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commit 54dc9edf999e8bee4ac6deaa462c239402f6334a
Author: Build Pelican (action) <[email protected]>
AuthorDate: Fri Mar 20 22:11:47 2026 +0000
Commit build products
---
blog/2026/03/20/writing-table-providers/index.html | 6 +-
blog/author/tim-saucer.html | 64 ---
blog/author/timsaucer.html | 32 ++
blog/category/blog.html | 4 +-
blog/feed.xml | 4 +-
blog/feeds/all-en.atom.xml | 2 +-
blog/feeds/blog.atom.xml | 2 +-
blog/feeds/tim-saucer.atom.xml | 597 ---------------------
blog/feeds/tim-saucer.rss.xml | 24 -
blog/feeds/timsaucer.atom.xml | 597 ++++++++++++++++++++-
blog/feeds/timsaucer.rss.xml | 24 +-
blog/index.html | 4 +-
12 files changed, 662 insertions(+), 698 deletions(-)
diff --git a/blog/2026/03/20/writing-table-providers/index.html
b/blog/2026/03/20/writing-table-providers/index.html
index 0f5312f..8636a6f 100644
--- a/blog/2026/03/20/writing-table-providers/index.html
+++ b/blog/2026/03/20/writing-table-providers/index.html
@@ -4,7 +4,7 @@
<meta charset="utf-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>"Writing Custom Table Providers in Apache DataFusion" - Apache
DataFusion Blog</title>
+ <title>Writing Custom Table Providers in Apache DataFusion - Apache
DataFusion Blog</title>
<link href="/blog/css/bootstrap.min.css" rel="stylesheet">
<link href="/blog/css/fontawesome.all.min.css" rel="stylesheet">
<link href="/blog/css/headerlink.css" rel="stylesheet">
@@ -46,9 +46,9 @@
<div class="row justify-content-center">
<div class="col-12 col-md-8 main-content">
<h1>
- "Writing Custom Table Providers in Apache DataFusion"
+ Writing Custom Table Providers in Apache DataFusion
</h1>
- <p>Posted on: Fri 20 March 2026 by Tim Saucer</p>
+ <p>Posted on: Fri 20 March 2026 by timsaucer</p>
<aside class="toc-container d-md-none mb-2">
<div class="toc"><span class="toctitle">Contents</span><ul>
diff --git a/blog/author/tim-saucer.html b/blog/author/tim-saucer.html
deleted file mode 100644
index c9faea3..0000000
--- a/blog/author/tim-saucer.html
+++ /dev/null
@@ -1,64 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
- <title>Apache DataFusion Blog - Articles by Tim Saucer</title>
- <meta charset="utf-8" />
- <meta name="generator" content="Pelican" />
- <link href="https://datafusion.apache.org/blog/feed.xml";
type="application/rss+xml" rel="alternate" title="Apache DataFusion Blog RSS
Feed" />
-</head>
-
-<body id="index" class="home">
- <header id="banner" class="body">
- <h1><a href="https://datafusion.apache.org/blog/";>Apache
DataFusion Blog</a></h1>
- </header><!-- /#banner -->
- <nav id="menu"><ul>
- <li><a
href="https://datafusion.apache.org/blog/pages/about.html";>About</a></li>
- <li><a
href="https://datafusion.apache.org/blog/pages/index.html";>index</a></li>
- <li><a
href="https://datafusion.apache.org/blog/category/blog.html";>blog</a></li>
- </ul></nav><!-- /#menu -->
-<section id="content">
-<h2>Articles by Tim Saucer</h2>
-
-<ol id="post-list">
- <li><article class="hentry">
- <header> <h2 class="entry-title"><a
href="https://datafusion.apache.org/blog/2026/03/20/writing-table-providers";
rel="bookmark" title="Permalink to "Writing Custom Table Providers in Apache
DataFusion"">"Writing Custom Table Providers in Apache DataFusion"</a></h2>
</header>
- <footer class="post-info">
- <time class="published"
datetime="2026-03-20T00:00:00+00:00"> Fri 20 March 2026 </time>
- <address class="vcard author">By
- <a class="url fn"
href="https://datafusion.apache.org/blog/author/tim-saucer.html";>Tim Saucer</a>
- </address>
- </footer><!-- /.post-info -->
- <div class="entry-content"> <!--
-{% comment %}
-Licensed to the Apache Software Foundation (ASF) under one or more
-contributor license agreements. See the NOTICE file distributed with
-this work for additional information regarding copyright ownership.
-The ASF licenses this file to you under the Apache License, Version 2.0
-(the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-{% endcomment %}
--->
-
-<p>One of DataFusion's greatest strengths is its extensibility. If your data
lives
-in a custom format, behind an API, or in a system that DataFusion does not
-natively support, you can teach DataFusion to read it by implementing a
-<strong>custom table provider</strong>. This post walks through the three
layers you …</p> </div><!-- /.entry-content -->
- </article></li>
-</ol><!-- /#posts-list -->
-</section><!-- /#content -->
- <footer id="contentinfo" class="body">
- <address id="about" class="vcard body">
- Proudly powered by <a
href="https://getpelican.com/";>Pelican</a>,
- which takes great advantage of <a
href="https://www.python.org/";>Python</a>.
- </address><!-- /#about -->
- </footer><!-- /#contentinfo -->
-</body>
-</html>
\ No newline at end of file
diff --git a/blog/author/timsaucer.html b/blog/author/timsaucer.html
index 2711c4a..aab5e7d 100644
--- a/blog/author/timsaucer.html
+++ b/blog/author/timsaucer.html
@@ -20,6 +20,38 @@
<h2>Articles by timsaucer</h2>
<ol id="post-list">
+ <li><article class="hentry">
+ <header> <h2 class="entry-title"><a
href="https://datafusion.apache.org/blog/2026/03/20/writing-table-providers";
rel="bookmark" title="Permalink to Writing Custom Table Providers in Apache
DataFusion">Writing Custom Table Providers in Apache DataFusion</a></h2>
</header>
+ <footer class="post-info">
+ <time class="published"
datetime="2026-03-20T00:00:00+00:00"> Fri 20 March 2026 </time>
+ <address class="vcard author">By
+ <a class="url fn"
href="https://datafusion.apache.org/blog/author/timsaucer.html";>timsaucer</a>
+ </address>
+ </footer><!-- /.post-info -->
+ <div class="entry-content"> <!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+<p>One of DataFusion's greatest strengths is its extensibility. If your data
lives
+in a custom format, behind an API, or in a system that DataFusion does not
+natively support, you can teach DataFusion to read it by implementing a
+<strong>custom table provider</strong>. This post walks through the three
layers you …</p> </div><!-- /.entry-content -->
+ </article></li>
<li><article class="hentry">
<header> <h2 class="entry-title"><a
href="https://datafusion.apache.org/blog/2025/03/30/datafusion-python-46.0.0";
rel="bookmark" title="Permalink to Apache DataFusion Python 46.0.0
Released">Apache DataFusion Python 46.0.0 Released</a></h2> </header>
<footer class="post-info">
diff --git a/blog/category/blog.html b/blog/category/blog.html
index 9c32175..f8add02 100644
--- a/blog/category/blog.html
+++ b/blog/category/blog.html
@@ -72,11 +72,11 @@ figcaption {
<p>Reading data efficiently means touching as little data as possible. The
fastest I/O is the I/O you never make …</p> </div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
- <header> <h2 class="entry-title"><a
href="https://datafusion.apache.org/blog/2026/03/20/writing-table-providers";
rel="bookmark" title="Permalink to "Writing Custom Table Providers in Apache
DataFusion"">"Writing Custom Table Providers in Apache DataFusion"</a></h2>
</header>
+ <header> <h2 class="entry-title"><a
href="https://datafusion.apache.org/blog/2026/03/20/writing-table-providers";
rel="bookmark" title="Permalink to Writing Custom Table Providers in Apache
DataFusion">Writing Custom Table Providers in Apache DataFusion</a></h2>
</header>
<footer class="post-info">
<time class="published"
datetime="2026-03-20T00:00:00+00:00"> Fri 20 March 2026 </time>
<address class="vcard author">By
- <a class="url fn"
href="https://datafusion.apache.org/blog/author/tim-saucer.html";>Tim Saucer</a>
+ <a class="url fn"
href="https://datafusion.apache.org/blog/author/timsaucer.html";>timsaucer</a>
</address>
</footer><!-- /.post-info -->
<div class="entry-content"> <!--
diff --git a/blog/feed.xml b/blog/feed.xml
index dafc16b..6b86d5b 100644
--- a/blog/feed.xml
+++ b/blog/feed.xml
@@ -39,7 +39,7 @@ figcaption {
}
</style>
<p><em>Xudong Wang, <a
href="https://www.massive.com/">Massive</a></em></p>
-<p>Reading data efficiently means touching as little data as possible.
The fastest I/O is the I/O you never make …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>xudong</dc:creator><pubDate>Fri, 20
Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/limit-pruning</guid><category>blog</category></item><item><title>"Writing
Custom Table Providers in Apache DataFusion"</title><link>https://dat [...]
+<p>Reading data efficiently means touching as little data as possible.
The fastest I/O is the I/O you never make …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>xudong</dc:creator><pubDate>Fri, 20
Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/limit-pruning</guid><category>blog</category></item><item><title>Writing
Custom Table Providers in Apache DataFusion</title><link>https://dataf [...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
@@ -61,7 +61,7 @@ limitations under the License.
<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
in a custom format, behind an API, or in a system that DataFusion does not
natively support, you can teach DataFusion to read it by implementing a
-<strong>custom table provider</strong>. This post walks through
the three layers you …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>Tim
Saucer</dc:creator><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/writing-table-providers</guid><category>blog</category></item><item><title>Optimizing
SQL CASE Expression Evaluation</title><link>https://datafusion.apache.org/b
[...]
