== Spring Cloud Stream and Schema Evolution in Action with Confluent Schema Registry Server!
This repo includes thress Spring Boot applications to demonstrate Schema Evolution using Spring Cloud Stream.
Producer V1 (`producer1`), Producer V2 (`producer2`), and Consumer (`consumer`) are included in this project.
This example in particular uses the Confluent Schema Registry server.
=== Requirement
As a developer, I'd like to design my consumer to be resilient to differing payload schemas and I want to use Confluent Schema Registry.
=== Assumptions
There are a lot of online literature on Schema Evolution, so we are going to skip defining them here. For this demonstration,
however, we will simply assume there are two producers producing events with different payload schemas. A consumer that
consumes both the payload versions will be designed to adapt to evolving schemas.
=== Running the application
Make sure you are in the directory `schema-registry-confluent`
Bring up Kafka: `docker-compose up -d`
Build the project: `./mvnw clean package`
- Start the Confluent Schema Registry server in a terminal window session (Make sure that you have it locally installed)
[source,bash]
----
<ROOT_OF_CONFLUENT_PLATFORM_INSTALLATION>/bin/schema-registry-start ./etc/schema-registry/schema-registry.properties
----
In order to run this sample, you need to set compatibility to `NONE` on Confluent schema registry server.
`curl -X PUT http://127.0.0.1:8081/config -d '{"compatibility": "NONE"}' -H "Content-Type:application/json"`
- Start `consumer` on another terminal session
[source,bash]
----
java -jar consumer/target/consumer-confluent-0.0.1-SNAPSHOT.jar
----
- Start `producer1` on another terminal session
[source,bash]
----
java -jar producer1/target/producer1-confluent-0.0.1-SNAPSHOT.jar
----
- Start `producer2` on another terminal session
[source,bash]
----
java -jar producer2/target/producer2-confluent-0.0.1-SNAPSHOT.jar
----
=== Sample Data
Both the producers in the demonstration are _also_ REST controllers. We will hit the `/messages` endpoint on each producer
to POST sample data.
_Example:_
[source,bash]
----
curl -X POST http://localhost:9009/messages
curl -X POST http://localhost:9010/messages
curl -X POST http://localhost:9009/messages
curl -X POST http://localhost:9009/messages
curl -X POST http://localhost:9010/messages
----
=== Output
The consumer should log the results.
[source,bash,options=nowrap,subs=attributes]
----
{"id": "d135efc3-72f8-4612-9497-184cae508e31-v1", "internalTemperature": 34.36362, "externalTemperature": 0.0, "acceleration": 9.656547, "velocity": 33.29733}
{"id": "fd2467ce-ae09-4fd4-9cde-d9ff33fac89b-v2", "internalTemperature": 34.840473, "externalTemperature": 0.0, "acceleration": 9.709609, "velocity": 23.046476}
{"id": "4ac70c32-9ffe-4c90-914a-fa28024f5faa-v1", "internalTemperature": 23.74807, "externalTemperature": 0.0, "acceleration": 7.5003176, "velocity": 15.848035}
{"id": "3ecaae18-3144-4570-800a-223ca3198001-v1", "internalTemperature": 28.410656, "externalTemperature": 0.0, "acceleration": 1.752817, "velocity": 69.82016}
{"id": "149637a9-c7a6-4ab8-b7aa-021c72d9ebd7-v2", "internalTemperature": 2.2332578, "externalTemperature": 0.0, "acceleration": 6.251889, "velocity": 65.84996}
----
NOTE: Refer to the payload suffix in the `id` field. Each of them are appended with `-v1` or `-v2` indicating they are from
`producer1` and `producer2` respectively.
=== What just happened?
The schema evolved on the `temperature` field. That field is now split into `internalTemperature` and `externalTemperature`,
as two separate fields. The `producer1` produces payload only with `temperature` and on the other hand, `producer2` produces
payload with `internalTemperature` and `externalTemperature` fields in it.
The `consumer` is coded against a base schema that include the split fields.
The `consumer` app can happily deserialize the payload with `internalTemperature` and `externalTemperature` fields. However, when
a `producer1` payload arrives (which includes `temperature` field), the schema evolution and compatibility check are automatically
applied.
Because each payload also includes the payload version in the header, Spring Cloud Stream with the help of Schema
Registry server and Avro, the schema evolution occurs behind the scenes. The automatic mapping of `temperature` to
`internalTemperature` field is applied, since that's the field where the `aliases` is defined in the link:https://github.com/sabbyanandan/schema/blob/master/consumer/src/main/resources/avro/sensor.avsc#L7[new schema].
=== Cleanup
Once you are done with running the samples, stop the docker containers
`docker-compose down`
and stop the Confluent Schema Registry server.
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