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new 6608f2e Commit build products
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commit 6608f2e91207945476ef33d8116faae0ab73b1d5
Author: Build Pelican (action) <priv...@infra.apache.org>
AuthorDate: Tue Sep 9 11:00:59 2025 +0000
Commit build products
---
blog/2025/09/10/dynamic-filters/index.html | 9 +++++----
...a-badaracco-pydantic-andrew-lamb-influxdata.atom.xml | 9 +++++----
blog/feeds/all-en.atom.xml | 9 +++++----
blog/feeds/blog.atom.xml | 9 +++++----
blog/images/dynamic-filters/join-performance.png | Bin 26776 -> 0 bytes
blog/images/dynamic-filters/join-performance.svg | 1 +
6 files changed, 21 insertions(+), 16 deletions(-)
diff --git a/blog/2025/09/10/dynamic-filters/index.html
b/blog/2025/09/10/dynamic-filters/index.html
index ea35fa7..c6e3b6d 100644
--- a/blog/2025/09/10/dynamic-filters/index.html
+++ b/blog/2025/09/10/dynamic-filters/index.html
@@ -303,13 +303,14 @@ key values from the build side. This simple approach is
fast to evaluate and the
filter improves performance significantly when combined with statistics
pruning,
late materialization, and other optimizations as shown in Figure 7.</p>
<div class="text-center">
-<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.png"
width="80%"/>
+<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.svg"
width="80%"/>
</div>
<p><strong>Figure 7</strong>: Join performance with and without dynamic
filters. In DataFusion
49.0.2 the join takes 2.5s, even with late materialization enabled. In
-DataFusion 50.0.0 with dynamic filters enabled the join takes only 0.7s, a 5x
-improvement, and with both dynamic filters and late materialization it takes
-only 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288";>discussion</a>
for more details.</p>
+DataFusion 50.0.0 with dynamic filters enabled (the default), the join takes
+only 0.7s, a 5x improvement. With both dynamic filters and late
materialization,
+DataFusion 50.0.0 takes 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288";>discussion</a>
for more
+details.</p>
<p>You can see dynamic join filters in action with the following example. </p>
<pre><code class="language-sql">-- create two tables: small_table with 1K rows
and large_table with 100K rows
copy (select i as k, i as v from generate_series(1, 1000) t(i)) to
'small_table.parquet';
diff --git
a/blog/feeds/adrian-garcia-badaracco-pydantic-andrew-lamb-influxdata.atom.xml
b/blog/feeds/adrian-garcia-badaracco-pydantic-andrew-lamb-influxdata.atom.xml
index 7b5c8e6..64cdffa 100644
---
a/blog/feeds/adrian-garcia-badaracco-pydantic-andrew-lamb-influxdata.atom.xml
+++
b/blog/feeds/adrian-garcia-badaracco-pydantic-andrew-lamb-influxdata.atom.xml
@@ -287,13 +287,14 @@ key values from the build side. This simple approach is
fast to evaluate and the
filter improves performance significantly when combined with statistics
pruning,
late materialization, and other optimizations as shown in Figure 7.</p>
<div class="text-center">
-<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.png"
width="80%"/>
+<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.svg"
width="80%"/>
</div>
<p><strong>Figure 7</strong>: Join performance with and
without dynamic filters. In DataFusion
49.0.2 the join takes 2.5s, even with late materialization enabled. In
-DataFusion 50.0.0 with dynamic filters enabled the join takes only 0.7s, a 5x
-improvement, and with both dynamic filters and late materialization it takes
-only 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more details.</p>
+DataFusion 50.0.0 with dynamic filters enabled (the default), the join takes
+only 0.7s, a 5x improvement. With both dynamic filters and late
materialization,
+DataFusion 50.0.0 takes 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more
+details.</p>
<p>You can see dynamic join filters in action with the following
example. </p>
<pre><code class="language-sql">-- create two tables: small_table
with 1K rows and large_table with 100K rows
copy (select i as k, i as v from generate_series(1, 1000) t(i)) to
'small_table.parquet';
