Spring Team
Soby Chacko

Soby Chacko

Spring Cloud Stream/Data Flow

Philadelphia, PA

Blog Posts by Soby Chacko

Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 5 - Application Customizations

Part 1 - Programming Model
Part 2 - Programming Model Continued
Part 3 - Data deserialization and serialization
Part 4 - Error Handling

In this blog post, we continue our discussion on the support for Kafka Streams in Spring Cloud Stream. We are going to elaborate on the ways in which you can customize a Kafka Streams application.

Customizing the StreamsBuilderFactoryBean

Kafka Streams binder uses the StreamsBuilderFactoryBean, provided by the Spring for Apache Kafka project, to build the StreamsBuilder object that is the foundation for a Kafka Streams application. This factory bean is a Spring lifecycle bean. Oftentimes, this factory bean must be customized before it is started, for various reasons. As described in the previous blog post on error handling, you need to customize the StreamsBuilderFactoryBean if you want to register a production exception handler. Let’s say that you have this producer exception handler:

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Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 4 - Error Handling

Part 1 - Programming Model
Part 2 - Programming Model Continued
Part 3 - Data deserialization and serialization

Continuing with the series on looking at the Spring Cloud Stream binder for Kafka Streams, in this blog post, we are looking at the various error-handling strategies that are available in the Kafka Streams binder.

The error handling in Kafka Streams is largely centered around errors that occur during deserialization on the inbound and during production on the outbound.

Handling Deserialization Exceptions

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Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 3 - Data deserialization and serialization

Part 1 - Programming Model
Part 2 - Programming Model Continued

Continuing on the previous two blog posts, in this series on writing stream processing applications with Spring Cloud Stream and Kafka Streams, now we will look at the details of how these applications handle deserialization on the inbound and serialization on the outbound.

All three major higher-level types in Kafka Streams - KStream<K,V>, KTable<K,V> and GlobalKTable<K,V> - work with a key and a value.

With Spring Cloud Stream Kafka Streams support, keys are always deserialized and serialized by using the native Serde mechanism. A Serde is a container object where it provides a deserializer and a serializer.

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Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 2 - Programming Model Continued

On the heels of the previous blog in which we introduced the basic functional programming model for writing streaming applications with Spring Cloud Stream and Kafka Streams, in this part, we are going to further explore that programming model.

Let’s look at a few scenarios.

Scenario 1: Single input and output binding

If your application consumes data from a single input binding and produces data into an output binding, you can use Java’s Function interface to do that. Keep in mind that binding in this sense is not necessarily mapped to a single input Kafka topic, because topics could be multiplexed and attached to a single input binding (with comma-separated multiple topics configured on a single binding - see below for an example). On the outbound case, the binding maps to a single topic here.

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Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 1 - Programming Model

This is the first in a series of blog posts in which we will look at how stream processing applications are written using Spring Cloud Stream and Kafka Streams.

The Spring Cloud Stream Horsham release (3.0.0) introduces several changes to the way applications can leverage Apache Kafka using the binders for Kafka and Kafka Streams.
One of the major enhancements that this release brings to the table is first class support for writing apps by using a fully functional programming paradigm. This blog post gives an introduction to how this functional programming model can be used to develop stream processing applications with Spring Cloud Stream and Kafka Streams. In the subsequent blog posts in this series, we will look into more details.

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Spring Cloud Stream Elmhurst.SR1 Released

On behalf of the team, I am pleased to announce the availability of Spring Cloud Stream Elmhurst.SR1 release train. This is the first maintenance release on the Elmhurst line and includes several bug fixes and minor enhancements in core Spring Cloud Stream (2.0.1.RELEASE), Kafka binder (2.0.1.RELEASE), and the Rabbit binder (2.0.1.RELEASE). Please take a look at the release notes for more details. The release can be found in maven central. Detailed descriptions can be found in the reference documentation.

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Spring Cloud Stream Ditmars.SR4 Released

On behalf of the team, I am pleased to announce the availability of Spring Cloud Stream Ditmars.SR4 release train. This is a maintenance release and includes several bug fixes and minor enhancements in core Spring Cloud Stream (1.3.3.RELEASE), Kafka binder (1.3.3.RELEASE), and the Rabbit binder (1.3.4.RELEASE). Please take a look at the release notes for more details. The release can be found in maven central. Detailed descriptions can be found in the reference documentation.

We thank all our community members who contributed valuable additions to this release.

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Kafka Streams and Spring Cloud Stream

On the heels of the recently announced Spring Cloud Stream Elmhurst.RELEASE, we are pleased to present another blog installment dedicated to Spring Cloud Stream’s native integration with the Apache Kafka Streams library. Let’s review the new improvements.

MessageChannel Binders

Spring Cloud Stream framework enables application developers to write event-driven applications that use the strong foundations of Spring Boot and Spring Integration. The underpinning of all these is the binder implementation, which is responsible for communication between the application and the message broker. These binders are MessageChannel-based implementations.

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Spring Cloud Stream Chelsea.SR2 Released

On behalf of the community, I am happy to announce the release of Spring Cloud Stream Chelsea.SR2. This is the second general availability release in the Chelsea release train, which fixes a number of issues over Chelsea.SR1.

The new release is available in Maven Central, and a detailed description of its features can be found in the reference documentation. For information about artifacts and most recent changes, please consult the release notes.

What is next:

After the release, we will start working on the next Spring Cloud Stream release train, named Ditmars, as well as expand the Spring Cloud Stream ecosystem. In parallel with developing the Ditmars release train, we will start work on the Elmhurst release train which will be based on Spring Framework 5 and Spring Boot 2.

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