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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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+
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+See the License for the specific language governing permissions and
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+{% endcomment %}
+-->
+
+[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 more information.
+
+
+[change log]: https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md
+
+## What 1.0 Means
+
+The 1.0.0 release is the culmination of more than two year's work since the project was [donated] as an Apache DataFusion subproject in March, 2024, and is less about any single new feature than about the accumulated maturity of the project:
+
+[donated]: https://datafusion.apache.org/blog/2024/03/06/comet-donation/
+
+- **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. The
+  [Spark version adoption and support-lifetime policy](https://github.com/apache/datafusion-comet/pull/4977)
+  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. The
+  [versioning policy](https://github.com/apache/datafusion-comet/pull/5056) spells out what that covers:
+  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 very
+  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
+  ([#5085](https://github.com/apache/datafusion-comet/pull/5085)), 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. Notes for bugs
+  that have since been fixed were removed in the same pass
+  ([#5154](https://github.com/apache/datafusion-comet/pull/5154)).
+
+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)), filter pushdown configuration has been
+revised ([#4722](https://github.com/apache/datafusion-comet/pull/4722)), the native scan now passes a metadata
+size hint so a single read usually captures the footer, matching the Iceberg path
+([#4717](https://github.com/apache/datafusion-comet/pull/4717)), and the native Parquet scan seeds its reader
+options from the session config so Parquet settings you already set take effect
+([#5107](https://github.com/apache/datafusion-comet/pull/5107)).
+
+Query planning and plan serialization also got cheaper. `QueryContext` SQL text is now interned into a per-plan
+pool ([#5204](https://github.com/apache/datafusion-comet/pull/5204)), which makes serialized plans up to 20x
+smaller on TPC-DS — every plan crosses the JNI boundary, so this shrinks per-query overhead across the board.
+Plan-data injection is now an O(1) lookup by operator kind
+([#4535](https://github.com/apache/datafusion-comet/pull/4535)) and no longer rebuilds operators it does not
+touch ([#5220](https://github.com/apache/datafusion-comet/pull/5220)). Comet also makes far fewer timer calls
+in the native execution loop ([#4739](https://github.com/apache/datafusion-comet/pull/4739)), and nested array
+equality now uses Arrow's comparator ([#5176](https://github.com/apache/datafusion-comet/pull/5176)).
+
+## Correctness
+
+A 1.0 release is only as good as its results. This release fixes a broad set of divergences from Spark, most of
+them found by running extensive AI-assisted audit sweeps of the code base, comparing Comet's expression implementations with all supported versions of Spark.
+
+- **Casts**: casting a string to `boolean`, an integral type, `float`/`double`, or `decimal` now uses Spark's
+  exact whitespace-trimming rules ([#5150](https://github.com/apache/datafusion-comet/pull/5150)). Comet's
+  kernels had used four different trim sets, three of them wrong, so results diverged in both directions —
+  returning null where Spark parses a value, and returning a value where Spark returns null. Casting
+  `float`/`double` to `decimal` now rounds the shortest decimal string form as Spark does, rather than the
+  binary value ([#5136](https://github.com/apache/datafusion-comet/pull/5136)), which matters for values such
+  as `0.5153125` whose binary form sits just below the rounding tie the string form lands on; `NaN` and infinity
+  now return null even in ANSI mode, matching Spark. Decimal promotion also uses the per-expression eval mode
+  ([#5171](https://github.com/apache/datafusion-comet/pull/5171)).
+- **ANSI error semantics**: the codegen dispatcher's null short-circuit no longer swallows errors Spark raises
+  ([#5219](https://github.com/apache/datafusion-comet/pull/5219)) — Spark evaluates null-intolerant expressions
+  per node and left to right, so short-circuiting on the union of input ordinals skipped subtrees Spark would
+  have evaluated, losing their errors. Roughly 70 built-in expressions route through this dispatcher and ANSI is
+  on by default in Spark 4, so this affected a wide surface. Also: `round` on a `Long` with a large negative
+  scale now overflows instead of silently returning zero
+  ([#5082](https://github.com/apache/datafusion-comet/pull/5082)), `Long.MinValue / -1` raises
+  `ARITHMETIC_OVERFLOW` ([#5084](https://github.com/apache/datafusion-comet/pull/5084)), floating-point
+  `x % 0.0` raises `REMAINDER_BY_ZERO` instead of returning `NaN`
+  ([#5081](https://github.com/apache/datafusion-comet/pull/5081)), `make_decimal` honors its
+  fail-on-error flag and throws `NUMERIC_VALUE_OUT_OF_RANGE` rather than returning null
+  ([#5080](https://github.com/apache/datafusion-comet/pull/5080)), invalid calendar dates raise
+  `CAST_INVALID_INPUT` ([#5014](https://github.com/apache/datafusion-comet/pull/5014)), and errors from nested
+  casts now propagate ([#4675](https://github.com/apache/datafusion-comet/pull/4675)).
