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blog/2026/08/07/datafusion-comet-1.0.0/index.html | 242 +++++++++++++++-------
blog/feeds/all-en.atom.xml | 196 ++++++++++++------
blog/feeds/blog.atom.xml | 196 ++++++++++++------
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diff --git a/blog/2026/08/07/datafusion-comet-1.0.0/index.html
b/blog/2026/08/07/datafusion-comet-1.0.0/index.html
index 27f0a25..792fc79 100644
--- a/blog/2026/08/07/datafusion-comet-1.0.0/index.html
+++ b/blog/2026/08/07/datafusion-comet-1.0.0/index.html
@@ -55,24 +55,31 @@
<aside class="toc-container d-md-none mb-2">
<div class="toc"><span class="toctitle">Contents</span><ul>
<li><a href="#the-road-to-10">The Road to 1.0</a><ul>
-<li><a href="#support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with
ANSI mode</a></li>
<li><a href="#correctness-testing">Correctness Testing</a></li>
<li><a href="#performance">Performance</a></li>
<li><a href="#codegen-dispatch">Codegen Dispatch</a></li>
+<li><a href="#iceberg-support">Iceberg Support</a></li>
</ul>
</li>
-<li><a href="#improvements-since-0171">Improvements since 0.17.1</a><ul>
-<li><a href="#experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support</a></li>
-<li><a href="#new-expression-and-aggregate-support">New Expression and
Aggregate Support</a></li>
-<li><a href="#faster-parquet-scans">Faster Parquet Scans</a></li>
-<li><a href="#iceberg-table-format-v3">Iceberg Table Format V3</a></li>
+<li><a href="#improvements-since-0171">Improvements since 0.17.1</a></li>
+<li><a href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD
AI CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN</a><ul>
<li><a href="#native-expression-performance">Native Expression
Performance</a></li>
-</ul>
-</li>
+<li><a href="#correctness">Correctness</a></li>
+<li><a href="#new-expression-and-aggregate-support">New Expression and
Aggregate Support</a></li>
+<li><a href="#native-sampling">Native Sampling</a></li>
+<li><a href="#experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support</a></li>
+<li><a href="#iceberg">Iceberg</a></li>
+<li><a href="#native-parquet-io-and-cloud">Native Parquet I/O and
Cloud</a></li>
+<li><a href="#execution-and-fallback-improvements">Execution and Fallback
Improvements</a></li>
+<li><a href="#shuffle-improvements">Shuffle Improvements</a></li>
+<li><a href="#configuration-changes">Configuration Changes</a></li>
+<li><a href="#documentation">Documentation</a></li>
<li><a href="#deprecation-notice">Deprecation Notice</a></li>
<li><a href="#compatibility">Compatibility</a></li>
<li><a href="#get-started-with-comet-100">Get Started with Comet 1.0.0</a></li>
</ul>
+</li>
+</ul>
</div>
</aside>
@@ -102,16 +109,9 @@ improved performance and efficiency without requiring any
code changes.</p>
contributors. See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md";>change
log</a> for the full list of changes.</p>
<h2 id="the-road-to-10">The Road to 1.0<a class="headerlink"
href="#the-road-to-10" title="Permanent link">¶</a></h2>
<p>Comet was <a
href="https://datafusion.apache.org/blog/2024/03/06/comet-donation/";>donated</a>
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.</p>
-<p>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.</p>
-<p>The rest of this section is a recap of the main advances since donation.</p>
-<h3 id="support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with ANSI
mode<a class="headerlink" href="#support-for-spark-40-with-ansi-mode"
title="Permanent link">¶</a></h3>
-<p>Comet 1.0.0 supports Spark versions 3.4 though 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.</p>
+months later with support for 13 operators, and 106 expressions.</p>
+<p>Comet 1.0.0 now supports more than 400 expressions, but that isn't the only
way the project has grown over this time.</p>
+<p>Here's a recap of some of the main advances over the past two years</p>
<h3 id="correctness-testing">Correctness Testing<a class="headerlink"
href="#correctness-testing" title="Permanent link">¶</a></h3>
<p>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.</p>
<ul>
@@ -119,46 +119,70 @@ Spark 3.4 announced below are scheduled for 1.1 rather
than landing in 1.0 itsel
<li>Scala tests: Comet has Scala tests that run queries end to end with Comet
enabled vs disabled and ensure that the results match</li>
<li>Fuzz testing: Many of the scala tests use a fuzz testing approach to
generate randomized data that queries run against, helping to catch regressions
around edge cases such as nulls, NaN, Infinity, and timezone issues</li>
<li>Comet SQL Tests: In an effort to make it easier to write tests, Comet now
provides a SQL-based testing approach that is inspired by sqllogictest</li>
-<li>Generative AI: More recently, Comet has taken advantage of agentic skills
to perform audit sweeps of all expressions, comparing the implementation to
Spark's source code and ensuring that Comet has tests covering all important
edge cases</li>
+<li>Generative AI: More recently, Comet has taken advantage of agentic skills
to perform audit sweeps of ll expressions, comapring the implementation to
Spark's souce code and ensuring that Comet has tests covering all important
edge cases</li>
</ul>
<h3 id="performance">Performance<a class="headerlink" href="#performance"
title="Permanent link">¶</a></h3>
-<p>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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark";>TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>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.</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>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.</p>
-<p>Codegen dispatch scopes that fallback down 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.</p>
-<ul>
-<li><strong>Coverage.</strong> 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.</li>
-<li><strong>Compatibility.</strong> 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 <em>is</em> Spark's
- implementation.</li>
-<li><strong>Scala and Java UDFs.</strong> 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 <a
href="https://datafusion.apache.org/comet/user-guide/latest/scala_java_udfs.html";>Scala
and Java UDF guide</a> for details.</li>
-</ul>
+<p>Matt TBD</p>
+<h3 id="iceberg-support">Iceberg Support<a class="headerlink"
href="#iceberg-support" title="Permanent link">¶</a></h3>
<h2 id="improvements-since-0171">Improvements since 0.17.1<a
class="headerlink" href="#improvements-since-0171" title="Permanent
link">¶</a></h2>
<p>The rest of this post covers what is new since the 0.17.1 release.</p>
-<h3 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF Support<a
class="headerlink" href="#experimental-pyarrow-udf-support" title="Permanent
link">¶</a></h3>
-<p>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.</p>
-<h3 id="new-expression-and-aggregate-support">New Expression and Aggregate
Support<a class="headerlink" href="#new-expression-and-aggregate-support"
title="Permanent link">¶</a></h3>
-<p>This release expands the set of Spark expressions and aggregates that are
accelerated by Comet:</p>
+<h1 id="old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD AI
CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN<a class="headerlink"
href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten"
title="Permanent link">¶</a></h1>
+<h2 id="native-expression-performance">Native Expression Performance<a
class="headerlink" href="#native-expression-performance" title="Permanent
link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
+ <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on binary-format
+ styles.</li>
+<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>, <code>regexp_extract</code>, and
<code>parse_url</code>.</li>
+<li><strong>Date/time and decimal kernels</strong>: <code>date_trunc</code>,
<code>spark_ceil</code>, and a vectorized
<code>spark_unscaled_value</code>.</li>
+<li><strong>String and array kernels</strong>: <code>lpad</code>,
<code>unhex</code>, <code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
+ arithmetic kernel.</li>
+</ul>
+<p>To make this kind of work repeatable, the release also adds a scalar
expression optimization guide
+documenting how to benchmark a kernel, keep its output bit-identical to Spark,
and gate changes on a
+no-regression check.</p>
+<p>Parquet reads pick up several improvements as well. Full Parquet metadata,
including the page index, is now
+cached via DataFusion's <code>CachedParquetFileReaderFactory</code>; 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.</p>
+<p>Query planning and plan serialization also got cheaper.
