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(datafusion-comet) branch asf-site updated: Publish built docs triggered by dba523d994f3f8336d2c5ca469c61672768611a1
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new 7339c4b23 Publish built docs triggered by dba523d994f3f8336d2c5ca469c61672768611a1
7339c4b23 is described below
commit 7339c4b23e22db6c1d30d1bc758e2a93df2f234c
Author: github-actions[bot] <gi...@users.noreply.github.com>
AuthorDate: Mon Nov 3 20:53:09 2025 +0000
Publish built docs triggered by dba523d994f3f8336d2c5ca469c61672768611a1
---
_sources/contributor-guide/index.md.txt | 1 +
_sources/contributor-guide/parquet_scans.md.txt | 137 ++++++++++++++++
_sources/user-guide/latest/compatibility.md.txt | 68 +-------
_sources/user-guide/latest/datasources.md.txt | 72 +--------
contributor-guide/adding_a_new_expression.html | 1 +
contributor-guide/benchmarking.html | 1 +
contributor-guide/contributing.html | 1 +
contributor-guide/debugging.html | 1 +
contributor-guide/development.html | 7 +-
contributor-guide/ffi.html | 7 +-
contributor-guide/index.html | 5 +
.../{tracing.html => parquet_scans.html} | 177 +++++++++++++++------
contributor-guide/plugin_overview.html | 1 +
contributor-guide/profiling_native_code.html | 1 +
contributor-guide/roadmap.html | 1 +
contributor-guide/spark-sql-tests.html | 1 +
contributor-guide/tracing.html | 1 +
objects.inv | Bin 1486 -> 1509 bytes
searchindex.js | 2 +-
user-guide/latest/compatibility.html | 80 +---------
user-guide/latest/datasources.html | 81 +---------
21 files changed, 315 insertions(+), 331 deletions(-)
diff --git a/_sources/contributor-guide/index.md.txt b/_sources/contributor-guide/index.md.txt
index ba4692a97..eb79f7ab5 100644
--- a/_sources/contributor-guide/index.md.txt
+++ b/_sources/contributor-guide/index.md.txt
@@ -26,6 +26,7 @@ under the License.
Getting Started <contributing>
Comet Plugin Overview <plugin_overview>
Arrow FFI <ffi>
+Parquet Scans <parquet_scans>
Development Guide <development>
Debugging Guide <debugging>
Benchmarking Guide <benchmarking>
diff --git a/_sources/contributor-guide/parquet_scans.md.txt b/_sources/contributor-guide/parquet_scans.md.txt
new file mode 100644
index 000000000..4aec9f347
--- /dev/null
+++ b/_sources/contributor-guide/parquet_scans.md.txt
@@ -0,0 +1,137 @@
+<!--
+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.
+-->
+
+# Comet Parquet Scan Implementations
+
+Comet currently has three distinct implementations of the Parquet scan operator. The configuration property
+`spark.comet.scan.impl` is used to select an implementation. The default setting is `spark.comet.scan.impl=auto`, and
+Comet will choose the most appropriate implementation based on the Parquet schema and other Comet configuration
+settings. Most users should not need to change this setting. However, it is possible to force Comet to try and use
+a particular implementation for all scan operations by setting this configuration property to one of the following
+implementations.
+
+| Implementation | Description |
+| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `native_comet` | This implementation provides strong compatibility with Spark but does not support complex types. This is the original scan implementation in Comet and may eventually be removed. |
+| `native_iceberg_compat` | This implementation delegates to DataFusion's `DataSourceExec` but uses a hybrid approach of JVM and native code. This scan is designed to be integrated with Iceberg in the future. |
+| `native_datafusion` | This experimental implementation delegates to DataFusion's `DataSourceExec` for full native execution. There are known compatibility issues when using this scan. |
+
+The `native_datafusion` and `native_iceberg_compat` scans provide the following benefits over the `native_comet`
+implementation:
+
+- Leverages the DataFusion community's ongoing improvements to `DataSourceExec`
+- Provides support for reading complex types (structs, arrays, and maps)
+- Removes the use of reusable mutable-buffers in Comet, which is complex to maintain
+- Improves performance
+
+The `native_datafusion` and `native_iceberg_compat` scans share the following limitations:
+
+- When reading Parquet files written by systems other than Spark that contain columns with the logical types `UINT_8`
+ or `UINT_16`, Comet will produce different results than Spark because Spark does not preserve or understand these
+ logical types. Arrow-based readers, such as DataFusion and Comet do respect these types and read the data as unsigned
+ rather than signed. By default, Comet will fall back to `native_comet` when scanning Parquet files containing `byte` or `short`
+ types (regardless of the logical type). This behavior can be disabled by setting
+ `spark.comet.scan.allowIncompatible=true`.
+- No support for default values that are nested types (e.g., maps, arrays, structs). Literal default values are supported.
+
+The `native_datafusion` scan has some additional limitations:
+
+- Bucketed scans are not supported
+- No support for row indexes
+- `PARQUET_FIELD_ID_READ_ENABLED` is not respected [#1758]
+- There are failures in the Spark SQL test suite [#1545]
+- Setting Spark configs `ignoreMissingFiles` or `ignoreCorruptFiles` to `true` is not compatible with Spark
+
+## S3 Support
+
+There are some
+
+### `native_comet`
+
+The default `native_comet` Parquet scan implementation reads data from S3 using the [Hadoop-AWS module](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html), which
+is identical to the approach commonly used with vanilla Spark. AWS credential configuration and other Hadoop S3A
+configurations works the same way as in vanilla Spark.
+
+### `native_datafusion` and `native_iceberg_compat`
+
+The `native_datafusion` and `native_iceberg_compat` Parquet scan implementations completely offload data loading
+to native code. They use the [`object_store` crate](https://crates.io/crates/object_store) to read data from S3 and
+support configuring S3 access using standard [Hadoop S3A configurations](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration) by translating them to
+the `object_store` crate's format.
+
+This implementation maintains compatibility with existing Hadoop S3A configurations, so existing code will
+continue to work as long as the configurations are supported and can be translated without loss of functionality.
+
+#### Additional S3 Configuration Options
+
+Beyond credential providers, the `native_datafusion` implementation supports additional S3 configuration options:
+
+| Option | Description |
+|--------|-------------|
+| `fs.s3a.endpoint` | The endpoint of the S3 service |
+| `fs.s3a.endpoint.region` | The AWS region for the S3 service. If not specified, the region will be auto-detected. |
+| `fs.s3a.path.style.access` | Whether to use path style access for the S3 service (true/false, defaults to virtual hosted style) |
+| `fs.s3a.requester.pays.enabled` | Whether to enable requester pays for S3 requests (true/false) |
+
+All configuration options support bucket-specific overrides using the pattern `fs.s3a.bucket.{bucket-name}.{option}`.
+
+#### Examples
+
+The following examples demonstrate how to configure S3 access with the `native_datafusion` Parquet scan implementation using different authentication methods.
+
+**Example 1: Simple Credentials**
+
+This example shows how to access a private S3 bucket using an access key and secret key. The `fs.s3a.aws.credentials.provider` configuration can be omitted since `org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider` is included in Hadoop S3A's default credential provider chain.