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index b9537ae..5cb00a5 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -287,13 +287,14 @@ key values from the build side. This simple approach is
fast to evaluate and the
filter improves performance significantly when combined with statistics
pruning,
late materialization, and other optimizations as shown in Figure 7.</p>
<div class="text-center">
-<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.png"
width="80%"/>
+<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.svg"
width="80%"/>
</div>
<p><strong>Figure 7</strong>: Join performance with and
without dynamic filters. In DataFusion
49.0.2 the join takes 2.5s, even with late materialization enabled. In
-DataFusion 50.0.0 with dynamic filters enabled the join takes only 0.7s, a 5x
-improvement, and with both dynamic filters and late materialization it takes
-only 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more details.</p>
+DataFusion 50.0.0 with dynamic filters enabled (the default), the join takes
+only 0.7s, a 5x improvement. With both dynamic filters and late
materialization,
+DataFusion 50.0.0 takes 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more
+details.</p>
<p>You can see dynamic join filters in action with the following
example. </p>
<pre><code class="language-sql">-- create two tables: small_table
with 1K rows and large_table with 100K rows
copy (select i as k, i as v from generate_series(1, 1000) t(i)) to
'small_table.parquet';
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index 8f7ca5b..200124d 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -287,13 +287,14 @@ key values from the build side. This simple approach is
fast to evaluate and the
filter improves performance significantly when combined with statistics
pruning,
late materialization, and other optimizations as shown in Figure 7.</p>
<div class="text-center">
-<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.png"
width="80%"/>
+<img alt="Join Performance Improvements with Dynamic Filters"
class="img-responsive" src="/blog/images/dynamic-filters/join-performance.svg"
width="80%"/>
</div>
<p><strong>Figure 7</strong>: Join performance with and
without dynamic filters. In DataFusion
49.0.2 the join takes 2.5s, even with late materialization enabled. In
-DataFusion 50.0.0 with dynamic filters enabled the join takes only 0.7s, a 5x
-improvement, and with both dynamic filters and late materialization it takes
-only 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more details.</p>
+DataFusion 50.0.0 with dynamic filters enabled (the default), the join takes
+only 0.7s, a 5x improvement. With both dynamic filters and late
materialization,
+DataFusion 50.0.0 takes 0.1s, a 25x improvement. See this <a
href="https://github.com/apache/datafusion-site/pull/103#issuecomment-3262612288">discussion</a>
for more
+details.</p>
<p>You can see dynamic join filters in action with the following
example. </p>
<pre><code class="language-sql">-- create two tables: small_table
with 1K rows and large_table with 100K rows
copy (select i as k, i as v from generate_series(1, 1000) t(i)) to
'small_table.parquet';
diff --git a/blog/images/dynamic-filters/join-performance.png
b/blog/images/dynamic-filters/join-performance.png
deleted file mode 100644
index 3762012..0000000
Binary files a/blog/images/dynamic-filters/join-performance.png and /dev/null
differ
diff --git a/blog/images/dynamic-filters/join-performance.svg
b/blog/images/dynamic-filters/join-performance.svg
new file mode 100644
index 0000000..e7fb986
--- /dev/null
+++ b/blog/images/dynamic-filters/join-performance.svg
@@ -0,0 +1 @@
+<svg version="1.1" viewBox="0.0 0.0 960.0 540.0" fill="none" stroke="none"
stroke-linecap="square" stroke-miterlimit="10"
xmlns:xlink="http://www.w3.org/1999/xlink";
xmlns="http://www.w3.org/2000/svg";><clipPath id="g37cfe6f370d_1_10.0"><path
d="m0 0l960.0 0l0 540.0l-960.0 0l0 -540.0z" clip-rule="nonzero"/></clipPath><g
clip-path="url(#g37cfe6f370d_1_10.0)"><path fill="#ffffff" d="m0 0l960.0 0l0
540.0l-960.0 0z" fill-rule="evenodd"/><path fill="#000000" fill-opacity="0.0"
d="m0 0l873.3165 [...]
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