<code>QueryContext</code> SQL text is now interned into a
+per-plan pool, 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 and no longer rebuilds operators it does not touch. Comet also
makes far fewer timer calls in
+the native execution loop.</p>
+<h2 id="correctness">Correctness<a class="headerlink" href="#correctness"
title="Permanent link">¶</a></h2>
+<p>Correctness fixes have been a regular part of every Comet release, but for
1.0 we made an extra push to
+clear out the bulk of the known issues before drawing a line under the stable
release. This release fixes
+roughly 30 divergences from Spark, spanning casts, ANSI error semantics,
wrong-result bugs, string and
+encoding handling, collations, and error reporting. Most were found by running
extensive AI-assisted audit
+sweeps of the code base, comparing Comet's expression implementations with all
supported versions of Spark.
+See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md";>change
log</a> for the full list; two representative fixes give a sense of the shape
of the work:</p>
+<ul>
+<li><strong>Whitespace trimming in string-to-numeric casts.</strong> Casts to
<code>boolean</code>, integral types, <code>float</code>/<code>double</code>,
+ and <code>decimal</code> now use Spark's exact trim rules. 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.</li>
+<li><strong>ANSI errors swallowed by null short-circuit.</strong> The codegen
dispatcher's null short-circuit no longer
+ swallows errors Spark raises. 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.</li>
+</ul>
+<p>Alongside the bug fixes, Comet also adds conservative fallbacks where
native behavior could not be made to
+match — decimal <code>SUM</code> / <code>AVG</code> over sliding window
frames, <code>FromUnixTime</code> with a non-default format, and a
+handful of others — and falls back for predicates and datetime expressions
under non-default collations
+rather than risking a wrong result.</p>
+<h2 id="new-expression-and-aggregate-support">New Expression and Aggregate
Support<a class="headerlink" href="#new-expression-and-aggregate-support"
title="Permanent link">¶</a></h2>
+<p>This release expands the set of Spark expressions and aggregates that run
natively:</p>
<ul>
<li><strong>Aggregates</strong>: <code>approx_percentile</code> /
<code>percentile_approx</code>, exact <code>percentile</code> /
<code>median</code>,
<code>approx_count_distinct</code>, and native <code>collect_list</code> /
<code>array_agg</code>.</li>
-<li><strong>Cast</strong>: Cast expressions where the native implementation is
marked as incompatible or unsupported
- are now routed through codegen dispatch.</li>
<li><strong>Grouping</strong>: <code>grouping()</code> and
<code>grouping_id()</code>.</li>
<li><strong>Intervals</strong>: interval types via
<code>make_ym_interval</code> and <code>make_dt_interval</code>,
<code>CalendarIntervalType</code>,
<code>multiply_dt_interval</code>, and interval codegen dispatch for nested
values and native shuffle.</li>
@@ -170,30 +194,89 @@ exists. This is an early-stage feature and we welcome
feedback from users experi
<code>PreciseTimestampConversion</code> for native time-window grouping.</li>
<li><strong>Windows</strong>: extended native window function support and
Spark 4 decimal window average.</li>
</ul>
-<h3 id="faster-parquet-scans">Faster Parquet Scans<a class="headerlink"
href="#faster-parquet-scans" title="Permanent link">¶</a></h3>
-<p>Parquet reads pick up several improvements as well. Full Parquet metadata,
including the page index, is now
-cached via DataFusion's <code>CachedParquetFileReaderFactory</code>; 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.</p>
-<h3 id="iceberg-table-format-v3">Iceberg Table Format V3<a class="headerlink"
href="#iceberg-table-format-v3" title="Permanent link">¶</a></h3>
+<h2 id="native-sampling">Native Sampling<a class="headerlink"
href="#native-sampling" title="Permanent link">¶</a></h2>
+<p>Spark's <code>SampleExec</code> now runs natively when sampling without
replacement, covering <code>DataFrame.sample</code>, SQL
+<code>TABLESAMPLE</code>, and <code>DataFrame.randomSplit</code>. The native
operator ports Spark's <code>BernoulliCellSampler</code> on top of
+<code>XorShiftRandom</code> 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.</p>
+<h2 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF Support<a
class="headerlink" href="#experimental-pyarrow-udf-support" title="Permanent
link">¶</a></h2>
+<p>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.</p>
+<h2 id="iceberg">Iceberg<a class="headerlink" href="#iceberg" title="Permanent
link">¶</a></h2>
<p>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
<code>_pos</code>, <code>_spec</code>, <code>_file</code>, and
<code>_partition</code> metadata columns, sizes delete files correctly to
avoid dropped deletes, disambiguates scans that
share a <code>metadata_location</code>, 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</p>
-<h3 id="native-expression-performance">Native Expression Performance<a
class="headerlink" href="#native-expression-performance" title="Permanent
link">¶</a></h3>
-<p>Many native expression implementations have been optimized to more
efficiently leverage Arrow kernels or to avoid per-row builders.</p>
+where Iceberg native scan exchange reuse with different pushed filters could
produce wrong results is also
+fixed.</p>
+<h2 id="native-parquet-io-and-cloud">Native Parquet I/O and Cloud<a
class="headerlink" href="#native-parquet-io-and-cloud" title="Permanent
link">¶</a></h2>
+<p>Native Parquet writes now support gzip compression, and the native Parquet
scan supports Azure authentication,
+complementing the existing S3 credential support.</p>
+<h2 id="execution-and-fallback-improvements">Execution and Fallback
Improvements<a class="headerlink" href="#execution-and-fallback-improvements"
title="Permanent link">¶</a></h2>
<ul>
-<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
- <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on binary-format
- styles.</li>
-<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>, <code>regexp_extract</code>, and
<code>parse_url</code>.</li>
-<li><strong>Date/time and decimal kernels</strong>: <code>date_trunc</code>,
<code>spark_ceil</code>, and a vectorized
<code>spark_unscaled_value</code>.</li>
-<li><strong>String and array kernels</strong>: <code>lpad</code>,
<code>unhex</code>, <code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
- arithmetic kernel.</li>
+<li><strong>Mixed partial/final aggregation.</strong> <code>sum</code> and
non-decimal <code>avg</code> can now run with the partial and final
+ aggregation stages split across Spark and Comet, keeping more aggregation
plans partially native.</li>
+<li><strong>Codegen dispatch for opt-in serdes.</strong> Expressions reported
as <code>Unsupported</code> can now route through JVM
+ codegen dispatch for opt-in serdes, and native opt-in expressions surface as
compatible-by-default with a
+ <code>COMET-INFO</code> plan hint. Casts join this path: 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. <code>sort_array</code> under
strict floating-point mode and <code>concat</code>
+ under non-<code>UTF8_BINARY</code> collations opt in the same way.</li>
+<li><strong>JVM columnar-to-row by default.</strong> 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.</li>
+<li><strong>Clearer fallback reasons.</strong> Unsupported scalar serde cases
are reported in the support level,
+ mixed-execution fallback messages name the incompatible aggregate functions,
unsupported metadata column
+ names appear in fallback reasons, and the spurious "WriteFilesExec is not
supported" message is suppressed.</li>
+<li><strong>Expression coverage in extended explain.</strong> The extended
explain summary previously reported operator
+ coverage but said nothing about expressions. It now ends with a line such as
<code>Comet accelerated 14
+ expressions (14 native, 1 codegen dispatch)</code>, 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.</li>
+<li><strong>Quieter logs and runtime cleanup.</strong> Logging levels on
frequent call sites have been lowered so Comet no
+ longer floods executor logs at default settings, and the Tokio runtime is
now released on driver and
+ executor exit.</li>
+</ul>
+<h2 id="shuffle-improvements">Shuffle Improvements<a class="headerlink"
href="#shuffle-improvements" title="Permanent link">¶</a></h2>
+<ul>
+<li><strong>Native shuffle memory cap.</strong> A new
<code>spark.comet.shuffle.maxBufferBytes</code> config caps native shuffle
writer
+ memory to bound worst-case usage.</li>
+<li><strong>Shuffle IPC schema encoding.</strong> The IPC schema is now
encoded once per writer instead of per block,
+ cutting per-batch shuffle overhead.</li>
+<li><strong>BatchCoalescer bypass.</strong> Shuffle bypasses the
<code>BatchCoalescer</code> for batches that are already appropriately
+ sized.</li>
+<li><strong>Single-partition shuffle.</strong> A redundant concatenation layer
has been removed from the single-partition
+ path.</li>
+</ul>
+<h2 id="configuration-changes">Configuration Changes<a class="headerlink"
href="#configuration-changes" title="Permanent link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Unified shuffle prefix.</strong> Shuffle configs were spread
across four disjoint prefixes
+ (<code>spark.comet.exec.shuffle.*</code>,
<code>spark.comet.columnar.shuffle.*</code>,
<code>spark.comet.native.shuffle.*</code>,
