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+---
+layout: post
+title: Apache DataFusion Comet 1.0.0 Release
+date: 2026-07-31
+author: pmc
+categories: [subprojects]
+---
+
+<!--
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+http://www.apache.org/licenses/LICENSE-2.0
+
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+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
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+{% endcomment %}
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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 is a major milestone. Comet began as a code donation in early 2024 and has shipped steadily ever since,
+one release at a time. Version 1.0.0 marks the point where the project is mature enough to commit to a stable
+release line: broad Apache Spark coverage, ANSI SQL semantics, native Parquet and Iceberg scans, and a native
+shuffle, all validated continuously against Spark's own test suites. This release covers roughly six weeks of
+development since 0.17.0 and is the result of merging over 190 PRs from 22 contributors. See the
+[change log] for more information.
+
+[change log]: https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md
+
+## What 1.0 Means
+
+Reaching 1.0 is less about any single new feature than about the accumulated maturity of the project:
+
+- **Broad Spark coverage.** Comet supports Apache Spark 3.4.3, 3.5.8, 4.0.2, and 4.1.1 out of the same
+  codebase, with dedicated Maven profiles, shim sources, and CI matrices for each.
+- **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. The very
+  first deprecations under that policy are announced in this release (see [Deprecation Notice](#deprecation-notice)
+  below).
+
+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:
+
+- **`get_json_object`** ([#4907](https://github.com/apache/datafusion-comet/pull/4907)): roughly 4x faster.
+- **`regexp_extract`** ([#4894](https://github.com/apache/datafusion-comet/pull/4894)) and **`parse_url`**
+  ([#4893](https://github.com/apache/datafusion-comet/pull/4893)): optimized regex and URL parsing paths.
+- **Casts**: a faster floating-point-to-decimal cast
+  ([#4940](https://github.com/apache/datafusion-comet/pull/4940)), an optimized integer-to-integer cast
+  ([#4920](https://github.com/apache/datafusion-comet/pull/4920)), shared no-overflow fast paths in
+  `CheckOverflow` ([#4937](https://github.com/apache/datafusion-comet/pull/4937)) and
+  `DecimalRescaleCheckOverflow` ([#4938](https://github.com/apache/datafusion-comet/pull/4938)), a ~40% faster
+  `float64`-to-`utf8` cast ([#4918](https://github.com/apache/datafusion-comet/pull/4918)), an optimized
+  `decimal128`-to-`utf8` cast ([#4924](https://github.com/apache/datafusion-comet/pull/4924)), string-to-date
+  parsing up to 2x faster ([#4917](https://github.com/apache/datafusion-comet/pull/4917)),
+  `parse_string_to_decimal` 30-40% faster ([#4916](https://github.com/apache/datafusion-comet/pull/4916)), and
+  `cast_binary_to_string` up to 27x faster on binary-format styles
+  ([#4912](https://github.com/apache/datafusion-comet/pull/4912)).
+- **Decimal and date/time kernels**: `date_trunc` more than 2x faster
+  ([#4915](https://github.com/apache/datafusion-comet/pull/4915)),
+  `spark_ceil` 3x faster ([#4926](https://github.com/apache/datafusion-comet/pull/4926)), and a vectorized
+  `spark_unscaled_value` 9x faster ([#4972](https://github.com/apache/datafusion-comet/pull/4972)).
+- **String and array kernels**: `lpad` ([#4919](https://github.com/apache/datafusion-comet/pull/4919)),
+  `unhex` ([#4876](https://github.com/apache/datafusion-comet/pull/4876)),
+  `size` ([#4877](https://github.com/apache/datafusion-comet/pull/4877)),
+  `arrays_overlap` ([#4906](https://github.com/apache/datafusion-comet/pull/4906)),
+  `escape_string` ([#4902](https://github.com/apache/datafusion-comet/pull/4902)), and the
+  `try_*` arithmetic kernel ([#4910](https://github.com/apache/datafusion-comet/pull/4910)).
+
+To make this kind of work repeatable, the release also adds a
+[scalar expression optimization guide](https://github.com/apache/datafusion-comet/pull/4933) documenting how
+to benchmark a kernel, keep its output bit-identical to Spark, and gate changes on a no-regression check.
