Hot on the heels of the recent Apache Kafka 2.4.0 release, I am pleased to announce the release candidate for Spring for Apache Kafka 2.4 - 2.4.0.RC1 - is available in the Spring milestone repository.

This version is essentially functionally equivalent to 2.3.x, but is compiled against the 2.4.0 kafka-clients and supports the new incremental rebalancing protocol.

The 2.4.0 kafka clients are not binary compatible with Spring for Apache Kafka 2.3 so if you wish to use the 2.4.0 clients, you must upgrade to this version. See the appendix in the reference manual for how to override the jar…

Hi, Spring fans! This week I am in beautiful Tokyo, Japan, where I just spoke at the always lovely annual Spring Fest event. I loved the show and I hope that they got something out of my performance.

Last week was tough. Possibly the toughest week of my life. I didn't publish an episode of A Bootiful Podcast, as such. You won't see that episode reflected on the blog because it was my heartbroken dedication to my father, who passed away last week at the age of 81. No interview in that brief, less-than-20 minutes episode.

I'm feeling a little better and was glad to come back to the work of…

This blog post is the third in a series of posts that aim at providing a deeper look into Reactor’s more advanced concepts and inner workings.

In this post, we explore the threading model, how some (most) operators are concurrent agnostic, the Scheduler abstraction and how to hop from one thread to another mid-sequence with operators like publishOn.

This series is derived from the Flight of the Flux talk, which content I found to be more adapted to a blog post format.

The table below will be updated with links when the other posts are published, but here is the planned content:

1. Assembly vs Subscription
2. Debugging caveats
3. Hopping Threads and Schedulers (this post)
4. Inner workings: work stealing
5. Inner workings: operator fusion
…

On behalf of the Spring, Apache Geode and Pivotal GemFire communities, it is my pleasure to announce the release of Spring Session for Apache Geode & Pivotal GemFire (SSDG) 2.1.7.RELEASE as well as 2.2.1.RELEASE.

Both releases are available in Maven Central.

What’s New

While SSDG 2.1.7.RELASE primarily aligns with the latest Spring bits in its respective line:

• Spring Framework 5.1.12.RELEASE

• Spring Boot 2.1.11.RELEASE

• Spring Data Lovelace-SR14/2.1.14.RELEASE

• Spring Session Bean-SR8/2.1.9.RELEASE

And SSDG 2.2.1.RELEASE aligns with:

• Spring Framework 5.2.2.RELEASE

• Spring Boot 2.2.2.RELEASE

• Spring Data Moore-SR3/2.2.3.RELEASE

• Spring Session Corn-RELEASE/2.2.0.RELEASE

…

On behalf of the Spring, Apache Geode & Pivotal GemFire communities, it is my pleasure to announce the release of Spring Boot for Apache Geode & Pivotal GemFire (SBDG) 1.1.4.RELEASE as well as 1.2.2.RELEASE.

Both releases are available in Maven Central.

What’s New

While SBDG 1.1.4.RELEASE primarily aligns with the latest Spring bits in its release line:

• Spring Framework 5.1.12.RELEASE

• Spring Boot 2.1.11.RELEASE

• Spring Data Lovelace-SR14/2.1.14.RELEASE

• Spring Session for Apache Geode & Pivotal GemFire (SSDG) Bean-SR8/2.1.7.RELEASE (NEW)

And SBDG 1.2.2.RELEASE builds on:

• Spring Framework 5.2.2.RELEASE

• Spring Boot 2.2.2.RELEASE

• Spring Data Moore-SR3/2.2.3.RELEASE

• Spring Session for Apache Geode & Pivotal GemFire (SSDG) Corn-RELEASE/2.2.1.RELEASE (NEW)

• Spring Test for Apache Geode & Pivotal GemFire (STDG) 0.0.11.RELEASE (NEW)

…

Hi, Spring fans! Welcome to another installment of This Week in Spring! Today I just wrapped up my appearance in Brisbane, Australia, where I have been for the epic YOW! conference. Truly, one of my all-time favorite shows on the planet. I feel like an imposter in the ranks of the other speakers. I can not recommend this show enough.

I'm just about to board my fight back to San Francisco, and we've got a ton of stuff to get to so let's press on!

• Spring Cloud Data Flow 2.3.0 GA Released
• Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 6 - State Stores and Interactive Queries
• Stream Processing with Spring Cloud Stream and Apache Kafka Streams. Part 5 - Application Customizations
• Spring Data R2DBC goes GA
• Spring Boot 2.2.2 is now available
• Spring Boot 2.1.11 is now available
• In last week's A Bootiful Podcast, I talked to Pivotal's Katrina Bakas about the Pivotal HealthWatch product, Kubernetes, Cloud Foundry and so much more.
• …

With the recent release of Spring Boot 2.2, we'd like to announce that maintenance for Spring Boot 2.1 will end on November 1st 2020.

We'll keep publishing the occasional maintenance release up until that point, but we recommend that all users consider upgrading to Spring Boot 2.2 as soon as possible. Upgrading to Spring Boot 2.2 from 2.1 should not be too difficult, and upgrade instructions are available on the WIKI.

In order to help track the state of supported releases we've also introduced a new "supported versions" WIKI page. You can see at a glance which Spring Boot versions are…

The release 2.3.0 delivers a lot of enhancements and generic compatibility changes for Spring Boot 2.2.x and Spring Cloud Hoxton.

Continuous Deployment For Tasks

As task applications evolve faster to keep up with business needs, the ability for new versions to be consumed via Data Flow in an automated way is needed. While Data Flow has supported the ability to register multiple versions of a task application in previous iterations, the ability to run them in a practical way by re-hydrating command line arguments, deployment properties, and application properties used in previous executions has…

Part 1 - Programming Model Part 2 - Programming Model Continued Part 3 - Data deserialization and serialization Part 4 - Error Handling Part 5 - Application Customizations

In this part (the sixth and final one of this series), we are going to look into the ways Spring Cloud Stream Binder for Kafka Streams supports state stores and interactive queries in Kafka Streams.

Named State Stores

When you have the need to maintain state in the application, Kafka Streams lets you materialize that state information into a named state store. There are several operations in Kafka Streams that require it to keep track of the state such as count, aggregate, reduce, various windowing operations, and others. Kafka Streams uses a special database called RocksDB for maintaining this state store in most cases (unless you explicitly change…