Add blog post: Announcing Spring Data Valkey#476
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| Across these use cases, the core theme is consistent: Spring Data Valkey preserves the developer experience, while GLIDE strengthens the operational layer. Cluster awareness, failover transparency, resilient messaging, cost-aware routing, and observability are handled in the client — so application code can remain simple even as infrastructure grows more complex. | ||
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| ## A New Season for Spring and Valkey |
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Instead of "A New Season for Spring and Valkey", it can be a bit more clear ex: "How to Get Started with Spring Data Valkey"
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I think the play on words is worth the slightly less clear title.
| link: "https://github.com/GITHUB_HANDLE" | ||
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| With the winter months winding down, it's a fitting time to introduce Spring Data Valkey, a new open source Spring Data module that provides first-class integration between Valkey and the Spring ecosystem. Valkey adoption has expanded rapidly across cloud providers, Linux distributions, and enterprise environments. As organizations standardize on Valkey for caching, session management, and real-time data workloads, demand has grown for native integrations with the frameworks developers already use. For the Java ecosystem, that means Spring and Spring Data. |
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Should we clarify that this is GA like this is a general availability/release
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| Spring Data Valkey makes leveraging Valkey in Spring applications simple. You keep the familiar Spring Data programming model — templates, repositories, cache annotations, and Spring Boot workflows — while gaining direct integration with Valkey and optional support for Valkey GLIDE. The result is a modern, cluster-aware client with built-in resilience, intelligent routing, and observability, all without requiring changes to how your Spring applications are written. As a drop-in replacement for Spring Data Redis, migrating is seamless and straightforward — often just a dependency update and a few configuration changes. | ||
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| Valkey is rapidly becoming the default high-performance caching engine across cloud providers and enterprise environments, and Spring Data Valkey provides a clear path for Spring developers to adopt it. To get started, explore the project on [GitHub](https://github.com/valkey-io/spring-data-valkey). For documentation and a step-by-step migration guide from Spring Data Redis, visit [spring.valkey.io](https://spring.valkey.io/overview/). |
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If we need community feedback, we should state that since this release is now generally available (GA) that we are excited for everyone to try it out and welcome their feedback—whether it’s feature suggestions, bug reports, or contributions to the project.
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crystalphamLF
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Hi Mas,
Here are some general comments you’re welcome to keep or skip.
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It feels like you wanted to write a GLIDE blog but had to integrate it with Spring Data Valkey. Almost the entire blog focuses on GLIDE, but you mention several times GLIDE is optional. Is there any benefit without GLIDE (it's fine if the answer is no, but I couldn't grok it). The blog also talks about too many things and doesn't tie it together by going deep, it's just all high level. There is a lot of repetition of GLIDE features.
We could move this to the issue, but who is supposed to read this blog? Is it new developers who might use spring data valkey, existing spring data that should migrate?
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| In addition, Spring Data Valkey introduces integration with [Valkey GLIDE](https://github.com/valkey-io/valkey-glide), one of Valkey's official client libraries. When used together, GLIDE extends the capabilities of Spring Data Valkey by adding enhanced reliability, fault tolerance, and operational resilience — strengthening the production characteristics of your application without changing its programming model. Valkey GLIDE delivers: | ||
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| - **Cluster awareness and intelligent routing** – Automatically discovers cluster topology, follows slot mappings, and routes requests to the appropriate primary or replica nodes based on real-time cluster state. |
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Doesn't Jedis and Lettuce do this? This list of features is super weird to have this at the top because it's in all clients. I would focus on availability aware routing. (Don't we intentionally disable resilient pub/sub?) I guess OpenTelemetry is cool. Is that supported?
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Some comments on the PR. Each sentence should be on its own line for reviewing. Also make sure the build is working, it's because you didn't add yourself as an author. |
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feedback addressed in this updated version. Fair feedback on the Valkey Advantage section being too glide focused. There are meaningful advantages to switching to Valkey from Redis engine, now mentioned in that section. |
madolson
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General comments are mostly to focus more on end user benefit and removing some unnecessary words. It looks mostly OK though.