+ <code>spark.comet.shuffle.*</code>). They now all live under
<code>spark.comet.shuffle.*</code>, with <code>.jvm.</code> and
<code>.native.</code>
+ sub-namespaces matching the <code>spark.comet.shuffle.mode</code> value you
already set.</li>
+<li><strong>Grouped explain configs.</strong> The orphan explain-related
configs are collected under a single
+ <code>spark.comet.explain.*</code> prefix, and the PyArrow UDF config is
renamed to <code>pyarrowUDF</code> for consistency.</li>
+<li><strong><code>spark.comet.version</code>.</strong> The loaded Comet build
version is now exposed as a runtime config, so you can
+ confirm which Comet a cluster is actually running with
<code>spark.conf.get</code> or <code>SET</code>.</li>
+<li><strong>Removed dead configs.</strong> The Parquet parallel-IO knobs,
<code>spark.comet.use.lazyMaterialization</code>, and
+ <code>spark.comet.exceptionOnDatetimeRebase</code> had no remaining effect
and were misleading anyone tuning against
+ them.</li>
+<li><strong>Removed async columnar shuffle.</strong> The
<code>spark.comet.columnar.shuffle.async.*</code> path was off by default and
+ untested, and has been removed rather than carried into a stable release
line.</li>
</ul>
+<h2 id="documentation">Documentation<a class="headerlink"
href="#documentation" title="Permanent link">¶</a></h2>
+<p>The documentation received a substantial overhaul for 1.0: a design refresh
of the docs site, versioned user
+guides with captioned sidebar sections, and a set of accessibility and
navigation fixes. The
+supported-expressions page now shows how each expression is implemented —
native, codegen dispatch, or hybrid
+— generated from the planner itself rather than maintained by hand. An
expanded tuning guide covers the
+performance and memory configs that matter in practice, and there is an
updated post-1.0 roadmap and a new
+blog posts and talks page.</p>
<h2 id="deprecation-notice">Deprecation Notice<a class="headerlink"
href="#deprecation-notice" title="Permanent link">¶</a></h2>
<p>With the move to a stable 1.0 release line, Comet begins deprecating older
platforms under semantic
versioning:</p>
@@ -250,24 +333,31 @@ to get up and running, then point Comet at your existing
Spark workloads and see
<aside class="toc-container d-none d-md-block col-md-4 col-xl-3 ms-xl-2">
<div class="toc"><span class="toctitle">Contents</span><ul>
<li><a href="#the-road-to-10">The Road to 1.0</a><ul>
-<li><a href="#support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with
ANSI mode</a></li>
<li><a href="#correctness-testing">Correctness Testing</a></li>
<li><a href="#performance">Performance</a></li>
<li><a href="#codegen-dispatch">Codegen Dispatch</a></li>
+<li><a href="#iceberg-support">Iceberg Support</a></li>
</ul>
</li>
-<li><a href="#improvements-since-0171">Improvements since 0.17.1</a><ul>
-<li><a href="#experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support</a></li>
-<li><a href="#new-expression-and-aggregate-support">New Expression and
Aggregate Support</a></li>
-<li><a href="#faster-parquet-scans">Faster Parquet Scans</a></li>
-<li><a href="#iceberg-table-format-v3">Iceberg Table Format V3</a></li>
+<li><a href="#improvements-since-0171">Improvements since 0.17.1</a></li>
+<li><a href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD
AI CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN</a><ul>
<li><a href="#native-expression-performance">Native Expression
Performance</a></li>
-</ul>
-</li>
+<li><a href="#correctness">Correctness</a></li>
+<li><a href="#new-expression-and-aggregate-support">New Expression and
Aggregate Support</a></li>
+<li><a href="#native-sampling">Native Sampling</a></li>
+<li><a href="#experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support</a></li>
+<li><a href="#iceberg">Iceberg</a></li>
+<li><a href="#native-parquet-io-and-cloud">Native Parquet I/O and
Cloud</a></li>
+<li><a href="#execution-and-fallback-improvements">Execution and Fallback
Improvements</a></li>
+<li><a href="#shuffle-improvements">Shuffle Improvements</a></li>
+<li><a href="#configuration-changes">Configuration Changes</a></li>
+<li><a href="#documentation">Documentation</a></li>
<li><a href="#deprecation-notice">Deprecation Notice</a></li>
<li><a href="#compatibility">Compatibility</a></li>
<li><a href="#get-started-with-comet-100">Get Started with Comet 1.0.0</a></li>
</ul>
+</li>
+</ul>
</div>
</aside>
</div>
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index f011187..a007647 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -47,16 +47,9 @@ improved performance and efficiency without requiring any
code changes.</p>
contributors. See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list of changes.</p>
<h2 id="the-road-to-10">The Road to 1.0<a class="headerlink"
href="#the-road-to-10" title="Permanent link">¶</a></h2>
<p>Comet was <a
href="https://datafusion.apache.org/blog/2024/03/06/comet-donation/">donated</a>
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.</p>
-<p>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.</p>
-<p>The rest of this section is a recap of the main advances since
donation.</p>
-<h3 id="support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with
ANSI mode<a class="headerlink" href="#support-for-spark-40-with-ansi-mode"
title="Permanent link">¶</a></h3>
-<p>Comet 1.0.0 supports Spark versions 3.4 though 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.</p>
+months later with support for 13 operators, and 106 expressions.</p>
+<p>Comet 1.0.0 now supports more than 400 expressions, but that isn't
the only way the project has grown over this time.</p>
+<p>Here's a recap of some of the main advances over the past two
years</p>
<h3 id="correctness-testing">Correctness Testing<a class="headerlink"
href="#correctness-testing" title="Permanent link">¶</a></h3>
<p>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.</p>
<ul>
@@ -64,46 +57,70 @@ Spark 3.4 announced below are scheduled for 1.1 rather than
landing in 1.0 itsel
<li>Scala tests: Comet has Scala tests that run queries end to end with
Comet enabled vs disabled and ensure that the results match</li>
<li>Fuzz testing: Many of the scala tests use a fuzz testing approach to
generate randomized data that queries run against, helping to catch regressions
around edge cases such as nulls, NaN, Infinity, and timezone issues</li>
<li>Comet SQL Tests: In an effort to make it easier to write tests,
Comet now provides a SQL-based testing approach that is inspired by
sqllogictest</li>
-<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of all expressions, comparing the implementation
to Spark's source code and ensuring that Comet has tests covering all important
edge cases</li>
+<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of ll expressions, comapring the implementation
to Spark's souce code and ensuring that Comet has tests covering all important
edge cases</li>
</ul>
<h3 id="performance">Performance<a class="headerlink"
href="#performance" title="Permanent link">¶</a></h3>
-<p>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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>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.</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>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.</p>
-<p>Codegen dispatch scopes that fallback down 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.</p>
-<ul>
-<li><strong>Coverage.</strong> 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.</li>
-<li><strong>Compatibility.</strong> 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 <em>is</em> Spark's
- implementation.</li>
-<li><strong>Scala and Java UDFs.</strong> 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 <a
href="https://datafusion.apache.org/comet/user-guide/latest/scala_java_udfs.html">Scala
and Java UDF guide</a> for details.</li>
-</ul>
+<p>Matt TBD</p>
+<h3 id="iceberg-support">Iceberg Support<a class="headerlink"
href="#iceberg-support" title="Permanent link">¶</a></h3>
<h2 id="improvements-since-0171">Improvements since 0.17.1<a
class="headerlink" href="#improvements-since-0171" title="Permanent
link">¶</a></h2>
<p>The rest of this post covers what is new since the 0.17.1
release.</p>
-<h3 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h3>
-<p>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.</p>
-<h3 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h3>
-<p>This release expands the set of Spark expressions and aggregates that
are accelerated by Comet:</p>
+<h1 id="old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD
AI CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN<a
class="headerlink"
href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten"
title="Permanent link">¶</a></h1>
+<h2 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
+ <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
+ styles.</li>
+<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
+<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
+<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
+ arithmetic kernel.</li>
+</ul>
+<p>To make this kind of work repeatable, the release also adds a scalar
expression optimization guide
+documenting how to benchmark a kernel, keep its output bit-identical to Spark,
and gate changes on a
+no-regression check.</p>
+<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
+cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
+<p>Query planning and plan serialization also got cheaper.
