Apache Spark Release Cycle and Version Support

Apache Spark follows a feature release roughly every six to nine months, with maintenance releases on the active branches in between. Each minor version is maintained for around eighteen months, which is shorter than many teams assume and shorter than the life of the clusters Spark usually runs on. This article covers the cadence, the semantic versioning policy the project follows, and what to check when moving between versions.

Cadence and versioning

Spark follows semantic versioning with the project’s own interpretation:

Minor releases arrive roughly every six to nine months. Maintenance releases on an active branch appear as needed, usually every few months.

The support window for a minor version is approximately eighteen months from release, with the caveat that the final maintenance release of a branch marks the real end. Because Spark clusters often outlive that window, checking which branch you are on is worth doing periodically rather than assuming continued coverage.

What changed in Spark 4

Spark 4 was the first major version in several years, and as with any major it took the opportunity to remove deprecated surface and change defaults. The themes:

• ANSI SQL mode by default, which changes how overflow, invalid casts and division by zero behave. Previously these silently produced null or wrapped values; under ANSI mode they raise errors. This is the single most likely source of behaviour change when upgrading.
• Spark Connect maturing into a first-class way to run Spark, separating the client from the driver over a gRPC protocol. It allows lighter clients and better isolation between applications and the cluster.
• Variant data type for semi-structured data, giving better performance than parsing JSON strings on every access.
• Python improvements, including a native plotting API on DataFrames and better user-defined function performance.
• Removal of long-deprecated APIs, including parts of the older RDD-era surface and previously deprecated configuration options.

# ANSI mode is the default in Spark 4; this raises rather than returning null
spark.conf.set("spark.sql.ansi.enabled", "false")  # opt out during migration

Disabling ANSI mode is a reasonable transitional step, but it should be treated as temporary. Leaving it off indefinitely means carrying the old silent-corruption semantics forward.

Compatibility considerations

Scala version. Spark binaries are built against a specific Scala version, and a mismatch between your application’s Scala version and the cluster’s is a frequent cause of cryptic failures. Check this before anything else.

Java version. Each Spark release supports a range of JDKs, and the supported range moves forward over time. Running an unsupported JDK often appears to work until a specific code path fails.

Python version. PySpark drops support for Python versions as they reach end of life, and the minimum supported version rises with each release.

Hadoop and cloud connectors. The Hadoop client libraries bundled with Spark determine which S3, ABFS and GCS connector versions you can use. Mixing versions here causes classpath problems that are tedious to diagnose.

Delta, Iceberg and Hudi. Table format libraries pin themselves to specific Spark minor versions. In practice, the table format often determines when you can upgrade Spark, not the other way round.

Upgrade approach

1. Read the migration guide. Spark publishes one per version, listing behaviour changes explicitly. For Spark it is genuinely necessary reading rather than a formality.
2. Run the old and new versions side by side. Execute representative jobs on both and compare output, not just success or failure. ANSI mode in particular changes results rather than causing crashes.
3. Check the ecosystem first. Confirm your table format, connectors and any custom data sources have builds for the target version.
4. Watch for configuration removals. Options removed in a major version are silently ignored rather than rejected in some cases, so a tuning setting you relied on may quietly stop applying.

Conclusion

Spark’s release cycle is a minor version every six to nine months and roughly eighteen months of support per branch. The upgrade risk is rarely the Spark API itself; it is the surrounding matrix of Scala, Java, Python, connectors and table formats, plus behaviour changes such as ANSI mode that alter results instead of failing loudly. Read the migration guide, compare output between versions, and let your table format dictate the timing.