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new d87b571a Publish built docs triggered by
36a2307487c528cdaf93fcfb55f23bcbf4b9b563
d87b571a is described below
commit d87b571a329301fcbaf246febbc3fb55b227f085
Author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
AuthorDate: Tue Dec 3 18:13:12 2024 +0000
Publish built docs triggered by 36a2307487c528cdaf93fcfb55f23bcbf4b9b563
---
_sources/contributor-guide/plugin_overview.md.txt | 67 +-
_static/images/CometNativeParquetReader.drawio | 100 -
_static/images/CometNativeParquetReader.drawio.svg | 4 -
_static/images/CometOverviewDetailed.drawio | 94 -
_static/images/CometOverviewDetailed.drawio.svg | 4 -
_static/images/comet-dataflow.excalidraw | 2134 ++++++++++++++++++++
_static/images/comet-dataflow.svg | 10 +
contributor-guide/plugin_overview.html | 101 +-
searchindex.js | 2 +-
9 files changed, 2276 insertions(+), 240 deletions(-)
diff --git a/_sources/contributor-guide/plugin_overview.md.txt
b/_sources/contributor-guide/plugin_overview.md.txt
index a211ca6b..3e7c24f5 100644
--- a/_sources/contributor-guide/plugin_overview.md.txt
+++ b/_sources/contributor-guide/plugin_overview.md.txt
@@ -28,11 +28,21 @@ following setting to the Spark configuration when launching
`spark-shell` or `sp
--conf spark.plugins=org.apache.spark.CometPlugin
```
+This class is loaded by Spark's plugin framework. It will be instantiated in
the Spark driver only. Comet does not
+provide any executor plugins.
+
+The plugin will update the current `SparkConf` with the extra configuration
provided by Comet, such as executor memory
+configuration.
+
+The plugin also registers `CometSparkSessionExtensions` with Spark's extension
API.
+
+## CometSparkSessionExtensions
+
On initialization, this class registers two physical plan optimization rules
with Spark: `CometScanRule`
and `CometExecRule`. These rules run whenever a query stage is being planned
during Adaptive Query Execution, and
run once for the entire plan when Adaptive Query Execution is disabled.
-## CometScanRule
+### CometScanRule
`CometScanRule` replaces any Parquet scans with Comet operators. There are
different paths for Spark v1 and v2 data sources.
@@ -43,13 +53,13 @@ Parquet data source but leverages native code for decoding
Parquet row groups di
Comet only supports a subset of data types and will fall back to Spark's scan
if unsupported types
exist. Comet can still accelerate the rest of the query execution in this case
because `CometSparkToColumnarExec` will
-convert the output from Spark's can to Arrow arrays. Note that both
`spark.comet.exec.enabled=true` and
+convert the output from Spark's scan to Arrow arrays. Note that both
`spark.comet.exec.enabled=true` and
`spark.comet.convert.parquet.enabled=true` must be set to enable this
conversion.
Refer to the [Supported Spark Data
Types](https://datafusion.apache.org/comet/user-guide/datatypes.html) section
in the contributor guide to see a list of currently supported data types.
-## CometExecRule
+### CometExecRule
This rule traverses bottom-up from the original Spark plan and attempts to
replace each operator with a Comet equivalent.
For example, a `ProjectExec` will be replaced by `CometProjectExec`.
@@ -64,25 +74,52 @@ of this could outweigh the benefits of running parts of the
query stage natively
## Query Execution
-Once the plan has been transformed, any consecutive Comet operators are
combined into a `CometNativeExec` which contains
-a serialized version of the plan (the serialization code can be found in
`QueryPlanSerde`). When this operator is
-executed, the serialized plan is passed to the native code when calling
`Native.createPlan`.
+Once the plan has been transformed, any consecutive native Comet operators are
combined into a `CometNativeExec` which contains
+a protocol buffer serialized version of the plan (the serialization code can
be found in `QueryPlanSerde`).
-In the native code there is a `PhysicalPlanner` struct (in `planner.rs`) which
converts the serialized plan into an
+Spark serializes the physical plan and sends it to the executors when
executing tasks. The executors deserialize the
+plan and invoke it.