+
+```shell
+$SPARK_HOME/bin/spark-shell \
+...
+--conf spark.comet.scan.impl=native_datafusion \
+--conf spark.hadoop.fs.s3a.access.key=my-access-key \
+--conf spark.hadoop.fs.s3a.secret.key=my-secret-key
+...
+```
+
+**Example 2: Assume Role with Web Identity Token**
+
+This example demonstrates using an assumed role credential to access a private S3 bucket, where the base credential for assuming the role is provided by a web identity token credentials provider.
+
+```shell
+$SPARK_HOME/bin/spark-shell \
+...
+--conf spark.comet.scan.impl=native_datafusion \
+--conf spark.hadoop.fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider \
+--conf spark.hadoop.fs.s3a.assumed.role.arn=arn:aws:iam::123456789012:role/my-role \
+--conf spark.hadoop.fs.s3a.assumed.role.session.name=my-session \
+--conf spark.hadoop.fs.s3a.assumed.role.credentials.provider=com.amazonaws.auth.WebIdentityTokenCredentialsProvider
+...
+```
+
+#### Limitations
+
+The S3 support of `native_datafusion` has the following limitations:
+
+1. **Partial Hadoop S3A configuration support**: Not all Hadoop S3A configurations are currently supported. Only the configurations listed in the tables above are translated and applied to the underlying `object_store` crate.
+
+2. **Custom credential providers**: Custom implementations of AWS credential providers are not supported. The implementation only supports the standard credential providers listed in the table above. We are planning to add support for custom credential providers through a JNI-based adapter that will allow calling Java credential providers from native code. See [issue #1829](https://github.com/apache/datafusion-comet/issues/1829) for more details.
+
+
+
+[#1545]: https://github.com/apache/datafusion-comet/issues/1545
+[#1758]: https://github.com/apache/datafusion-comet/issues/1758
diff --git a/_sources/user-guide/latest/compatibility.md.txt b/_sources/user-guide/latest/compatibility.md.txt
index ac2be802d..908693ff5 100644
--- a/_sources/user-guide/latest/compatibility.md.txt
+++ b/_sources/user-guide/latest/compatibility.md.txt
@@ -25,59 +25,11 @@ This guide offers information about areas of functionality where there are known
## Parquet
-### Data Type Support
+Comet has the following limitations when reading Parquet files:
-Comet does not support reading decimals encoded in binary format.
-
-### Parquet Scans
-
-Comet currently has three distinct implementations of the Parquet scan operator. The configuration property
-`spark.comet.scan.impl` is used to select an implementation. The default setting is `spark.comet.scan.impl=auto`, and
-Comet will choose the most appropriate implementation based on the Parquet schema and other Comet configuration
-settings. Most users should not need to change this setting. However, it is possible to force Comet to try and use
-a particular implementation for all scan operations by setting this configuration property to one of the following
-implementations.
-
-| Implementation | Description |
-| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `native_comet` | This implementation provides strong compatibility with Spark but does not support complex types. This is the original scan implementation in Comet and may eventually be removed. |
-| `native_iceberg_compat` | This implementation delegates to DataFusion's `DataSourceExec` but uses a hybrid approach of JVM and native code. This scan is designed to be integrated with Iceberg in the future. |
-| `native_datafusion` | This experimental implementation delegates to DataFusion's `DataSourceExec` for full native execution. There are known compatibility issues when using this scan. |
-
-The `native_datafusion` and `native_iceberg_compat` scans provide the following benefits over the `native_comet`
-implementation:
-
-- Leverages the DataFusion community's ongoing improvements to `DataSourceExec`
-- Provides support for reading complex types (structs, arrays, and maps)
-- Removes the use of reusable mutable-buffers in Comet, which is complex to maintain
-- Improves performance
-
-The `native_datafusion` and `native_iceberg_compat` scans share the following limitations:
-
-- When reading Parquet files written by systems other than Spark that contain columns with the logical types `UINT_8`
- or `UINT_16`, Comet will produce different results than Spark because Spark does not preserve or understand these
- logical types. Arrow-based readers, such as DataFusion and Comet do respect these types and read the data as unsigned
- rather than signed. By default, Comet will fall back to `native_comet` when scanning Parquet files containing `byte` or `short`
- types (regardless of the logical type). This behavior can be disabled by setting
- `spark.comet.scan.allowIncompatible=true`.
+- Comet does not support reading decimals encoded in binary format.
- No support for default values that are nested types (e.g., maps, arrays, structs). Literal default values are supported.
-The `native_datafusion` scan has some additional limitations:
-
-- Bucketed scans are not supported
-- No support for row indexes
-- `PARQUET_FIELD_ID_READ_ENABLED` is not respected [#1758]
-- There are failures in the Spark SQL test suite [#1545]
-- Setting Spark configs `ignoreMissingFiles` or `ignoreCorruptFiles` to `true` is not compatible with Spark
-
-[#1545]: https://github.com/apache/datafusion-comet/issues/1545
-[#1758]: https://github.com/apache/datafusion-comet/issues/1758
-
-### S3 Support with `native_iceberg_compat`
-
-- When using the default AWS S3 endpoint (no custom endpoint configured), a valid region is required. Comet
- will attempt to resolve the region if it is not provided.
-
## ANSI Mode
Comet will fall back to Spark for the following expressions when ANSI mode is enabled, unless
@@ -101,18 +53,14 @@ Sorting on floating-point data types (or complex types containing floating-point
Spark if the data contains both zero and negative zero. This is likely an edge case that is not of concern for many users
and sorting on floating-point data can be enabled by setting `spark.comet.expression.SortOrder.allowIncompatible=true`.
-There is a known bug with using count(distinct) within aggregate queries, where each NaN value will be counted
-separately [#1824](https://github.com/apache/datafusion-comet/issues/1824).
-
## Incompatible Expressions
-Some Comet native expressions are not 100% compatible with Spark and are disabled by default. These expressions
-will fall back to Spark but can be enabled by setting `spark.comet.expression.allowIncompatible=true`.
-
-## Array Expressions
+Expressions that are not 100% Spark-compatible will fall back to Spark by default and can be enabled by setting
+`spark.comet.expression.EXPRNAME.allowIncompatible=true`, where `EXPRNAME` is the Spark expression class name. See
+the [Comet Supported Expressions Guide](expressions.md) for more information on this configuration setting.
-Comet has experimental support for a number of array expressions. These are experimental and currently marked
-as incompatible and can be enabled by setting `spark.comet.expression.allowIncompatible=true`.
+It is also possible to specify `spark.comet.expression.allowIncompatible=true` to enable all
+incompatible expressions.
## Regular Expressions
@@ -127,7 +75,7 @@ Cast operations in Comet fall into three levels of support:
- **Compatible**: The results match Apache Spark
- **Incompatible**: The results may match Apache Spark for some inputs, but there are known issues where some inputs
will result in incorrect results or exceptions. The query stage will fall back to Spark by default. Setting
- `spark.comet.expression.allowIncompatible=true` will allow all incompatible casts to run natively in Comet, but this is not
+ `spark.comet.expression.Cast.allowIncompatible=true` will allow all incompatible casts to run natively in Comet, but this is not
recommended for production use.
