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Head to head · Figures checked September 2026

Apache Gluten vs DataFusion Comet: open-source Spark accelerators compared

ETL Compare staff · Figures checked September 2026 · Sourced from vendor docs, project pages and public benchmarks

In brief

The two are close: Gluten scores 3.9 and Comet 3.8. Gluten has more contributors, a longer history and an extra backend option; Comet is lighter to install from Maven Central, supports current Spark 4.x releases and documents its spill limits more fully. Both need memory tuning and neither comes with a vendor.

Gluten if contributor breadth and maturity matter most; Comet if you run Spark 4.x and want the quicker first trial.

Gluten + Velox

3.9 / 5

ETL fit score (editorial assessment, 0-5)

ETL fit score (editorial assessment, 0-5)

Rank 1 of 7

Highest ETL fit score: open-source native engine for self-managed Spark

Comet

3.8 / 5

ETL fit score (editorial assessment, 0-5)

ETL fit score (editorial assessment, 0-5)

Rank 2 of 7

Best for teams on recent Spark 4.x releases

Side-by-side

Gluten + Velox and Comet compared. A filled square marks the higher score.
 Gluten + VeloxComet
Adoption effort 20%3.8Tie3.8Tie
Stage coverage 20%3.8Higher3.6
Tuning and operating burden 15%2.62.8Higher
Platform and instance portability 10%4.3Higher4.2
Cost model transparency 10%5.0Tie5.0Tie
Published evidence 15%3.8Tie3.8Tie
Maturity and community 10%4.4Higher3.9
ETL fit score3.9Higher3.8
ApproachOpen-source plugin that offloads Spark SQL execution to a native C++ engine (Velox or ClickHouse)Open-source Spark plugin that runs supported operators on the Apache DataFusion engine (Rust, Arrow)
Native engineVelox, C++, or ClickHouseDataFusion, Rust on Arrow
Spark versions3.4, 3.5, 4.0, 4.13.5, 4.0, 4.1; 3.4 deprecated; 4.2 experimental
DistributionDownload or buildJars on Maven Central, Linux amd64 and arm64
ShuffleColumnarShuffleManagerCometShuffleManager, native or columnar
SpillExperimental in Velox backendNative operators spill up to a per-task limit
GovernanceApache top-level project since March 2026Subproject of Apache DataFusion
Published benchmarkTPC-H and TPC-DSTPC-DS at 1 TB

Which job stages does each address?

Spark ETL job stages and which stages each accelerator documents addressingA waterfall of five Spark ETL job stages (read, transform, shuffle, spill, write) with illustrative proportions, and below it a grid showing, for each accelerator, whether its own documentation says it addresses that stage.Anatomy of a Spark ETL jobRead: scan and decode filesTransform: filter, join, aggregateShuffle: write and fetch between stagesSpill: memory pressure pushes data to diskWrite: encode and commit outputIllustrative proportions, not measured data. Your own split comes from the Spark UI: see Profile a slow Spark job.
Stage coverage by accelerator, from vendor documentation
AcceleratorReadTransformShuffleSpillWrite
Gluten + VeloxPartialPartialDocumentedDocumentedDocumentedDocumentedPartialPartialNot statedNot stated
CometDocumentedDocumentedDocumentedDocumentedDocumentedDocumentedPartialPartialNot statedNot stated
  • DocumentedDocumented: the vendor's own documentation says it addresses this stage
  • PartialPartial: indirect or experimental, or covered only by an end-to-end claim
  • Not statedNot stated: not found on the pages we reviewed

This shows what each vendor says, not what we measured. Sources are listed on each review.

Which one is more mature?

Gluten: started by Intel and Kyligence in 2022, an Apache top-level project since March 2026, with contributors across several large companies (maturity 4.4 against 3.9).

Which one is easier to try?

Comet, slightly: prebuilt jars with bundled native libraries on Maven Central, and a tuning guide that lists every setting you will need. Both need off-heap memory configured and a shuffle manager switched, so both score 3.8 on adoption effort.

Which one handles spill better?

Neither documents spill as a solved problem. Gluten's Velox spill is experimental; Comet's native operators spill within a per-task limit and its guide warns that memory accounting is not exact. For spill-heavy jobs, test both on your worst job and watch Shuffle spill (disk).

Choose Gluten if

  • You value contributor breadth and maturity
  • You may want the ClickHouse backend
  • You run Spark 3.4

Choose Comet if

  • You run Spark 3.5 or 4.x
  • You want Maven Central jars
  • You prefer DataFusion and Rust

Frequently asked questions

Can I run Gluten and Comet together?

Both replace Spark's physical execution and shuffle manager, so they are alternatives for the same job, not layers to stack.

Are both free?

Yes. Both are Apache License 2.0.

Sources

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