+<strong>custom table provider</strong>. This post walks through
the three layers you …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>timsaucer</dc:creator><pubDate>Fri,
20 Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/writing-table-providers</guid><category>blog</category></item><item><title>Optimizing
SQL CASE Expression Evaluation</title><link>https://datafusion.apache.org/bl
[...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
diff --git a/blog/feeds/all-en.atom.xml b/blog/feeds/all-en.atom.xml
index 00a7bfd..bd45c8b 100644
--- a/blog/feeds/all-en.atom.xml
+++ b/blog/feeds/all-en.atom.xml
@@ -310,7 +310,7 @@ limit_pruned_row_groups=3 total → 1 matched
<p><a href="https://datafusion.apache.org/">Apache
DataFusion</a> is an extensible query engine, written in <a
href="https://www.rust-lang.org/">Rust</a>, that uses <a
href="https://arrow.apache.org">Apache Arrow</a> as its in-memory
format. DataFusion is used by developers to create new, fast, data-centric
systems such as databases, dataframe libraries, and machine learning and
streaming applications.</p>
<p>DataFusion's core thesis is that, as a community, together we can
build much more advanced technology than any of us as individuals or companies
could build alone.</p>
<h2 id="how-to-get-involved">How to Get Involved<a class="headerlink"
href="#how-to-get-involved" title="Permanent link">¶</a></h2>
-<p>If you are interested in contributing, we would love to have you. You
can try out DataFusion on some of your own data and projects and let us know
how it goes, contribute suggestions, documentation, bug reports, or a PR with
documentation, tests, or code. A list of open issues suitable for beginners is
<a
href="https://github.com/apache/datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>,
and you can find out how to reach us on the <a [...]
+<p>If you are interested in contributing, we would love to have you. You
can try out DataFusion on some of your own data and projects and let us know
how it goes, contribute suggestions, documentation, bug reports, or a PR with
documentation, tests, or code. A list of open issues suitable for beginners is
<a
href="https://github.com/apache/datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>,
and you can find out how to reach us on the <a [...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
diff --git a/blog/feeds/blog.atom.xml b/blog/feeds/blog.atom.xml
index 7f8f4a9..9ab9a85 100644
--- a/blog/feeds/blog.atom.xml
+++ b/blog/feeds/blog.atom.xml
@@ -310,7 +310,7 @@ limit_pruned_row_groups=3 total → 1 matched
<p><a href="https://datafusion.apache.org/">Apache
DataFusion</a> is an extensible query engine, written in <a
href="https://www.rust-lang.org/">Rust</a>, that uses <a
href="https://arrow.apache.org">Apache Arrow</a> as its in-memory
format. DataFusion is used by developers to create new, fast, data-centric
systems such as databases, dataframe libraries, and machine learning and
streaming applications.</p>
<p>DataFusion's core thesis is that, as a community, together we can
build much more advanced technology than any of us as individuals or companies
could build alone.</p>
<h2 id="how-to-get-involved">How to Get Involved<a class="headerlink"
href="#how-to-get-involved" title="Permanent link">¶</a></h2>
-<p>If you are interested in contributing, we would love to have you. You
can try out DataFusion on some of your own data and projects and let us know
how it goes, contribute suggestions, documentation, bug reports, or a PR with
documentation, tests, or code. A list of open issues suitable for beginners is
<a
href="https://github.com/apache/datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>,
and you can find out how to reach us on the <a [...]
+<p>If you are interested in contributing, we would love to have you. You
can try out DataFusion on some of your own data and projects and let us know
how it goes, contribute suggestions, documentation, bug reports, or a PR with
documentation, tests, or code. A list of open issues suitable for beginners is
<a
href="https://github.com/apache/datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>,
and you can find out how to reach us on the <a [...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
diff --git a/blog/feeds/tim-saucer.atom.xml b/blog/feeds/tim-saucer.atom.xml
deleted file mode 100644
index 81bd8f6..0000000
--- a/blog/feeds/tim-saucer.atom.xml
+++ /dev/null
@@ -1,597 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<feed xmlns="http://www.w3.org/2005/Atom";><title>Apache DataFusion Blog - Tim
Saucer</title><link href="https://datafusion.apache.org/blog/";
rel="alternate"></link><link
href="https://datafusion.apache.org/blog/feeds/tim-saucer.atom.xml";
rel="self"></link><id>https://datafusion.apache.org/blog/</id><updated>2026-03-20T00:00:00+00:00</updated><subtitle></subtitle><entry><title>"Writing
Custom Table Providers in Apache DataFusion"</title><link
href="https://datafusion.apache.org/blog/2026/ [...]
-{% comment %}
-Licensed to the Apache Software Foundation (ASF) under one or more
-contributor license agreements. See the NOTICE file distributed with
-this work for additional information regarding copyright ownership.
-The ASF licenses this file to you under the Apache License, Version 2.0
-(the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-{% endcomment %}
--->
-
-<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
-in a custom format, behind an API, or in a system that DataFusion does not
-natively support, you can teach DataFusion to read it by implementing a
-<strong>custom table provider</strong>. This post walks through
the three layers you …</p></summary><content type="html"><!--
-{% comment %}
-Licensed to the Apache Software Foundation (ASF) under one or more
-contributor license agreements. See the NOTICE file distributed with
-this work for additional information regarding copyright ownership.
-The ASF licenses this file to you under the Apache License, Version 2.0
-(the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-{% endcomment %}
--->
-
-<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
-in a custom format, behind an API, or in a system that DataFusion does not
-natively support, you can teach DataFusion to read it by implementing a
-<strong>custom table provider</strong>. This post walks through
the three layers you need to
-understand and explains where your work should actually happen.</p>
-<h2 id="the-three-layers">The Three Layers<a class="headerlink"
href="#the-three-layers" title="Permanent link">¶</a></h2>
-<hr/>
-<p>When DataFusion executes a query against a table, three abstractions
collaborate
-to produce results:</p>
-<ol>
-<li><strong>[<code>TableProvider</code>]</strong>
-- Describes the table (schema, capabilities) and
- produces an execution plan when queried.</li>
-<li><strong>[<code>ExecutionPlan</code>]</strong>
-- Describes <em>how</em> to compute the result: partitioning,
- ordering, and child plan relationships.</li>
-<li><strong>[<code>SendableRecordBatchStream</code>]</strong>
-- The async stream that <em>actually does the
- work</em>, yielding <code>RecordBatch</code>es one at a
time.</li>
-</ol>
-<p>Think of these as a funnel:
<code>TableProvider::scan()</code> is called once during
-planning to create an <code>ExecutionPlan</code>, then
<code>ExecutionPlan::execute()</code> is called
-once per partition to create a stream, and those streams are where rows are
-actually produced during execution.</p>
-<h2 id="layer-1-tableprovider">Layer 1: TableProvider<a
class="headerlink" href="#layer-1-tableprovider" title="Permanent
link">¶</a></h2>
-<hr/>
-<p>A [<code>TableProvider</code>] represents a queryable
data source. For a minimal read-only
-table, you need four methods:</p>
-<pre><code class="language-rust">impl TableProvider for MyTable {
- fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
-
- fn schema(&amp;self) -&gt; SchemaRef {
- Arc::clone(&amp;self.schema)
- }
-
- fn table_type(&amp;self) -&gt; TableType {
- TableType::Base
- }
-
- async fn scan(
- &amp;self,
- state: &amp;dyn Session,
- projection: Option&lt;&amp;Vec&lt;usize&gt;&gt;,
- filters: &amp;[Expr],
- limit: Option&lt;usize&gt;,
- ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
- // Build and return an ExecutionPlan -- keep this lightweight!
- Ok(Arc::new(MyExecPlan::new(
- Arc::clone(&amp;self.schema),
- projection,
- limit,
- )))
- }
-}
-</code></pre>
-<p>The <code>scan</code> method is the heart of
<code>TableProvider</code>. It receives three pushdown
-hints from the optimizer, each reducing the amount of data your source needs
-to produce:</p>
-<ul>
-<li><strong><code>projection</code></strong> --
Which columns are needed. This reduces the <strong>width</strong> of
- the output. If your source supports it, read only these columns rather than
- the full schema.</li>
-<li><strong><code>filters</code></strong> --
Predicates the engine would like you to apply during the
- scan. This reduces the <strong>number of rows</strong> by
skipping data that does not
- match. Implement <code>supports_filters_pushdown</code> to
advertise which filters you
- can handle.</li>
-<li><strong><code>limit</code></strong> -- A row
count cap. This also reduces the <strong>number of rows</strong> --
- if you can stop reading early once you have produced enough rows, this avoids
- unnecessary work.</li>
-</ul>
-<h3 id="keep-scan-lightweight">Keep <code>scan()</code>
Lightweight<a class="headerlink" href="#keep-scan-lightweight"
title="Permanent link">¶</a></h3>
-<p>This is a critical point:
<strong><code>scan()</code> runs during planning, not
execution.</strong> It
-should return quickly. Best practices are to avoid performing I/O, network
-calls, or heavy computation here. The <code>scan</code> method's
job is to <em>describe</em> how
-the data will be produced, not to produce it. All the real work belongs in the
-stream (Layer 3).</p>
-<p>A common pitfall is to fetch data or open connections in
<code>scan()</code>. This blocks
-the planning thread and can cause timeouts or deadlocks, especially if the
query
-involves multiple tables or subqueries that all need to be planned before
-execution begins.</p>
-<h3 id="existing-implementations-to-learn-from">Existing Implementations
to Learn From<a class="headerlink"
href="#existing-implementations-to-learn-from" title="Permanent
link">¶</a></h3>
-<p>DataFusion ships several <code>TableProvider</code>
implementations that are excellent
-references:</p>
-<ul>
-<li><strong>[<code>MemTable</code>]</strong> --
Holds data in memory as
<code>Vec&lt;RecordBatch&gt;</code>. The simplest
- possible provider; great for tests and small datasets.</li>
-<li><strong>[<code>StreamTable</code>]</strong>
-- Wraps a user-provided stream factory. Useful when your
- data arrives as a continuous stream (e.g., from Kafka or a
socket).</li>
-<li><strong>[<code>SortedTableProvider</code>]</strong>
-- Wraps another <code>TableProvider</code> and advertises a
- known sort order, enabling the optimizer to skip redundant sorts.</li>
-</ul>
-<h2 id="layer-2-executionplan">Layer 2: ExecutionPlan<a
class="headerlink" href="#layer-2-executionplan" title="Permanent
link">¶</a></h2>
-<hr/>
-<p>An [<code>ExecutionPlan</code>] is a node in the physical
query plan tree. Your table
-provider's <code>scan()</code> method returns one. The required
methods are:</p>
-<pre><code class="language-rust">impl ExecutionPlan for MyExecPlan
{
- fn name(&amp;self) -&gt; &amp;str { "MyExecPlan" }
-
- fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
-
- fn properties(&amp;self) -&gt; &amp;PlanProperties {
- &amp;self.properties
- }
-
- fn children(&amp;self) -&gt; Vec&lt;&amp;Arc&lt;dyn
ExecutionPlan&gt;&gt; {
- vec![] // Leaf node -- no children
- }
-
- fn with_new_children(
- self: Arc&lt;Self&gt;,
- children: Vec&lt;Arc&lt;dyn ExecutionPlan&gt;&gt;,
- ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
- assert!(children.is_empty());
- Ok(self)
- }
-
- fn execute(
- &amp;self,
- partition: usize,
- context: Arc&lt;TaskContext&gt;,
- ) -&gt; Result&lt;SendableRecordBatchStream&gt; {
- // This is where you build and return your stream
- // ...