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| The final graph measures CPU efficiency, expressed as requests per second per unit of CPU utilization. | ||
| This metric highlights how efficiently a client translates compute resources into completed operations. | ||
| GLIDE is roughly twice as efficient, compared to the other clients, indicating that it can deliver similar or higher throughput while consuming fewer compute resources. |
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GLIDE is roughly twice as a efficient doesn't come across in the benchmarks at all. It looks slighter better to worse in various situations.
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Use a smaller image, otherwise we'll be sending 1MB to folks on phones for no reason. We won't send this large of an image anyways.
| The programming model remains the same. | ||
| The templates and repositories remain the same. | ||
| What changes is the foundation underneath. | ||
| Spring Data Valkey is directly integrated with Valkey and can optionally be used with [Valkey GLIDE](https://github.com/valkey-io/valkey-glide), one of Valkey's official client libraries, that brings new operational-excellence capabilities to Spring applications. |
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I'm not sure what "directly integrated" with Valkey means. It's in the Valkey repo. It's officially supported maybe?
| The templates and repositories remain the same. | ||
| What changes is the foundation underneath. | ||
| Spring Data Valkey is directly integrated with Valkey and can optionally be used with [Valkey GLIDE](https://github.com/valkey-io/valkey-glide), one of Valkey's official client libraries, that brings new operational-excellence capabilities to Spring applications. | ||
| When enabled, GLIDE adds cluster awareness, intelligent routing, automatic failover handling, and production-grade reliability — extending the operational capabilities of Spring Data Valkey while preserving the existing developer experience. |
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I still feel like all of theses were their before? Maybe intelligent routing to replicas is new, but I'm not sure what that is.
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"Replica selection and availability-zone–aware routing" is meant to be "AZ Affinity" which I don't think is a common term so I wanted to be more descriptive.
Otel integratin: Last I checked, Jedis does not have it. Lettuce yes via instrumentation.
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| Spring Data Valkey is available through standard Maven repositories, so you can manage it using your existing dependency management workflows and align versions with the broader Spring ecosystem. | ||
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| ## The Valkey Advantage |
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This section is confusing. It splits between Valkey core improvements (which I think are a tangent for this blog) vs new features that need client support (which I think is fine).
| GLIDE is designed to take full advantage of Valkey's architecture, offering enhanced reliability, fault tolerance, and operational resilience. | ||
| When used with Spring Data Valkey, it strengthens production characteristics while preserving the same developer experience. |
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| GLIDE is designed to take full advantage of Valkey's architecture, offering enhanced reliability, fault tolerance, and operational resilience. | |
| When used with Spring Data Valkey, it strengthens production characteristics while preserving the same developer experience. |
These two sentences don't say a lot, can we do less with less here?
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| ## Value-add for Real-World Use Cases | ||
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| Let's explore how the new capabilities introduced through Valkey GLIDE integration with Spring Data Valkey solve real-world problems in real-world use cases. |
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I'm not convinced we need this paragraph either. A lot of it is re-hashing past statements.
| ## Value-add for Real-World Use Cases | ||
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| Let's explore how the new capabilities introduced through Valkey GLIDE integration with Spring Data Valkey solve real-world problems in real-world use cases. | ||
| Spring Data Valkey supports common production patterns such as caching, session storage, messaging, rate limiting, and real-time data services. |
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Session storage is mentioned, but not hit later, consider removing.
| ## A New Season for Spring and Valkey | ||
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| Spring Data Valkey makes leveraging Valkey in Spring applications simple. | ||
| You keep the familiar Spring Data programming model — templates, repositories, cache annotations, and Spring Boot workflows — while gaining direct integration with Valkey and optional support for Valkey GLIDE. |
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I think one thing that feels wrong is that the emphasis is on the glide integration, not on the features that glide provides. You could merge this and the next sentence together.
| The result is a modern, cluster-aware client with built-in resilience, intelligent routing, and observability, all without requiring changes to how your Spring applications are written. | ||
| Fully compatible with Spring Data Redis, migrating is seamless and straightforward — just a dependency update and a few configuration changes. | ||
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| Valkey is rapidly becoming the default high-performance caching engine across cloud providers and enterprise environments, and Spring Data Valkey provides a clear path for Spring developers to adopt it. |
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| Valkey is rapidly becoming the default high-performance caching engine across cloud providers and enterprise environments, and Spring Data Valkey provides a clear path for Spring developers to adopt it. |
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- Blog post about Spring Data Valkey general availability - Author profile and photo for makubo-aws - Benchmark charts for throughput and latency - One sentence per line for easier review Signed-off-by: makubo <makubo@amazon.com>
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Signed-off-by: Madelyn Olson <madelyneolson@gmail.com>
stockholmux
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:sigh: I had this on my desk and didn't finish it off before it got published. Consider these to be useful feedback if you want to touch up the already published blog.