<code>QueryContext</code> SQL text is now interned into a
+per-plan pool, 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 and no longer rebuilds operators it does not touch. Comet also
makes far fewer timer calls in
+the native execution loop.</p>
+<h2 id="correctness">Correctness<a class="headerlink"
href="#correctness" title="Permanent link">¶</a></h2>
+<p>Correctness fixes have been a regular part of every Comet release,
but for 1.0 we made an extra push to
+clear out the bulk of the known issues before drawing a line under the stable
release. This release fixes
+roughly 30 divergences from Spark, spanning casts, ANSI error semantics,
wrong-result bugs, string and
+encoding handling, collations, and error reporting. Most were found by running
extensive AI-assisted audit
+sweeps of the code base, comparing Comet's expression implementations with all
supported versions of Spark.
+See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list; two representative fixes give a sense of the
shape of the work:</p>
+<ul>
+<li><strong>Whitespace trimming in string-to-numeric
casts.</strong> Casts to <code>boolean</code>, integral
types, <code>float</code>/<code>double</code>,
+ and <code>decimal</code> now use Spark's exact trim rules.
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.</li>
+<li><strong>ANSI errors swallowed by null
short-circuit.</strong> The codegen dispatcher's null short-circuit no
longer
+ swallows errors Spark raises. 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.</li>
+</ul>
+<p>Alongside the bug fixes, Comet also adds conservative fallbacks where
native behavior could not be made to
+match — decimal <code>SUM</code> / <code>AVG</code>
over sliding window frames, <code>FromUnixTime</code> with a
non-default format, and a
+handful of others — and falls back for predicates and datetime expressions
under non-default collations
+rather than risking a wrong result.</p>
+<h2 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h2>
+<p>This release expands the set of Spark expressions and aggregates that
run natively:</p>
<ul>
<li><strong>Aggregates</strong>:
<code>approx_percentile</code> /
<code>percentile_approx</code>, exact
<code>percentile</code> / <code>median</code>,
<code>approx_count_distinct</code>, and native
<code>collect_list</code> /
<code>array_agg</code>.</li>
-<li><strong>Cast</strong>: Cast expressions where the native
implementation is marked as incompatible or unsupported
- are now routed through codegen dispatch.</li>
<li><strong>Grouping</strong>:
<code>grouping()</code> and
<code>grouping_id()</code>.</li>
<li><strong>Intervals</strong>: interval types via
<code>make_ym_interval</code> and
<code>make_dt_interval</code>,
<code>CalendarIntervalType</code>,
<code>multiply_dt_interval</code>, and interval codegen dispatch
for nested values and native shuffle.</li>
@@ -115,30 +132,89 @@ exists. This is an early-stage feature and we welcome
feedback from users experi
<code>PreciseTimestampConversion</code> for native time-window
grouping.</li>
<li><strong>Windows</strong>: extended native window
function support and Spark 4 decimal window average.</li>
</ul>
-<h3 id="faster-parquet-scans">Faster Parquet Scans<a
class="headerlink" href="#faster-parquet-scans" title="Permanent
link">¶</a></h3>
-<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
-cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
-<h3 id="iceberg-table-format-v3">Iceberg Table Format V3<a
class="headerlink" href="#iceberg-table-format-v3" title="Permanent
link">¶</a></h3>
+<h2 id="native-sampling">Native Sampling<a class="headerlink"
href="#native-sampling" title="Permanent link">¶</a></h2>
+<p>Spark's <code>SampleExec</code> now runs natively when
sampling without replacement, covering
<code>DataFrame.sample</code>, SQL
+<code>TABLESAMPLE</code>, and
<code>DataFrame.randomSplit</code>. The native operator ports
Spark's <code>BernoulliCellSampler</code> on top of
+<code>XorShiftRandom</code> 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.</p>
+<h2 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h2>
+<p>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.</p>
+<h2 id="iceberg">Iceberg<a class="headerlink" href="#iceberg"
title="Permanent link">¶</a></h2>
<p>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
<code>_pos</code>, <code>_spec</code>,
<code>_file</code>, and
<code>_partition</code> metadata columns, sizes delete files
correctly to avoid dropped deletes, disambiguates scans that
share a <code>metadata_location</code>, 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</p>
-<h3 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h3>
-<p>Many native expression implementations have been optimized to more
efficiently leverage Arrow kernels or to avoid per-row builders.</p>
+where Iceberg native scan exchange reuse with different pushed filters could
produce wrong results is also
+fixed.</p>
+<h2 id="native-parquet-io-and-cloud">Native Parquet I/O and Cloud<a
class="headerlink" href="#native-parquet-io-and-cloud" title="Permanent
link">¶</a></h2>
+<p>Native Parquet writes now support gzip compression, and the native
Parquet scan supports Azure authentication,
+complementing the existing S3 credential support.</p>
+<h2 id="execution-and-fallback-improvements">Execution and Fallback
Improvements<a class="headerlink"
href="#execution-and-fallback-improvements" title="Permanent
link">¶</a></h2>
<ul>
-<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
- <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
- styles.</li>
-<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
-<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
-<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
- arithmetic kernel.</li>
+<li><strong>Mixed partial/final aggregation.</strong>
<code>sum</code> and non-decimal <code>avg</code> can
now run with the partial and final
+ aggregation stages split across Spark and Comet, keeping more aggregation
plans partially native.</li>
+<li><strong>Codegen dispatch for opt-in serdes.</strong>
Expressions reported as <code>Unsupported</code> can now route
through JVM
+ codegen dispatch for opt-in serdes, and native opt-in expressions surface as
compatible-by-default with a
+ <code>COMET-INFO</code> plan hint. Casts join this path: 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.