- }
-}
-</code></pre>
-<p>The key properties to set correctly in
[<code>PlanProperties</code>] are <strong>output
-partitioning</strong> and <strong>output
ordering</strong>.</p>
-<p><strong>Output partitioning</strong> tells the engine how
many partitions your data has,
-which determines parallelism. If your source naturally partitions data (e.g.,
-by file or by shard), expose that here.</p>
-<p><strong>Output ordering</strong> declares whether your
data is naturally sorted. This
-enables the optimizer to avoid inserting a <code>SortExec</code>
when a query requires
-ordered data. Getting this right can be a significant performance
win.</p>
-<h3 id="partitioning-strategies">Partitioning Strategies<a
class="headerlink" href="#partitioning-strategies" title="Permanent
link">¶</a></h3>
-<p>Since <code>execute()</code> is called once per
partition, partitioning directly controls
-the parallelism of your table scan. Each partition runs on its own task, so
-more partitions means more concurrent work -- up to the number of available
-cores.</p>
-<p>Consider how your data source naturally divides its data:</p>
-<ul>
-<li><strong>By file or object:</strong> If you are reading
from S3, each file can be a
- partition. DataFusion will read them in parallel.</li>
-<li><strong>By shard or region:</strong> If your source is a
sharded database, each shard
- maps naturally to a partition.</li>
-<li><strong>By key range:</strong> If your data is keyed
(e.g., by timestamp or customer ID),
- you can split it into ranges.</li>
-</ul>
-<p>Getting partitioning right matters because it affects everything
downstream in
-the plan. When DataFusion needs to perform an aggregation or join, it
-repartitions data by hashing the relevant columns. If your source already
-produces data partitioned by the join or group-by key, DataFusion can skip the
-repartition step entirely -- avoiding a potentially expensive
shuffle.</p>
-<p>For example, if you are building a table provider for a system that
stores
-data partitioned by <code>customer_id</code>, and a common query
groups by <code>customer_id</code>:</p>
-<pre><code class="language-sql">SELECT customer_id, SUM(amount)
-FROM my_table
-GROUP BY customer_id;
-</code></pre>
-<p>If you declare your output partitioning as
<code>Hash([customer_id], N)</code>, the
-optimizer recognizes that the data is already distributed correctly for the
-aggregation and eliminates the <code>RepartitionExec</code> that
would otherwise appear
-in the plan. You can verify this with <code>EXPLAIN</code> (more
on this below).</p>
-<p>Conversely, if you report
<code>UnknownPartitioning</code>, DataFusion must assume the
-worst case and will always insert repartitioning operators as needed.</p>
-<h3 id="keep-execute-lightweight-too">Keep
<code>execute()</code> Lightweight Too<a class="headerlink"
href="#keep-execute-lightweight-too" title="Permanent
link">¶</a></h3>
-<p>Like <code>scan()</code>, the
<code>execute()</code> method should construct and return a stream
-without doing heavy work. The actual data production happens when the stream
-is polled. Do not block on async operations here -- build the stream and let
-the runtime drive it.</p>
-<h3 id="existing-implementations-to-learn-from_1">Existing
Implementations to Learn From<a class="headerlink"
href="#existing-implementations-to-learn-from_1" title="Permanent
link">¶</a></h3>
-<ul>
-<li><strong>[<code>StreamingTableExec</code>]</strong>
-- Executes a streaming table scan. It takes a
- stream factory (a closure that produces streams) and handles partitioning.
- Good reference for wrapping external streams.</li>
-<li><strong>[<code>DataSourceExec</code>]</strong>
-- The execution plan behind DataFusion's built-in file
- scanning (Parquet, CSV, JSON). It demonstrates sophisticated partitioning,
- filter pushdown, and projection pushdown.</li>
-</ul>
-<h2 id="layer-3-sendablerecordbatchstream">Layer 3:
SendableRecordBatchStream<a class="headerlink"
href="#layer-3-sendablerecordbatchstream" title="Permanent
link">¶</a></h2>
-<hr/>
-<p>[<code>SendableRecordBatchStream</code>] is where the
real work happens. It is defined as:</p>
-<pre><code class="language-rust">type SendableRecordBatchStream =
- Pin&lt;Box&lt;dyn RecordBatchStream&lt;Item =
Result&lt;RecordBatch&gt;&gt; + Send&gt;&gt;;
-</code></pre>
-<p>This is an async stream of <code>RecordBatch</code>es
that can be sent across threads. When
-the DataFusion runtime polls this stream, your code runs: reading files,
calling
-APIs, transforming data, etc.</p>
-<h3 id="using-recordbatchstreamadapter">Using
RecordBatchStreamAdapter<a class="headerlink"
href="#using-recordbatchstreamadapter" title="Permanent
link">¶</a></h3>
-<p>The easiest way to create a
<code>SendableRecordBatchStream</code> is with
-[<code>RecordBatchStreamAdapter</code>]. It bridges any
<code>futures::Stream&lt;Item =
-Result&lt;RecordBatch&gt;&gt;</code> into the
<code>SendableRecordBatchStream</code> type:</p>
-<pre><code class="language-rust">use
datafusion::physical_plan::stream::RecordBatchStreamAdapter;
-
-fn execute(
- &amp;self,
- partition: usize,
- context: Arc&lt;TaskContext&gt;,
-) -&gt; Result&lt;SendableRecordBatchStream&gt; {
- let schema = self.schema();
- let config = self.config.clone();
-
- let stream = futures::stream::once(async move {
- // ALL the heavy work happens here, inside the stream:
- // - Open connections
- // - Read data from external sources
- // - Transform and batch the results
- let batches = fetch_data_from_source(&amp;config).await?;
- Ok(batches)
- })
- .flat_map(|result| match result {
- Ok(batch) =&gt; futures::stream::iter(vec![Ok(batch)]),
- Err(e) =&gt; futures::stream::iter(vec![Err(e)]),
- });
-
- Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
-}
-</code></pre>
-<h3 id="cpu-intensive-work-use-a-separate-thread-pool">CPU-Intensive
Work: Use a Separate Thread Pool<a class="headerlink"
href="#cpu-intensive-work-use-a-separate-thread-pool" title="Permanent
link">¶</a></h3>
-<p>If your stream performs CPU-intensive work (parsing, decompression,
complex
-transformations), avoid blocking the tokio runtime. Instead, offload to a
-dedicated thread pool and send results back through a channel:</p>
-<pre><code class="language-rust">fn execute(
- &amp;self,
- partition: usize,
- context: Arc&lt;TaskContext&gt;,
-) -&gt; Result&lt;SendableRecordBatchStream&gt; {
- let schema = self.schema();
- let config = self.config.clone();
-
- let (tx, rx) = tokio::sync::mpsc::channel(2);
-
- // Spawn CPU-heavy work on a blocking thread pool
- tokio::task::spawn_blocking(move || {
- let batches = generate_data(&amp;config);
- for batch in batches {
- if tx.blocking_send(Ok(batch)).is_err() {
- break; // Receiver dropped, query was cancelled
- }
- }
- });
-
- let stream = tokio_stream::wrappers::ReceiverStream::new(rx);
- Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
-}
-</code></pre>
-<p>This pattern keeps the async runtime responsive while your data
generation
-runs on its own threads.</p>
-<h2 id="where-should-the-work-happen">Where Should the Work Happen?<a
class="headerlink" href="#where-should-the-work-happen" title="Permanent
link">¶</a></h2>
-<hr/>
-<p>This table summarizes what belongs at each layer:</p>
-<table class="table">
-<thead>
-<tr>
-<th>Layer</th>
-<th>Runs During</th>
-<th>Should Do</th>
-<th>Should NOT Do</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><code>TableProvider::scan()</code></td>
-<td>Planning</td>
-<td>Build an <code>ExecutionPlan</code> with
metadata</td>
-<td>I/O, network calls, heavy computation</td>
-</tr>
-<tr>
-<td><code>ExecutionPlan::execute()</code></td>
-<td>Execution (once per partition)</td>
-<td>Construct a stream, set up channels</td>
-<td>Block on async work, read data</td>
-</tr>
-<tr>
-<td><code>RecordBatchStream</code> (polling)</td>
-<td>Execution</td>
-<td>All I/O, computation, data production</td>
-<td>--</td>
-</tr>
-</tbody>
-</table>
-<p>The guiding principle: <strong>push work as late as
possible.</strong> Planning should be