- **Unsupported**: Comet does not provide a native version of this cast expression and the query stage will fall back to
Spark.
diff --git a/_sources/user-guide/latest/datasources.md.txt b/_sources/user-guide/latest/datasources.md.txt
index 98bd61f71..14d0ecc15 100644
--- a/_sources/user-guide/latest/datasources.md.txt
+++ b/_sources/user-guide/latest/datasources.md.txt
@@ -163,23 +163,11 @@ Or use `spark-shell` with HDFS support as described [above](#using-experimental-
## S3
-DataFusion Comet has [multiple Parquet scan implementations](./compatibility.md#parquet-scans) that use different approaches to read data from S3.
-
-### `native_comet`
-
-The default `native_comet` Parquet scan implementation reads data from S3 using the [Hadoop-AWS module](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html), which is identical to the approach commonly used with vanilla Spark. AWS credential configuration and other Hadoop S3A configurations works the same way as in vanilla Spark.
-
-### `native_datafusion` and `native_iceberg_compat`
-
-The `native_datafusion` and `native_iceberg_compat` Parquet scan implementations completely offload data loading to native code. They use the [`object_store` crate](https://crates.io/crates/object_store) to read data from S3 and support configuring S3 access using standard [Hadoop S3A configurations](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration) by translating them to the `object_store` crate's format.
-
-This implementation maintains compatibility with existing Hadoop S3A configurations, so existing code will continue to work as long as the configurations are supported and can be translated without loss of functionality.
-
#### Root CA Certificates
-One major difference between `native_comet` and the other scan implementations is the mechanism for discovering Root
-CA Certificates. The `native_comet` scan uses the JVM to read CA Certificates from the Java Trust Store, but the native
-scan implementations `native_datafusion` and `native_iceberg_compat` use system Root CA Certificates (typically stored
+One major difference between Spark and Comet is the mechanism for discovering Root
+CA Certificates. Spark uses the JVM to read CA Certificates from the Java Trust Store, but native Comet
+scans use system Root CA Certificates (typically stored
in `/etc/ssl/certs` on Linux). These scans will not be able to interact with S3 if the Root CA Certificates are not
installed.
@@ -200,57 +188,3 @@ AWS credential providers can be configured using the `fs.s3a.aws.credentials.pro
| `com.amazonaws.auth.WebIdentityTokenCredentialsProvider`<br/>`software.amazon.awssdk.auth.credentials.WebIdentityTokenFileCredentialsProvider` | Authenticate using web identity token file | None |
Multiple credential providers can be specified in a comma-separated list using the `fs.s3a.aws.credentials.provider` configuration, just as Hadoop AWS supports. If `fs.s3a.aws.credentials.provider` is not configured, Hadoop S3A's default credential provider chain will be used. All configuration options also support bucket-specific overrides using the pattern `fs.s3a.bucket.{bucket-name}.{option}`.
-
-#### Additional S3 Configuration Options
-
-Beyond credential providers, the `native_datafusion` implementation supports additional S3 configuration options:
-
-| Option | Description |
-|--------|-------------|
-| `fs.s3a.endpoint` | The endpoint of the S3 service |
-| `fs.s3a.endpoint.region` | The AWS region for the S3 service. If not specified, the region will be auto-detected. |
-| `fs.s3a.path.style.access` | Whether to use path style access for the S3 service (true/false, defaults to virtual hosted style) |
-| `fs.s3a.requester.pays.enabled` | Whether to enable requester pays for S3 requests (true/false) |
-
-All configuration options support bucket-specific overrides using the pattern `fs.s3a.bucket.{bucket-name}.{option}`.
-
-#### Examples
-
-The following examples demonstrate how to configure S3 access with the `native_datafusion` Parquet scan implementation using different authentication methods.
-
-**Example 1: Simple Credentials**
-
-This example shows how to access a private S3 bucket using an access key and secret key. The `fs.s3a.aws.credentials.provider` configuration can be omitted since `org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider` is included in Hadoop S3A's default credential provider chain.
-
-```shell
-$SPARK_HOME/bin/spark-shell \
-...
---conf spark.comet.scan.impl=native_datafusion \
---conf spark.hadoop.fs.s3a.access.key=my-access-key \
---conf spark.hadoop.fs.s3a.secret.key=my-secret-key
-...
-```
-
-**Example 2: Assume Role with Web Identity Token**
-
-This example demonstrates using an assumed role credential to access a private S3 bucket, where the base credential for assuming the role is provided by a web identity token credentials provider.
-
-```shell
-$SPARK_HOME/bin/spark-shell \
-...
---conf spark.comet.scan.impl=native_datafusion \
---conf spark.hadoop.fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider \
---conf spark.hadoop.fs.s3a.assumed.role.arn=arn:aws:iam::123456789012:role/my-role \
---conf spark.hadoop.fs.s3a.assumed.role.session.name=my-session \
---conf spark.hadoop.fs.s3a.assumed.role.credentials.provider=com.amazonaws.auth.WebIdentityTokenCredentialsProvider
-...
-```
-
-#### Limitations
-
-The S3 support of `native_datafusion` has the following limitations:
-
-1. **Partial Hadoop S3A configuration support**: Not all Hadoop S3A configurations are currently supported. Only the configurations listed in the tables above are translated and applied to the underlying `object_store` crate.
-
-2. **Custom credential providers**: Custom implementations of AWS credential providers are not supported. The implementation only supports the standard credential providers listed in the table above. We are planning to add support for custom credential providers through a JNI-based adapter that will allow calling Java credential providers from native code. See [issue #1829](https://github.com/apache/datafusion-comet/issues/1829) for more details.
-
diff --git a/contributor-guide/adding_a_new_expression.html b/contributor-guide/adding_a_new_expression.html
index d749ae49b..28c2a9c48 100644
--- a/contributor-guide/adding_a_new_expression.html
+++ b/contributor-guide/adding_a_new_expression.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/benchmarking.html b/contributor-guide/benchmarking.html
index 3d65c3ff5..6723a56bf 100644
--- a/contributor-guide/benchmarking.html
+++ b/contributor-guide/benchmarking.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Benchmarking Guide</a></li>
diff --git a/contributor-guide/contributing.html b/contributor-guide/contributing.html
index 4c91dfb07..31fba8be2 100644
--- a/contributor-guide/contributing.html
+++ b/contributor-guide/contributing.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/debugging.html b/contributor-guide/debugging.html
index 197b3a3e5..445e001bd 100644
--- a/contributor-guide/debugging.html
+++ b/contributor-guide/debugging.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/development.html b/contributor-guide/development.html
index c60c4dd6b..a034984d1 100644
--- a/contributor-guide/development.html
+++ b/contributor-guide/development.html
@@ -66,7 +66,7 @@ under the License.
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="Comet Debugging Guide" href="debugging.html" />
- <link rel="prev" title="Arrow FFI Usage in Comet" href="ffi.html" />
+ <link rel="prev" title="Comet Parquet Scan Implementations" href="parquet_scans.html" />
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@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
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<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
@@ -601,12 +602,12 @@ cargo<span class="w"> </span>clippy<span class="w"> </span>--color<span class="o
<div class="prev-next-area">
<a class="left-prev"
- href="ffi.html"
+ href="parquet_scans.html"
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- <p class="prev-next-title">Arrow FFI Usage in Comet</p>
+ <p class="prev-next-title">Comet Parquet Scan Implementations</p>
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diff --git a/contributor-guide/ffi.html b/contributor-guide/ffi.html
index 93dfc9e12..c8585787a 100644
--- a/contributor-guide/ffi.html
+++ b/contributor-guide/ffi.html
@@ -65,7 +65,7 @@ under the License.
