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Posted to commits@datafusion.apache.org by ag...@apache.org on 2025/11/03 20:52:37 UTC
(datafusion-comet) branch main updated: docs: Various documentation updates (#2674)
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in repository https://gitbox.apache.org/repos/asf/datafusion-comet.git
The following commit(s) were added to refs/heads/main by this push:
new dba523d99 docs: Various documentation updates (#2674)
dba523d99 is described below
commit dba523d994f3f8336d2c5ca469c61672768611a1
Author: Andy Grove <ag...@apache.org>
AuthorDate: Mon Nov 3 13:52:32 2025 -0700
docs: Various documentation updates (#2674)
---
docs/source/contributor-guide/index.md | 1 +
docs/source/contributor-guide/parquet_scans.md | 137 +++++++++++++++++++++++++
docs/source/user-guide/latest/compatibility.md | 68 ++----------
docs/source/user-guide/latest/datasources.md | 72 +------------
4 files changed, 149 insertions(+), 129 deletions(-)
diff --git a/docs/source/contributor-guide/index.md b/docs/source/contributor-guide/index.md
index ba4692a97..eb79f7ab5 100644
--- a/docs/source/contributor-guide/index.md
+++ b/docs/source/contributor-guide/index.md
@@ -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/docs/source/contributor-guide/parquet_scans.md b/docs/source/contributor-guide/parquet_scans.md
new file mode 100644
index 000000000..4aec9f347
--- /dev/null
+++ b/docs/source/contributor-guide/parquet_scans.md
@@ -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/docs/source/user-guide/latest/compatibility.md b/docs/source/user-guide/latest/compatibility.md
index ac2be802d..908693ff5 100644
--- a/docs/source/user-guide/latest/compatibility.md
+++ b/docs/source/user-guide/latest/compatibility.md
@@ -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/docs/source/user-guide/latest/datasources.md b/docs/source/user-guide/latest/datasources.md
index 7525c2f45..e2f3f8d1a 100644
--- a/docs/source/user-guide/latest/datasources.md
+++ b/docs/source/user-guide/latest/datasources.md
@@ -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.
-
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