-fast so the optimizer can do its job. Execution setup should be fast so all
-partitions can start promptly. The stream is where you spend time producing
-data.</p>
-<h3 id="why-this-matters">Why This Matters<a class="headerlink"
href="#why-this-matters" title="Permanent link">¶</a></h3>
-<p>When <code>scan()</code> does heavy work, several
problems arise:</p>
-<ol>
-<li><strong>Planning becomes slow.</strong> If a query
touches 10 tables and each <code>scan()</code>
- takes 500ms, planning alone takes 5 seconds before any data
flows.</li>
-<li><strong>The optimizer cannot help.</strong> The
optimizer runs between planning and
- execution. If you have already fetched data during planning, optimizations
- like predicate pushdown or partition pruning cannot reduce the
work.</li>
-<li><strong>Resource management breaks down.</strong>
DataFusion manages concurrency and
- memory during execution. Work done during planning bypasses these
controls.</li>
-</ol>
-<h2 id="filter-pushdown-doing-less-work">Filter Pushdown: Doing Less
Work<a class="headerlink" href="#filter-pushdown-doing-less-work"
title="Permanent link">¶</a></h2>
-<hr/>
-<p>One of the most impactful optimizations you can add to a custom table
provider
-is <strong>filter pushdown</strong> -- letting the source skip
data that the query does not
-need, rather than reading everything and filtering it afterward.</p>
-<h3 id="how-filter-pushdown-works">How Filter Pushdown Works<a
class="headerlink" href="#how-filter-pushdown-works" title="Permanent
link">¶</a></h3>
-<p>When DataFusion plans a query with a <code>WHERE</code>
clause, it passes the filter
-predicates to your <code>scan()</code> method as the
<code>filters</code> parameter. By default,
-DataFusion assumes your provider cannot handle any filters and inserts a
-<code>FilterExec</code> node above your scan to apply them. But if
your source <em>can</em>
-evaluate some predicates during scanning -- for example, by skipping files,
-partitions, or row groups that cannot match -- you can eliminate a huge amount
-of unnecessary I/O.</p>
-<p>To opt in, implement
<code>supports_filters_pushdown</code>:</p>
-<pre><code class="language-rust">fn supports_filters_pushdown(
- &amp;self,
- filters: &amp;[&amp;Expr],
-) -&gt;
Result&lt;Vec&lt;TableProviderFilterPushDown&gt;&gt; {
- Ok(filters.iter().map(|f| {
- match f {
- // We can fully evaluate equality filters on
- // the partition column at the source
- Expr::BinaryExpr(BinaryExpr {
- left, op: Operator::Eq, right
- }) if is_partition_column(left) || is_partition_column(right)
=&gt; {
- TableProviderFilterPushDown::Exact
- }
- // All other filters: let DataFusion handle them
- _ =&gt; TableProviderFilterPushDown::Unsupported,
- }
- }).collect())
-}
-</code></pre>
-<p>The three possible responses for each filter are:</p>
-<ul>
-<li><strong><code>Exact</code></strong> -- Your
source guarantees that no output rows will have a false
- value for this predicate. Because the filter is fully evaluated at the
source,
- DataFusion will <strong>not</strong> add a
<code>FilterExec</code> for it.</li>
-<li><strong><code>Inexact</code></strong> --
Your source has the ability to reduce the data produced, but
- the output may still include rows that do not satisfy the predicate. For
- example, you might skip entire files based on metadata statistics but not
- filter individual rows within a file. DataFusion will still add a
<code>FilterExec</code>
- above your scan to remove any remaining rows that slipped through.</li>
-<li><strong><code>Unsupported</code></strong> --
Your source ignores this filter entirely. DataFusion
- handles it.</li>
-</ul>
-<h3 id="why-filter-pushdown-matters">Why Filter Pushdown Matters<a
class="headerlink" href="#why-filter-pushdown-matters" title="Permanent
link">¶</a></h3>
-<p>Consider a table with 1 billion rows partitioned by
<code>region</code>, and a query:</p>
-<pre><code class="language-sql">SELECT * FROM events WHERE region
= 'us-east-1' AND event_type = 'click';
-</code></pre>
-<p><strong>Without filter pushdown:</strong> Your table
provider reads all 1 billion rows
-across all regions. DataFusion then applies both filters, discarding the vast
-majority of the data.</p>
-<p><strong>With filter pushdown on
<code>region</code>:</strong> Your
<code>scan()</code> method sees the
-<code>region = 'us-east-1'</code> filter and constructs an
execution plan that only reads
-the <code>us-east-1</code> partition. If that partition holds 100
million rows, you have
-just eliminated 90% of the I/O. DataFusion still applies the
<code>event_type</code>
-filter via <code>FilterExec</code> if you reported it as
<code>Unsupported</code>.</p>
-<h3 id="using-explain-to-debug-your-table-provider">Using EXPLAIN to
Debug Your Table Provider<a class="headerlink"
href="#using-explain-to-debug-your-table-provider" title="Permanent
link">¶</a></h3>
-<p>The <code>EXPLAIN</code> statement is your best tool for
understanding what DataFusion is
-actually doing with your table provider. It shows the physical plan that
-DataFusion will execute, including any operators it inserted:</p>
-<pre><code class="language-sql">EXPLAIN SELECT * FROM events WHERE
region = 'us-east-1' AND event_type = 'click';
-</code></pre>
-<p>If you are using DataFrames, call <code>.explain(false,
false)</code> for the logical plan
-or <code>.explain(false, true)</code> for the physical plan. You
can also print the plans
-in verbose mode with <code>.explain(true, true)</code>.</p>
-<p><strong>Before filter pushdown</strong>, the plan might
look like:</p>
-<pre><code class="language-text">FilterExec: region@0 = us-east-1
AND event_type@1 = click
- MyExecPlan: partitions=50
-</code></pre>
-<p>Here DataFusion is reading all 50 partitions and filtering everything
-afterward. The <code>FilterExec</code> above your scan is doing
all the predicate work.</p>
-<p><strong>After implementing pushdown for
<code>region</code></strong> (reported as
<code>Exact</code>):</p>
-<pre><code class="language-text">FilterExec: event_type@1 = click
- MyExecPlan: partitions=5, filter=[region = us-east-1]
-</code></pre>
-<p>Now your exec reads only the 5 partitions for
<code>us-east-1</code>, and the remaining
-<code>FilterExec</code> only handles the
<code>event_type</code> predicate. The
<code>region</code> filter has
-been fully absorbed by your scan.</p>
-<p><strong>After implementing pushdown for both
filters</strong> (both <code>Exact</code>):</p>
-<pre><code class="language-text">MyExecPlan: partitions=5,
filter=[region = us-east-1 AND event_type = click]
-</code></pre>
-<p>No <code>FilterExec</code> at all -- your source handles
everything.</p>
-<p>Similarly, <code>EXPLAIN</code> will reveal whether
DataFusion is inserting unnecessary
-<code>SortExec</code> or <code>RepartitionExec</code>
nodes that you could eliminate by declaring
-better output properties. Whenever your queries seem slower than expected,
-<code>EXPLAIN</code> is the first place to look.</p>
-<h2 id="putting-it-all-together">Putting It All Together<a
class="headerlink" href="#putting-it-all-together" title="Permanent
link">¶</a></h2>
-<hr/>
-<p>Here is a minimal but complete example of a custom table provider
that generates
-data lazily during streaming:</p>
-<pre><code class="language-rust">use std::any::Any;
-use std::sync::Arc;
-
-use arrow::array::{Int64Array, StringArray};
-use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
-use arrow::record_batch::RecordBatch;
-use datafusion::catalog::TableProvider;
-use datafusion::common::Result;
-use datafusion::datasource::TableType;
-use datafusion::execution::context::SessionState;
-use datafusion::execution::SendableRecordBatchStream;
-use datafusion::logical_expr::Expr;
-use datafusion::physical_expr::EquivalenceProperties;
-use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
-use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion::physical_plan::{
- ExecutionPlan, Partitioning, PlanProperties,
-};
-use futures::stream;
-
-/// A table provider that generates sequential numbers on demand.
