--- layout: global title: Versioning Policy type: "page" navigation: weight: 5 show: true --- Starting with Spark 1.0.0, the Spark project will follow the semantic versioning guidelines with a few deviations. These small differences account for Spark's nature as a multi-module project.

Spark versions

Each Spark release will be versioned: `[MAJOR].[FEATURE].[MAINTENANCE]` - **MAJOR**: Major releases occur annually (every 12 months) as **x.0.0** releases. These releases may include **breaking changes**, third-party dependency upgrades, API removals and deprecations, and other changes that are not compatible with the previous major line. Within a major line, all **x.y.z** releases share API compatibility as described in the **FEATURE** and **MAINTENANCE** bullets below. - **FEATURE**: Feature releases (**x.y.0** with **y ≥ 1**) occur quarterly (every 3 months) and contain new features, performance improvements, API additions, and bug fixes. To ensure safe and predictable upgrades for downstream projects, feature releases have the following compatibility: - No third-party dependency upgrades (e.g. Parquet, Arrow, ORC, Hadoop, Netty) by default. Upgrades required to address **security** issues may be allowed; any other exception is decided case by case by the release managers and the community. - No behavior or semantic changes (e.g. SQL semantics, execution behavior, configuration defaults). Optimizer changes that only improve performance while preserving query results are allowed; optimizer changes that alter results are not. Exceptions might occur case by case (e.g., security issues). - Public APIs may be added but not changed or removed. Each feature release will have a merge window where new patches can be merged, a QA window when only fixes can be merged, then a final period where voting occurs on release candidates. These windows will be announced immediately after the previous feature release to give people plenty of time. - **MAINTENANCE**: Maintenance releases (**x.y.z** with **z ≥ 1**) will occur on an ad hoc basis depending on specific patches introduced (e.g. critical bug fixes and security patches) and their urgency. They contain bug fixes and security patches only; they do not introduce new features.

Alpha components

When new components are added to Spark, they may initially be marked as "alpha". Alpha components do not have to abide by the above guidelines, however, to the maximum extent possible, they should try to. Once they are marked "stable" they have to follow these guidelines.

API compatibility

In general, An API is any public class or interface documented in Spark, e.g., ScalaDoc. We try to guarantee both source compatibility and binary compatibility between releases. Note, however, that even for features "developer API" and "experimental", we strive to maintain maximum compatibility. Code should not be merged into the project as "experimental" if there is a plan to change the API later, because users expect the maximum compatibility from all available APIs.

Considerations when breaking APIs

The Spark project strives to avoid breaking APIs or silently changing behavior, even at major versions. While this is not always possible, the balance of the following factors should be considered before choosing to break an API.

Cost of breaking an API

Breaking an API almost always has a non-trivial cost to the users of Spark. A broken API means that Spark programs need to be rewritten before they can be upgraded. However, there are a few considerations when thinking about what the cost will be: - **Usage** - an API that is actively used in many different places, is always very costly to break. While it is hard to know usage for sure, there are a bunch of ways that we can estimate: - How long has the API been in Spark? - Is the API common even for basic programs? - How often do we see recent questions in JIRA or mailing lists? - How often does it appear in StackOverflow or blogs? - **Behavior after the break** - How will a program that works today, work after the break? The following are listed roughly in order of increasing severity: - Will there be a compiler or linker error? - Will there be a runtime exception? - Will that exception happen after significant processing has been done? - Will we silently return different answers? (very hard to debug, might not even notice!)

Cost of maintaining an API

Of course, the above does not mean that we will **never** break **any** APIs. We must also consider the cost both to the project and to our users of keeping the API in question. - **Project Costs** - Every API we have needs to be tested and needs to keep working as other parts of the project changes. These costs are significantly exacerbated when external dependencies change (the JVM, Scala, etc). In some cases, while not completely technically infeasible, the cost of maintaining a particular API can become too high. - **User Costs** - APIs also have a cognitive cost to users learning Spark or trying to understand Spark programs. This cost becomes even higher when the API in question has confusing or undefined semantics.

