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Rick Hightower
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This comprehensive Kafka tutorial covers Kafka architecture and design. The Kafka tutorial has example Java Kafka producers and Kafka consumers. The Kafka tutorial also covers Avro and Schema Registry.

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This comprehensive Kafka tutorial covers Kafka architecture and design. The Kafka tutorial has example Java Kafka producers and Kafka consumers. The Kafka tutorial also covers Avro and Schema Registry.

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Kafka Tutorial: Kafka, Avro Serialization and the Schema Registry: Confluent Schema Registry stores Avro Schemas for Kafka producers and consumers. The Schema Registry and provides RESTful interface for managing Avro schemas It allows the storage of a history of schemas which are versioned. the Confluent Schema Registry supports checking schema compatibility for Kafka. You can configure compatibility setting which supports the evolution of schemas using Avro. Kafka Avro serialization project provides serializers. Kafka Producers and Consumers that use Kafka Avro serialization handle schema management and serialization of records using Avro and the Schema Registry. When using the Confluent Schema Registry, Producers don’t have to send schema just the schema id which is unique. The consumer uses the schema id to look up the full schema from the Confluent Schema Registry if not already cached. Since you don’t have to send the schema with each set of records, this saves time. Not sending the schema with each record or batch of records, speeds up the serialization as only the id of the schema is sent.

If you have never used Avro before, please read Avro Introduction for Big Data and Data Streams.

This article is going to cover what is the Schema Registry and cover why you want to use it with Kafka. We drill down into understanding Avro schema evolution and setting up and using Schema Registry with Kafka Avro Serializers. We show how to manage Avro Schemas with REST interface of the Schema Registry and then how to write Avro Serializer based Producers and Avro Deserializer based Consumers for Kafka.

The Kafka Producer creates a record/message, which is an Avro record. The record contains a schema id and data. With Kafka Avro Serializer, the schema is registered if needed and then it serializes the data and schema id. The Kafka Avro Serializer keeps a cache of registered schemas from Schema Registry their schema ids.

Consumers receive payloads and deserialize them with Kafka Avro Deserializers which use the Confluent Schema Registry. Deserializer looks up the full schema from cache or Schema Registry based on id.

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Kafka Tutorial: Kafka, Avro Serialization and the Schema Registry: Confluent Schema Registry stores Avro Schemas for Kafka producers and consumers. The Schema Registry and provides RESTful interface for managing Avro schemas It allows the storage of a history of schemas which are versioned. the Confluent Schema Registry supports checking schema compatibility for Kafka. You can configure compatibility setting which supports the evolution of schemas using Avro. Kafka Avro serialization project provides serializers. Kafka Producers and Consumers that use Kafka Avro serialization handle schema management and serialization of records using Avro and the Schema Registry. When using the Confluent Schema Registry, Producers don’t have to send schema just the schema id which is unique. The consumer uses the schema id to look up the full schema from the Confluent Schema Registry if not already cached. Since you don’t have to send the schema with each set of records, this saves time. Not sending the schema with each record or batch of records, speeds up the serialization as only the id of the schema is sent.

If you have never used Avro before, please read Avro Introduction for Big Data and Data Streams.

This article is going to cover what is the Schema Registry and cover why you want to use it with Kafka. We drill down into understanding Avro schema evolution and setting up and using Schema Registry with Kafka Avro Serializers. We show how to manage Avro Schemas with REST interface of the Schema Registry and then how to write Avro Serializer based Producers and Avro Deserializer based Consumers for Kafka.

The Kafka Producer creates a record/message, which is an Avro record. The record contains a schema id and data. With Kafka Avro Serializer, the schema is registered if needed and then it serializes the data and schema id. The Kafka Avro Serializer keeps a cache of registered schemas from Schema Registry their schema ids.

Consumers receive payloads and deserialize them with Kafka Avro Deserializers which use the Confluent Schema Registry. Deserializer looks up the full schema from cache or Schema Registry based on id.

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Avro Introduction for Big Data and Data Streaming Architectures: Apache Avro™ is a data serialization system. Avro provides data structures, binary data format, container file format to store persistent data, and provides RPC capabilities. Avro does not require code generation to use and integrates well with JavaScript, Python, Ruby, C, C#, C++ and Java. Avro gets used in the Hadoop ecosystem as well as by Kafka.

Avro is similar to Thrift, Protocol Buffers, JSON, etc. Avro does not require code generation. Avro needs less encoding as part of the data since it stores names and types in the schema reducing duplication. Avro supports the evolution of schemas.

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Avro Introduction for Big Data and Data Streaming Architectures: Apache Avro™ is a data serialization system. Avro provides data structures, binary data format, container file format to store persistent data, and provides RPC capabilities. Avro does not require code generation to use and integrates well with JavaScript, Python, Ruby, C, C#, C++ and Java. Avro gets used in the Hadoop ecosystem as well as by Kafka.

Avro is similar to Thrift, Protocol Buffers, JSON, etc. Avro does not require code generation. Avro needs less encoding as part of the data since it stores names and types in the schema reducing duplication. Avro supports the evolution of schemas.

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Kafka Tutorial 14: Creating Advanced Kafka Consumers in Java

In this tutorial, you are going to create advanced Kafka Consumers.

UNDER CONSTRUCTION.

Before you start

The prerequisites to this tutorial are

Kafka from the command line
Kafka clustering and failover basics
and Creating a Kafka Consumer in Java.
This tutorial picks up right where Kafka Tutorial Part 11: Writing a Kafka Producer example in Java left off. In the last tutorial, we created advanced Java producers, now we will do the same with Consumers.

Kafka Tutorial 14: Creating Advanced Kafka Consumers in Java Slides

This tutorial covers advanced consumer topics like custom deserializers, ConsumerRebalanceListener to rewind to a certain offset, manual assignment of partitions to implement a priority queue, “at least once” message delivery semantics Consumer Java example, “at most once” message delivery semantics Consumer Java example, “exactly once” message delivery semantics Consumer Java example, and a lot more. We also cover various threading models for the Consumer from the easiest (thread per consumer) to a more complex (consumer that is multi-threaded).

This tutorial is under construction, but we have complete example code and slides explaining all of the above. Contact us if you would like the code examples from these slides.

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Kafka Tutorial 14: Creating Advanced Kafka Consumers in Java:
In this tutorial, you are going to create advanced Kafka Consumers.


Before you start: The prerequisites to this tutorial are
Kafka from the command line
Kafka clustering and failover basics
and Creating a Kafka Consumer in Java.
This tutorial picks up right where Kafka Tutorial Part 11: Writing a Kafka Producer example in Java left off. In the last tutorial, we created advanced Java producers, now we will do the same with Consumers.

Kafka Tutorial 14: Creating Advanced Kafka Consumers in Java Slides

This tutorial covers advanced consumer topics like custom deserializers, ConsumerRebalanceListener to rewind to a certain offset, manual assignment of partitions to implement a priority queue, “at least once” message delivery semantics Consumer Java example, “at most once” message delivery semantics Consumer Java example, “exactly once” message delivery semantics Consumer Java example, and a lot more. We also cover various threading models for the Consumer from the easiest (thread per consumer) to a more complex (consumer that is multi-threaded).

This tutorial is under construction, but we have complete example code and slides explaining all of the above. Contact us if you would like the code examples from these slides.
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