-struct CountingTable {
- schema: SchemaRef,
- num_partitions: usize,
- rows_per_partition: usize,
-}
-
-impl CountingTable {
- fn new(num_partitions: usize, rows_per_partition: usize) -&gt; Self {
- let schema = Arc::new(Schema::new(vec![
- Field::new("partition", DataType::Int64, false),
- Field::new("value", DataType::Int64, false),
- ]));
- Self { schema, num_partitions, rows_per_partition }
- }
-}
-
-#[async_trait::async_trait]
-impl TableProvider for CountingTable {
- fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
- fn schema(&amp;self) -&gt; SchemaRef {
Arc::clone(&amp;self.schema) }
- fn table_type(&amp;self) -&gt; TableType { TableType::Base }
-
- async fn scan(
- &amp;self,
- _state: &amp;dyn Session,
- projection: Option&lt;&amp;Vec&lt;usize&gt;&gt;,
- _filters: &amp;[Expr],
- limit: Option&lt;usize&gt;,
- ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
- // Light work only: build the plan with metadata
- Ok(Arc::new(CountingExec {
- schema: Arc::clone(&amp;self.schema),
- num_partitions: self.num_partitions,
- rows_per_partition: limit
- .unwrap_or(self.rows_per_partition)
- .min(self.rows_per_partition),
- properties: PlanProperties::new(
- EquivalenceProperties::new(Arc::clone(&amp;self.schema)),
- Partitioning::UnknownPartitioning(self.num_partitions),
- EmissionType::Incremental,
- Boundedness::Bounded,
- ),
- }))
- }
-}
-
-struct CountingExec {
- schema: SchemaRef,
- num_partitions: usize,
- rows_per_partition: usize,
- properties: PlanProperties,
-}
-
-impl ExecutionPlan for CountingExec {
- fn name(&amp;self) -&gt; &amp;str { "CountingExec" }
- fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
- fn properties(&amp;self) -&gt; &amp;PlanProperties {
&amp;self.properties }
- fn children(&amp;self) -&gt; Vec&lt;&amp;Arc&lt;dyn
ExecutionPlan&gt;&gt; { vec![] }
-
- fn with_new_children(
- self: Arc&lt;Self&gt;,
- _children: Vec&lt;Arc&lt;dyn ExecutionPlan&gt;&gt;,
- ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
- Ok(self)
- }
-
- fn execute(
- &amp;self,
- partition: usize,
- _context: Arc&lt;TaskContext&gt;,
- ) -&gt; Result&lt;SendableRecordBatchStream&gt; {
- let schema = Arc::clone(&amp;self.schema);
- let rows = self.rows_per_partition;
-
- // The heavy work (data generation) happens inside the stream,
- // not here in execute().
- let batch_stream = stream::once(async move {
- let partitions = Int64Array::from(
- vec![partition as i64; rows],
- );
- let values = Int64Array::from(
- (0..rows as
i64).collect::&lt;Vec&lt;_&gt;&gt;(),
- );
- let batch = RecordBatch::try_new(
- Arc::clone(&amp;schema),
- vec![Arc::new(partitions), Arc::new(values)],
- )?;
- Ok(batch)
- });
-
- Ok(Box::pin(RecordBatchStreamAdapter::new(
- Arc::clone(&amp;self.schema),
- batch_stream,
- )))
- }
-}
-</code></pre>
-<h2 id="choosing-the-right-starting-point">Choosing the Right Starting
Point<a class="headerlink" href="#choosing-the-right-starting-point"
title="Permanent link">¶</a></h2>
-<hr/>
-<p>Not every custom data source requires implementing all three layers
from
-scratch. DataFusion provides building blocks that let you plug in at whatever
-level makes sense:</p>
-<table class="table">
-<thead>
-<tr>
-<th>If your data is...</th>
-<th>Start with</th>
-<th>You implement</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Already in <code>RecordBatch</code>es in
memory</td>
-<td>[<code>MemTable</code>]</td>
-<td>Nothing -- just construct it</td>
-</tr>
-<tr>
-<td>An async stream of batches</td>
-<td>[<code>StreamTable</code>]</td>
-<td>A stream factory</td>
-</tr>
-<tr>
-<td>A table with known sort order</td>
-<td>[<code>SortedTableProvider</code>] wrapping another
provider</td>
-<td>The inner provider</td>
-</tr>
-<tr>
-<td>A custom source needing full control</td>
-<td><code>TableProvider</code> +
<code>ExecutionPlan</code> + stream</td>
-<td>All three layers</td>
-</tr>
-</tbody>
-</table>
-<p>For most integrations, [<code>StreamTable</code>]
combined with
-[<code>RecordBatchStreamAdapter</code>] provides a good balance of
simplicity and
-flexibility. You provide a closure that returns a stream, and DataFusion
handles
-the rest.</p>
-<h2 id="further-reading">Further Reading<a class="headerlink"
href="#further-reading" title="Permanent link">¶</a></h2>
-<hr/>
-<ul>
-<li>[TableProvider API
docs][<code>TableProvider</code>]</li>
-<li>[ExecutionPlan API
docs][<code>ExecutionPlan</code>]</li>
-<li>[SendableRecordBatchStream API
docs][<code>SendableRecordBatchStream</code>]</li>
-<li><a
href="https://github.com/apache/datafusion/issues/16821">GitHub issue
discussing table provider examples</a></li>
-<li><a
href="https://github.com/apache/datafusion/tree/main/datafusion-examples/examples">DataFusion
examples directory</a> --
- contains working examples including custom table providers</li>
-</ul>
-<hr/>
-<p><em>Note: Portions of this blog post were written with the
assistance of an AI agent.</em></p></content><category
term="blog"></category></entry></feed>
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--- a/blog/feeds/tim-saucer.rss.xml
+++ /dev/null
@@ -1,24 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<rss version="2.0"><channel><title>Apache DataFusion Blog - Tim
Saucer</title><link>https://datafusion.apache.org/blog/</link><description></description><lastBuildDate>Fri,
20 Mar 2026 00:00:00 +0000</lastBuildDate><item><title>"Writing Custom Table
Providers in Apache
DataFusion"</title><link>https://datafusion.apache.org/blog/2026/03/20/writing-table-providers</link><description><!--
-{% comment %}
-Licensed to the Apache Software Foundation (ASF) under one or more
-contributor license agreements. See the NOTICE file distributed with
-this work for additional information regarding copyright ownership.
-The ASF licenses this file to you under the Apache License, Version 2.0
-(the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-{% endcomment %}
--->
-
-<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
-in a custom format, behind an API, or in a system that DataFusion does not
-natively support, you can teach DataFusion to read it by implementing a
-<strong>custom table provider</strong>. This post walks through
the three layers you …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>Tim
Saucer</dc:creator><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/writing-table-providers</guid><category>blog</category></item></channel></rss>
\ No newline at end of file
diff --git a/blog/feeds/timsaucer.atom.xml b/blog/feeds/timsaucer.atom.xml
index 268635c..2637a9c 100644
--- a/blog/feeds/timsaucer.atom.xml
+++ b/blog/feeds/timsaucer.atom.xml
@@ -1,5 +1,600 @@
<?xml version="1.0" encoding="utf-8"?>
-<feed xmlns="http://www.w3.org/2005/Atom";><title>Apache DataFusion Blog -
timsaucer</title><link href="https://datafusion.apache.org/blog/";
rel="alternate"></link><link
href="https://datafusion.apache.org/blog/feeds/timsaucer.atom.xml";
rel="self"></link><id>https://datafusion.apache.org/blog/</id><updated>2025-03-30T00:00:00+00:00</updated><subtitle></subtitle><entry><title>Apache
DataFusion Python 46.0.0 Released</title><link
href="https://datafusion.apache.org/blog/2025/03/30/datafusio [...]
+<feed xmlns="http://www.w3.org/2005/Atom";><title>Apache DataFusion Blog -
timsaucer</title><link href="https://datafusion.apache.org/blog/";
rel="alternate"></link><link
href="https://datafusion.apache.org/blog/feeds/timsaucer.atom.xml";
rel="self"></link><id>https://datafusion.apache.org/blog/</id><updated>2026-03-20T00:00:00+00:00</updated><subtitle></subtitle><entry><title>Writing
Custom Table Providers in Apache DataFusion</title><link
href="https://datafusion.apache.org/blog/2026/03/2 [...]
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
+in a custom format, behind an API, or in a system that DataFusion does not
+natively support, you can teach DataFusion to read it by implementing a
+<strong>custom table provider</strong>. This post walks through
the three layers you …</p></summary><content type="html"><!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
+in a custom format, behind an API, or in a system that DataFusion does not
+natively support, you can teach DataFusion to read it by implementing a
+<strong>custom table provider</strong>. This post walks through
the three layers you need to
+understand and explains where your work should actually happen.</p>
+<h2 id="the-three-layers">The Three Layers<a class="headerlink"
href="#the-three-layers" title="Permanent link">¶</a></h2>
+<hr/>
+<p>When DataFusion executes a query against a table, three abstractions
collaborate
+to produce results:</p>
+<ol>
+<li><strong>[<code>TableProvider</code>]</strong>
-- Describes the table (schema, capabilities) and
+ produces an execution plan when queried.</li>
+<li><strong>[<code>ExecutionPlan</code>]</strong>
-- Describes <em>how</em> to compute the result: partitioning,
+ ordering, and child plan relationships.</li>
+<li><strong>[<code>SendableRecordBatchStream</code>]</strong>
-- The async stream that <em>actually does the
+ work</em>, yielding <code>RecordBatch</code>es one at a
time.</li>
+</ol>
+<p>Think of these as a funnel:
<code>TableProvider::scan()</code> is called once during
+planning to create an <code>ExecutionPlan</code>, then
<code>ExecutionPlan::execute()</code> is called
+once per partition to create a stream, and those streams are where rows are
+actually produced during execution.</p>
+<h2 id="layer-1-tableprovider">Layer 1: TableProvider<a
class="headerlink" href="#layer-1-tableprovider" title="Permanent
link">¶</a></h2>
+<hr/>
+<p>A [<code>TableProvider</code>] represents a queryable
data source. For a minimal read-only
+table, you need four methods:</p>
+<pre><code class="language-rust">impl TableProvider for MyTable {
+ fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
+
+ fn schema(&amp;self) -&gt; SchemaRef {
+ Arc::clone(&amp;self.schema)
+ }
+
+ fn table_type(&amp;self) -&gt; TableType {
+ TableType::Base
+ }
+
+ async fn scan(
+ &amp;self,
+ state: &amp;dyn Session,
+ projection: Option&lt;&amp;Vec&lt;usize&gt;&gt;,
+ filters: &amp;[Expr],
+ limit: Option&lt;usize&gt;,
+ ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
+ // Build and return an ExecutionPlan -- keep this lightweight!
