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commit bf739a8e3920475df459f1722ddc5ad63dfa2d58
Author: Build Pelican (action) <[email protected]>
AuthorDate: Tue Aug 4 19:35:57 2026 +0000
Commit build products
---
blog/2026/08/07/datafusion-comet-1.0.0/index.html | 6 +++---
blog/feeds/all-en.atom.xml | 6 +++---
blog/feeds/blog.atom.xml | 6 +++---
blog/feeds/pmc.atom.xml | 6 +++---
4 files changed, 12 insertions(+), 12 deletions(-)
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 9a96499..0474cdb 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
@@ -101,12 +101,12 @@ contributors. See the <a
href="https://github.com/apache/datafusion-comet/blob/m
<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 15 data types, 13 operators, 106 expressions, and a "modest
performance speedup." The
-nineteen releases between then and now cover a lot of ground:</p>
+sixteen major releases between then and now cover a lot of ground:</p>
<ul>
<li><strong>Query coverage went from a handful of operators to the shape of a
real Spark query.</strong> Native
SortMergeJoin, HashJoin, and BroadcastHashJoin landed early; native columnar
and native shuffle,
broadcast nested loop joins, native window functions, native sampling, and
mixed partial/final aggregation
- followed. Supported expressions grew from 106 at 0.1.0 to 404 in 1.0, and
the introduction of the JVM
+ followed. Supported expressions grew from 106 at 0.1.0 to more than 400 in
1.0.0, and the introduction of the JVM
codegen dispatcher in 0.17.0 gave Comet a way to keep unsupported
expressions Arrow-native by running
Spark's own generated code inside the pipeline rather than falling back to
row-based execution.</li>
<li><strong>Spark support broadened.</strong> The 0.1.0 release targeted Spark
3.3, 3.4, and 3.5, with experimental 4.0. The
@@ -114,7 +114,7 @@ nineteen releases between then and now cover a lot of
ground:</p>
Spark 4 — moved from partial to a supported default across the natively
implemented surface.</li>
<li><strong>The ecosystem story filled in.</strong> Native Iceberg support
arrived in 0.10.0 and has been extended through
1.11 and format V3, native Parquet writes and CSV reads landed as
experimental features and matured,
- Azure joined S3 on the native cloud path, and 1.0 adds experimental
accelerated PyArrow UDFs alongside the
+ Azure joined S3 on the native cloud path, and 1.0.0 adds experimental
accelerated PyArrow UDFs alongside the
Java and Scala UDF support that shipped in 0.17.0.</li>
<li><strong>Correctness rigor grew alongside the surface area.</strong> Early
releases relied on fuzz testing to surface
divergences; later releases added a full Spark SQL test-suite run against
every supported Spark version,
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index 76c6bd6..b9fa4fa 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -48,12 +48,12 @@ contributors. See the <a
href="https://github.com/apache/datafusion-comet/blo
<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 15 data types, 13 operators, 106 expressions, and a "modest
performance speedup." The
-nineteen releases between then and now cover a lot of ground:</p>
+sixteen major releases between then and now cover a lot of ground:</p>
<ul>
<li><strong>Query coverage went from a handful of operators to the
shape of a real Spark query.</strong> Native
SortMergeJoin, HashJoin, and BroadcastHashJoin landed early; native columnar
and native shuffle,
broadcast nested loop joins, native window functions, native sampling, and
mixed partial/final aggregation
- followed. Supported expressions grew from 106 at 0.1.0 to 404 in 1.0, and
the introduction of the JVM
+ followed. Supported expressions grew from 106 at 0.1.0 to more than 400 in
1.0.0, and the introduction of the JVM
codegen dispatcher in 0.17.0 gave Comet a way to keep unsupported
expressions Arrow-native by running
Spark's own generated code inside the pipeline rather than falling back to
row-based execution.</li>
<li><strong>Spark support broadened.</strong> The 0.1.0
release targeted Spark 3.3, 3.4, and 3.5, with experimental 4.0. The
@@ -61,7 +61,7 @@ nineteen releases between then and now cover a lot of
ground:</p>
Spark 4 — moved from partial to a supported default across the natively
implemented surface.</li>
<li><strong>The ecosystem story filled in.</strong> Native
Iceberg support arrived in 0.10.0 and has been extended through
1.11 and format V3, native Parquet writes and CSV reads landed as
experimental features and matured,
- Azure joined S3 on the native cloud path, and 1.0 adds experimental
accelerated PyArrow UDFs alongside the
+ Azure joined S3 on the native cloud path, and 1.0.0 adds experimental
accelerated PyArrow UDFs alongside the
Java and Scala UDF support that shipped in 0.17.0.</li>
<li><strong>Correctness rigor grew alongside the surface