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<link rel="index" title="Index" href="../genindex.html" />
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- <link rel="next" title="Comet Development Guide" href="development.html" />
+ <link rel="next" title="Comet Parquet Scan Implementations" href="parquet_scans.html" />
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@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
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<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
@@ -832,11 +833,11 @@ t4 Batch handle released ArrowBuf freed Data freed
</div>
</a>
<a class="right-next"
- href="development.html"
+ href="parquet_scans.html"
title="next page">
<div class="prev-next-info">
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- <p class="prev-next-title">Comet Development Guide</p>
+ <p class="prev-next-title">Comet Parquet Scan Implementations</p>
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diff --git a/contributor-guide/index.html b/contributor-guide/index.html
index 40f181483..52f0039f6 100644
--- a/contributor-guide/index.html
+++ b/contributor-guide/index.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
@@ -478,6 +479,10 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="ffi.html#further-reading">Further Reading</a></li>
</ul>
</li>
+<li class="toctree-l1"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html#s3-support">S3 Support</a></li>
+</ul>
+</li>
<li class="toctree-l1"><a class="reference internal" href="development.html">Development Guide</a><ul>
<li class="toctree-l2"><a class="reference internal" href="development.html#project-layout">Project Layout</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html#development-setup">Development Setup</a></li>
diff --git a/contributor-guide/tracing.html b/contributor-guide/parquet_scans.html
similarity index 55%
copy from contributor-guide/tracing.html
copy to contributor-guide/parquet_scans.html
index 74cffacaa..e0589b79b 100644
--- a/contributor-guide/tracing.html
+++ b/contributor-guide/parquet_scans.html
@@ -27,7 +27,7 @@ under the License.
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
- <title>Tracing — Apache DataFusion Comet documentation</title>
+ <title>Comet Parquet Scan Implementations — Apache DataFusion Comet documentation</title>
@@ -61,12 +61,12 @@ under the License.
<script src="../_static/documentation_options.js?v=5929fcd5"></script>
<script src="../_static/doctools.js?v=9a2dae69"></script>
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
- <script>DOCUMENTATION_OPTIONS.pagename = 'contributor-guide/tracing';</script>
+ <script>DOCUMENTATION_OPTIONS.pagename = 'contributor-guide/parquet_scans';</script>
<script async="true" defer="true" src="https://buttons.github.io/buttons.js"></script>
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
- <link rel="next" title="Profiling Native Code" href="profiling_native_code.html" />
- <link rel="prev" title="Adding a New Expression" href="adding_a_new_expression.html" />
+ <link rel="next" title="Comet Development Guide" href="development.html" />
+ <link rel="prev" title="Arrow FFI Usage in Comet" href="ffi.html" />
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@@ -357,11 +357,12 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="adding_a_new_expression.html">Adding a New Expression</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Tracing</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tracing.html">Tracing</a></li>
<li class="toctree-l2"><a class="reference internal" href="profiling_native_code.html">Profiling Native Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="spark-sql-tests.html">Spark SQL Tests</a></li>
<li class="toctree-l2"><a class="reference internal" href="roadmap.html">Roadmap</a></li>
@@ -414,7 +415,7 @@ under the License.
<li class="breadcrumb-item"><a href="index.html" class="nav-link">Comet Contributor Guide</a></li>
- <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Tracing</span></li>
+ <li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Comet Parquet Scan Implementations</span></li>
</ul>
</nav>
</div>
@@ -449,56 +450,138 @@ KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
-<section id="tracing">
-<h1>Tracing<a class="headerlink" href="#tracing" title="Link to this heading">#</a></h1>
-<p>Tracing can be enabled by setting <code class="docutils literal notranslate"><span class="pre">spark.comet.tracing.enabled=true</span></code>.</p>
-<p>With this feature enabled, each Spark executor will write a JSON event log file in
-Chrome’s <a class="reference external" href="https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview?tab=t.0#heading=h.yr4qxyxotyw">Trace Event Format</a>. The file will be written to the executor’s current working
-directory with the filename <code class="docutils literal notranslate"><span class="pre">comet-event-trace.json</span></code>.</p>
-<p>Additionally, enabling the <code class="docutils literal notranslate"><span class="pre">jemalloc</span></code> feature will enable tracing of native memory allocations.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>release<span class="w"> </span><span class="nv">COMET_FEATURES</span><span class="o">=</span><span class="s2">"jemalloc"</span>
-</pre></div>
-</div>
-<p>Example output:</p>
-<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"decodeShuffleBlock"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </spa [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"decodeShuffleBlock"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span class="w"> [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"decodeShuffleBlock"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span class="w"> [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"decodeShuffleBlock"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span class="w"> [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"execute_plan"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span class="w"> </span [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"CometExecIterator_getNextBatch"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span [...]
-<span class="p">{</span><span class="w"> </span><span class="nt">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"CometExecIterator_getNextBatch"</span><span class="p">,</span><span class="w"> </span><span class="nt">"cat"</span><span class="p">:</span><span class="w"> </span><span class="s2">"PERF"</span><span class="p">,</span><span class="w"> </span><span class="nt">"ph"</span><span class="p">:</span><span [...]
