This is an automated email from the ASF dual-hosted git repository.
github-actions[bot] pushed a commit to branch asf-staging
in repository https://gitbox.apache.org/repos/asf/datafusion-site.git
The following commit(s) were added to refs/heads/asf-staging by this push:
new 45ff506 Commit build products
45ff506 is described below
commit 45ff5062f5aa2834255c6e5141d78b4f3f212785
Author: Build Pelican (action) <[email protected]>
AuthorDate: Thu Aug 6 19:45:22 2026 +0000
Commit build products
---
blog/2026/08/07/datafusion-comet-1.0.0/index.html | 5 +++--
blog/feeds/all-en.atom.xml | 5 +++--
blog/feeds/blog.atom.xml | 5 +++--
blog/feeds/pmc.atom.xml | 5 +++--
4 files changed, 12 insertions(+), 8 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 bbe879c..10b6e0d 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
@@ -114,9 +114,10 @@ months later with support for 13 operators, and 106
expressions.</p>
<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 TPC-DS @ 3TB running in EKS.</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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark";>TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>AWS Labs also published performance results <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-gluten-velox-comet-benchmark";>comparing
Comet with Apache Gluten</a>, concluding that "Gluten + Velox v1.6.0 and
DataFusion Comet v0.16.0 deliver similar overall runtime".</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>Matt TBD</p>
+<p>A significant innovation that landed in 0.17.0 was to change the approach
to "native" acceleration. Rather than falling back to Spark row-based execution
whenever Comet lacked a native Rust implementation of an expression, Comet is
now able to execute Spark's own expression evaluation logic directly against
Arrow data in the Comet pipeline. This immediately expanded the number of
expressions that Comet could support without an expensive transition from
columnar to row-based data.</p>
<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>
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index 409a0da..02869f7 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -60,9 +60,10 @@ months later with support for 13 operators, and 106
expressions.</p>
<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 TPC-DS @ 3TB running in
EKS.</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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>AWS Labs also published performance results <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-gluten-velox-comet-benchmark">comparing
Comet with Apache Gluten</a>, concluding that "Gluten + Velox v1.6.0 and
DataFusion Comet v0.16.0 deliver similar overall runtime".</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>Matt TBD</p>
+<p>A significant innovation that landed in 0.17.0 was to change the
approach to "native" acceleration. Rather than falling back to Spark row-based
execution whenever Comet lacked a native Rust implementation of an expression,
Comet is now able to execute Spark's own expression evaluation logic directly
against Arrow data in the Comet pipeline. This immediately expanded the number
of expressions that Comet could support without an expensive transition from
columnar to row-based data [...]
<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>
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index 809b36a..141b1e2 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -60,9 +60,10 @@ months later with support for 13 operators, and 106
expressions.</p>
<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 TPC-DS @ 3TB running in
EKS.</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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>AWS Labs also published performance results <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-gluten-velox-comet-benchmark">comparing
Comet with Apache Gluten</a>, concluding that "Gluten + Velox v1.6.0 and
DataFusion Comet v0.16.0 deliver similar overall runtime".</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>Matt TBD</p>
+<p>A significant innovation that landed in 0.17.0 was to change the
approach to "native" acceleration. Rather than falling back to Spark row-based
execution whenever Comet lacked a native Rust implementation of an expression,
Comet is now able to execute Spark's own expression evaluation logic directly
against Arrow data in the Comet pipeline. This immediately expanded the number
of expressions that Comet could support without an expensive transition from
columnar to row-based data [...]
<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>
diff --git a/blog/feeds/pmc.atom.xml b/blog/feeds/pmc.atom.xml
index 8b170e6..b09f4e6 100644
--- a/blog/feeds/pmc.atom.xml
+++ b/blog/feeds/pmc.atom.xml
@@ -60,9 +60,10 @@ months later with support for 13 operators, and 106
expressions.</p>
<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 TPC-DS @ 3TB running in
EKS.</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 <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark">TPC-DS
@ 3TB running in EKS</a>.</p>
+<p>AWS Labs also published performance results <a
href="https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-gluten-velox-comet-benchmark">comparing
Comet with Apache Gluten</a>, concluding that "Gluten + Velox v1.6.0 and
DataFusion Comet v0.16.0 deliver similar overall runtime".</p>
<h3 id="codegen-dispatch">Codegen Dispatch<a class="headerlink"
href="#codegen-dispatch" title="Permanent link">¶</a></h3>
-<p>Matt TBD</p>
+<p>A significant innovation that landed in 0.17.0 was to change the
approach to "native" acceleration. Rather than falling back to Spark row-based
execution whenever Comet lacked a native Rust implementation of an expression,
Comet is now able to execute Spark's own expression evaluation logic directly
against Arrow data in the Comet pipeline. This immediately expanded the number
of expressions that Comet could support without an expensive transition from
columnar to row-based data [...]
<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>
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
