DataFusion Comet: What It Accelerates and What It Doesn't

Apache DataFusion Comet is an open-source Apache Spark plugin that accelerates supported SQL and DataFrame operations by translating portions of Spark physical plans to the Rust-based, vectorized DataFusion engine using Apache Arrow data, while unsupported operations silently fall back to standard JVM-based Spark execution. It is designed as a drop-in addition requiring no application code changes and performs best on CPU-bound, scan-heavy Iceberg and Parquet workloads involving filters, joins, aggregations, sorts, and columnar shuffles, but it does not natively accelerate Iceberg writes or Delta Lake, Hudi, and Paimon reads, making its real benefit dependent on the proportion of a job that executes natively. Users are advised to enable fallback explanations and inspect query plans rather than infer coverage from deployment alone, since expensive stages may still run on Spark. Comet’s codegen dispatcher can preserve native pipelines for some partially compatible expressions and Java or Scala UDFs, while compatibility settings and unsupported expressions can require ongoing tuning. Compared with Apache Gluten, which uses the C++ Velox engine and supports more table formats, Comet emphasizes Rust memory safety and its dispatcher, although benchmark differences vary by workload. The discussion also presents Quanton as a commercial open-core alternative that claims to extend optimization beyond compute into storage-aware planning, native writes, compaction, and MERGE operations, while recommending that teams benchmark Comet, Gluten, and other options using their own workloads.

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