area.</strong> Early releases relied on fuzz testing to surface
divergences; later releases added a full Spark SQL test-suite run against
every supported Spark version,
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index 281afbb..30a64fb 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -48,12 +48,12 @@ contributors. See the <a
href="https://github.com/apache/datafusion-comet/blo
<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 15 data types, 13 operators, 106 expressions, and a "modest
performance speedup." The
-nineteen releases between then and now cover a lot of ground:</p>
+sixteen major releases between then and now cover a lot of ground:</p>
<ul>
<li><strong>Query coverage went from a handful of operators to the
shape of a real Spark query.</strong> Native
SortMergeJoin, HashJoin, and BroadcastHashJoin landed early; native columnar
and native shuffle,
broadcast nested loop joins, native window functions, native sampling, and
mixed partial/final aggregation
- followed. Supported expressions grew from 106 at 0.1.0 to 404 in 1.0, and
the introduction of the JVM
+ followed. Supported expressions grew from 106 at 0.1.0 to more than 400 in
1.0.0, and the introduction of the JVM
codegen dispatcher in 0.17.0 gave Comet a way to keep unsupported
expressions Arrow-native by running
Spark's own generated code inside the pipeline rather than falling back to
row-based execution.</li>
<li><strong>Spark support broadened.</strong> The 0.1.0
release targeted Spark 3.3, 3.4, and 3.5, with experimental 4.0. The
@@ -61,7 +61,7 @@ nineteen releases between then and now cover a lot of
ground:</p>
Spark 4 — moved from partial to a supported default across the natively
implemented surface.</li>
<li><strong>The ecosystem story filled in.</strong> Native
Iceberg support arrived in 0.10.0 and has been extended through
1.11 and format V3, native Parquet writes and CSV reads landed as
experimental features and matured,
- Azure joined S3 on the native cloud path, and 1.0 adds experimental
accelerated PyArrow UDFs alongside the
+ Azure joined S3 on the native cloud path, and 1.0.0 adds experimental
accelerated PyArrow UDFs alongside the
Java and Scala UDF support that shipped in 0.17.0.</li>
<li><strong>Correctness rigor grew alongside the surface
area.</strong> Early releases relied on fuzz testing to surface
divergences; later releases added a full Spark SQL test-suite run against
every supported Spark version,
diff --git a/blog/feeds/pmc.atom.xml b/blog/feeds/pmc.atom.xml
index 3bda8b4..2982b8b 100644
--- a/blog/feeds/pmc.atom.xml
+++ b/blog/feeds/pmc.atom.xml
@@ -48,12 +48,12 @@ contributors. See the <a
href="https://github.com/apache/datafusion-comet/blo
<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 15 data types, 13 operators, 106 expressions, and a "modest
performance speedup." The
-nineteen releases between then and now cover a lot of ground:</p>
+sixteen major releases between then and now cover a lot of ground:</p>
<ul>
<li><strong>Query coverage went from a handful of operators to the
shape of a real Spark query.</strong> Native
SortMergeJoin, HashJoin, and BroadcastHashJoin landed early; native columnar
and native shuffle,
broadcast nested loop joins, native window functions, native sampling, and
mixed partial/final aggregation
- followed. Supported expressions grew from 106 at 0.1.0 to 404 in 1.0, and
the introduction of the JVM
+ followed. Supported expressions grew from 106 at 0.1.0 to more than 400 in
1.0.0, and the introduction of the JVM
codegen dispatcher in 0.17.0 gave Comet a way to keep unsupported
expressions Arrow-native by running
Spark's own generated code inside the pipeline rather than falling back to
row-based execution.</li>
<li><strong>Spark support broadened.</strong> The 0.1.0
release targeted Spark 3.3, 3.4, and 3.5, with experimental 4.0. The
@@ -61,7 +61,7 @@ nineteen releases between then and now cover a lot of
ground:</p>
Spark 4 — moved from partial to a supported default across the natively
implemented surface.</li>
<li><strong>The ecosystem story filled in.</strong> Native
Iceberg support arrived in 0.10.0 and has been extended through
1.11 and format V3, native Parquet writes and CSV reads landed as
experimental features and matured,
- Azure joined S3 on the native cloud path, and 1.0 adds experimental
accelerated PyArrow UDFs alongside the
+ Azure joined S3 on the native cloud path, and 1.0.0 adds experimental
accelerated PyArrow UDFs alongside the
Java and Scala UDF support that shipped in 0.17.0.</li>
<li><strong>Correctness rigor grew alongside the surface
area.</strong> Early releases relied on fuzz testing to surface
divergences; later releases added a full Spark SQL test-suite run against
every supported Spark version,
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