+
+On the scan side, Parquet reads pick up several improvements: full Parquet metadata (including the page index)
+is now cached via DataFusion's `CachedParquetFileReaderFactory`
+([#4707](https://github.com/apache/datafusion-comet/pull/4707)), identity casts are unwrapped in the schema
+adapter so Parquet statistics pruning can engage
+([#4730](https://github.com/apache/datafusion-comet/pull/4730)), and filter pushdown configuration has been
+revised ([#4722](https://github.com/apache/datafusion-comet/pull/4722)).
+
+## New Expression and Aggregate Support
+
+This release expands the set of Spark expressions and aggregates that run natively:
+
+- **Aggregates**: `approx_percentile` / `percentile_approx`
+  ([#4801](https://github.com/apache/datafusion-comet/pull/4801)), exact `percentile` / `median`
+  ([#4542](https://github.com/apache/datafusion-comet/pull/4542)),
+  `approx_count_distinct` ([#4819](https://github.com/apache/datafusion-comet/pull/4819)), and native
+  `collect_list` / `array_agg` ([#4720](https://github.com/apache/datafusion-comet/pull/4720)).
+- **Grouping**: `grouping()` and `grouping_id()` indicator functions
+  ([#4815](https://github.com/apache/datafusion-comet/pull/4815)).
+- **Intervals**: interval types with `make_ym_interval` and `make_dt_interval`
+  ([#4541](https://github.com/apache/datafusion-comet/pull/4541)),
+  `CalendarIntervalType` support ([#4898](https://github.com/apache/datafusion-comet/pull/4898)), and
+  `multiply_dt_interval` via codegen dispatch
+  ([#4900](https://github.com/apache/datafusion-comet/pull/4900)).
+- **String**: `base64` ([#4778](https://github.com/apache/datafusion-comet/pull/4778)),
+  `split_part` via `StringSplitSQL` ([#4592](https://github.com/apache/datafusion-comet/pull/4592)),
+  native `levenshtein` ([#4105](https://github.com/apache/datafusion-comet/pull/4105)), and native
+  `randstr` compatible with Spark ([#5035](https://github.com/apache/datafusion-comet/pull/5035)).
+- **Array / map**: `array_prepend` ([#4716](https://github.com/apache/datafusion-comet/pull/4716)),
+  `size()` for `MapType` ([#4580](https://github.com/apache/datafusion-comet/pull/4580)), `ElementAt` over
+  `MapType` ([#4697](https://github.com/apache/datafusion-comet/pull/4697)), and removal of the constraint on
+  arrays of nested elements ([#4714](https://github.com/apache/datafusion-comet/pull/4714)).
+- **Date/time**: native `TimestampNTZ` inputs for `hour` / `minute` / `second`
+  ([#4753](https://github.com/apache/datafusion-comet/pull/4753)) and
+  `PreciseTimestampConversion` for native time-window grouping
+  ([#4784](https://github.com/apache/datafusion-comet/pull/4784)).
+- **Windows**: extended native window function support
+  ([#4209](https://github.com/apache/datafusion-comet/pull/4209)) and Spark 4 decimal window average
+  ([#4749](https://github.com/apache/datafusion-comet/pull/4749)).
+
+## Experimental PyArrow UDF Support
+
+This release adds experimental support for accelerated PyArrow UDFs
+([#4234](https://github.com/apache/datafusion-comet/pull/4234)), 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
+([#4892](https://github.com/apache/datafusion-comet/pull/4892)) so users know the option exists. This is an
+early-stage feature and we welcome feedback from users experimenting with it.
+
+## Iceberg
+
+- **Iceberg 1.11 support** ([#4840](https://github.com/apache/datafusion-comet/pull/4840)): adds support for
+  Iceberg 1.11, audits the existing Iceberg diffs, bumps the iceberg-rust dependency, and adds a
+  `run-iceberg-tests` CI trigger.
+- **Iceberg table format V3** ([#4991](https://github.com/apache/datafusion-comet/pull/4991)): native table
+  decryption for V3 tables, with fallback to Spark for other V3 features. Follow-up
+  [#5020](https://github.com/apache/datafusion-comet/pull/5020) applies the same diff changes across other
+  Iceberg versions.
+- **Delete-file correctness** ([#4760](https://github.com/apache/datafusion-comet/pull/4760)): the native scan
+  now sizes Iceberg delete files correctly, avoiding dropped deletes.
