How can you transform a stream of events with nested data into a flattened dataset that is simpler to handle?
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 flatten-nested-data && cd flatten-nested-data
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:
zookeeper:
image: confluentinc/cp-zookeeper:7.3.0
hostname: zookeeper
container_name: zookeeper
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
broker:
image: confluentinc/cp-kafka:7.3.0
hostname: broker
container_name: broker
depends_on:
- zookeeper
ports:
- "29092:29092"
environment:
KAFKA_BROKER_ID: 1
KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
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
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);
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"}}}
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
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;
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
}
]
}
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!
Launch your statements into production by sending them to the REST API with the following command:
tr '\n' ' ' < src/statements.sql | \
sed 's/;/;\'$'\n''/g' | \
while read stmt; do
echo '{"ksql":"'$stmt'", "streamsProperties": {}}' | \
curl -s -X "POST" "http://localhost:8088/ksql" \
-H "Content-Type: application/vnd.ksql.v1+json; charset=utf-8" \
-d @- | \
jq
done
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.