+
+When `CometNativeExec` is invoked, it will pass the serialized protobuf plan
into
+`Native.createPlan`, which invokes the native code via JNI, where the plan is
then deserialized.
+
+In the native code there is a `PhysicalPlanner` struct (in `planner.rs`) which
converts the deserialized plan into an
Apache DataFusion `ExecutionPlan`. In some cases, Comet provides specialized
physical operators and expressions to
override the DataFusion versions to ensure compatibility with Apache Spark.
-`CometExecIterator` will invoke `Native.executePlan` to pull the next batch
from the native plan. This is repeated
-until no more batches are available (meaning that all data has been processed
by the native plan).
+The leaf nodes in the physical plan are always `ScanExec` and each of these
operators will make a JNI call to
+`CometBatchIterator.next()` to fetch the next input batch. The input could be
a Comet native Parquet scan,
+a Spark exchange, or another native plan.
+
+`CometNativeExec` creates a `CometExecIterator` and applies this iterator to
the input RDD
+partitions. Each call to `CometExecIterator.next()` will invoke
`Native.executePlan`. Once the plan finishes
+executing, the resulting Arrow batches are imported into the JVM using Arrow
FFI.
+
+## Arrow
+
+Due to the hybrid execution model, it is necessary to pass batches of data
between the JVM and native code.
+
+Comet uses a combination of Arrow FFI and Arrow IPC to achieve this.
+
+### Arrow FFI
+
+The foundation for Arrow FFI is the [Arrow C Data Interface], which provides a
stable ABI-compatible interface for
+accessing Arrow data structures from multiple languages.
+
+[Arrow C Data Interface]:
https://arrow.apache.org/docs/format/CDataInterface.html
+
+- `CometExecIterator` invokes native plans and uses Arrow FFI to read the
output batches
+- Native `ScanExec` operators call `CometBatchIterator` via JNI to fetch input
batches from the JVM
+
+### Arrow IPC
-The leaf nodes in the physical plan are always `ScanExec` and these operators
consume batches of Arrow data that were
-prepared before the plan is executed. When `CometExecIterator` invokes
`Native.executePlan` it passes the memory
-addresses of these Arrow arrays to the native code.
+Comet native shuffle uses Arrow IPC to write batches to the shuffle files.
-
+- `CometShuffleWriteProcessor` invokes a native plan to fetch batches and then
passes them to native `ShuffleWriterExec`
+- `CometBlockStoreShuffleReader` reads batches from shuffle files
## End to End Flow
-The following diagram shows the end-to-end flow.
+The following diagram shows an example of the end-to-end flow for a query
stage.
-
+
diff --git a/_static/images/CometNativeParquetReader.drawio
b/_static/images/CometNativeParquetReader.drawio
deleted file mode 100644
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diff --git a/_static/images/CometNativeParquetReader.drawio.svg
b/_static/images/CometNativeParquetReader.drawio.svg
deleted file mode 100644
index 0c1f93c7..00000000
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diff --git a/_static/images/comet-dataflow.svg
b/_static/images/comet-dataflow.svg
new file mode 100644
index 00000000..20a573c1
--- /dev/null
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diff --git a/contributor-guide/plugin_overview.html
b/contributor-guide/plugin_overview.html
index 5d6640f5..edb1d7a2 100644
--- a/contributor-guide/plugin_overview.html
+++ b/contributor-guide/plugin_overview.html
@@ -287,19 +287,43 @@ under the License.