Alternatives to breaking an API

In cases where there is a "Bad API", but where the cost of removal is also high, there are alternatives that should be considered that do not hurt existing users but do address some of the maintenance costs. - **Avoid Bad APIs** - While this is a bit obvious, it is an important point. Anytime we are adding a new interface to Spark we should consider that we might be stuck with this API forever. Think deeply about how new APIs relate to existing ones, as well as how you expect them to evolve over time. - **Deprecation Warnings** - All deprecation warnings should point to a clear alternative and should never just say that an API is deprecated. - **Updated Docs** - Documentation should point to the "best" recommended way of performing a given task. In the cases where we maintain legacy documentation, we should clearly point to newer APIs and suggest to users the "right" way. - **Community Work** - Many people learn Spark by reading blogs and other sites such as StackOverflow. However, many of these resources are out of date. Update them, to reduce the cost of eventually removing deprecated APIs.

Release cadence

Starting with Apache Spark 4.3.0 (the first quarterly feature release after 4.2.0), feature releases occur quarterly (every 3 months), containing new features, improvements, and bug fixes. Major releases occur annually (every 12 months), typically in a stable window once per year (subject to release discussion on the dev list), allowing breaking changes and dependency upgrades. Maintenance releases happen as needed in between for critical bug fixes and security patches.

Spark 4.2 release window

| Date | Event | | ----- | ----- | | May 1st 2026 | Code freeze. Release branch cut.| | Mid May 2026 | QA period. Focus on bug fixes, tests, stability and docs. Generally, no new features merged.| | Late May 2026 | Release candidates (RC), voting, etc. until final release passes|

Illustrative transition: 2026 and 2027

The calendar below is an **example** to show how the community expects to bootstrap the faster cadence; exact dates stay subject to the usual release-discussion and voting process. - **2026**: Ship **Apache Spark 4.2.0** on the timeline above (the last *large* feature drop before the transition). **4.2.x** is not affected by this policy (development began before the SPIP); existing commitments, including the **18**-month maintenance window for **4.2.x**, are unchanged. After **4.2.0** is generally available, plan **Apache Spark 4.3.0** as the first quarterly feature release on the new train (for example, roughly **three months** after the **4.2.0** GA date for the start of the **4.3** merge/RC cycle—this is not a fixed rule, only an illustration of quarterly feature releases). - **2027**: Ship **Apache Spark 5.0.0** as the next **annual** major. Follow with quarterly **5.1.0**, **5.2.0**, and **5.3.0** feature releases; **5.3.0** is the **5.x** LTS as the last **5.x** feature release (for example targeting calendar quarters **2027 Q1** through **Q4** if the **5.0.0** major lands early in the year).

Maintenance releases and EOL

We plan to ship a release every 3 months. Every fourth release bumps the major version (**x.0.0**). Within each major line, the first release is the major release, the releases in between are feature releases, and the last release in the line is the LTS release. The following table summarizes the maintenance window for each release type: | Release Type | Maintenance Window | | ----- | ----- | | Major (x.0.0) | 6 months | | Feature (x.[1–2].0) | 6 months | | LTS (last x.y.0 in the major line) | 18 months | | Maintenance (x.y.z, z ≥ 1) | N/A (patches only) | Release branches **other than LTS** will, generally, be maintained with bug fix releases for a period of 6 months (see the **Major** and **Feature** rows in the table above). Under this cadence, the **last** release in each major line is designated as the LTS (Long-Term Support) release for that major line and is maintained for **18 months** (for example Apache Spark 5.3.0 and 6.3.0). The **x.0.0**, **x.1.0**, and **x.2.0** trains are maintained for **6 months** each as usual. LTS releases provide a stable target for ecosystem projects and downstream vendors to standardize around. As an exception while the project transitions into this cadence, **Spark 4.x** still ships **4.3.0** as the first quarterly release on the new train, but the **4.x** LTS will be **Apache Spark 4.5.0** (the last planned **4.x** release) rather than **4.3.0**. **4.5.x** therefore receives the **18**-month LTS window in the same role as the last release on newer major lines. Critical security patches will be backported to all actively maintained branches. Critical bug fixes (e.g., correctness issues) that may introduce behavior changes will be evaluated by the community on a case-by-case basis. As an exception from the normal versioning policy, version 3.5.x has an "extended" LTS period to allow for migrations to be completed. This extended LTS period will end *November 2027*. During the 3.5.x extended LTS period, we will only include security fixes. This extended LTS only applies to the primary Apache Spark project/repo and does not apply to sub projects with separate repos/releases (namely: Spark Connect for Swift/Rust/Go and Spark Kubernetes operator). Additionally, as Java 8 support may be removed from other projects (including Hadoop), should a dependency have a security fix that is not backported to a Java 8 compatible version we may decide to mark that vulnerability as a won't fix or release the new version without Java 8 support.