-</pre></div>
-</div>
-<p>Traces can be viewed with <a class="reference external" href="https://github.com/catapult-project/catapult/blob/main/tracing/README.md">Trace Viewer</a>.</p>
-<p>Example trace visualization:</p>
-<p><img alt="tracing" src="../_images/tracing.png" /></p>
-<section id="definition-of-labels">
-<h2>Definition of Labels<a class="headerlink" href="#definition-of-labels" title="Link to this heading">#</a></h2>
+<section id="comet-parquet-scan-implementations">
+<h1>Comet Parquet Scan Implementations<a class="headerlink" href="#comet-parquet-scan-implementations" title="Link to this heading">#</a></h1>
+<p>Comet currently has three distinct implementations of the Parquet scan operator. The configuration property
+<code class="docutils literal notranslate"><span class="pre">spark.comet.scan.impl</span></code> is used to select an implementation. The default setting is <code class="docutils literal notranslate"><span class="pre">spark.comet.scan.impl=auto</span></code>, and
+Comet will choose the most appropriate implementation based on the Parquet schema and other Comet configuration
+settings. Most users should not need to change this setting. However, it is possible to force Comet to try and use
+a particular implementation for all scan operations by setting this configuration property to one of the following
+implementations.</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
-<tr class="row-odd"><th class="head"><p>Label</p></th>
-<th class="head"><p>Meaning</p></th>
+<tr class="row-odd"><th class="head"><p>Implementation</p></th>
+<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
-<tr class="row-even"><td><p>jvm_heapUsed</p></td>
-<td><p>JVM heap memory usage of live objects for the executor process</p></td>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">native_comet</span></code></p></td>
+<td><p>This implementation provides strong compatibility with Spark but does not support complex types. This is the original scan implementation in Comet and may eventually be removed.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code></p></td>
+<td><p>This implementation delegates to DataFusion’s <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code> but uses a hybrid approach of JVM and native code. This scan is designed to be integrated with Iceberg in the future.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code></p></td>
+<td><p>This experimental implementation delegates to DataFusion’s <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code> for full native execution. There are known compatibility issues when using this scan.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> scans provide the following benefits over the <code class="docutils literal notranslate"><span class="pre">native_comet</span></code>
+implementation:</p>
+<ul class="simple">
+<li><p>Leverages the DataFusion community’s ongoing improvements to <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code></p></li>
+<li><p>Provides support for reading complex types (structs, arrays, and maps)</p></li>
+<li><p>Removes the use of reusable mutable-buffers in Comet, which is complex to maintain</p></li>
+<li><p>Improves performance</p></li>
+</ul>
+<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> scans share the following limitations:</p>
+<ul class="simple">
+<li><p>When reading Parquet files written by systems other than Spark that contain columns with the logical types <code class="docutils literal notranslate"><span class="pre">UINT_8</span></code>
+or <code class="docutils literal notranslate"><span class="pre">UINT_16</span></code>, Comet will produce different results than Spark because Spark does not preserve or understand these
+logical types. Arrow-based readers, such as DataFusion and Comet do respect these types and read the data as unsigned
+rather than signed. By default, Comet will fall back to <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> when scanning Parquet files containing <code class="docutils literal notranslate"><span class="pre">byte</span></code> or <code class="docutils literal notranslate"><span class="pre">short</span></code>
+types (regardless of the logical type). This behavior can be disabled by setting
+<code class="docutils literal notranslate"><span class="pre">spark.comet.scan.allowIncompatible=true</span></code>.</p></li>
+<li><p>No support for default values that are nested types (e.g., maps, arrays, structs). Literal default values are supported.</p></li>
+</ul>
+<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> scan has some additional limitations:</p>
+<ul class="simple">
+<li><p>Bucketed scans are not supported</p></li>
+<li><p>No support for row indexes</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">PARQUET_FIELD_ID_READ_ENABLED</span></code> is not respected <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1758">#1758</a></p></li>
+<li><p>There are failures in the Spark SQL test suite <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1545">#1545</a></p></li>
+<li><p>Setting Spark configs <code class="docutils literal notranslate"><span class="pre">ignoreMissingFiles</span></code> or <code class="docutils literal notranslate"><span class="pre">ignoreCorruptFiles</span></code> to <code class="docutils literal notranslate"><span class="pre">true</span></code> is not compatible with Spark</p></li>
+</ul>
+<section id="s3-support">
+<h2>S3 Support<a class="headerlink" href="#s3-support" title="Link to this heading">#</a></h2>
+<p>There are some</p>
+<section id="native-comet">
+<h3><code class="docutils literal notranslate"><span class="pre">native_comet</span></code><a class="headerlink" href="#native-comet" title="Link to this heading">#</a></h3>
+<p>The default <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> Parquet scan implementation reads data from S3 using the <a class="reference external" href="https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html">Hadoop-AWS module</a>, which
+is identical to the approach commonly used with vanilla Spark. AWS credential configuration and other Hadoop S3A
+configurations works the same way as in vanilla Spark.</p>
+</section>
+<section id="native-datafusion-and-native-iceberg-compat">
+<h3><code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code><a class="headerlink" href="#native-datafusion-and-native-iceberg-compat" title="Link to this heading">#</a></h3>
+<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> Parquet scan implementations completely offload data loading
+to native code. They use the <a class="reference external" href="https://crates.io/crates/object_store"><code class="docutils literal notranslate"><span class="pre">object_store</span></code> crate</a> to read data from S3 and
+support configuring S3 access using standard <a class="reference external" href="https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration">Hadoop S3A configurations</a> by translating them to
+the <code class="docutils literal notranslate"><span class="pre">object_store</span></code> crate’s format.</p>
+<p>This implementation maintains compatibility with existing Hadoop S3A configurations, so existing code will
+continue to work as long as the configurations are supported and can be translated without loss of functionality.</p>
+<section id="additional-s3-configuration-options">
+<h4>Additional S3 Configuration Options<a class="headerlink" href="#additional-s3-configuration-options" title="Link to this heading">#</a></h4>
+<p>Beyond credential providers, the <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> implementation supports additional S3 configuration options:</p>
+<div class="pst-scrollable-table-container"><table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Option</p></th>
+<th class="head"><p>Description</p></th>
</tr>
-<tr class="row-odd"><td><p>jemalloc_allocated</p></td>
-<td><p>Native memory usage for the executor process</p></td>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.endpoint</span></code></p></td>
+<td><p>The endpoint of the S3 service</p></td>
</tr>
-<tr class="row-even"><td><p>task_memory_comet_NNN</p></td>
-<td><p>Off-heap memory allocated by Comet for query execution</p></td>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.endpoint.region</span></code></p></td>
+<td><p>The AWS region for the S3 service. If not specified, the region will be auto-detected.</p></td>
</tr>
-<tr class="row-odd"><td><p>task_memory_spark_NNN</p></td>
-<td><p>On-heap & Off-heap memory allocated by Spark</p></td>
+<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.path.style.access</span></code></p></td>
+<td><p>Whether to use path style access for the S3 service (true/false, defaults to virtual hosted style)</p></td>
</tr>
-<tr class="row-even"><td><p>comet_shuffle_NNN</p></td>
-<td><p>Off-heap memory allocated by Comet for columnar shuffle</p></td>
+<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.requester.pays.enabled</span></code></p></td>
+<td><p>Whether to enable requester pays for S3 requests (true/false)</p></td>
</tr>
</tbody>
</table>
</div>
+<p>All configuration options support bucket-specific overrides using the pattern <code class="docutils literal notranslate"><span class="pre">fs.s3a.bucket.{bucket-name}.{option}</span></code>.</p>
+</section>
+<section id="examples">
+<h4>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h4>
+<p>The following examples demonstrate how to configure S3 access with the <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> Parquet scan implementation using different authentication methods.</p>
+<p><strong>Example 1: Simple Credentials</strong></p>
+<p>This example shows how to access a private S3 bucket using an access key and secret key. The <code class="docutils literal notranslate"><span class="pre">fs.s3a.aws.credentials.provider</span></code> configuration can be omitted since <code class="docutils literal notranslate"><span class="pre">org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider</span></code> is included in Hadoop S3A’s default credential provider chain.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">$SPARK_HOME</span>/bin/spark-shell<span class="w"> </span><span class="se">\</span>
+...
+--conf<span class="w"> </span>spark.comet.scan.impl<span class="o">=</span>native_datafusion<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.access.key<span class="o">=</span>my-access-key<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.secret.key<span class="o">=</span>my-secret-key
+...
+</pre></div>
+</div>
+<p><strong>Example 2: Assume Role with Web Identity Token</strong></p>
+<p>This example demonstrates using an assumed role credential to access a private S3 bucket, where the base credential for assuming the role is provided by a web identity token credentials provider.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">$SPARK_HOME</span>/bin/spark-shell<span class="w"> </span><span class="se">\</span>
+...