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#cometscanrule">
- CometScanRule
+ <a class="reference internal nav-link" href="#cometsparksessionextensions">
+ CometSparkSessionExtensions
</a>
+ <ul class="nav section-nav flex-column">
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#cometscanrule">
+ CometScanRule
+ </a>
+ </li>
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#cometexecrule">
+ CometExecRule
+ </a>
+ </li>
+ </ul>
</li>
<li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#cometexecrule">
- CometExecRule
+ <a class="reference internal nav-link" href="#query-execution">
+ Query Execution
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
- <a class="reference internal nav-link" href="#query-execution">
- Query Execution
+ <a class="reference internal nav-link" href="#arrow">
+ Arrow
</a>
+ <ul class="nav section-nav flex-column">
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#arrow-ffi">
+ Arrow FFI
+ </a>
+ </li>
+ <li class="toc-h3 nav-item toc-entry">
+ <a class="reference internal nav-link" href="#arrow-ipc">
+ Arrow IPC
+ </a>
+ </li>
+ </ul>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#end-to-end-flow">
@@ -361,12 +385,19 @@ following setting to the Spark configuration when
launching <code class="docutil
<div class="highlight-default notranslate"><div
class="highlight"><pre><span></span><span class="o">--</span><span
class="n">conf</span> <span class="n">spark</span><span class="o">.</span><span
class="n">plugins</span><span class="o">=</span><span class="n">org</span><span
class="o">.</span><span class="n">apache</span><span class="o">.</span><span
class="n">spark</span><span class="o">.</span><span class="n">CometPlugin</span>
</pre></div>
</div>
+<p>This class is loaded by Spark’s plugin framework. It will be instantiated
in the Spark driver only. Comet does not
+provide any executor plugins.</p>
+<p>The plugin will update the current <code class="docutils literal
notranslate"><span class="pre">SparkConf</span></code> with the extra
configuration provided by Comet, such as executor memory
+configuration.</p>
+<p>The plugin also registers <code class="docutils literal notranslate"><span
class="pre">CometSparkSessionExtensions</span></code> with Spark’s extension
API.</p>
+</section>
+<section id="cometsparksessionextensions">
+<h2>CometSparkSessionExtensions<a class="headerlink"
href="#cometsparksessionextensions" title="Link to this heading">¶</a></h2>
<p>On initialization, this class registers two physical plan optimization
rules with Spark: <code class="docutils literal notranslate"><span
class="pre">CometScanRule</span></code>
and <code class="docutils literal notranslate"><span
class="pre">CometExecRule</span></code>. These rules run whenever a query stage
is being planned during Adaptive Query Execution, and
run once for the entire plan when Adaptive Query Execution is disabled.</p>
-</section>
<section id="cometscanrule">
-<h2>CometScanRule<a class="headerlink" href="#cometscanrule" title="Link to
this heading">¶</a></h2>
+<h3>CometScanRule<a class="headerlink" href="#cometscanrule" title="Link to
this heading">¶</a></h3>
<p><code class="docutils literal notranslate"><span
class="pre">CometScanRule</span></code> replaces any Parquet scans with Comet
operators. There are different paths for Spark v1 and v2 data sources.</p>
<p>When reading from Parquet v1 data sources, Comet replaces <code
class="docutils literal notranslate"><span
class="pre">FileSourceScanExec</span></code> with a <code class="docutils
literal notranslate"><span class="pre">CometScanExec</span></code>, and for v2
data sources, <code class="docutils literal notranslate"><span
class="pre">BatchScanExec</span></code> is replaced with <code class="docutils
literal notranslate"><span class="pre">CometBatchScanExec</span></code>. In
both cases, Comet replaces Spark’s Parquet
@@ -374,13 +405,13 @@ reader with a custom vectorized Parquet reader. This is
similar to Spark’s vec
Parquet data source but leverages native code for decoding Parquet row groups
directly into Arrow format.</p>
<p>Comet only supports a subset of data types and will fall back to Spark’s
scan if unsupported types
exist. Comet can still accelerate the rest of the query execution in this case
because <code class="docutils literal notranslate"><span
class="pre">CometSparkToColumnarExec</span></code> will
-convert the output from Spark’s can to Arrow arrays. Note that both <code
class="docutils literal notranslate"><span
class="pre">spark.comet.exec.enabled=true</span></code> and
+convert the output from Spark’s scan to Arrow arrays. Note that both <code
class="docutils literal notranslate"><span
class="pre">spark.comet.exec.enabled=true</span></code> and
<code class="docutils literal notranslate"><span
class="pre">spark.comet.convert.parquet.enabled=true</span></code> must be set
to enable this conversion.</p>
<p>Refer to the <a class="reference external"
href="https://datafusion.apache.org/comet/user-guide/datatypes.html";>Supported
Spark Data Types</a> section
in the contributor guide to see a list of currently supported data types.</p>
</section>
<section id="cometexecrule">
-<h2>CometExecRule<a class="headerlink" href="#cometexecrule" title="Link to
this heading">¶</a></h2>
+<h3>CometExecRule<a class="headerlink" href="#cometexecrule" title="Link to
this heading">¶</a></h3>
<p>This rule traverses bottom-up from the original Spark plan and attempts to
replace each operator with a Comet equivalent.