<code>sort_array</code> under strict floating-point mode and
<code>concat</code>
+ under non-<code>UTF8_BINARY</code> collations opt in the same
way.</li>
+<li><strong>JVM columnar-to-row by default.</strong>
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.</li>
+<li><strong>Clearer fallback reasons.</strong> Unsupported
scalar serde cases are reported in the support level,
+ mixed-execution fallback messages name the incompatible aggregate functions,
unsupported metadata column
+ names appear in fallback reasons, and the spurious "WriteFilesExec is not
supported" message is suppressed.</li>
+<li><strong>Expression coverage in extended
explain.</strong> The extended explain summary previously reported
operator
+ coverage but said nothing about expressions. It now ends with a line such as
<code>Comet accelerated 14
+ expressions (14 native, 1 codegen dispatch)</code>, 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.</li>
+<li><strong>Quieter logs and runtime cleanup.</strong>
Logging levels on frequent call sites have been lowered so Comet no
+ longer floods executor logs at default settings, and the Tokio runtime is
now released on driver and
+ executor exit.</li>
</ul>
+<h2 id="shuffle-improvements">Shuffle Improvements<a
class="headerlink" href="#shuffle-improvements" title="Permanent
link">¶</a></h2>
+<ul>
+<li><strong>Native shuffle memory cap.</strong> A new
<code>spark.comet.shuffle.maxBufferBytes</code> config caps native
shuffle writer
+ memory to bound worst-case usage.</li>
+<li><strong>Shuffle IPC schema encoding.</strong> The IPC
schema is now encoded once per writer instead of per block,
+ cutting per-batch shuffle overhead.</li>
+<li><strong>BatchCoalescer bypass.</strong> Shuffle bypasses
the <code>BatchCoalescer</code> for batches that are already
appropriately
+ sized.</li>
+<li><strong>Single-partition shuffle.</strong> A redundant
concatenation layer has been removed from the single-partition
+ path.</li>
+</ul>
+<h2 id="configuration-changes">Configuration Changes<a
class="headerlink" href="#configuration-changes" title="Permanent
link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Unified shuffle prefix.</strong> Shuffle configs
were spread across four disjoint prefixes
+ (<code>spark.comet.exec.shuffle.*</code>,
<code>spark.comet.columnar.shuffle.*</code>,
<code>spark.comet.native.shuffle.*</code>,
+ <code>spark.comet.shuffle.*</code>). They now all live under
<code>spark.comet.shuffle.*</code>, with
<code>.jvm.</code> and <code>.native.</code>
+ sub-namespaces matching the
<code>spark.comet.shuffle.mode</code> value you already
set.</li>
+<li><strong>Grouped explain configs.</strong> The orphan
explain-related configs are collected under a single
+ <code>spark.comet.explain.*</code> prefix, and the PyArrow UDF
config is renamed to <code>pyarrowUDF</code> for
consistency.</li>
+<li><strong><code>spark.comet.version</code>.</strong>
The loaded Comet build version is now exposed as a runtime config, so you can
+ confirm which Comet a cluster is actually running with
<code>spark.conf.get</code> or
<code>SET</code>.</li>
+<li><strong>Removed dead configs.</strong> The Parquet
parallel-IO knobs,
<code>spark.comet.use.lazyMaterialization</code>, and
+ <code>spark.comet.exceptionOnDatetimeRebase</code> had no
remaining effect and were misleading anyone tuning against
+ them.</li>
+<li><strong>Removed async columnar shuffle.</strong> The
<code>spark.comet.columnar.shuffle.async.*</code> path was off by
default and
+ untested, and has been removed rather than carried into a stable release
line.</li>
+</ul>
+<h2 id="documentation">Documentation<a class="headerlink"
href="#documentation" title="Permanent link">¶</a></h2>
+<p>The documentation received a substantial overhaul for 1.0: a design
refresh of the docs site, versioned user
+guides with captioned sidebar sections, and a set of accessibility and
navigation fixes. The
+supported-expressions page now shows how each expression is implemented —
native, codegen dispatch, or hybrid
+— generated from the planner itself rather than maintained by hand. An
expanded tuning guide covers the
+performance and memory configs that matter in practice, and there is an
updated post-1.0 roadmap and a new
+blog posts and talks page.</p>
<h2 id="deprecation-notice">Deprecation Notice<a class="headerlink"
href="#deprecation-notice" title="Permanent link">¶</a></h2>
<p>With the move to a stable 1.0 release line, Comet begins deprecating
older platforms under semantic
versioning:</p>
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index 9904838..22d9c41 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -47,16 +47,9 @@ improved performance and efficiency without requiring any
code changes.</p>
contributors. See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list of changes.</p>
<h2 id="the-road-to-10">The Road to 1.0<a class="headerlink"
href="#the-road-to-10" title="Permanent link">¶</a></h2>
<p>Comet was <a
href="https://datafusion.apache.org/blog/2024/03/06/comet-donation/">donated</a>
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.</p>
-<p>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.</p>
-<p>The rest of this section is a recap of the main advances since
donation.</p>
-<h3 id="support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with
ANSI mode<a class="headerlink" href="#support-for-spark-40-with-ansi-mode"
title="Permanent link">¶</a></h3>
-<p>Comet 1.0.0 supports Spark versions 3.4 though 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.</p>
+months later with support for 13 operators, and 106 expressions.</p>
+<p>Comet 1.0.0 now supports more than 400 expressions, but that isn't
the only way the project has grown over this time.</p>
+<p>Here's a recap of some of the main advances over the past two
years</p>
<h3 id="correctness-testing">Correctness Testing<a class="headerlink"
href="#correctness-testing" title="Permanent link">¶</a></h3>
<p>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.</p>
<ul>
@@ -64,46 +57,70 @@ Spark 3.4 announced below are scheduled for 1.1 rather than
landing in 1.0 itsel
<li>Scala tests: Comet has Scala tests that run queries end to end with
Comet enabled vs disabled and ensure that the results match</li>
<li>Fuzz testing: Many of the scala tests use a fuzz testing approach to
generate randomized data that queries run against, helping to catch regressions
around edge cases such as nulls, NaN, Infinity, and timezone issues</li>
<li>Comet SQL Tests: In an effort to make it easier to write tests,
Comet now provides a SQL-based testing approach that is inspired by
sqllogictest</li>
-<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of all expressions, comparing the implementation
to Spark's source code and ensuring that Comet has tests covering all important
edge cases</li>
+<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of ll expressions, comapring the implementation
to Spark's souce code and ensuring that Comet has tests covering all important
edge cases</li>
</ul>
<h3 id="performance">Performance<a class="headerlink"
href="#performance" title="Permanent link">¶</a></h3>
-<p>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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>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.</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>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.</p>
-<p>Codegen dispatch scopes that fallback down 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.</p>
-<ul>
-<li><strong>Coverage.</strong> 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.</li>
-<li><strong>Compatibility.</strong> 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 <em>is</em> Spark's
- implementation.</li>
-<li><strong>Scala and Java UDFs.</strong> 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 <a
href="https://datafusion.apache.org/comet/user-guide/latest/scala_java_udfs.html">Scala
and Java UDF guide</a> for details.</li>
-</ul>
+<p>Matt TBD</p>
+<h3 id="iceberg-support">Iceberg Support<a class="headerlink"
href="#iceberg-support" title="Permanent link">¶</a></h3>
<h2 id="improvements-since-0171">Improvements since 0.17.1<a
class="headerlink" href="#improvements-since-0171" title="Permanent
link">¶</a></h2>
<p>The rest of this post covers what is new since the 0.17.1
release.</p>
-<h3 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h3>
-<p>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.</p>
-<h3 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h3>
-<p>This release expands the set of Spark expressions and aggregates that
are accelerated by Comet:</p>
+<h1 id="old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD
AI CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN<a
class="headerlink"
href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten"
title="Permanent link">¶</a></h1>
+<h2 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
+ <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
+ styles.</li>
+<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
+<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
+<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
+ arithmetic kernel.</li>
+</ul>
+<p>To make this kind of work repeatable, the release also adds a scalar
expression optimization guide
+documenting how to benchmark a kernel, keep its output bit-identical to Spark,
and gate changes on a
+no-regression check.</p>
+<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
+cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
+<p>Query planning and plan serialization also got cheaper.
<code>QueryContext</code> SQL text is now interned into a
+per-plan pool, 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 and no longer rebuilds operators it does not touch. Comet also
makes far fewer timer calls in
+the native execution loop.</p>
+<h2 id="correctness">Correctness<a class="headerlink"
href="#correctness" title="Permanent link">¶</a></h2>
+<p>Correctness fixes have been a regular part of every Comet release,
but for 1.0 we made an extra push to
+clear out the bulk of the known issues before drawing a line under the stable
release. This release fixes
+roughly 30 divergences from Spark, spanning casts, ANSI error semantics,
wrong-result bugs, string and
+encoding handling, collations, and error reporting. Most were found by running
extensive AI-assisted audit
+sweeps of the code base, comparing Comet's expression implementations with all
supported versions of Spark.
+See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list; two representative fixes give a sense of the
shape of the work:</p>
+<ul>
+<li><strong>Whitespace trimming in string-to-numeric
casts.</strong> Casts to <code>boolean</code>, integral
types, <code>float</code>/<code>double</code>,
+ and <code>decimal</code> now use Spark's exact trim rules.