+- **Wrong results**: `count` no longer returns zero when the native scan is disabled
+  ([#4795](https://github.com/apache/datafusion-comet/pull/4795)), Spark's legacy `null IN ()` behavior is
+  honored ([#5127](https://github.com/apache/datafusion-comet/pull/5127)), `pow` matches Java's `Math.pow` on
+  the edge cases where C and Java disagree ([#5033](https://github.com/apache/datafusion-comet/pull/5033)),
+  `percentile` matches Spark's interpolation precision
+  ([#4792](https://github.com/apache/datafusion-comet/pull/4792)), `date_trunc` handles DST boundaries in
+  non-UTC sessions and no longer produces schema mismatches through shuffle and sort
+  ([#4761](https://github.com/apache/datafusion-comet/pull/4761)), `flatten` handles null sub-arrays
+  ([#4822](https://github.com/apache/datafusion-comet/pull/4822)), `ArrayInsert` handles null source arrays
+  ([#4726](https://github.com/apache/datafusion-comet/pull/4726)), and the `array_filter` / `array_compact`
+  fast path is restricted to the lambda variable
+  ([#4848](https://github.com/apache/datafusion-comet/pull/4848)).
+- **Strings and encodings**: `CAST(binary AS string)` now decodes exactly as the JVM's
+  `new String(bytes, UTF_8)` does, including the surrogate cases where Rust's lossy decoder diverges, instead
+  of reinterpreting bytes unchecked ([#4763](https://github.com/apache/datafusion-comet/pull/4763)), and
+  shuffle tolerates non-UTF-8 bytes rather than failing
+  ([#4524](https://github.com/apache/datafusion-comet/pull/4524)).
+- **Timestamps**: reading a Parquet timestamp-with-timezone column as `TimestampNTZ` is now rejected on Spark
+  3.x, matching Spark's own refusal, instead of silently returning the UTC instant
+  ([#4357](https://github.com/apache/datafusion-comet/pull/4357)).
+- **Deep expression trees**: long chains of associative bitwise, `Add`, and `Multiply` operators are rebalanced
+  so plans no longer hit protobuf's recursion limit
+  ([#4588](https://github.com/apache/datafusion-comet/pull/4588)).
+- **Native shuffle**: scalar subqueries used in partitioning expressions are now registered, fixing
+  "Subquery N not found" failures ([#4869](https://github.com/apache/datafusion-comet/pull/4869)), and constant
+  column vectors are materialized on Comet's serialize and export paths
+  ([#4532](https://github.com/apache/datafusion-comet/pull/4532)).
+- **Collations**: Comet falls back for predicates whose operands use non-default collations
+  ([#4948](https://github.com/apache/datafusion-comet/pull/4948)), for Spark 4 datetime expressions under
+  non-default collations ([#4693](https://github.com/apache/datafusion-comet/pull/4693)), and for
+  `str_to_map` ([#4701](https://github.com/apache/datafusion-comet/pull/4701)).
+- **Conservative fallbacks** where native behavior could not be made to match: decimal `SUM` / `AVG` over
+  sliding window frames ([#4732](https://github.com/apache/datafusion-comet/pull/4732)),
+  `FromUnixTime` with a non-default format ([#4847](https://github.com/apache/datafusion-comet/pull/4847)),
+  `CreateArray` with struct-nullability-divergent children
+  ([#4533](https://github.com/apache/datafusion-comet/pull/4533)), and native V1 scans on filesystem schemes
+  that `object_store` does not support ([#4525](https://github.com/apache/datafusion-comet/pull/4525)).
