Comet 1.0.0 Blog Post

content/blog/2026-08-07-datafusion-comet-1.0.0.md

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+---
+layout: post
+title: Apache DataFusion Comet 1.0.0 Release
+date: 2026-08-07
+author: pmc
+categories: [subprojects]
+---
+
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+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 1.0.0 of the [Comet](https://datafusion.apache.org/comet/) subproject.
+
+Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for
+improved performance and efficiency without requiring any code changes.
+
+This release covers roughly six weeks of development since 0.17.0 and consists of 244 commits from 23
+contributors. See the [change log] for the full list of changes.
+
+[change log]: https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md
+
+## The Road to 1.0
+
+Comet was [donated] to the Apache DataFusion project in March 2024 and cut its first release, 0.1.0, five
+months later with support for 13 operators and 106 expressions. Since then, the project has shipped 20
+releases and drawn contributions from more than 120 developers, and the codebase now recognizes over 400
+Spark expressions.
+
+[donated]: https://datafusion.apache.org/blog/2024/03/06/comet-donation/
+
+The 1.0 release marks the point at which Comet begins following [semantic versioning]. Users upgrading
+within the 1.x line can expect backward-compatible changes only; features slated for removal will be
+deprecated in a minor release before being dropped in the next major version. This is why the deprecations
+of JDK 11 and Spark 3.4 announced below are scheduled for 1.1 rather than landing in 1.0 itself.
+
+[semantic versioning]: https://datafusion.apache.org/comet/about/versioning_policy.html
+
+### Support for Spark 4.0+ with ANSI mode
+
+Comet 1.0.0 supports Spark versions 3.4 through 4.1, with experimental support for 4.2. Comet fully supports Spark's ANSI mode, which is enabled by default starting with Spark 4.0.
+
+### Correctness Testing
+
+It is important that queries accelerated by Comet produce the same results as Spark. Correctness checking has always been a large effort in Comet development, but the approach has evolved over time.
+
+- **Upstream Spark tests**: Comet runs Spark's own test suite with Comet enabled, providing more than 24,000 unit tests effectively for free. These tests run in Comet's CI for all supported Spark versions.
+- **Scala tests**: end-to-end queries that run with Comet enabled versus disabled, checking that results match.
+- **Fuzz testing**: many of the Scala tests generate randomized data to catch regressions around edge cases such as nulls, NaN, Infinity, and timezone issues.
+- **Comet SQL tests**: a sqllogictest-inspired approach that makes end-to-end tests easier to write.
+- **Generative AI audits**: agentic skills sweep every expression, comparing Comet's implementation to Spark's source and ensuring tests cover important edge cases.
+
+### Performance
+
+The early Comet releases provided a very modest speedup and the published benchmark results were based on running TPC workloads at small scale factors on a single node. There are now independent benchmark results published by AWS Labs that show significant speedups for [TPC-DS @ 3TB running in EKS](https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark).
+
+### Codegen Dispatch
+
+Comet 0.17.0 introduced a new approach to filling gaps in expression coverage. In earlier releases,
+whenever Comet's planner encountered an expression that lacked a native Rust implementation, it fell back
+to executing an entire subtree of the plan in Spark. That required converting Arrow columns back to Spark
+rows before the expression ran and back to Arrow after, and the cost was often enough to erase the speedup
+Comet had bought elsewhere in the plan.
+
+Codegen dispatch narrows that fallback to the expression itself: the batch stays in the Comet pipeline
+and Comet invokes Spark's own generated code for just the missing expression, leaving the rest of the query
+running natively. Three consequences are worth calling out.
+
+- **Coverage.** Expressions that would previously have blocked native execution of a whole subtree are
+  now supported immediately, without a Rust port. In 1.0, this pathway also handles cast fallbacks,
+  several interval expressions, and additional string and timestamp functions.
+- **Compatibility.** For categories where a native reimplementation would inevitably diverge from Spark's
+  semantics — regular expressions being the canonical case, given the gap between Java's regex engine and
+  any Rust or C++ equivalent — codegen dispatch delivers bit-for-bit Spark parity because it *is* Spark's
+  implementation.
+- **Scala and Java UDFs.** User-defined functions are compiled to the same codegen surface as built-in
+  expressions, so they can flow through codegen dispatch without any change from the user. Queries that
+  were previously disqualified from acceleration only because they contained a UDF can now benefit as
+  long as the surrounding operators are supported. See the [Scala and Java UDF guide] for details.
+
+[Scala and Java UDF guide]: https://datafusion.apache.org/comet/user-guide/latest/scala_java_udfs.html
+
+## Improvements since 0.17.0
+
+The rest of this post covers what is new since the 0.17.0 release.
+
+### Experimental PyArrow UDF Support
+
+This release adds experimental support for accelerated PyArrow UDFs, allowing PyArrow-based user-defined
+functions to participate in native execution instead of forcing a fallback to Spark. When the feature is
+disabled, Comet now hints at the native PyArrow UDF path in its fallback reasons so users know the option
+exists. This is an early-stage feature and we welcome feedback from users experimenting with it.
