PRODUCT->NAME AS PROD_NAME,
        PRODUCT->VENDOR->VENDORNAME AS PROD_VENDOR_NAME,
        PRODUCT->VENDOR->COUNTRY AS PROD_VENDOR_COUNTRY
    FROM
        ORDERS;

Test it

Create the test data

1

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

{
  "inputs": [
    {
      "topic": "ORDERS",
      "value": {
        "id": "1",
        "timestamp": "2020-01-18 01:12:05",
        "amount": 84.02,
        "customer": {
          "firstName": "Ricardo",
          "lastName": "Ferreira",
          "phoneNumber": "1234567899",
          "address": {
            "street": "Street SDF",
            "number": "8602",
            "zipcode": "27640",
            "city": "Raleigh",
            "state": "NC"
          }
        },
        "product": {
          "sku": "P12345",
          "name": "Highly Durable Glue",
          "vendor": {
            "vendorName": "Acme Corp",
            "country": "US"
          }
        }
      }
    },
    {
      "topic": "ORDERS",
      "value": {
        "id": "2",
        "timestamp": "2020-01-18 01:35:12",
        "amount": 84.02,
        "customer": {
          "firstName": "Tim",
          "lastName": "Berglund",
          "phoneNumber": "9987654321",
          "address": {
            "street": "Street UOI",
            "number": "1124",
            "zipcode": "85756",
            "city": "Littletown",
            "state": "CO"
          }
        },
        "product": {
          "sku": "P12345",
          "name": "Highly Durable Glue",
          "vendor": {
            "vendorName": "Acme Corp",
            "country": "US"
          }
        }
      }
    },
    {
      "topic": "ORDERS",
      "value": {
        "id": "3",
        "timestamp": "2020-01-18 01:58:55",
        "amount": 84.02,
        "customer": {
          "firstName": "Robin",
          "lastName": "Moffatt",
          "phoneNumber": "4412356789",
          "address": {
            "street": "Street YUP",
            "number": "9066",
            "zipcode": "BD111NE",
            "city": "Leeds",
            "state": "YS"
          }
        },
        "product": {
          "sku": "P12345",
          "name": "Highly Durable Glue",
          "vendor": {
            "vendorName": "Acme Corp",
            "country": "US"
          }
        }
      }
    },
    {
      "topic": "ORDERS",
      "value": {
        "id": "4",
        "timestamp": "2020-01-18 02:31:43",
        "amount": 84.02,
        "customer": {
          "firstName": "Viktor",
          "lastName": "Gamov",
          "phoneNumber": "9874563210",
          "address": {
            "street": "Street SHT",
            "number": "12450",
            "zipcode": "07003",
            "city": "New Jersey",
            "state": "NJ"
          }
        },
        "product": {
          "sku": "P12345",
          "name": "Highly Durable Glue",
          "vendor": {
            "vendorName": "Acme Corp",
            "country": "US"
          }
        }
      }
    }
  ]
}

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

{
  "outputs": [
    {
      "topic": "FLATTENED_ORDERS",
      "value": {
        "ORDER_ID": "1",
        "ORDER_TS": "2020-01-18 01:12:05",
        "ORDER_AMOUNT": 84.02,
        "CUST_FIRST_NAME": "Ricardo",
        "CUST_LAST_NAME": "Ferreira",
        "CUST_PHONE_NUMBER": "1234567899",
        "CUST_ADDR_STREET": "Street SDF",
        "CUST_ADDR_NUMBER": "8602",
        "CUST_ADDR_ZIPCODE": "27640",
        "CUST_ADDR_CITY": "Raleigh",
        "CUST_ADDR_STATE": "NC",
        "PROD_SKU": "P12345",
        "PROD_NAME": "Highly Durable Glue",
        "PROD_VENDOR_NAME": "Acme Corp",
        "PROD_VENDOR_COUNTRY": "US"
      },
      "timestamp": 1579309925000
    },
    {
      "topic": "FLATTENED_ORDERS",
      "value": {
        "ORDER_ID": "2",
        "ORDER_TS": "2020-01-18 01:35:12",
        "ORDER_AMOUNT": 84.02,
        "CUST_FIRST_NAME": "Tim",
        "CUST_LAST_NAME": "Berglund",
        "CUST_PHONE_NUMBER": "9987654321",
        "CUST_ADDR_STREET": "Street UOI",
        "CUST_ADDR_NUMBER": "1124",
        "CUST_ADDR_ZIPCODE": "85756",
        "CUST_ADDR_CITY": "Littletown",
        "CUST_ADDR_STATE": "CO",
        "PROD_SKU": "P12345",
        "PROD_NAME": "Highly Durable Glue",
        "PROD_VENDOR_NAME": "Acme Corp",
        "PROD_VENDOR_COUNTRY": "US"
      },
      "timestamp": 1579311312000
    },
    {
      "topic": "FLATTENED_ORDERS",
      "value": {
        "ORDER_ID": "3",
        "ORDER_TS": "2020-01-18 01:58:55",
        "ORDER_AMOUNT": 84.02,
        "CUST_FIRST_NAME": "Robin",
        "CUST_LAST_NAME": "Moffatt",
        "CUST_PHONE_NUMBER": "4412356789",
        "CUST_ADDR_STREET": "Street YUP",
        "CUST_ADDR_NUMBER": "9066",
        "CUST_ADDR_ZIPCODE": "BD111NE",
        "CUST_ADDR_CITY": "Leeds",
        "CUST_ADDR_STATE": "YS",
        "PROD_SKU": "P12345",
        "PROD_NAME": "Highly Durable Glue",
        "PROD_VENDOR_NAME": "Acme Corp",
        "PROD_VENDOR_COUNTRY": "US"
      },
      "timestamp": 1579312735000
    },
    {
      "topic": "FLATTENED_ORDERS",
      "value": {
        "ORDER_ID": "4",
        "ORDER_TS": "2020-01-18 02:31:43",
        "ORDER_AMOUNT": 84.02,
        "CUST_FIRST_NAME": "Viktor",
        "CUST_LAST_NAME": "Gamov",
        "CUST_PHONE_NUMBER": "9874563210",
        "CUST_ADDR_STREET": "Street SHT",
        "CUST_ADDR_NUMBER": "12450",
        "CUST_ADDR_ZIPCODE": "07003",
        "CUST_ADDR_CITY": "New Jersey",
        "CUST_ADDR_STATE": "NJ",
        "PROD_SKU": "P12345",
        "PROD_NAME": "Highly Durable Glue",
        "PROD_VENDOR_NAME": "Acme Corp",
        "PROD_VENDOR_COUNTRY": "US"
      },
      "timestamp": 1579314703000
    }
  ]
}

Invoke the tests

2

Lastly, 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

Which should pass:

	 >>> 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).

  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.