| github: makubo-aws | ||
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| Mas is a Product Manager in the In-Memory Databases team at AWS focused on Valkey, the open-source high-performance datastore engine for Amazon ElastiCache. Outside of work Mas follows the wind and the ocean while freediving, paragliding, kitesurfing, and sailing. |
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Feels weird to describe the relationship between Valkey and ElastiCache in your bio.
Also, I want to talk to you about paragliding.
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| With the winter months winding down, it's a fitting time to introduce the general availability of Spring Data Valkey, a new open source Spring Data module that provides first-class integration between Valkey and the Spring ecosystem. |
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Winter is over officially in the northern hemisphere (and would be weird for our southern neighbours!)
Maybe tweak it a bit.
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I would link Spring, Spring Data, and Spring-data-valkey.
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| ## Spring Data Redis Compatible | ||
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| Spring Data Valkey was built to align closely with the established Spring Data programming model while supporting Valkey's Redis-compatible protocol and data structures. |
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Nit: we should include the API not just protocol and data structures.
Typically we talk compatibility as far as Valkey sharing the same protocol as Redis (instead of saying Redis compatible) as well as the same 7.2.4 API. This frames it a more as Valkey being compatible with Redis.
| If your application already relies on Spring Data templates, repository-based data access, Spring's cache abstraction (such as `@Cacheable` and `@CacheEvict`), or Spring Boot auto-configuration, you can seamlessly move to Valkey while continuing to use those same abstractions. | ||
| The core programming model does not change, data access patterns remain consistent, and your application architecture remains intact. | ||
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| To move your existing Spring Data applications to use Spring Data Valkey, simply update a few dependencies, switch to Valkey-specific package namespaces, and adjust configuration property names; after that, your application continues to operate using the same Spring Data APIs and behaviors it always has. |
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the term 'simply' or 'simple' is probably should be avoided as it's relative (and it kind of sounds complicated as described, tbh).
I'm might consilidate and make this section a little more high level.
| The core programming model does not change, data access patterns remain consistent, and your application architecture remains intact. | ||
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| To move your existing Spring Data applications to use Spring Data Valkey, simply update a few dependencies, switch to Valkey-specific package namespaces, and adjust configuration property names; after that, your application continues to operate using the same Spring Data APIs and behaviors it always has. | ||
| For a step-by-step guide, you can refer to the [Spring Data Valkey migration guide](https://spring.valkey.io/commons/migration/). |
| With Spring Data Valkey and GLIDE, cluster changes, failovers, reconnections, observability, and cross-AZ traffic optimization are handled transparently, allowing teams to focus on business logic instead of infrastructure edge cases. | ||
| The result is a Spring-native development experience backed by a client designed for resilient, production-grade operation in modern distributed environments. | ||
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| You can learn more about GLIDE here: [https://github.com/valkey-io/valkey-glide](https://github.com/valkey-io/valkey-glide) |
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Raw links are an anti-pattern. Link a word or words instead of a URL.
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| ### High-Availability Caching in Microservices | ||
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| In modern microservices architectures, caches are often deployed in clustered, multi-AZ environments. |
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'modern' vs 'traditional'? Is that needed?
- Add embedded hyperlinks throughout the blog (Valkey, Spring, GLIDE, AZ affinity, Pub/Sub, OpenTelemetry, Habby case study) - Add second author: Ilia Kolominsky (ikolomi) - Minor text updates from latest draft to address Kyle's feedback ### Description Makes it amazing ### Issues Resolved Kyle's feedback from #476 ### Check List Check By submitting this pull request, I confirm that my contribution is made under the terms of the BSD-3-Clause License. Signed-off-by: makubo <makubo@amazon.com>
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Description
Adds a blog announcing Spring Data Valkey
Issues Resolved
#475
Check List
--signoffBy submitting this pull request, I confirm that my contribution is made under the terms of the BSD-3-Clause License.