Example :- I have a field named
"abc":[
{"key1":"value1", "key2":"value2"},
{"key1":"value1", "key2":"value2"}
]
Consumer1, consumer2 consuming this variable, where as now consumer2 require few more fields and need to change the structure.
How to address this issue by following best practice?
You can use type map in Avro schema. key is always a string and value can be any type but should one type for the whole map.
So, in your case, introduce a map into your schema. consumer_1 can consume the event and get they keys needed only for the consumer_1 and do the same for consumer_2. But still same Avro schema.
Note: you can not send null to the map in schema. you need to send empty map.
If possible introduce Schema Registry server for schema versioning. Register all the different avro schema's at schema registry and a version Id will be given. Connect your producer and consumer app with schema registry server to fetch the registered schema for the respective Kafka message. Now message with any kind of schema can be received by any consumer with full compatibility.
Related
Let's say I have a bunch of different topics, each with their own json schema. In schema registry, I indicated which schemas exist within the different topics, not directly refering to which topic a schema applies. Then, in my sink connector, I only refer to the endpoint (URL) of the schema registry. So to my knowledge, I never indicated which registered schema a kafka connector (e.g., JDBC sink) should be used in order to deserialize a message from a certain topic?
Asking here as I can't seem to find anything online.
I am trying to decrease my kafka message size by removing overhead of having to specify the schema in each message, and using schema registry instead. However, I cannot seem to understand how this could work.
Your producer serializes the schema id directly in the bytes of the record. Connect (or consumers with the json deserializer) use the schema that's part of each record.
https://docs.confluent.io/platform/current/schema-registry/serdes-develop/index.html#wire-format
If you're trying to decrease message size, don't use JSON, but rather a binary format and enable topic compression such as ZSTD
Let us assume I am using Avro serialization while sending data to kafka.
While consuming record from Apache Kafka, I get both the schema and the record. I can use the schema to parse the record. I am not getting the scenario why consumer would use a different version of schema to deserialize the record. Can someone help?
The message is serialized with the the unique id for a specific version of the schema when produced onto Kafka. The consumer would use that unique schema id to deserialize.
Taken from https://docs.confluent.io/current/schema-registry/avro.html#schema-evolution
I'm currently studying the Avro schema system and from what I understand the flow of a schema update is:
1) A client changes the schema (maybe by adding a new field with a default value for backwards compatibility) and sends data to Kafka serialized with the updated schema.
2) Schema registry does compatibility checks and registers the new version of the schema with a new version and a unique Id.
3) The consumer (still using the old schema) attempts to deserialize the data and schema evolution drops the new field, allowing the consumer to deserialize the data.
From I understand we need to explicitly update the
consumers after a schema change in order to supply them with the latest schema.
But why the consumer just pull the latest schema when it sees that the ID has changed?
You need to update consumer schemas if they are using a SpecificRecord subclass. That's effectively skipping the schema ID lookup
If you want it to always match the latest, then you can make an http call to the registry to /latest and get it, then restart the app.
Or if you always want the consumer to use the ID of the message, use GenericRecord as the object type
Is there any order that must be followed - e.g. person should create a topic first and then schema in schema registry or vice versa?
Can two topics use the same schema from Schema Registry?
Does every topic needs to have Key and Value? (and therefore needs to exist 2 schemas for each topic?)
What is the relation and possible combinations?
Thanks.
is there any order that must be followed
Nope. If you have auto topic creation enabled, you could even start producing Avro immediately to a non existing topic. The Confluent serializers automatically register the schema, and the broker will create a topic with default partitions and replicas
Can two topics use the same schema
Yes, the Avro Schema ID of two distinct topics can be the same. For example, Avro key of a string shared over more than one topic will cause two subjects to be entered into the registry, however, only one schema ID will back them
Does every topic needs to have Key and Value?
Yes. Thats part of the Kafka Record protocol. The key can be nullable, however. If you're not using Avro serializer for either key or value, no entry will be made. You're not required to use Avro for both options if one or the other is
I am working on Kafka and as a beginner the following question popped out of my mind.
Every time we design the schema for Avro, we create the Java object out of it through its jars.
Now we use that object to populate data and send it from Producer.
For consuming the message we generate the Object again in Consumer. Now the objects generated in both places Producer & Consumer contains a field
"public static final org.apache.avro.Schema SCHEMA$" which actually stores the schema as a String.
If that is the case then why should kafka use schema registry at all ? The schema is already available as part of the Avro objects.
Hope my question is clear. If someone can answer me, It would be of great help.
Schema Registry is the repository which store the schema of all the records sent to Kafka. So when a Kafka producer send some records using KafkaAvroSerializer. The schema of the record is extracted and stored in schema registry and the actual record in Kafka only contains the schema-id.
The consumer when de-serializing the record fetches the schema-id and use it to fetch the actual schema from schema- registry. The record is then de-serialized using the fetched schema.
So in short Kafka does not keep a copy of schema in every record instead it is stored in schema registry and referenced via schema-id.
This helps in saving space while storing records also to detect any schema compatibility issue between various clients.
https://docs.confluent.io/current/schema-registry/docs/serializer-formatter.html
Schema registry is a central repo for all the schemas and helps in enforcing schema compatibility rules while registering new schemas , without which schema evolution would be difficult.
Based on the configured compatibility ( backward, forward , full etc) , the schema registry will restrict adding new schema which doesn't confirm to the configured compatibility.