+ Ok(Arc::new(MyExecPlan::new(
+ Arc::clone(&amp;self.schema),
+ projection,
+ limit,
+ )))
+ }
+}
+</code></pre>
+<p>The <code>scan</code> method is the heart of
<code>TableProvider</code>. It receives three pushdown
+hints from the optimizer, each reducing the amount of data your source needs
+to produce:</p>
+<ul>
+<li><strong><code>projection</code></strong> --
Which columns are needed. This reduces the <strong>width</strong> of
+ the output. If your source supports it, read only these columns rather than
+ the full schema.</li>
+<li><strong><code>filters</code></strong> --
Predicates the engine would like you to apply during the
+ scan. This reduces the <strong>number of rows</strong> by
skipping data that does not
+ match. Implement <code>supports_filters_pushdown</code> to
advertise which filters you
+ can handle.</li>
+<li><strong><code>limit</code></strong> -- A row
count cap. This also reduces the <strong>number of rows</strong> --
+ if you can stop reading early once you have produced enough rows, this avoids
+ unnecessary work.</li>
+</ul>
+<h3 id="keep-scan-lightweight">Keep <code>scan()</code>
Lightweight<a class="headerlink" href="#keep-scan-lightweight"
title="Permanent link">¶</a></h3>
+<p>This is a critical point:
<strong><code>scan()</code> runs during planning, not
execution.</strong> It
+should return quickly. Best practices are to avoid performing I/O, network
+calls, or heavy computation here. The <code>scan</code> method's
job is to <em>describe</em> how
+the data will be produced, not to produce it. All the real work belongs in the
+stream (Layer 3).</p>
+<p>A common pitfall is to fetch data or open connections in
<code>scan()</code>. This blocks
+the planning thread and can cause timeouts or deadlocks, especially if the
query
+involves multiple tables or subqueries that all need to be planned before
+execution begins.</p>
+<h3 id="existing-implementations-to-learn-from">Existing Implementations
to Learn From<a class="headerlink"
href="#existing-implementations-to-learn-from" title="Permanent
link">¶</a></h3>
+<p>DataFusion ships several <code>TableProvider</code>
implementations that are excellent
+references:</p>
+<ul>
+<li><strong>[<code>MemTable</code>]</strong> --
Holds data in memory as
<code>Vec&lt;RecordBatch&gt;</code>. The simplest
+ possible provider; great for tests and small datasets.</li>
+<li><strong>[<code>StreamTable</code>]</strong>
-- Wraps a user-provided stream factory. Useful when your
+ data arrives as a continuous stream (e.g., from Kafka or a
socket).</li>
+<li><strong>[<code>SortedTableProvider</code>]</strong>
-- Wraps another <code>TableProvider</code> and advertises a
+ known sort order, enabling the optimizer to skip redundant sorts.</li>
+</ul>
+<h2 id="layer-2-executionplan">Layer 2: ExecutionPlan<a
class="headerlink" href="#layer-2-executionplan" title="Permanent
link">¶</a></h2>
+<hr/>
+<p>An [<code>ExecutionPlan</code>] is a node in the physical
query plan tree. Your table
+provider's <code>scan()</code> method returns one. The required
methods are:</p>
+<pre><code class="language-rust">impl ExecutionPlan for MyExecPlan
{
+ fn name(&amp;self) -&gt; &amp;str { "MyExecPlan" }
+
+ fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
+
+ fn properties(&amp;self) -&gt; &amp;PlanProperties {
+ &amp;self.properties
+ }
+
+ fn children(&amp;self) -&gt; Vec&lt;&amp;Arc&lt;dyn
ExecutionPlan&gt;&gt; {
+ vec![] // Leaf node -- no children
+ }
+
+ fn with_new_children(
+ self: Arc&lt;Self&gt;,
+ children: Vec&lt;Arc&lt;dyn ExecutionPlan&gt;&gt;,
+ ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
+ assert!(children.is_empty());
+ Ok(self)
+ }
+
+ fn execute(
+ &amp;self,
+ partition: usize,
+ context: Arc&lt;TaskContext&gt;,
+ ) -&gt; Result&lt;SendableRecordBatchStream&gt; {
+ // This is where you build and return your stream
+ // ...
+ }
+}
+</code></pre>
+<p>The key properties to set correctly in
[<code>PlanProperties</code>] are <strong>output
+partitioning</strong> and <strong>output
ordering</strong>.</p>
+<p><strong>Output partitioning</strong> tells the engine how
many partitions your data has,
+which determines parallelism. If your source naturally partitions data (e.g.,
+by file or by shard), expose that here.</p>
+<p><strong>Output ordering</strong> declares whether your
data is naturally sorted. This
+enables the optimizer to avoid inserting a <code>SortExec</code>
when a query requires
+ordered data. Getting this right can be a significant performance
win.</p>
+<h3 id="partitioning-strategies">Partitioning Strategies<a
class="headerlink" href="#partitioning-strategies" title="Permanent
link">¶</a></h3>
+<p>Since <code>execute()</code> is called once per
partition, partitioning directly controls
+the parallelism of your table scan. Each partition runs on its own task, so
+more partitions means more concurrent work -- up to the number of available
+cores.</p>
+<p>Consider how your data source naturally divides its data:</p>
+<ul>
+<li><strong>By file or object:</strong> If you are reading
from S3, each file can be a
+ partition. DataFusion will read them in parallel.</li>
+<li><strong>By shard or region:</strong> If your source is a
sharded database, each shard
+ maps naturally to a partition.</li>
+<li><strong>By key range:</strong> If your data is keyed
(e.g., by timestamp or customer ID),
+ you can split it into ranges.</li>
+</ul>
+<p>Getting partitioning right matters because it affects everything
downstream in
+the plan. When DataFusion needs to perform an aggregation or join, it
+repartitions data by hashing the relevant columns. If your source already
+produces data partitioned by the join or group-by key, DataFusion can skip the
+repartition step entirely -- avoiding a potentially expensive
shuffle.</p>
+<p>For example, if you are building a table provider for a system that
stores
+data partitioned by <code>customer_id</code>, and a common query
groups by <code>customer_id</code>:</p>
+<pre><code class="language-sql">SELECT customer_id, SUM(amount)
+FROM my_table
+GROUP BY customer_id;
+</code></pre>
+<p>If you declare your output partitioning as
<code>Hash([customer_id], N)</code>, the
+optimizer recognizes that the data is already distributed correctly for the
+aggregation and eliminates the <code>RepartitionExec</code> that
would otherwise appear
+in the plan. You can verify this with <code>EXPLAIN</code> (more
on this below).</p>
+<p>Conversely, if you report
<code>UnknownPartitioning</code>, DataFusion must assume the
+worst case and will always insert repartitioning operators as needed.</p>
+<h3 id="keep-execute-lightweight-too">Keep
<code>execute()</code> Lightweight Too<a class="headerlink"
href="#keep-execute-lightweight-too" title="Permanent
link">¶</a></h3>
+<p>Like <code>scan()</code>, the
<code>execute()</code> method should construct and return a stream
+without doing heavy work. The actual data production happens when the stream
+is polled. Do not block on async operations here -- build the stream and let
+the runtime drive it.</p>
+<h3 id="existing-implementations-to-learn-from_1">Existing
Implementations to Learn From<a class="headerlink"
href="#existing-implementations-to-learn-from_1" title="Permanent
link">¶</a></h3>
+<ul>
+<li><strong>[<code>StreamingTableExec</code>]</strong>
-- Executes a streaming table scan. It takes a
+ stream factory (a closure that produces streams) and handles partitioning.