+
+### New Expression and Aggregate Support
+
+This release expands the set of Spark expressions and aggregates that are accelerated by Comet:
+
+- **Aggregates**: `approx_percentile` / `percentile_approx`, exact `percentile` / `median`,
+  `approx_count_distinct`, and native `collect_list` / `array_agg`.
+- **Cast**: Cast expressions where the native implementation is marked as incompatible or unsupported
+  are now routed through codegen dispatch.
+- **Grouping**: `grouping()` and `grouping_id()`.
+- **Intervals**: interval types via `make_ym_interval` and `make_dt_interval`, `CalendarIntervalType`,
+  `multiply_dt_interval`, and interval codegen dispatch for nested values and native shuffle.
+- **String**: `base64`, `split_part` via `StringSplitSQL`, native `levenshtein`, and native `randstr` and
+  `uuid` — both bit-for-bit compatible with Spark for a given seed.
+- **Array / map**: `array_prepend`, the `shuffle()` array function, `size()` for `MapType`, and `ElementAt`
+  over `MapType`.
+- **Date/time**: native `TimestampNTZ` inputs for `hour` / `minute` / `second` and
+  `PreciseTimestampConversion` for native time-window grouping.
+- **Windows**: extended native window function support and Spark 4 decimal window average.
+
+### Faster Parquet Scans
+
+Parquet reads pick up several improvements as well. Full Parquet metadata, including the page index, is now
+cached via DataFusion's `CachedParquetFileReaderFactory`; identity casts are unwrapped in the schema adapter
+so Parquet statistics pruning can engage; filter pushdown configuration has been revised; the native scan
+passes a metadata size hint so a single read usually captures the footer; and the native Parquet scan seeds
+its reader options from the session config so Parquet settings you already set take effect.
+
+### Iceberg Table Format V3
+
+Comet now supports Iceberg 1.11 and Iceberg table format V3 (with native table decryption for V3 tables and
+fallback to Spark for other V3 features). The native Iceberg scan supports the `_pos`, `_spec`, `_file`, and
+`_partition` metadata columns, sizes delete files correctly to avoid dropped deletes, disambiguates scans that
+share a `metadata_location`, and dedupes residuals and delete files in the native scan serde. A prior case
+where Iceberg native scan exchange reuse with different pushed filters could produce wrong results is also
+fixed.
+
+### Native Expression Performance
+
+Many native expression implementations have been optimized to more efficiently leverage Arrow kernels or to avoid per-row builders.
+
+- **Casts** between numeric, string, decimal, and date types, including a faster float-to-decimal cast, an
+  optimized integer-to-integer cast, shared no-overflow fast paths in `CheckOverflow` and
+  `DecimalRescaleCheckOverflow`, and a `cast_binary_to_string` that is up to 27x faster on binary-format
+  styles.
+- **JSON, regex, and URL parsing**: `get_json_object`, `regexp_extract`, and `parse_url`.
+- **Date/time and decimal kernels**: `date_trunc`, `spark_ceil`, and a vectorized `spark_unscaled_value`.
+- **String and array kernels**: `lpad`, `unhex`, `size`, `arrays_overlap`, `escape_string`, and the `try_*`
+  arithmetic kernel.
+
+## Deprecation Notice
+
+With the move to a stable 1.0 release line, Comet begins deprecating older platforms under its
+[versioning policy]:
+
+- **JDK 11** is deprecated and scheduled for removal in Comet 1.1.0.
+- **Apache Spark 3.4** is deprecated and scheduled for removal in Comet 1.1.0.
+
+Users on these platforms should plan to move to JDK 17+ and Spark 3.5 or later before upgrading to 1.1.0.
+
+[versioning policy]: https://datafusion.apache.org/comet/about/versioning_policy.html
+
+## Compatibility
+
+Supported platforms include:
+
+- **Spark 3.4.3** with Java 11/17 and Scala 2.12/2.13 (deprecated, removal in 1.1.0)
+- **Spark 3.5.9** with Java 11/17 and Scala 2.12/2.13
+- **Spark 4.0.4** with Java 17 and Scala 2.13
+- **Spark 4.1.3** with Java 17/21 and Scala 2.13
+- **Spark 4.2** with Java 17 and Scala 2.13 (experimental, for early evaluation only)
+
+See the [Spark Version Compatibility] page for known limitations specific to each version.
+
+[Spark Version Compatibility]: https://datafusion.apache.org/comet/user-guide/latest/compatibility/spark-versions.html
+
+This release upgrades to **DataFusion 54.1** and **Arrow 58.4**.
+
+## Get Started with Comet 1.0.0
+
+Ready to try it out? Follow the [Comet 1.0.0 Installation Guide](https://datafusion.apache.org/comet/user-guide/1.0/installation.html)
+to get up and running, then point Comet at your existing Spark workloads and see the speedup for yourself.