+--conf<span class="w"> </span>spark.comet.scan.impl<span class="o">=</span>native_datafusion<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.aws.credentials.provider<span class="o">=</span>org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.arn<span class="o">=</span>arn:aws:iam::123456789012:role/my-role<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.session.name<span class="o">=</span>my-session<span class="w"> </span><span class="se">\</span>
+--conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.credentials.provider<span class="o">=</span>com.amazonaws.auth.WebIdentityTokenCredentialsProvider
+...
+</pre></div>
+</div>
+</section>
+<section id="limitations">
+<h4>Limitations<a class="headerlink" href="#limitations" title="Link to this heading">#</a></h4>
+<p>The S3 support of <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> has the following limitations:</p>
+<ol class="arabic simple">
+<li><p><strong>Partial Hadoop S3A configuration support</strong>: Not all Hadoop S3A configurations are currently supported. Only the configurations listed in the tables above are translated and applied to the underlying <code class="docutils literal notranslate"><span class="pre">object_store</span></code> crate.</p></li>
+<li><p><strong>Custom credential providers</strong>: Custom implementations of AWS credential providers are not supported. The implementation only supports the standard credential providers listed in the table above. We are planning to add support for custom credential providers through a JNI-based adapter that will allow calling Java credential providers from native code. See <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1829">issue #1829</a> for [...]
+</ol>
+</section>
+</section>
</section>
</section>
@@ -513,20 +596,20 @@ directory with the filename <code class="docutils literal notranslate"><span cla
<div class="prev-next-area">
<a class="left-prev"
- href="adding_a_new_expression.html"
+ href="ffi.html"
title="previous page">
<i class="fa-solid fa-angle-left"></i>
<div class="prev-next-info">
<p class="prev-next-subtitle">previous</p>
- <p class="prev-next-title">Adding a New Expression</p>
+ <p class="prev-next-title">Arrow FFI Usage in Comet</p>
</div>
</a>
<a class="right-next"
- href="profiling_native_code.html"
+ href="development.html"
title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
- <p class="prev-next-title">Profiling Native Code</p>
+ <p class="prev-next-title">Comet Development Guide</p>
</div>
<i class="fa-solid fa-angle-right"></i>
</a>
diff --git a/contributor-guide/plugin_overview.html b/contributor-guide/plugin_overview.html
index 43df5e530..cd632fef3 100644
--- a/contributor-guide/plugin_overview.html
+++ b/contributor-guide/plugin_overview.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2 current"><a class="current reference internal" href="#">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/profiling_native_code.html b/contributor-guide/profiling_native_code.html
index eef562b10..12afcbc1c 100644
--- a/contributor-guide/profiling_native_code.html
+++ b/contributor-guide/profiling_native_code.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/roadmap.html b/contributor-guide/roadmap.html
index ce9b33aa3..8bd2dd75d 100644
--- a/contributor-guide/roadmap.html
+++ b/contributor-guide/roadmap.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/spark-sql-tests.html b/contributor-guide/spark-sql-tests.html
index 6652284c8..33b687156 100644
--- a/contributor-guide/spark-sql-tests.html
+++ b/contributor-guide/spark-sql-tests.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/contributor-guide/tracing.html b/contributor-guide/tracing.html
index 74cffacaa..2245a3ce0 100644
--- a/contributor-guide/tracing.html
+++ b/contributor-guide/tracing.html
@@ -357,6 +357,7 @@ under the License.
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html">Comet Plugin Architecture</a></li>
<li class="toctree-l2"><a class="reference internal" href="plugin_overview.html#plugin-components">Plugin Components</a></li>
<li class="toctree-l2"><a class="reference internal" href="ffi.html">Arrow FFI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="parquet_scans.html">Parquet Scans</a></li>
<li class="toctree-l2"><a class="reference internal" href="development.html">Development Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="debugging.html">Debugging Guide</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarking.html">Benchmarking Guide</a></li>
diff --git a/objects.inv b/objects.inv
index 6771033be..cc6ac2420 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/searchindex.js b/searchindex.js
index 60ce38c92..32384baf1 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. Install Comet": [[17, "install-comet"]], "2. Clone Spark and Apply Diff": [[17, "clone-spark-and-apply-diff"]], "3. Run Spark SQL Tests": [[17, "run-spark-sql-tests"]], "ANSI Mode": [[20, "ansi-mode"], [33, "ansi-mode"], [73, "ansi-mode"]], "ANSI mode": [[46, "ansi-mode"], [59, "ansi-mode"]], "API Differences Between Spark Versions": [[3, "api-differences-between-spark-versions"]], "ASF Links": [[2, null], [2, null]], "Accelerating Apache Iceberg Parque [...]
\ No newline at end of file
+Search.setIndex({"alltitles": {"1. Install Comet": [[18, "install-comet"]], "2. Clone Spark and Apply Diff": [[18, "clone-spark-and-apply-diff"]], "3. Run Spark SQL Tests": [[18, "run-spark-sql-tests"]], "ANSI Mode": [[21, "ansi-mode"], [34, "ansi-mode"], [74, "ansi-mode"]], "ANSI mode": [[47, "ansi-mode"], [60, "ansi-mode"]], "API Differences Between Spark Versions": [[3, "api-differences-between-spark-versions"]], "ASF Links": [[2, null], [2, null]], "Accelerating Apache Iceberg Parque [...]
\ No newline at end of file
diff --git a/user-guide/latest/compatibility.html b/user-guide/latest/compatibility.html
index b55c99b35..5903394bd 100644
--- a/user-guide/latest/compatibility.html
+++ b/user-guide/latest/compatibility.html
@@ -464,71 +464,11 @@ under the License.