+ Good reference for wrapping external streams.</li>
+<li><strong>[<code>DataSourceExec</code>]</strong>
-- The execution plan behind DataFusion's built-in file
+ scanning (Parquet, CSV, JSON). It demonstrates sophisticated partitioning,
+ filter pushdown, and projection pushdown.</li>
+</ul>
+<h2 id="layer-3-sendablerecordbatchstream">Layer 3:
SendableRecordBatchStream<a class="headerlink"
href="#layer-3-sendablerecordbatchstream" title="Permanent
link">¶</a></h2>
+<hr/>
+<p>[<code>SendableRecordBatchStream</code>] is where the
real work happens. It is defined as:</p>
+<pre><code class="language-rust">type SendableRecordBatchStream =
+ Pin&lt;Box&lt;dyn RecordBatchStream&lt;Item =
Result&lt;RecordBatch&gt;&gt; + Send&gt;&gt;;
+</code></pre>
+<p>This is an async stream of <code>RecordBatch</code>es
that can be sent across threads. When
+the DataFusion runtime polls this stream, your code runs: reading files,
calling
+APIs, transforming data, etc.</p>
+<h3 id="using-recordbatchstreamadapter">Using
RecordBatchStreamAdapter<a class="headerlink"
href="#using-recordbatchstreamadapter" title="Permanent
link">¶</a></h3>
+<p>The easiest way to create a
<code>SendableRecordBatchStream</code> is with
+[<code>RecordBatchStreamAdapter</code>]. It bridges any
<code>futures::Stream&lt;Item =
+Result&lt;RecordBatch&gt;&gt;</code> into the
<code>SendableRecordBatchStream</code> type:</p>
+<pre><code class="language-rust">use
datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+
+fn execute(
+ &amp;self,
+ partition: usize,
+ context: Arc&lt;TaskContext&gt;,
+) -&gt; Result&lt;SendableRecordBatchStream&gt; {
+ let schema = self.schema();
+ let config = self.config.clone();
+
+ let stream = futures::stream::once(async move {
+ // ALL the heavy work happens here, inside the stream:
+ // - Open connections
+ // - Read data from external sources
+ // - Transform and batch the results
+ let batches = fetch_data_from_source(&amp;config).await?;
+ Ok(batches)
+ })
+ .flat_map(|result| match result {
+ Ok(batch) =&gt; futures::stream::iter(vec![Ok(batch)]),
+ Err(e) =&gt; futures::stream::iter(vec![Err(e)]),
+ });
+
+ Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
+}
+</code></pre>
+<h3 id="cpu-intensive-work-use-a-separate-thread-pool">CPU-Intensive
Work: Use a Separate Thread Pool<a class="headerlink"
href="#cpu-intensive-work-use-a-separate-thread-pool" title="Permanent
link">¶</a></h3>
+<p>If your stream performs CPU-intensive work (parsing, decompression,
complex
+transformations), avoid blocking the tokio runtime. Instead, offload to a
+dedicated thread pool and send results back through a channel:</p>
+<pre><code class="language-rust">fn execute(
+ &amp;self,
+ partition: usize,
+ context: Arc&lt;TaskContext&gt;,
+) -&gt; Result&lt;SendableRecordBatchStream&gt; {
+ let schema = self.schema();
+ let config = self.config.clone();
+
+ let (tx, rx) = tokio::sync::mpsc::channel(2);
+
+ // Spawn CPU-heavy work on a blocking thread pool
+ tokio::task::spawn_blocking(move || {
+ let batches = generate_data(&amp;config);
+ for batch in batches {
+ if tx.blocking_send(Ok(batch)).is_err() {
+ break; // Receiver dropped, query was cancelled
+ }
+ }
+ });
+
+ let stream = tokio_stream::wrappers::ReceiverStream::new(rx);
+ Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
+}
+</code></pre>
+<p>This pattern keeps the async runtime responsive while your data
generation
+runs on its own threads.</p>
+<h2 id="where-should-the-work-happen">Where Should the Work Happen?<a
class="headerlink" href="#where-should-the-work-happen" title="Permanent
link">¶</a></h2>
+<hr/>
+<p>This table summarizes what belongs at each layer:</p>
+<table class="table">
+<thead>
+<tr>
+<th>Layer</th>
+<th>Runs During</th>
+<th>Should Do</th>
+<th>Should NOT Do</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><code>TableProvider::scan()</code></td>
+<td>Planning</td>
+<td>Build an <code>ExecutionPlan</code> with
metadata</td>
+<td>I/O, network calls, heavy computation</td>
+</tr>
+<tr>
+<td><code>ExecutionPlan::execute()</code></td>
+<td>Execution (once per partition)</td>
+<td>Construct a stream, set up channels</td>
+<td>Block on async work, read data</td>
+</tr>
+<tr>
+<td><code>RecordBatchStream</code> (polling)</td>
+<td>Execution</td>
+<td>All I/O, computation, data production</td>
+<td>--</td>
+</tr>
+</tbody>
+</table>
+<p>The guiding principle: <strong>push work as late as
possible.</strong> Planning should be
+fast so the optimizer can do its job. Execution setup should be fast so all
+partitions can start promptly. The stream is where you spend time producing
+data.</p>
+<h3 id="why-this-matters">Why This Matters<a class="headerlink"
href="#why-this-matters" title="Permanent link">¶</a></h3>
+<p>When <code>scan()</code> does heavy work, several
problems arise:</p>
+<ol>
+<li><strong>Planning becomes slow.</strong> If a query
touches 10 tables and each <code>scan()</code>
+ takes 500ms, planning alone takes 5 seconds before any data
flows.</li>
+<li><strong>The optimizer cannot help.</strong> The
optimizer runs between planning and
+ execution. If you have already fetched data during planning, optimizations
+ like predicate pushdown or partition pruning cannot reduce the
work.</li>
+<li><strong>Resource management breaks down.</strong>
DataFusion manages concurrency and
+ memory during execution. Work done during planning bypasses these
controls.</li>
+</ol>
+<h2 id="filter-pushdown-doing-less-work">Filter Pushdown: Doing Less
Work<a class="headerlink" href="#filter-pushdown-doing-less-work"
title="Permanent link">¶</a></h2>
+<hr/>
+<p>One of the most impactful optimizations you can add to a custom table
provider
+is <strong>filter pushdown</strong> -- letting the source skip
data that the query does not
+need, rather than reading everything and filtering it afterward.</p>
+<h3 id="how-filter-pushdown-works">How Filter Pushdown Works<a
class="headerlink" href="#how-filter-pushdown-works" title="Permanent
link">¶</a></h3>
+<p>When DataFusion plans a query with a <code>WHERE</code>
clause, it passes the filter
+predicates to your <code>scan()</code> method as the
<code>filters</code> parameter. By default,
+DataFusion assumes your provider cannot handle any filters and inserts a
+<code>FilterExec</code> node above your scan to apply them. But if
your source <em>can</em>
+evaluate some predicates during scanning -- for example, by skipping files,
+partitions, or row groups that cannot match -- you can eliminate a huge amount
+of unnecessary I/O.</p>
+<p>To opt in, implement
<code>supports_filters_pushdown</code>:</p>
+<pre><code class="language-rust">fn supports_filters_pushdown(
+ &amp;self,
+ filters: &amp;[&amp;Expr],
+) -&gt;
Result&lt;Vec&lt;TableProviderFilterPushDown&gt;&gt; {
+ Ok(filters.iter().map(|f| {
+ match f {
+ // We can fully evaluate equality filters on
+ // the partition column at the source
+ Expr::BinaryExpr(BinaryExpr {
+ left, op: Operator::Eq, right
+ }) if is_partition_column(left) || is_partition_column(right)
=&gt; {
+ TableProviderFilterPushDown::Exact
+ }
+ // All other filters: let DataFusion handle them
+ _ =&gt; TableProviderFilterPushDown::Unsupported,
+ }
+ }).collect())
+}
+</code></pre>
+<p>The three possible responses for each filter are:</p>
+<ul>
+<li><strong><code>Exact</code></strong> -- Your
source guarantees that no output rows will have a false
+ value for this predicate. Because the filter is fully evaluated at the
source,
+ DataFusion will <strong>not</strong> add a
<code>FilterExec</code> for it.</li>
+<li><strong><code>Inexact</code></strong> --
Your source has the ability to reduce the data produced, but
+ the output may still include rows that do not satisfy the predicate. For
+ example, you might skip entire files based on metadata statistics but not
+ filter individual rows within a file. DataFusion will still add a
<code>FilterExec</code>
+ above your scan to remove any remaining rows that slipped through.</li>
+<li><strong><code>Unsupported</code></strong> --
Your source ignores this filter entirely. DataFusion
+ handles it.</li>
+</ul>
+<h3 id="why-filter-pushdown-matters">Why Filter Pushdown Matters<a
class="headerlink" href="#why-filter-pushdown-matters" title="Permanent
link">¶</a></h3>
+<p>Consider a table with 1 billion rows partitioned by
<code>region</code>, and a query:</p>
+<pre><code class="language-sql">SELECT * FROM events WHERE region
= 'us-east-1' AND event_type = 'click';
+</code></pre>
+<p><strong>Without filter pushdown:</strong> Your table
provider reads all 1 billion rows
+across all regions. DataFusion then applies both filters, discarding the vast
+majority of the data.</p>
+<p><strong>With filter pushdown on
<code>region</code>:</strong> Your
<code>scan()</code> method sees the
+<code>region = 'us-east-1'</code> filter and constructs an
execution plan that only reads
+the <code>us-east-1</code> partition. If that partition holds 100
million rows, you have
+just eliminated 90% of the I/O. DataFusion still applies the
<code>event_type</code>
+filter via <code>FilterExec</code> if you reported it as
<code>Unsupported</code>.</p>
+<h3 id="using-explain-to-debug-your-table-provider">Using EXPLAIN to
Debug Your Table Provider<a class="headerlink"
href="#using-explain-to-debug-your-table-provider" title="Permanent
link">¶</a></h3>
+<p>The <code>EXPLAIN</code> statement is your best tool for
understanding what DataFusion is
+actually doing with your table provider. It shows the physical plan that
+DataFusion will execute, including any operators it inserted:</p>
+<pre><code class="language-sql">EXPLAIN SELECT * FROM events WHERE
region = 'us-east-1' AND event_type = 'click';
+</code></pre>
+<p>If you are using DataFrames, call <code>.explain(false,
false)</code> for the logical plan
+or <code>.explain(false, true)</code> for the physical plan. You
can also print the plans
+in verbose mode with <code>.explain(true, true)</code>.</p>
+<p><strong>Before filter pushdown</strong>, the plan might
look like:</p>
+<pre><code class="language-text">FilterExec: region@0 = us-east-1
AND event_type@1 = click
+ MyExecPlan: partitions=50
+</code></pre>
+<p>Here DataFusion is reading all 50 partitions and filtering everything
+afterward. The <code>FilterExec</code> above your scan is doing
all the predicate work.</p>
+<p><strong>After implementing pushdown for
<code>region</code></strong> (reported as
<code>Exact</code>):</p>
+<pre><code class="language-text">FilterExec: event_type@1 = click
+ MyExecPlan: partitions=5, filter=[region = us-east-1]
+</code></pre>
+<p>Now your exec reads only the 5 partitions for
<code>us-east-1</code>, and the remaining
+<code>FilterExec</code> only handles the
<code>event_type</code> predicate. The
<code>region</code> filter has
+been fully absorbed by your scan.</p>
+<p><strong>After implementing pushdown for both
filters</strong> (both <code>Exact</code>):</p>
+<pre><code class="language-text">MyExecPlan: partitions=5,
filter=[region = us-east-1 AND event_type = click]
+</code></pre>
+<p>No <code>FilterExec</code> at all -- your source handles
everything.</p>
+<p>Similarly, <code>EXPLAIN</code> will reveal whether
DataFusion is inserting unnecessary
+<code>SortExec</code> or <code>RepartitionExec</code>
nodes that you could eliminate by declaring
+better output properties. Whenever your queries seem slower than expected,
+<code>EXPLAIN</code> is the first place to look.</p>
+<h2 id="putting-it-all-together">Putting It All Together<a
class="headerlink" href="#putting-it-all-together" title="Permanent
link">¶</a></h2>
+<hr/>
+<p>Here is a minimal but complete example of a custom table provider
that generates
+data lazily during streaming:</p>
+<pre><code class="language-rust">use std::any::Any;
+use std::sync::Arc;
+
+use arrow::array::{Int64Array, StringArray};
+use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
+use arrow::record_batch::RecordBatch;
+use datafusion::catalog::TableProvider;
+use datafusion::common::Result;
+use datafusion::datasource::TableType;
+use datafusion::execution::context::SessionState;
+use datafusion::execution::SendableRecordBatchStream;
+use datafusion::logical_expr::Expr;
+use datafusion::physical_expr::EquivalenceProperties;
+use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
+use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
+use datafusion::physical_plan::{
+ ExecutionPlan, Partitioning, PlanProperties,
+};
+use futures::stream;
+
+/// A table provider that generates sequential numbers on demand.
