Delta Lake Support#
The RAPIDS Accelerator for Apache Spark provides limited support for Delta Lake tables. This document details the Delta Lake features that are supported.
Delta versions supported by the RAPIDS Accelerator:
Delta Lake version [3.3.0,3.3.2] on Apache Spark [3.5.3,4.0.0). Most comprehensively benchmarked combination is Delta 3.3.0/Spark 3.5.3
Delta Lake version 2.0.1 on Apache Spark 3.2.x
Delta Lake version 2.1.1, 2.2.0, and 2.3.0 on Apache Spark 3.3.x
Delta Lake version 2.4.0 on Apache Spark 3.4.x
Delta Lake on Databricks 12.2 LTS
The Information about compatible Spark versions is derived from Delta Lake Release Notes, for instance, 3.3.0
Reading Delta Lake Tables#
Data Queries#
Delta Lake scans of the underlying Parquet files are presented in the query as normal Parquet reads, so the Parquet reads will be accelerated in the same way raw Parquet file reads are accelerated.
Starting with version 25.10, reading from Delta Lake tables with liquid clustering is supported. And the RAPIDS Accelerator provides limited read support for Delta Lake tables with deletion vectors enabled. To use this feature, you must set the following configuration:
spark.databricks.delta.deletionVectors.useMetadataRowIndex=false
Without this configuration, reads against tables with deletion vectors will fallback to the CPU.
Parquet Reader Limitations: When using deletion vectors, setting the parquet readerType to COALESCING will automatically fall back to the MULTITHREADED reader, as file concatenation (combining) is not supported with deletion vectors.
Metadata Queries#
Reads of Delta Lake metadata, that is: the Delta log detailing the history of snapshots, won’t be GPU accelerated. The CPU will continue to process metadata queries on Delta Lake tables.
Writing Delta Lake Tables#
Delta Lake write acceleration is enabled by default. To disable acceleration of Delta Lake writes, set spark.rapids.sql.format.delta.write.enabled=false.
Write Operations Supported#
Most write operations to Delta Lake tables are supported. Starting with version 25.10, writing to Delta Lake tables with liquid clustering is supported.
Some notable Delta Lake write operations that will fallback to the CPU include:
DML operations (DELETE, UPDATE, MERGE) on tables with deletion vectors enabled that may result in deletion vector creation
Automatic Optimization of Writes#
Delta Lake on Databricks has automatic optimization features for optimized writes and automatic compaction.
The optimized write is supported for Delta 3.3.0 and Databricks. The plugin uses a similar, but slightly different algorithm from the Databricks version. The following table describes configuration settings that control the operation of the optimized write within the plugin.
Configuration |
Default |
Description |
|---|---|---|
spark.databricks.delta.optimizeWrite.binSize |
512 |
Target uncompressed partition size in megabytes. See Delta Lake documentation for details. |
spark.databricks.delta.optimizeWrite.smallPartitionFactor |
0.5 |
Merge partitions smaller than this factor multiplied by the target partition size |
spark.databricks.delta.optimizeWrite.mergedPartitionFactor |
1.2 |
Avoid combining partitions larger than this factor multiplied by the target partition size |
Automatic Compaction is also supported on Delta 3.3.0 and Databricks. The plugin uses different algorithms for OSS Delta and Databricks Delta. The following table describes configuration settings supported by the plugin for OSS Delta.
Configuration |
Default |
Description |
|---|---|---|
spark.databricks.delta.autoCompact.enabled |
false |
Enable/disable auto compaction for writes to Delta directories |
spark.databricks.delta.properties.defaults.autoOptimize.autoCompact |
false |
Whether to enable auto compaction by default, if spark.databricks.delta.autoCompact.enabled isn’t set |
spark.databricks.delta.autoCompact.minNumFiles |
50 |
Minimum number of files that must exist in the Delta directory before Auto Optimize begins compaction. See Delta Lake documentation for details. |
spark.databricks.delta.autoCompact.minFileSize |
Half of spark.databricks.delta.autoCompact.maxFileSize |
Files which are smaller than this threshold (in bytes) will be grouped together and rewritten as larger files by the auto compaction. |
spark.databricks.delta.autoCompact.maxFileSize |
128MB |
Target file size produced by auto compaction. |
The following table describes configuration settings supported by the plugin for Databricks Delta.