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.</li>
+<li><strong>ANSI errors swallowed by null
short-circuit.</strong> The codegen dispatcher's null short-circuit no
longer
+ swallows errors Spark raises. 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.</li>
+</ul>
+<p>Alongside the bug fixes, Comet also adds conservative fallbacks where
native behavior could not be made to
+match — decimal <code>SUM</code> / <code>AVG</code>
over sliding window frames, <code>FromUnixTime</code> with a
non-default format, and a
+handful of others — and falls back for predicates and datetime expressions
under non-default collations
+rather than risking a wrong result.</p>
+<h2 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h2>
+<p>This release expands the set of Spark expressions and aggregates that
run natively:</p>
<ul>
<li><strong>Aggregates</strong>:
<code>approx_percentile</code> /
<code>percentile_approx</code>, exact
<code>percentile</code> / <code>median</code>,
<code>approx_count_distinct</code>, and native
<code>collect_list</code> /
<code>array_agg</code>.</li>
-<li><strong>Cast</strong>: Cast expressions where the native
implementation is marked as incompatible or unsupported
- are now routed through codegen dispatch.</li>
<li><strong>Grouping</strong>:
<code>grouping()</code> and
<code>grouping_id()</code>.</li>
<li><strong>Intervals</strong>: interval types via
<code>make_ym_interval</code> and
<code>make_dt_interval</code>,
<code>CalendarIntervalType</code>,
<code>multiply_dt_interval</code>, and interval codegen dispatch
for nested values and native shuffle.</li>
@@ -115,30 +132,89 @@ exists. This is an early-stage feature and we welcome
feedback from users experi
<code>PreciseTimestampConversion</code> for native time-window
grouping.</li>
<li><strong>Windows</strong>: extended native window
function support and Spark 4 decimal window average.</li>
</ul>
-<h3 id="faster-parquet-scans">Faster Parquet Scans<a
class="headerlink" href="#faster-parquet-scans" title="Permanent
link">¶</a></h3>
-<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
-cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
-<h3 id="iceberg-table-format-v3">Iceberg Table Format V3<a
class="headerlink" href="#iceberg-table-format-v3" title="Permanent
link">¶</a></h3>
+<h2 id="native-sampling">Native Sampling<a class="headerlink"
href="#native-sampling" title="Permanent link">¶</a></h2>
+<p>Spark's <code>SampleExec</code> now runs natively when
sampling without replacement, covering
<code>DataFrame.sample</code>, SQL
+<code>TABLESAMPLE</code>, and
<code>DataFrame.randomSplit</code>. The native operator ports
Spark's <code>BernoulliCellSampler</code> on top of
+<code>XorShiftRandom</code> 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.</p>
+<h2 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h2>
+<p>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.</p>
+<h2 id="iceberg">Iceberg<a class="headerlink" href="#iceberg"
title="Permanent link">¶</a></h2>
<p>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
<code>_pos</code>, <code>_spec</code>,
<code>_file</code>, and
<code>_partition</code> metadata columns, sizes delete files
correctly to avoid dropped deletes, disambiguates scans that
share a <code>metadata_location</code>, 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</p>
-<h3 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h3>
-<p>Many native expression implementations have been optimized to more
efficiently leverage Arrow kernels or to avoid per-row builders.</p>
+where Iceberg native scan exchange reuse with different pushed filters could
produce wrong results is also
+fixed.</p>
+<h2 id="native-parquet-io-and-cloud">Native Parquet I/O and Cloud<a
class="headerlink" href="#native-parquet-io-and-cloud" title="Permanent
link">¶</a></h2>
+<p>Native Parquet writes now support gzip compression, and the native
Parquet scan supports Azure authentication,
+complementing the existing S3 credential support.</p>
+<h2 id="execution-and-fallback-improvements">Execution and Fallback
Improvements<a class="headerlink"
href="#execution-and-fallback-improvements" title="Permanent
link">¶</a></h2>
<ul>
-<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
- <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
- styles.</li>
-<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
-<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
-<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
- arithmetic kernel.</li>
+<li><strong>Mixed partial/final aggregation.</strong>
<code>sum</code> and non-decimal <code>avg</code> can
now run with the partial and final
+ aggregation stages split across Spark and Comet, keeping more aggregation
plans partially native.</li>
+<li><strong>Codegen dispatch for opt-in serdes.</strong>
Expressions reported as <code>Unsupported</code> can now route
through JVM
+ codegen dispatch for opt-in serdes, and native opt-in expressions surface as
compatible-by-default with a
+ <code>COMET-INFO</code> plan hint. Casts join this path: 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.
<code>sort_array</code> under strict floating-point mode and
<code>concat</code>
+ under non-<code>UTF8_BINARY</code> collations opt in the same
way.</li>
+<li><strong>JVM columnar-to-row by default.</strong>
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.</li>
+<li><strong>Clearer fallback reasons.</strong> Unsupported
scalar serde cases are reported in the support level,
+ mixed-execution fallback messages name the incompatible aggregate functions,
unsupported metadata column
+ names appear in fallback reasons, and the spurious "WriteFilesExec is not
supported" message is suppressed.</li>
+<li><strong>Expression coverage in extended
explain.</strong> The extended explain summary previously reported
operator
+ coverage but said nothing about expressions. It now ends with a line such as
<code>Comet accelerated 14
+ expressions (14 native, 1 codegen dispatch)</code>, 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.</li>
+<li><strong>Quieter logs and runtime cleanup.</strong>
Logging levels on frequent call sites have been lowered so Comet no
+ longer floods executor logs at default settings, and the Tokio runtime is
now released on driver and
+ executor exit.</li>
</ul>
+<h2 id="shuffle-improvements">Shuffle Improvements<a
class="headerlink" href="#shuffle-improvements" title="Permanent
link">¶</a></h2>
+<ul>
+<li><strong>Native shuffle memory cap.</strong> A new
<code>spark.comet.shuffle.maxBufferBytes</code> config caps native
shuffle writer
+ memory to bound worst-case usage.</li>
+<li><strong>Shuffle IPC schema encoding.</strong> The IPC
schema is now encoded once per writer instead of per block,
+ cutting per-batch shuffle overhead.</li>
+<li><strong>BatchCoalescer bypass.</strong> Shuffle bypasses
the <code>BatchCoalescer</code> for batches that are already
appropriately
+ sized.</li>
+<li><strong>Single-partition shuffle.</strong> A redundant
concatenation layer has been removed from the single-partition
+ path.</li>
+</ul>
+<h2 id="configuration-changes">Configuration Changes<a
class="headerlink" href="#configuration-changes" title="Permanent
link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Unified shuffle prefix.</strong> Shuffle configs
were spread across four disjoint prefixes
+ (<code>spark.comet.exec.shuffle.*</code>,
<code>spark.comet.columnar.shuffle.*</code>,
<code>spark.comet.native.shuffle.*</code>,
+ <code>spark.comet.shuffle.*</code>). They now all live under
<code>spark.comet.shuffle.*</code>, with
<code>.jvm.</code> and <code>.native.</code>
+ sub-namespaces matching the
<code>spark.comet.shuffle.mode</code> value you already
set.</li>
+<li><strong>Grouped explain configs.</strong> The orphan
explain-related configs are collected under a single
+ <code>spark.comet.explain.*</code> prefix, and the PyArrow UDF
config is renamed to <code>pyarrowUDF</code> for
consistency.</li>
+<li><strong><code>spark.comet.version</code>.</strong>
The loaded Comet build version is now exposed as a runtime config, so you can