+- **Exchange-reuse correctness** ([#4812](https://github.com/apache/datafusion-comet/pull/4812)): fixed a case
+  where Iceberg native scan exchange reuse with different pushed filters could produce wrong results.
+- **Native serde dedup** ([#4982](https://github.com/apache/datafusion-comet/pull/4982)): dedupes Iceberg
+  residuals and delete files in the native scan serde, reducing planning overhead.
+
+## Native Parquet I/O and Cloud
+
+- **gzip Parquet writes** ([#4930](https://github.com/apache/datafusion-comet/pull/4930)): native Parquet
+  writes now support gzip compression.
+- **Azure authentication** ([#4783](https://github.com/apache/datafusion-comet/pull/4783)): the native
+  Parquet scan now supports Azure authentication, complementing the existing S3 credential support.
+
+## Execution and Fallback Improvements
+
+- **Mixed partial/final aggregation** ([#4861](https://github.com/apache/datafusion-comet/pull/4861)):
+  `sum` and non-decimal `avg` can now run with the partial and final aggregation stages split across Spark and
+  Comet, keeping more aggregation plans partially native.
+- **Stage-based fallback** ([#4519](https://github.com/apache/datafusion-comet/pull/4519)): a more granular
+  fallback mechanism that operates at the stage level.
+- **Codegen dispatch for opt-in serdes** ([#4728](https://github.com/apache/datafusion-comet/pull/4728)):
+  expressions reported as `Unsupported` can now route through JVM codegen dispatch for opt-in serdes, and
+  native opt-in expressions surface as compatible-by-default with a `COMET-INFO` plan hint
+  ([#4721](https://github.com/apache/datafusion-comet/pull/4721)).
+- **Collated predicate fallback** ([#4948](https://github.com/apache/datafusion-comet/pull/4948)): Comet
+  now falls back to Spark for predicates whose operands use non-default collations, avoiding subtle
+  correctness issues.
+- **ANSI cast to date** ([#5014](https://github.com/apache/datafusion-comet/pull/5014)): invalid calendar
+  dates now raise `CAST_INVALID_INPUT` under ANSI mode, matching Spark's behavior.
+- **Local scan nullability** ([#4843](https://github.com/apache/datafusion-comet/pull/4843)): local table scan
+  child nullability is now widened to match the native kernels, fixing a class of nullability mismatches.
+- **Config aliases** ([#4979](https://github.com/apache/datafusion-comet/pull/4979)): a `withAlternative`
+  alias mechanism lets `CometConf` entries carry old names during renames without breaking existing
+  configurations.
+- **Runtime cleanup** ([#4734](https://github.com/apache/datafusion-comet/pull/4734)): the Tokio runtime is
+  now released on driver and executor exit.
+- **Contrib scan SPI** ([#4700](https://github.com/apache/datafusion-comet/pull/4700)): a core SPI for
+  contrib leaf scans (`CometScanWithPlanData`), the first part of splitting the Delta integration into a
+  contrib module.
+
+## Shuffle Improvements
+
+- **Native shuffle memory cap** ([#4989](https://github.com/apache/datafusion-comet/pull/4989)): a new
+  `spark.comet.shuffle.maxBufferBytes` config caps native shuffle writer memory to bound worst-case usage.
+- **Shuffle IPC schema encoding** ([#5006](https://github.com/apache/datafusion-comet/pull/5006)): the IPC
+  schema is now encoded once per writer instead of per block, cutting per-batch shuffle overhead.
+- **BatchCoalescer bypass** ([#5003](https://github.com/apache/datafusion-comet/pull/5003)): shuffle bypasses
+  the `BatchCoalescer` for batches that are already appropriately sized.
+
+## Deprecation Notice
+
+With the move to a stable 1.0 release line, Comet begins deprecating older platforms under semantic versioning
+([#4857](https://github.com/apache/datafusion-comet/pull/4857)):
+
+- **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.
+
+## 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 3.5.8** with Java 11/17 and Scala 2.12/2.13
+- **Spark 4.0.2** with Java 17 and Scala 2.13
+- **Spark 4.1.1** with Java 17 and Scala 2.13
+
+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 58.3**.
+
+## 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.