For example, a <code class="docutils literal notranslate"><span
class="pre">ProjectExec</span></code> will be replaced by <code class="docutils
literal notranslate"><span class="pre">CometProjectExec</span></code>.</p>
<p>When replacing a node, various checks are performed to determine if Comet
can support the operator and its expressions.
@@ -390,25 +421,51 @@ underlying Spark node and the plan will not be
converted.</p>
transitions to convert between row-based and columnar data between Spark
operators and Comet operators and the overhead
of this could outweigh the benefits of running parts of the query stage
natively in Comet.</p>
</section>
+</section>
<section id="query-execution">
<h2>Query Execution<a class="headerlink" href="#query-execution" title="Link
to this heading">¶</a></h2>
-<p>Once the plan has been transformed, any consecutive Comet operators are
combined into a <code class="docutils literal notranslate"><span
class="pre">CometNativeExec</span></code> which contains
-a serialized version of the plan (the serialization code can be found in <code
class="docutils literal notranslate"><span
class="pre">QueryPlanSerde</span></code>). When this operator is
-executed, the serialized plan is passed to the native code when calling <code
class="docutils literal notranslate"><span
class="pre">Native.createPlan</span></code>.</p>
-<p>In the native code there is a <code class="docutils literal
notranslate"><span class="pre">PhysicalPlanner</span></code> struct (in <code
class="docutils literal notranslate"><span
class="pre">planner.rs</span></code>) which converts the serialized plan into an
+<p>Once the plan has been transformed, any consecutive native Comet operators
are combined into a <code class="docutils literal notranslate"><span
class="pre">CometNativeExec</span></code> which contains
+a protocol buffer serialized version of the plan (the serialization code can
be found in <code class="docutils literal notranslate"><span
class="pre">QueryPlanSerde</span></code>).</p>
+<p>Spark serializes the physical plan and sends it to the executors when
executing tasks. The executors deserialize the
+plan and invoke it.</p>
+<p>When <code class="docutils literal notranslate"><span
class="pre">CometNativeExec</span></code> is invoked, it will pass the
serialized protobuf plan into
+<code class="docutils literal notranslate"><span
class="pre">Native.createPlan</span></code>, which invokes the native code via
JNI, where the plan is then deserialized.</p>
+<p>In the native code there is a <code class="docutils literal
notranslate"><span class="pre">PhysicalPlanner</span></code> struct (in <code
class="docutils literal notranslate"><span
class="pre">planner.rs</span></code>) which converts the deserialized plan into
an
Apache DataFusion <code class="docutils literal notranslate"><span
class="pre">ExecutionPlan</span></code>. In some cases, Comet provides
specialized physical operators and expressions to
override the DataFusion versions to ensure compatibility with Apache Spark.</p>
-<p><code class="docutils literal notranslate"><span
class="pre">CometExecIterator</span></code> will invoke <code class="docutils
literal notranslate"><span class="pre">Native.executePlan</span></code> to pull
the next batch from the native plan. This is repeated
-until no more batches are available (meaning that all data has been processed
by the native plan).</p>
-<p>The leaf nodes in the physical plan are always <code class="docutils
literal notranslate"><span class="pre">ScanExec</span></code> and these
operators consume batches of Arrow data that were
-prepared before the plan is executed. When <code class="docutils literal
notranslate"><span class="pre">CometExecIterator</span></code> invokes <code
class="docutils literal notranslate"><span
class="pre">Native.executePlan</span></code> it passes the memory
-addresses of these Arrow arrays to the native code.</p>
-<p><img alt="Diagram of Comet Native Execution"
src="../_static/images/CometOverviewDetailed.drawio.svg" /></p>
+<p>The leaf nodes in the physical plan are always <code class="docutils
literal notranslate"><span class="pre">ScanExec</span></code> and each of these
operators will make a JNI call to
+<code class="docutils literal notranslate"><span