<p>This guide offers information about areas of functionality where there are known differences.</p>
<section id="parquet">
<h2>Parquet<a class="headerlink" href="#parquet" title="Link to this heading">#</a></h2>
-<section id="data-type-support">
-<h3>Data Type Support<a class="headerlink" href="#data-type-support" title="Link to this heading">#</a></h3>
-<p>Comet does not support reading decimals encoded in binary format.</p>
-</section>
-<section id="parquet-scans">
-<h3>Parquet Scans<a class="headerlink" href="#parquet-scans" title="Link to this heading">#</a></h3>
-<p>Comet currently has three distinct implementations of the Parquet scan operator. The configuration property
-<code class="docutils literal notranslate"><span class="pre">spark.comet.scan.impl</span></code> is used to select an implementation. The default setting is <code class="docutils literal notranslate"><span class="pre">spark.comet.scan.impl=auto</span></code>, and
-Comet will choose the most appropriate implementation based on the Parquet schema and other Comet configuration
-settings. Most users should not need to change this setting. However, it is possible to force Comet to try and use
-a particular implementation for all scan operations by setting this configuration property to one of the following
-implementations.</p>
-<div class="pst-scrollable-table-container"><table class="table">
-<thead>
-<tr class="row-odd"><th class="head"><p>Implementation</p></th>
-<th class="head"><p>Description</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">native_comet</span></code></p></td>
-<td><p>This implementation provides strong compatibility with Spark but does not support complex types. This is the original scan implementation in Comet and may eventually be removed.</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code></p></td>
-<td><p>This implementation delegates to DataFusion’s <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code> but uses a hybrid approach of JVM and native code. This scan is designed to be integrated with Iceberg in the future.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code></p></td>
-<td><p>This experimental implementation delegates to DataFusion’s <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code> for full native execution. There are known compatibility issues when using this scan.</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> scans provide the following benefits over the <code class="docutils literal notranslate"><span class="pre">native_comet</span></code>
-implementation:</p>
-<ul class="simple">
-<li><p>Leverages the DataFusion community’s ongoing improvements to <code class="docutils literal notranslate"><span class="pre">DataSourceExec</span></code></p></li>
-<li><p>Provides support for reading complex types (structs, arrays, and maps)</p></li>
-<li><p>Removes the use of reusable mutable-buffers in Comet, which is complex to maintain</p></li>
-<li><p>Improves performance</p></li>
-</ul>
-<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> scans share the following limitations:</p>
+<p>Comet has the following limitations when reading Parquet files:</p>
<ul class="simple">
-<li><p>When reading Parquet files written by systems other than Spark that contain columns with the logical types <code class="docutils literal notranslate"><span class="pre">UINT_8</span></code>
-or <code class="docutils literal notranslate"><span class="pre">UINT_16</span></code>, Comet will produce different results than Spark because Spark does not preserve or understand these
-logical types. Arrow-based readers, such as DataFusion and Comet do respect these types and read the data as unsigned
-rather than signed. By default, Comet will fall back to <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> when scanning Parquet files containing <code class="docutils literal notranslate"><span class="pre">byte</span></code> or <code class="docutils literal notranslate"><span class="pre">short</span></code>
-types (regardless of the logical type). This behavior can be disabled by setting
-<code class="docutils literal notranslate"><span class="pre">spark.comet.scan.allowIncompatible=true</span></code>.</p></li>
+<li><p>Comet does not support reading decimals encoded in binary format.</p></li>
<li><p>No support for default values that are nested types (e.g., maps, arrays, structs). Literal default values are supported.</p></li>
</ul>
-<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> scan has some additional limitations:</p>
-<ul class="simple">
-<li><p>Bucketed scans are not supported</p></li>
-<li><p>No support for row indexes</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">PARQUET_FIELD_ID_READ_ENABLED</span></code> is not respected <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1758">#1758</a></p></li>
-<li><p>There are failures in the Spark SQL test suite <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1545">#1545</a></p></li>
-<li><p>Setting Spark configs <code class="docutils literal notranslate"><span class="pre">ignoreMissingFiles</span></code> or <code class="docutils literal notranslate"><span class="pre">ignoreCorruptFiles</span></code> to <code class="docutils literal notranslate"><span class="pre">true</span></code> is not compatible with Spark</p></li>
-</ul>
-</section>
-<section id="s3-support-with-native-iceberg-compat">
-<h3>S3 Support with <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code><a class="headerlink" href="#s3-support-with-native-iceberg-compat" title="Link to this heading">#</a></h3>
-<ul class="simple">
-<li><p>When using the default AWS S3 endpoint (no custom endpoint configured), a valid region is required. Comet
-will attempt to resolve the region if it is not provided.</p></li>
-</ul>
-</section>
</section>
<section id="ansi-mode">
<h2>ANSI Mode<a class="headerlink" href="#ansi-mode" title="Link to this heading">#</a></h2>
@@ -551,18 +491,14 @@ So Comet will add additional normalization expression of NaN and zero for compar
<p>Sorting on floating-point data types (or complex types containing floating-point values) is not compatible with
Spark if the data contains both zero and negative zero. This is likely an edge case that is not of concern for many users
and sorting on floating-point data can be enabled by setting <code class="docutils literal notranslate"><span class="pre">spark.comet.expression.SortOrder.allowIncompatible=true</span></code>.</p>
-<p>There is a known bug with using count(distinct) within aggregate queries, where each NaN value will be counted
-separately <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1824">#1824</a>.</p>
</section>
<section id="incompatible-expressions">
<h2>Incompatible Expressions<a class="headerlink" href="#incompatible-expressions" title="Link to this heading">#</a></h2>
-<p>Some Comet native expressions are not 100% compatible with Spark and are disabled by default. These expressions
-will fall back to Spark but can be enabled by setting <code class="docutils literal notranslate"><span class="pre">spark.comet.expression.allowIncompatible=true</span></code>.</p>
-</section>
-<section id="array-expressions">
-<h2>Array Expressions<a class="headerlink" href="#array-expressions" title="Link to this heading">#</a></h2>
-<p>Comet has experimental support for a number of array expressions. These are experimental and currently marked
-as incompatible and can be enabled by setting <code class="docutils literal notranslate"><span class="pre">spark.comet.expression.allowIncompatible=true</span></code>.</p>
+<p>Expressions that are not 100% Spark-compatible will fall back to Spark by default and can be enabled by setting
+<code class="docutils literal notranslate"><span class="pre">spark.comet.expression.EXPRNAME.allowIncompatible=true</span></code>, where <code class="docutils literal notranslate"><span class="pre">EXPRNAME</span></code> is the Spark expression class name. See
+the <a class="reference internal" href="expressions.html"><span class="std std-doc">Comet Supported Expressions Guide</span></a> for more information on this configuration setting.</p>
+<p>It is also possible to specify <code class="docutils literal notranslate"><span class="pre">spark.comet.expression.allowIncompatible=true</span></code> to enable all
+incompatible expressions.</p>
</section>
<section id="regular-expressions">
<h2>Regular Expressions<a class="headerlink" href="#regular-expressions" title="Link to this heading">#</a></h2>
@@ -577,7 +513,7 @@ this can be overridden by setting <code class="docutils literal notranslate"><sp
<li><p><strong>Compatible</strong>: The results match Apache Spark</p></li>
<li><p><strong>Incompatible</strong>: The results may match Apache Spark for some inputs, but there are known issues where some inputs
will result in incorrect results or exceptions. The query stage will fall back to Spark by default. Setting
-<code class="docutils literal notranslate"><span class="pre">spark.comet.expression.allowIncompatible=true</span></code> will allow all incompatible casts to run natively in Comet, but this is not
+<code class="docutils literal notranslate"><span class="pre">spark.comet.expression.Cast.allowIncompatible=true</span></code> will allow all incompatible casts to run natively in Comet, but this is not
recommended for production use.</p></li>
<li><p><strong>Unsupported</strong>: Comet does not provide a native version of this cast expression and the query stage will fall back to
Spark.</p></li>
diff --git a/user-guide/latest/datasources.html b/user-guide/latest/datasources.html
index 0cc56d422..0b951336e 100644
--- a/user-guide/latest/datasources.html
+++ b/user-guide/latest/datasources.html
@@ -598,25 +598,16 @@ Input<span class="w"> </span><span class="o">[</span><span class="m">3</span><sp
</section>
<section id="s3">
<h2>S3<a class="headerlink" href="#s3" title="Link to this heading">#</a></h2>
-<p>DataFusion Comet has <a class="reference internal" href="compatibility.html#parquet-scans"><span class="std std-ref">multiple Parquet scan implementations</span></a> that use different approaches to read data from S3.</p>
-<section id="native-comet">
-<h3><code class="docutils literal notranslate"><span class="pre">native_comet</span></code><a class="headerlink" href="#native-comet" title="Link to this heading">#</a></h3>
-<p>The default <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> Parquet scan implementation reads data from S3 using the <a class="reference external" href="https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html">Hadoop-AWS module</a>, which is identical to the approach commonly used with vanilla Spark. AWS credential configuration and other Hadoop S3A configurations works the same way as in vanilla Spark.</p>
-</section>
-<section id="native-datafusion-and-native-iceberg-compat">
-<h3><code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code><a class="headerlink" href="#native-datafusion-and-native-iceberg-compat" title="Link to this heading">#</a></h3>
-<p>The <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> Parquet scan implementations completely offload data loading to native code. They use the <a class="reference external" href="https://crates.io/crates/object_store"><code class="docutils literal notranslate"><span class="pre">object_store</span></code> crate</a> to read data from S3 and sup [...]