+ confirm which Comet a cluster is actually running with
<code>spark.conf.get</code> or
<code>SET</code>.</li>
+<li><strong>Removed dead configs.</strong> The Parquet
parallel-IO knobs,
<code>spark.comet.use.lazyMaterialization</code>, and
+ <code>spark.comet.exceptionOnDatetimeRebase</code> had no
remaining effect and were misleading anyone tuning against
+ them.</li>
+<li><strong>Removed async columnar shuffle.</strong> The
<code>spark.comet.columnar.shuffle.async.*</code> path was off by
default and
+ untested, and has been removed rather than carried into a stable release
line.</li>
+</ul>
+<h2 id="documentation">Documentation<a class="headerlink"
href="#documentation" title="Permanent link">¶</a></h2>
+<p>The documentation received a substantial overhaul for 1.0: a design
refresh of the docs site, versioned user
+guides with captioned sidebar sections, and a set of accessibility and
navigation fixes. The
+supported-expressions page now shows how each expression is implemented —
native, codegen dispatch, or hybrid
+— generated from the planner itself rather than maintained by hand. An
expanded tuning guide covers the
+performance and memory configs that matter in practice, and there is an
updated post-1.0 roadmap and a new
+blog posts and talks page.</p>
<h2 id="deprecation-notice">Deprecation Notice<a class="headerlink"
href="#deprecation-notice" title="Permanent link">¶</a></h2>
<p>With the move to a stable 1.0 release line, Comet begins deprecating
older platforms under semantic
versioning:</p>
diff --git a/blog/feeds/pmc.atom.xml b/blog/feeds/pmc.atom.xml
index a8353b9..251af08 100644
--- a/blog/feeds/pmc.atom.xml
+++ b/blog/feeds/pmc.atom.xml
@@ -47,16 +47,9 @@ improved performance and efficiency without requiring any
code changes.</p>
contributors. See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list of changes.</p>
<h2 id="the-road-to-10">The Road to 1.0<a class="headerlink"
href="#the-road-to-10" title="Permanent link">¶</a></h2>
<p>Comet was <a
href="https://datafusion.apache.org/blog/2024/03/06/comet-donation/">donated</a>
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.</p>
-<p>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.</p>
-<p>The rest of this section is a recap of the main advances since
donation.</p>
-<h3 id="support-for-spark-40-with-ansi-mode">Support for Spark 4.0+ with
ANSI mode<a class="headerlink" href="#support-for-spark-40-with-ansi-mode"
title="Permanent link">¶</a></h3>
-<p>Comet 1.0.0 supports Spark versions 3.4 though 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.</p>
+months later with support for 13 operators, and 106 expressions.</p>
+<p>Comet 1.0.0 now supports more than 400 expressions, but that isn't
the only way the project has grown over this time.</p>
+<p>Here's a recap of some of the main advances over the past two
years</p>
<h3 id="correctness-testing">Correctness Testing<a class="headerlink"
href="#correctness-testing" title="Permanent link">¶</a></h3>
<p>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.</p>
<ul>
@@ -64,46 +57,70 @@ Spark 3.4 announced below are scheduled for 1.1 rather than
landing in 1.0 itsel
<li>Scala tests: Comet has Scala tests that run queries end to end with
Comet enabled vs disabled and ensure that the results match</li>
<li>Fuzz testing: Many of the scala tests use a fuzz testing approach to
generate randomized data that queries run against, helping to catch regressions
around edge cases such as nulls, NaN, Infinity, and timezone issues</li>
<li>Comet SQL Tests: In an effort to make it easier to write tests,
Comet now provides a SQL-based testing approach that is inspired by
sqllogictest</li>
-<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of all expressions, comparing the implementation
to Spark's source code and ensuring that Comet has tests covering all important
edge cases</li>
+<li>Generative AI: More recently, Comet has taken advantage of agentic
skills to perform audit sweeps of ll expressions, comapring the implementation
to Spark's souce code and ensuring that Comet has tests covering all important
edge cases</li>
</ul>
<h3 id="performance">Performance<a class="headerlink"
href="#performance" title="Permanent link">¶</a></h3>
-<p>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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>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.</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>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.</p>
-<p>Codegen dispatch scopes that fallback down 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.</p>
-<ul>
-<li><strong>Coverage.</strong> 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.</li>
-<li><strong>Compatibility.</strong> 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 <em>is</em> Spark's
- implementation.</li>
-<li><strong>Scala and Java UDFs.</strong> 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 <a
href="https://datafusion.apache.org/comet/user-guide/latest/scala_java_udfs.html">Scala
and Java UDF guide</a> for details.</li>
-</ul>
+<p>Matt TBD</p>
+<h3 id="iceberg-support">Iceberg Support<a class="headerlink"
href="#iceberg-support" title="Permanent link">¶</a></h3>
<h2 id="improvements-since-0171">Improvements since 0.17.1<a
class="headerlink" href="#improvements-since-0171" title="Permanent
link">¶</a></h2>
<p>The rest of this post covers what is new since the 0.17.1
release.</p>
-<h3 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h3>
-<p>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.</p>
-<h3 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h3>
-<p>This release expands the set of Spark expressions and aggregates that
are accelerated by Comet:</p>
+<h1 id="old-ai-content-below-needs-to-be-reviewed-andor-rewritten">OLD
AI CONTENT BELOW - NEEDS TO BE REVIEWED AND/OR REWRITTEN<a
class="headerlink"
href="#old-ai-content-below-needs-to-be-reviewed-andor-rewritten"
title="Permanent link">¶</a></h1>
+<h2 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
+ <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
+ styles.</li>
+<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
+<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
+<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
+ arithmetic kernel.</li>
+</ul>
+<p>To make this kind of work repeatable, the release also adds a scalar
expression optimization guide
+documenting how to benchmark a kernel, keep its output bit-identical to Spark,
and gate changes on a
+no-regression check.</p>
+<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
+cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
+<p>Query planning and plan serialization also got cheaper.
<code>QueryContext</code> SQL text is now interned into a
+per-plan pool, 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 and no longer rebuilds operators it does not touch. Comet also
makes far fewer timer calls in
+the native execution loop.</p>
+<h2 id="correctness">Correctness<a class="headerlink"
href="#correctness" title="Permanent link">¶</a></h2>
+<p>Correctness fixes have been a regular part of every Comet release,
but for 1.0 we made an extra push to
+clear out the bulk of the known issues before drawing a line under the stable
release. This release fixes
+roughly 30 divergences from Spark, spanning casts, ANSI error semantics,
wrong-result bugs, string and
+encoding handling, collations, and error reporting. Most were found by running
extensive AI-assisted audit
+sweeps of the code base, comparing Comet's expression implementations with all
supported versions of Spark.
+See the <a
href="https://github.com/apache/datafusion-comet/blob/main/docs/source/changelog/1.0.0.md">change
log</a> for the full list; two representative fixes give a sense of the
shape of the work:</p>
+<ul>
+<li><strong>Whitespace trimming in string-to-numeric
casts.</strong> Casts to <code>boolean</code>, integral
types, <code>float</code>/<code>double</code>,
+ and <code>decimal</code> now use Spark's exact trim rules.