+- **Error reporting**: native Parquet read failures now surface as Spark's `FAILED_READ_FILE`
+  ([#4536](https://github.com/apache/datafusion-comet/pull/4536)), and a DataFusion 54.1.0 Parquet page-index
+  regression is worked around ([#5132](https://github.com/apache/datafusion-comet/pull/5132)).
+
+## 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)),
+  `multiply_dt_interval` via codegen dispatch
+  ([#4900](https://github.com/apache/datafusion-comet/pull/4900)), and interval codegen dispatch for nested
+  values and native shuffle ([#4976](https://github.com/apache/datafusion-comet/pull/4976)).
+- **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` ([#5035](https://github.com/apache/datafusion-comet/pull/5035)) and `uuid`
+  ([#5034](https://github.com/apache/datafusion-comet/pull/5034)), both bit-for-bit compatible with Spark for a
+  given seed.
+- **Array / map**: `array_prepend` ([#4716](https://github.com/apache/datafusion-comet/pull/4716)),
+  the `shuffle()` array function ([#4797](https://github.com/apache/datafusion-comet/pull/4797)),
+  `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)).
+- **Internal expressions**: native `empty2null`
+  ([#4683](https://github.com/apache/datafusion-comet/pull/4683)), which appears in write paths, and the
+  shuffle-side infrastructure for Spark's `TimeType`
+  ([#4398](https://github.com/apache/datafusion-comet/pull/4398)).
+
+## Native Sampling
+
+Spark's `SampleExec` now runs natively when sampling without replacement
+([#5110](https://github.com/apache/datafusion-comet/pull/5110)), covering `DataFrame.sample`, SQL
+`TABLESAMPLE`, and `DataFrame.randomSplit`. The native operator ports Spark's `BernoulliCellSampler` on top of
+`XorShiftRandom` and seeds per partition exactly as Spark does, so it selects the same rows for a given seed —
+sampling stays reproducible whether or not Comet is enabled. Sampling with replacement still falls back to
+Spark.
+
+## 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.
+- **Metadata columns** ([#4752](https://github.com/apache/datafusion-comet/pull/4752)): the native Iceberg scan
+  now supports the `_pos`, `_spec`, `_file`, and `_partition` metadata columns.
+- **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.
+- **Scan disambiguation** ([#5180](https://github.com/apache/datafusion-comet/pull/5180)): Iceberg scans that
+  share a `metadata_location` are now told apart, rather than being conflated.
+- **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)). Casts join this path
+  ([#5079](https://github.com/apache/datafusion-comet/pull/5079)): an incompatible or unsupported cast now runs
+  Spark's own generated code inside the Comet pipeline instead of pulling the whole operator back to Spark, so
+  one awkward cast no longer de-accelerates a query. `sort_array` under strict floating-point mode
+  ([#4637](https://github.com/apache/datafusion-comet/pull/4637)) and `concat` under non-`UTF8_BINARY`
+  collations ([#4640](https://github.com/apache/datafusion-comet/pull/4640)) opt in the same way.
+- **JVM columnar-to-row by default** ([#5114](https://github.com/apache/datafusion-comet/pull/5114)): isolated
+  benchmarking showed the native columnar-to-row converter is roughly 3.7x slower per row than the JVM
+  implementation at the default batch size — and up to 15.7x slower for small batches — because of a fixed
+  per-batch JNI and FFI cost, with no end-to-end benefit in TPC-DS runs. It is now disabled by default.
+- **Clearer fallback reasons**: unsupported scalar serde cases are reported in the support level
+  ([#4745](https://github.com/apache/datafusion-comet/pull/4745)), mixed-execution fallback messages name the
+  incompatible aggregate functions ([#4750](https://github.com/apache/datafusion-comet/pull/4750)), unsupported
+  metadata column names appear in fallback reasons
+  ([#4758](https://github.com/apache/datafusion-comet/pull/4758)), the compatibility guide URLs in fallback
+  messages are corrected ([#4854](https://github.com/apache/datafusion-comet/pull/4854)), and the spurious
+  "WriteFilesExec is not supported" message is suppressed
+  ([#4928](https://github.com/apache/datafusion-comet/pull/4928)).