-<p>This implementation maintains compatibility with existing Hadoop S3A configurations, so existing code will continue to work as long as the configurations are supported and can be translated without loss of functionality.</p>
<section id="root-ca-certificates">
-<h4>Root CA Certificates<a class="headerlink" href="#root-ca-certificates" title="Link to this heading">#</a></h4>
-<p>One major difference between <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> and the other scan implementations is the mechanism for discovering Root
-CA Certificates. The <code class="docutils literal notranslate"><span class="pre">native_comet</span></code> scan uses the JVM to read CA Certificates from the Java Trust Store, but the native
-scan implementations <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> and <code class="docutils literal notranslate"><span class="pre">native_iceberg_compat</span></code> use system Root CA Certificates (typically stored
+<h3>Root CA Certificates<a class="headerlink" href="#root-ca-certificates" title="Link to this heading">#</a></h3>
+<p>One major difference between Spark and Comet is the mechanism for discovering Root
+CA Certificates. Spark uses the JVM to read CA Certificates from the Java Trust Store, but native Comet
+scans use system Root CA Certificates (typically stored
in <code class="docutils literal notranslate"><span class="pre">/etc/ssl/certs</span></code> on Linux). These scans will not be able to interact with S3 if the Root CA Certificates are not
installed.</p>
</section>
<section id="supported-credential-providers">
-<h4>Supported Credential Providers<a class="headerlink" href="#supported-credential-providers" title="Link to this heading">#</a></h4>
+<h3>Supported Credential Providers<a class="headerlink" href="#supported-credential-providers" title="Link to this heading">#</a></h3>
<p>AWS credential providers can be configured using the <code class="docutils literal notranslate"><span class="pre">fs.s3a.aws.credentials.provider</span></code> configuration. The following table shows the supported credential providers and their configuration options:</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
@@ -667,68 +658,6 @@ installed.</p>
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<p>Multiple credential providers can be specified in a comma-separated list using the <code class="docutils literal notranslate"><span class="pre">fs.s3a.aws.credentials.provider</span></code> configuration, just as Hadoop AWS supports. If <code class="docutils literal notranslate"><span class="pre">fs.s3a.aws.credentials.provider</span></code> is not configured, Hadoop S3A’s default credential provider chain will be used. All configuration options also support bucket-specific overrides [...]
</section>
-<section id="additional-s3-configuration-options">
-<h4>Additional S3 Configuration Options<a class="headerlink" href="#additional-s3-configuration-options" title="Link to this heading">#</a></h4>
-<p>Beyond credential providers, the <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> implementation supports additional S3 configuration options:</p>
-<div class="pst-scrollable-table-container"><table class="table">
-<thead>
-<tr class="row-odd"><th class="head"><p>Option</p></th>
-<th class="head"><p>Description</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.endpoint</span></code></p></td>
-<td><p>The endpoint of the S3 service</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.endpoint.region</span></code></p></td>
-<td><p>The AWS region for the S3 service. If not specified, the region will be auto-detected.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.path.style.access</span></code></p></td>
-<td><p>Whether to use path style access for the S3 service (true/false, defaults to virtual hosted style)</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">fs.s3a.requester.pays.enabled</span></code></p></td>
-<td><p>Whether to enable requester pays for S3 requests (true/false)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>All configuration options support bucket-specific overrides using the pattern <code class="docutils literal notranslate"><span class="pre">fs.s3a.bucket.{bucket-name}.{option}</span></code>.</p>
-</section>
-<section id="examples">
-<h4>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h4>
-<p>The following examples demonstrate how to configure S3 access with the <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> Parquet scan implementation using different authentication methods.</p>
-<p><strong>Example 1: Simple Credentials</strong></p>
-<p>This example shows how to access a private S3 bucket using an access key and secret key. The <code class="docutils literal notranslate"><span class="pre">fs.s3a.aws.credentials.provider</span></code> configuration can be omitted since <code class="docutils literal notranslate"><span class="pre">org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider</span></code> is included in Hadoop S3A’s default credential provider chain.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">$SPARK_HOME</span>/bin/spark-shell<span class="w"> </span><span class="se">\</span>
-...
---conf<span class="w"> </span>spark.comet.scan.impl<span class="o">=</span>native_datafusion<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.access.key<span class="o">=</span>my-access-key<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.secret.key<span class="o">=</span>my-secret-key
-...
-</pre></div>
-</div>
-<p><strong>Example 2: Assume Role with Web Identity Token</strong></p>
-<p>This example demonstrates using an assumed role credential to access a private S3 bucket, where the base credential for assuming the role is provided by a web identity token credentials provider.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">$SPARK_HOME</span>/bin/spark-shell<span class="w"> </span><span class="se">\</span>
-...
---conf<span class="w"> </span>spark.comet.scan.impl<span class="o">=</span>native_datafusion<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.aws.credentials.provider<span class="o">=</span>org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.arn<span class="o">=</span>arn:aws:iam::123456789012:role/my-role<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.session.name<span class="o">=</span>my-session<span class="w"> </span><span class="se">\</span>
---conf<span class="w"> </span>spark.hadoop.fs.s3a.assumed.role.credentials.provider<span class="o">=</span>com.amazonaws.auth.WebIdentityTokenCredentialsProvider
-...
-</pre></div>
-</div>
-</section>
-<section id="limitations">
-<h4>Limitations<a class="headerlink" href="#limitations" title="Link to this heading">#</a></h4>
-<p>The S3 support of <code class="docutils literal notranslate"><span class="pre">native_datafusion</span></code> has the following limitations:</p>
-<ol class="arabic simple">
-<li><p><strong>Partial Hadoop S3A configuration support</strong>: Not all Hadoop S3A configurations are currently supported. Only the configurations listed in the tables above are translated and applied to the underlying <code class="docutils literal notranslate"><span class="pre">object_store</span></code> crate.</p></li>
-<li><p><strong>Custom credential providers</strong>: Custom implementations of AWS credential providers are not supported. The implementation only supports the standard credential providers listed in the table above. We are planning to add support for custom credential providers through a JNI-based adapter that will allow calling Java credential providers from native code. See <a class="reference external" href="https://github.com/apache/datafusion-comet/issues/1829">issue #1829</a> for [...]
-</ol>
-</section>
-</section>
</section>
</section>
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