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.</li>
+<li><strong>ANSI errors swallowed by null
short-circuit.</strong> The codegen dispatcher's null short-circuit no
longer
+ swallows errors Spark raises. 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.</li>
+</ul>
+<p>Alongside the bug fixes, Comet also adds conservative fallbacks where
native behavior could not be made to
+match — decimal <code>SUM</code> / <code>AVG</code>
over sliding window frames, <code>FromUnixTime</code> with a
non-default format, and a
+handful of others — and falls back for predicates and datetime expressions
under non-default collations
+rather than risking a wrong result.</p>
+<h2 id="new-expression-and-aggregate-support">New Expression and
Aggregate Support<a class="headerlink"
href="#new-expression-and-aggregate-support" title="Permanent
link">¶</a></h2>
+<p>This release expands the set of Spark expressions and aggregates that
run natively:</p>
<ul>
<li><strong>Aggregates</strong>:
<code>approx_percentile</code> /
<code>percentile_approx</code>, exact
<code>percentile</code> / <code>median</code>,
<code>approx_count_distinct</code>, and native
<code>collect_list</code> /
<code>array_agg</code>.</li>
-<li><strong>Cast</strong>: Cast expressions where the native
implementation is marked as incompatible or unsupported
- are now routed through codegen dispatch.</li>
<li><strong>Grouping</strong>:
<code>grouping()</code> and
<code>grouping_id()</code>.</li>
<li><strong>Intervals</strong>: interval types via
<code>make_ym_interval</code> and
<code>make_dt_interval</code>,
<code>CalendarIntervalType</code>,
<code>multiply_dt_interval</code>, and interval codegen dispatch
for nested values and native shuffle.</li>
@@ -115,30 +132,89 @@ exists. This is an early-stage feature and we welcome
feedback from users experi
<code>PreciseTimestampConversion</code> for native time-window
grouping.</li>
<li><strong>Windows</strong>: extended native window
function support and Spark 4 decimal window average.</li>
</ul>
-<h3 id="faster-parquet-scans">Faster Parquet Scans<a
class="headerlink" href="#faster-parquet-scans" title="Permanent
link">¶</a></h3>
-<p>Parquet reads pick up several improvements as well. Full Parquet
metadata, including the page index, is now
-cached via DataFusion's
<code>CachedParquetFileReaderFactory</code>; 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.</p>
-<h3 id="iceberg-table-format-v3">Iceberg Table Format V3<a
class="headerlink" href="#iceberg-table-format-v3" title="Permanent
link">¶</a></h3>
+<h2 id="native-sampling">Native Sampling<a class="headerlink"
href="#native-sampling" title="Permanent link">¶</a></h2>
+<p>Spark's <code>SampleExec</code> now runs natively when
sampling without replacement, covering
<code>DataFrame.sample</code>, SQL
+<code>TABLESAMPLE</code>, and
<code>DataFrame.randomSplit</code>. The native operator ports
Spark's <code>BernoulliCellSampler</code> on top of
+<code>XorShiftRandom</code> 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.</p>
+<h2 id="experimental-pyarrow-udf-support">Experimental PyArrow UDF
Support<a class="headerlink" href="#experimental-pyarrow-udf-support"
title="Permanent link">¶</a></h2>
+<p>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.</p>
+<h2 id="iceberg">Iceberg<a class="headerlink" href="#iceberg"
title="Permanent link">¶</a></h2>
<p>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
<code>_pos</code>, <code>_spec</code>,
<code>_file</code>, and
<code>_partition</code> metadata columns, sizes delete files
correctly to avoid dropped deletes, disambiguates scans that
share a <code>metadata_location</code>, 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</p>
-<h3 id="native-expression-performance">Native Expression
Performance<a class="headerlink" href="#native-expression-performance"
title="Permanent link">¶</a></h3>
-<p>Many native expression implementations have been optimized to more
efficiently leverage Arrow kernels or to avoid per-row builders.</p>
+where Iceberg native scan exchange reuse with different pushed filters could
produce wrong results is also
+fixed.</p>
+<h2 id="native-parquet-io-and-cloud">Native Parquet I/O and Cloud<a
class="headerlink" href="#native-parquet-io-and-cloud" title="Permanent
link">¶</a></h2>
+<p>Native Parquet writes now support gzip compression, and the native
Parquet scan supports Azure authentication,
+complementing the existing S3 credential support.</p>
+<h2 id="execution-and-fallback-improvements">Execution and Fallback
Improvements<a class="headerlink"
href="#execution-and-fallback-improvements" title="Permanent
link">¶</a></h2>
+<ul>
+<li><strong>Mixed partial/final aggregation.</strong>
<code>sum</code> and non-decimal <code>avg</code> can
now run with the partial and final
+ aggregation stages split across Spark and Comet, keeping more aggregation
plans partially native.</li>
+<li><strong>Codegen dispatch for opt-in serdes.</strong>
Expressions reported as <code>Unsupported</code> can now route
through JVM
+ codegen dispatch for opt-in serdes, and native opt-in expressions surface as
compatible-by-default with a
+ <code>COMET-INFO</code> plan hint. Casts join this path: 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.
<code>sort_array</code> under strict floating-point mode and
<code>concat</code>
+ under non-<code>UTF8_BINARY</code> collations opt in the same
way.</li>
+<li><strong>JVM columnar-to-row by default.</strong>
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.</li>
+<li><strong>Clearer fallback reasons.</strong> Unsupported
scalar serde cases are reported in the support level,
+ mixed-execution fallback messages name the incompatible aggregate functions,
unsupported metadata column
+ names appear in fallback reasons, and the spurious "WriteFilesExec is not
supported" message is suppressed.</li>
+<li><strong>Expression coverage in extended
explain.</strong> The extended explain summary previously reported
operator
+ coverage but said nothing about expressions. It now ends with a line such as
<code>Comet accelerated 14
+ expressions (14 native, 1 codegen dispatch)</code>, 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.</li>
+<li><strong>Quieter logs and runtime cleanup.</strong>
Logging levels on frequent call sites have been lowered so Comet no
+ longer floods executor logs at default settings, and the Tokio runtime is
now released on driver and
+ executor exit.</li>
+</ul>
+<h2 id="shuffle-improvements">Shuffle Improvements<a
class="headerlink" href="#shuffle-improvements" title="Permanent
link">¶</a></h2>
<ul>
-<li><strong>Casts</strong> 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
<code>CheckOverflow</code> and
- <code>DecimalRescaleCheckOverflow</code>, and a
<code>cast_binary_to_string</code> that is up to 27x faster on
binary-format
- styles.</li>
-<li><strong>JSON, regex, and URL parsing</strong>:
<code>get_json_object</code>,
<code>regexp_extract</code>, and
<code>parse_url</code>.</li>
-<li><strong>Date/time and decimal kernels</strong>:
<code>date_trunc</code>, <code>spark_ceil</code>, and a
vectorized <code>spark_unscaled_value</code>.</li>
-<li><strong>String and array kernels</strong>:
<code>lpad</code>, <code>unhex</code>,
<code>size</code>, <code>arrays_overlap</code>,
<code>escape_string</code>, and the <code>try_*</code>
- arithmetic kernel.</li>
+<li><strong>Native shuffle memory cap.</strong> A new
<code>spark.comet.shuffle.maxBufferBytes</code> config caps native
shuffle writer
+ memory to bound worst-case usage.</li>
+<li><strong>Shuffle IPC schema encoding.</strong> The IPC
schema is now encoded once per writer instead of per block,
+ cutting per-batch shuffle overhead.</li>
+<li><strong>BatchCoalescer bypass.</strong> Shuffle bypasses
the <code>BatchCoalescer</code> for batches that are already
appropriately
+ sized.</li>
+<li><strong>Single-partition shuffle.</strong> A redundant
concatenation layer has been removed from the single-partition
+ path.</li>
+</ul>
+<h2 id="configuration-changes">Configuration Changes<a
class="headerlink" href="#configuration-changes" title="Permanent
link">¶</a></h2>
+<p>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:</p>
+<ul>
+<li><strong>Unified shuffle prefix.</strong> Shuffle configs
were spread across four disjoint prefixes
+ (<code>spark.comet.exec.shuffle.*</code>,
<code>spark.comet.columnar.shuffle.*</code>,
<code>spark.comet.native.shuffle.*</code>,
+ <code>spark.comet.shuffle.*</code>). They now all live under
<code>spark.comet.shuffle.*</code>, with
<code>.jvm.</code> and <code>.native.</code>
+ sub-namespaces matching the
<code>spark.comet.shuffle.mode</code> value you already
set.</li>
+<li><strong>Grouped explain configs.</strong> The orphan
explain-related configs are collected under a single
+ <code>spark.comet.explain.*</code> prefix, and the PyArrow UDF
config is renamed to <code>pyarrowUDF</code> for
consistency.</li>
+<li><strong><code>spark.comet.version</code>.</strong>
The loaded Comet build version is now exposed as a runtime config, so you can
+ confirm which Comet a cluster is actually running with
<code>spark.conf.get</code> or
<code>SET</code>.</li>
+<li><strong>Removed dead configs.</strong> The Parquet
parallel-IO knobs,
<code>spark.comet.use.lazyMaterialization</code>, and
+ <code>spark.comet.exceptionOnDatetimeRebase</code> had no
remaining effect and were misleading anyone tuning against
+ them.</li>
+<li><strong>Removed async columnar shuffle.</strong> The
<code>spark.comet.columnar.shuffle.async.*</code> path was off by
default and
+ untested, and has been removed rather than carried into a stable release
line.</li>
</ul>
+<h2 id="documentation">Documentation<a class="headerlink"
href="#documentation" title="Permanent link">¶</a></h2>
+<p>The documentation received a substantial overhaul for 1.0: a design
refresh of the docs site, versioned user
+guides with captioned sidebar sections, and a set of accessibility and
navigation fixes. The
+supported-expressions page now shows how each expression is implemented —
native, codegen dispatch, or hybrid
+— generated from the planner itself rather than maintained by hand. An
expanded tuning guide covers the
+performance and memory configs that matter in practice, and there is an
updated post-1.0 roadmap and a new
+blog posts and talks page.</p>
<h2 id="deprecation-notice">Deprecation Notice<a class="headerlink"
href="#deprecation-notice" title="Permanent link">¶</a></h2>
<p>With the move to a stable 1.0 release line, Comet begins deprecating
older platforms under semantic
versioning:</p>
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