"JSONType1": {
"fieldA": "some data",
"numberField": 1.001,
"oneOnlyField": "more data", (1)
"randomField": "random data"
}
How do you select fields from a stream of records with different structures and possibly different values?
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;
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
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
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
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.
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.
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
.
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, '$.fieldD') AS DESCRIPTION
FROM
DATA_STREAM;
We’re done with the ksqlDB CLI for now so go ahead and type exit
to quit.
Now that you have a series of statements that’s extracting the fields you care about, the last step is to put them into a file so that they can be used outside the CLI session. Create a file at src/statements.sql
with the following content:
CREATE STREAM DATA_STREAM (
JSONType1 VARCHAR,
JSONType2 VARCHAR,
JSONType3 VARCHAR
)
WITH (KAFKA_TOPIC='source_data',
VALUE_FORMAT='JSON',
PARTITIONS=1);
CREATE STREAM SUMMARY_REPORTS AS
SELECT
EXTRACTJSONFIELD (JSONType1, '$.oneOnlyField') AS SPECIAL_INFO,
CAST(EXTRACTJSONFIELD (JSONType2, '$.numberField') AS DOUBLE) AS RUNFLD,
EXTRACTJSONFIELD (JSONType3, '$.fieldD') AS DESCRIPTION
FROM
DATA_STREAM;
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 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!
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 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).
Click on LEARN and follow the instructions to launch a Kafka cluster and to 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.