+- **Expression coverage in extended explain** ([#5201](https://github.com/apache/datafusion-comet/pull/5201)):
+  the extended explain summary previously reported operator coverage but said nothing about expressions. It now
+  ends with a line such as `Comet accelerated 14 expressions (14 native, 1 codegen dispatch)`, so you can see
+  how much of a plan's expression evaluation runs in native DataFusion kernels versus Spark's generated code
+  inside the dispatcher. Operator counts also handle `ReusedSubquery` and `CometSubqueryBroadcast` correctly
+  ([#5206](https://github.com/apache/datafusion-comet/pull/5206)).
+- **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.
+- **Quieter logs** ([#5155](https://github.com/apache/datafusion-comet/pull/5155)): logging levels on frequent
+  call sites have been lowered so Comet no longer floods executor logs at default settings.
+- **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.
+- **Single-partition shuffle** ([#5004](https://github.com/apache/datafusion-comet/pull/5004)): a redundant
+  concatenation layer has been removed from the single-partition path.
+
+## Configuration Changes
+
+Because configuration is Comet's primary API surface, 1.0 is the release where the naming gets cleaned up.
+Every rename below registers the old key as an alias, so existing configurations keep working and log a
+deprecation warning when an old key is read:
+
+- **Unified shuffle prefix** ([#4986](https://github.com/apache/datafusion-comet/pull/4986)): shuffle configs
+  were spread across four disjoint prefixes (`spark.comet.exec.shuffle.*`, `spark.comet.columnar.shuffle.*`,
+  `spark.comet.native.shuffle.*`, `spark.comet.shuffle.*`). They now all live under `spark.comet.shuffle.*`,
+  with `.jvm.` and `.native.` sub-namespaces matching the `spark.comet.shuffle.mode` value you already set.
+- **Grouped explain configs** ([#5026](https://github.com/apache/datafusion-comet/pull/5026)): the orphan
+  explain-related configs are collected under a single `spark.comet.explain.*` prefix, and the PyArrow UDF
+  config is renamed to `pyarrowUDF` for consistency
+  ([#5197](https://github.com/apache/datafusion-comet/pull/5197)).
+- **`spark.comet.version`** ([#5049](https://github.com/apache/datafusion-comet/pull/5049)): the loaded Comet
+  build version is now exposed as a runtime config, so you can confirm which Comet a cluster is actually
+  running with `spark.conf.get` or `SET`.
+- **Removed dead configs**: the Parquet parallel-IO knobs
+  ([#4981](https://github.com/apache/datafusion-comet/pull/4981)),
+  `spark.comet.use.lazyMaterialization` ([#4998](https://github.com/apache/datafusion-comet/pull/4998)), and
+  `spark.comet.exceptionOnDatetimeRebase` ([#5221](https://github.com/apache/datafusion-comet/pull/5221)) had no
+  remaining effect and were misleading anyone tuning against them.
+- **Removed async columnar shuffle** ([#4985](https://github.com/apache/datafusion-comet/pull/4985)): the
+  `spark.comet.columnar.shuffle.async.*` path was off by default and untested, and has been removed rather than
+  carried into a stable release line.
+
+## Documentation
+
+The documentation received a substantial overhaul for 1.0:
+
+- A [design refresh](https://github.com/apache/datafusion-comet/pull/4353) of the docs site, with versioned
+  user guides gaining captioned sidebar sections
+  ([#4699](https://github.com/apache/datafusion-comet/pull/4699)) and a set of accessibility and navigation
+  fixes ([#4858](https://github.com/apache/datafusion-comet/pull/4858)).
+- The supported-expressions page now shows how each expression is implemented — native, codegen dispatch, or
+  hybrid ([#5028](https://github.com/apache/datafusion-comet/pull/5028)), generated from the planner itself
+  rather than maintained by hand.
+- An expanded [tuning guide](https://github.com/apache/datafusion-comet/pull/4908) covering the performance and
+  memory configs that matter in practice, plus guidance on `spark.sql.files.maxPartitionBytes`
+  ([#4931](https://github.com/apache/datafusion-comet/pull/4931)).
+- An [updated roadmap](https://github.com/apache/datafusion-comet/pull/5064) for the post-1.0 work, and a new
+  [blog posts and talks page](https://github.com/apache/datafusion-comet/pull/5043).
+
+## 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 3.5.9** 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.