class="pre">CometBatchIterator.next()</span></code> to fetch the next input
batch. The input could be a Comet native Parquet scan,
+a Spark exchange, or another native plan.</p>
+<p><code class="docutils literal notranslate"><span
class="pre">CometNativeExec</span></code> creates a <code class="docutils
literal notranslate"><span class="pre">CometExecIterator</span></code> and
applies this iterator to the input RDD
+partitions. Each call to <code class="docutils literal notranslate"><span
class="pre">CometExecIterator.next()</span></code> will invoke <code
class="docutils literal notranslate"><span
class="pre">Native.executePlan</span></code>. Once the plan finishes
+executing, the resulting Arrow batches are imported into the JVM using Arrow
FFI.</p>
+</section>
+<section id="arrow">
+<h2>Arrow<a class="headerlink" href="#arrow" title="Link to this
heading">¶</a></h2>
+<p>Due to the hybrid execution model, it is necessary to pass batches of data
between the JVM and native code.</p>
+<p>Comet uses a combination of Arrow FFI and Arrow IPC to achieve this.</p>
+<section id="arrow-ffi">
+<h3>Arrow FFI<a class="headerlink" href="#arrow-ffi" title="Link to this
heading">¶</a></h3>
+<p>The foundation for Arrow FFI is the <a class="reference external"
href="https://arrow.apache.org/docs/format/CDataInterface.html";>Arrow C Data
Interface</a>, which provides a stable ABI-compatible interface for
+accessing Arrow data structures from multiple languages.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span
class="pre">CometExecIterator</span></code> invokes native plans and uses Arrow
FFI to read the output batches</p></li>
+<li><p>Native <code class="docutils literal notranslate"><span
class="pre">ScanExec</span></code> operators call <code class="docutils literal
notranslate"><span class="pre">CometBatchIterator</span></code> via JNI to
fetch input batches from the JVM</p></li>
+</ul>
+</section>
+<section id="arrow-ipc">
+<h3>Arrow IPC<a class="headerlink" href="#arrow-ipc" title="Link to this
heading">¶</a></h3>
+<p>Comet native shuffle uses Arrow IPC to write batches to the shuffle
files.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span
class="pre">CometShuffleWriteProcessor</span></code> invokes a native plan to
fetch batches and then passes them to native <code class="docutils literal
notranslate"><span class="pre">ShuffleWriterExec</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span
class="pre">CometBlockStoreShuffleReader</span></code> reads batches from
shuffle files</p></li>
+</ul>
+</section>
</section>
<section id="end-to-end-flow">
<h2>End to End Flow<a class="headerlink" href="#end-to-end-flow" title="Link
to this heading">¶</a></h2>
-<p>The following diagram shows the end-to-end flow.</p>
-<p><img alt="Diagram of Comet Native Parquet Scan"
src="../_static/images/CometNativeParquetReader.drawio.svg" /></p>
+<p>The following diagram shows an example of the end-to-end flow for a query
stage.</p>
+<p><img alt="Diagram of Comet Data Flow"
src="../_static/images/comet-dataflow.svg" /></p>
</section>
</section>
diff --git a/searchindex.js b/searchindex.js
index 68be9de8..8a441ad6 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. Install Comet": [[9, "install-comet"]], "2.
Clone Spark and Apply Diff": [[9, "clone-spark-and-apply-diff"]], "3. Run Spark
SQL Tests": [[9, "run-spark-sql-tests"]], "ANSI mode": [[11, "ansi-mode"]],
"API Differences Between Spark Versions": [[0,
"api-differences-between-spark-versions"]], "ASF Links": [[10, null]], "Adding
Spark-side Tests for the New Expression": [[0,
"adding-spark-side-tests-for-the-new-expression"]], "Adding a New Expression":
[[0, [...]
\ No newline at end of file
+Search.setIndex({"alltitles": {"1. Install Comet": [[9, "install-comet"]], "2.
Clone Spark and Apply Diff": [[9, "clone-spark-and-apply-diff"]], "3. Run Spark
SQL Tests": [[9, "run-spark-sql-tests"]], "ANSI mode": [[11, "ansi-mode"]],
"API Differences Between Spark Versions": [[0,
"api-differences-between-spark-versions"]], "ASF Links": [[10, null]], "Adding
Spark-side Tests for the New Expression": [[0,
"adding-spark-side-tests-for-the-new-expression"]], "Adding a New Expression":
[[0, [...]
\ No newline at end of file
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