hsiang-c commented on code in PR #201:
URL: https://github.com/apache/datafusion-site/pull/201#discussion_r3722515843
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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 15 data types, 13 operators, 106 expressions, and a "modest
performance speedup." The
+sixteen major releases between then and now cover a lot of ground:
+
+[donated]: https://datafusion.apache.org/blog/2024/03/06/comet-donation/
+
+- **Query coverage went from a handful of operators to the shape of a real
Spark query.** Native
+ SortMergeJoin, HashJoin, and BroadcastHashJoin landed early; native columnar
and native shuffle,
+ broadcast nested loop joins, native window functions, native sampling, and
mixed partial/final aggregation
+ followed. Supported expressions grew from 106 at 0.1.0 to more than 400 in
1.0.0, and the introduction of the JVM
+ codegen dispatcher in 0.17.0 gave Comet a way to keep unsupported
expressions Arrow-native by running
+ Spark's own generated code inside the pipeline rather than falling back to
row-based execution.
+- **Spark support broadened.** The 0.1.0 release targeted Spark 3.3, 3.4, and
3.5, with experimental 4.0. The
+ 1.0 line drops 3.3, adds 4.1, and ships an experimental 4.2 profile. ANSI
semantics — on by default in
+ Spark 4 — moved from partial to a supported default across the natively
implemented surface.
+- **The ecosystem story filled in.** Native Iceberg support arrived in 0.10.0
and has been extended through
+ 1.11 and format V3, native Parquet writes and CSV reads landed as
experimental features and matured,
+ Azure joined S3 on the native cloud path, and 1.0.0 adds experimental
accelerated PyArrow UDFs alongside the
+ Java and Scala UDF support that shipped in 0.17.0.
+- **Correctness rigor grew alongside the surface area.** Early releases relied
on fuzz testing to surface
+ divergences; later releases added a full Spark SQL test-suite run against
every supported Spark version,
+ followed by AI-assisted expression audits comparing Comet's behavior against
every supported Spark version
+ edge case by edge case. 1.0 ships with every known open correctness gap
documented in the compatibility
+ guide.
+- **Performance moved from "modest speedup" to workload-level wins.** TPC-DS
at 1TB has become the reference
+ workload, with successive releases removing FFI round trips, caching Parquet
metadata, shrinking plan
+ serialization, and tightening the shuffle write path. 0.17.0 alone was ~9%
faster than 0.16.0 on TPC-DS
+ 1TB, and the per-expression optimization work in 1.0 continues that
trajectory.
+
+## What 1.0 Means
+
+Beyond the accumulated features, 1.0 is where the project commits to being
something you can build on:
+
+- **Broad Spark coverage.** Comet supports Apache Spark 3.4.3, 3.5.9, 4.0.4,
and 4.1.3 out of the same
+ codebase, with dedicated Maven profiles, shim sources, and CI matrices for
each, plus an experimental
+ Spark 4.2 profile for early evaluation. A published Spark version adoption
and support-lifetime policy
+ now states how long each Spark minor stays supported.
+- **ANSI SQL by default.** Spark 4 enables ANSI semantics by default. Comet
implements ANSI behavior for the
+ expressions it supports natively, including arithmetic overflow checks, ANSI
cast behavior, and `try_*`
+ variants, so ANSI workloads keep accelerating rather than falling back.
+- **A correctness harness, not just unit tests.** Comet runs the full Apache
Spark SQL test suite through its
+ native execution path against every supported Spark version. Running Spark's
own correctness tests is what
+ surfaces semantic shifts before they reach user workloads, and it is the
foundation of the confidence behind
+ a 1.0 release.
+- **A stable release line going forward.** With 1.0, the project commits to
semantic versioning. Because Comet
+ is a plugin rather than a library, its configuration is its primary API
surface, so `spark.comet.*` keys, an
+ explicitly enumerated public Java and Scala API, and query results
documented as Compatible are all part of
+ the compatibility surface. Behavior changes in a minor release now require a
`spark.comet.legacy.*` key that
+ restores the previous behavior, and each release records its behavior
changes in a user-facing upgrade
+ guide. Correctness fixes are exempt from being treated as breaking changes.
The first deprecations under
+ that policy are announced in this release (see [Deprecation
Notice](#deprecation-notice) below).
+- **Documented limitations.** Every open correctness issue is now surfaced in
the generated compatibility
+ guide, down to the expression level, so you can see where Comet is known to
diverge from Spark before you
+ hit it in production rather than after.
+
+The rest of this post covers what is new since 0.17.0.
+
+## Native Expression Performance
+
+A large share of this release is dedicated to making Comet's native scalar
expressions faster. These kernels
+run per-row or per-batch, so improvements here compound across every query
that uses them. Expressions with
+optimized implementations in this release include:
+
+- **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.
+
+To make this kind of work repeatable, the release also adds a scalar
expression optimization guide
Review Comment:
(nit) Add a link to
[guide](https://datafusion.apache.org/comet/contributor-guide/optimizing_expressions.html)
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