Configuration |
Default |
Description |
|---|---|---|
spark.databricks.delta.autoCompact.enabled |
false |
Enable/disable auto compaction for writes to Delta directories |
spark.databricks.delta.properties.defaults.autoOptimize.autoCompact |
false |
Whether to enable auto compaction by default, if spark.databricks.delta.autoCompact.enabled isn’t set |
spark.databricks.delta.autoCompact.minNumFiles |
50 |
Minimum number of files that must exist in the Delta directory before Auto Optimize begins compaction. See Databricks Delta Lake documentation for details. |
spark.databricks.delta.autoCompact.maxFileSize |
128MB |
Target file size produced by auto compaction. See Databricks Delta Lake documentation for details. |
spark.databricks.delta.autoCompact.target |
partition |
Target files for auto compaction. “table”, “commit”, “partition” options are available. If “table”, all files in the table are eligible for auto compaction. If “commit”, added/updated files by the commit are eligible. If “partition”, all files in partitions containing any added/updated files by the commit are eligible. |
spark.databricks.delta.autoCompact.maxCompactBytes |
20GB |
Maximum amount of data to compact together |
Optimized write support requires round-robin partitioning of the data, and round-robin partitioning requires sorting across all columns for deterministic operation. If the GPU can’t support sorting a particular column type in order to support the round-robin partitioning, the Delta Lake write will fallback to the CPU.
RapidsDeltaWrite Node in Query Plans#
A side-effect of performing a GPU accelerated Delta Lake write is a new node will appear in the query plan, RapidsDeltaWrite. Normally, the writing of Delta Lake files isn’t represented by a dedicated node in query plans, as it’s implicitly covered by higher-level operations such as SaveIntoDataSourceCommand that wrap the entire query along with the write operation afterwards. The RAPIDS Accelerator places a node in the plan being written to mark the point at which the write occurs and adds statistics showing the time spent performing the low-level write operation.
Merging Into Delta Lake Tables#
Delta Lake merge acceleration is enabled by default. To disable acceleration of Delta Lake merge operations, set spark.rapids.sql.command.MergeIntoCommand=false and also set spark.rapids.sql.command.MergeIntoCommandEdge=false on Databricks platforms.
Merging into Delta Lake tables via the SQL MERGE INTO statement or via the DeltaTable merge API on non-Databricks platforms is supported.
Note: Merging into Delta Lake tables with liquid clustering is not supported and will fallback to the CPU.
RapidsProcessDeltaMergeJoin Node in Query Plans#
A side-effect of performing GPU accelerated Delta Lake merge operations is a new node will appear in the query plan, RapidsProcessDeltaMergeJoin. Normally, the Delta Lake merge is performed via a join and then post-processing of the join via a MapPartitions node. Instead the GPU performs the join post-processing via this new RapidsProcessDeltaMergeJoin node.
Delete Operations on Delta Lake Tables#
Delta Lake delete acceleration is enabled by default. To disable acceleration of Delta Lake delete operations, set spark.rapids.sql.command.DeleteCommand=false and also set spark.rapids.sql.command.DeleteCommandEdge=false on Databricks platforms.
Deleting data from Delta Lake tables via the SQL DELETE FROM statement or via the DeltaTable delete API is supported.
Note: Delete operations on Delta Lake tables with liquid clustering are not supported and will fallback to the CPU.
num_affected_rows Difference with Databricks#
The Delta Lake delete command returns a single row result with a num_affected_rows column. When entire partition files in the table are deleted, the open source Delta Lake and RAPIDS Acclerator implementations of delete can return -1 for num_affected_rows since it could be expensive to open the files and produce an accurate row count. Databricks changed the behavior of delete operations that delete entire partition files to return the actual row count. This is only a difference in the statistics of the operation, and the table contents will still be accurately deleted with the RAPIDS Accelerator.
Update Operations on Delta Lake Tables#
Delta Lake update acceleration is enabled by default. To disable acceleration of Delta Lake update operations, set spark.rapids.sql.command.UpdateCommand=false and also set spark.rapids.sql.command.UpdateCommandEdge=false on Databricks platforms.
Updating data from Delta Lake tables via the SQL UPDATE statement or via the DeltaTable update API is supported.
Note: Update operations on Delta Lake tables with liquid clustering are not supported and will fallback to the CPU.