+struct CountingTable {
+ schema: SchemaRef,
+ num_partitions: usize,
+ rows_per_partition: usize,
+}
+
+impl CountingTable {
+ fn new(num_partitions: usize, rows_per_partition: usize) -&gt; Self {
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("partition", DataType::Int64, false),
+ Field::new("value", DataType::Int64, false),
+ ]));
+ Self { schema, num_partitions, rows_per_partition }
+ }
+}
+
+#[async_trait::async_trait]
+impl TableProvider for CountingTable {
+ fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
+ fn schema(&amp;self) -&gt; SchemaRef {
Arc::clone(&amp;self.schema) }
+ fn table_type(&amp;self) -&gt; TableType { TableType::Base }
+
+ async fn scan(
+ &amp;self,
+ _state: &amp;dyn Session,
+ projection: Option&lt;&amp;Vec&lt;usize&gt;&gt;,
+ _filters: &amp;[Expr],
+ limit: Option&lt;usize&gt;,
+ ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
+ // Light work only: build the plan with metadata
+ Ok(Arc::new(CountingExec {
+ schema: Arc::clone(&amp;self.schema),
+ num_partitions: self.num_partitions,
+ rows_per_partition: limit
+ .unwrap_or(self.rows_per_partition)
+ .min(self.rows_per_partition),
+ properties: PlanProperties::new(
+ EquivalenceProperties::new(Arc::clone(&amp;self.schema)),
+ Partitioning::UnknownPartitioning(self.num_partitions),
+ EmissionType::Incremental,
+ Boundedness::Bounded,
+ ),
+ }))
+ }
+}
+
+struct CountingExec {
+ schema: SchemaRef,
+ num_partitions: usize,
+ rows_per_partition: usize,
+ properties: PlanProperties,
+}
+
+impl ExecutionPlan for CountingExec {
+ fn name(&amp;self) -&gt; &amp;str { "CountingExec" }
+ fn as_any(&amp;self) -&gt; &amp;dyn Any { self }
+ fn properties(&amp;self) -&gt; &amp;PlanProperties {
&amp;self.properties }
+ fn children(&amp;self) -&gt; Vec&lt;&amp;Arc&lt;dyn
ExecutionPlan&gt;&gt; { vec![] }
+
+ fn with_new_children(
+ self: Arc&lt;Self&gt;,
+ _children: Vec&lt;Arc&lt;dyn ExecutionPlan&gt;&gt;,
+ ) -&gt; Result&lt;Arc&lt;dyn ExecutionPlan&gt;&gt; {
+ Ok(self)
+ }
+
+ fn execute(
+ &amp;self,
+ partition: usize,
+ _context: Arc&lt;TaskContext&gt;,
+ ) -&gt; Result&lt;SendableRecordBatchStream&gt; {
+ let schema = Arc::clone(&amp;self.schema);
+ let rows = self.rows_per_partition;
+
+ // The heavy work (data generation) happens inside the stream,
+ // not here in execute().
+ let batch_stream = stream::once(async move {
+ let partitions = Int64Array::from(
+ vec![partition as i64; rows],
+ );
+ let values = Int64Array::from(
+ (0..rows as
i64).collect::&lt;Vec&lt;_&gt;&gt;(),
+ );
+ let batch = RecordBatch::try_new(
+ Arc::clone(&amp;schema),
+ vec![Arc::new(partitions), Arc::new(values)],
+ )?;
+ Ok(batch)
+ });
+
+ Ok(Box::pin(RecordBatchStreamAdapter::new(
+ Arc::clone(&amp;self.schema),
+ batch_stream,
+ )))
+ }
+}
+</code></pre>
+<h2 id="choosing-the-right-starting-point">Choosing the Right Starting
Point<a class="headerlink" href="#choosing-the-right-starting-point"
title="Permanent link">¶</a></h2>
+<hr/>
+<p>Not every custom data source requires implementing all three layers
from
+scratch. DataFusion provides building blocks that let you plug in at whatever
+level makes sense:</p>
+<table class="table">
+<thead>
+<tr>
+<th>If your data is...</th>
+<th>Start with</th>
+<th>You implement</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Already in <code>RecordBatch</code>es in
memory</td>
+<td>[<code>MemTable</code>]</td>
+<td>Nothing -- just construct it</td>
+</tr>
+<tr>
+<td>An async stream of batches</td>
+<td>[<code>StreamTable</code>]</td>
+<td>A stream factory</td>
+</tr>
+<tr>
+<td>A table with known sort order</td>
+<td>[<code>SortedTableProvider</code>] wrapping another
provider</td>
+<td>The inner provider</td>
+</tr>
+<tr>
+<td>A custom source needing full control</td>
+<td><code>TableProvider</code> +
<code>ExecutionPlan</code> + stream</td>
+<td>All three layers</td>
+</tr>
+</tbody>
+</table>
+<p>For most integrations, [<code>StreamTable</code>]
combined with
+[<code>RecordBatchStreamAdapter</code>] provides a good balance of
simplicity and
+flexibility. You provide a closure that returns a stream, and DataFusion
handles
+the rest.</p>
+<h2 id="further-reading">Further Reading<a class="headerlink"
href="#further-reading" title="Permanent link">¶</a></h2>
+<hr/>
+<ul>
+<li>[TableProvider API
docs][<code>TableProvider</code>]</li>
+<li>[ExecutionPlan API
docs][<code>ExecutionPlan</code>]</li>
+<li>[SendableRecordBatchStream API
docs][<code>SendableRecordBatchStream</code>]</li>
+<li><a
href="https://github.com/apache/datafusion/issues/16821">GitHub issue
discussing table provider examples</a></li>
+<li><a
href="https://github.com/apache/datafusion/tree/main/datafusion-examples/examples">DataFusion
examples directory</a> --
+ contains working examples including custom table providers</li>
+</ul>
+<hr/>
+<p><em>Note: Portions of this blog post were written with the
assistance of an AI agent.</em></p></content><category
term="blog"></category></entry><entry><title>Apache DataFusion Python 46.0.0
Released</title><link
href="https://datafusion.apache.org/blog/2025/03/30/datafusion-python-46.0.0";
rel="alternate"></link><published>2025-03-30T00:00:00+00:00</published><updated>2025-03-30T00:00:00+00:00</updated><author><name>timsaucer</name></author><id>tag:datafusion.a
[...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
diff --git a/blog/feeds/timsaucer.rss.xml b/blog/feeds/timsaucer.rss.xml
index 22d32ef..4274e9d 100644
--- a/blog/feeds/timsaucer.rss.xml
+++ b/blog/feeds/timsaucer.rss.xml
@@ -1,5 +1,27 @@
<?xml version="1.0" encoding="utf-8"?>
-<rss version="2.0"><channel><title>Apache DataFusion Blog -
timsaucer</title><link>https://datafusion.apache.org/blog/</link><description></description><lastBuildDate>Sun,
30 Mar 2025 00:00:00 +0000</lastBuildDate><item><title>Apache DataFusion
Python 46.0.0
Released</title><link>https://datafusion.apache.org/blog/2025/03/30/datafusion-python-46.0.0</link><description><!--
+<rss version="2.0"><channel><title>Apache DataFusion Blog -
timsaucer</title><link>https://datafusion.apache.org/blog/</link><description></description><lastBuildDate>Fri,
20 Mar 2026 00:00:00 +0000</lastBuildDate><item><title>Writing Custom Table
Providers in Apache
DataFusion</title><link>https://datafusion.apache.org/blog/2026/03/20/writing-table-providers</link><description><!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+<p>One of DataFusion's greatest strengths is its extensibility. If your
data lives
+in a custom format, behind an API, or in a system that DataFusion does not
+natively support, you can teach DataFusion to read it by implementing a
+<strong>custom table provider</strong>. This post walks through
the three layers you …</p></description><dc:creator
xmlns:dc="http://purl.org/dc/elements/1.1/";>timsaucer</dc:creator><pubDate>Fri,
20 Mar 2026 00:00:00 +0000</pubDate><guid
isPermaLink="false">tag:datafusion.apache.org,2026-03-20:/blog/2026/03/20/writing-table-providers</guid><category>blog</category></item><item><title>Apache
DataFusion Python 46.0.0
Released</title><link>https://datafusion.apache.org/blo [...]
{% comment %}
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements. See the NOTICE file distributed with
diff --git a/blog/index.html b/blog/index.html
index 8d2f852..b41526b 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -116,8 +116,8 @@ figcaption {
<article class="post">
<header>
<div class="title">
- <h1><a
href="/blog/2026/03/20/writing-table-providers">"Writing Custom Table Providers
in Apache DataFusion"</a></h1>
- <p>Posted on: Fri 20 March 2026 by Tim Saucer</p>
+ <h1><a
href="/blog/2026/03/20/writing-table-providers">Writing Custom Table Providers
in Apache DataFusion</a></h1>
+ <p>Posted on: Fri 20 March 2026 by timsaucer</p>
<p><!--
{% comment %}
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