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.

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. Reads against tables that have deletion vectors enabled will fallback to the CPU.

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.

Delta Lake write acceleration is enabled by default. To disable acceleration of Delta Lake writes, set spark.rapids.sql.format.delta.write.enabled=false.

Delta Lake Versions Supported For Write

The RAPIDS Accelerator supports the following software configurations for accelerating Delta Lake writes:

• 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 10.4 LTS

• Delta Lake on Databricks 11.3 LTS

• Delta Lake on Databricks 12.2 LTS

Delta Lake writes won’t be accelerated on Spark 3.1.x or earlier.

Write Operations Supported

Most write operations to Delta Lake tables are supported. Some notable Delta Lake write operations that will fallback to the CPU include:

• Writes against tables that have deletion vectors enabled

• The OPTIMIZE TABLE operation

Automatic Optimization of Writes

Delta Lake on Databricks has automatic optimization features for optimized writes and automatic compaction.

Optimized writes are supported only on Databricks platforms. The algorithm used is similar but not identical to the Databricks version. The following table describes configuration settings that control the operation of the optimized write.

Automatic compaction is supported only on Databricks platforms. The algorithm is similar but not identical to the Databricks version. The following table describes configuration settings that control the operation of automatic compaction.

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.

Delta Lake merge acceleration is experimental and is disabled by default. To enable acceleration of Delta Lake merge operations, set spark.rapids.sql.command.MergeIntoCommand=true and also set spark.rapids.sql.command.MergeIntoCommandEdge=true 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.

Limitations with DeltaTable merge API on non-Databricks Platforms

For non-Databricks platforms, the DeltaTable merge API directly instantiates a CPU MergeIntoCommand instance and invokes it. This doesn’t go through the normal Spark Catalyst optimizer, and the merge operation won’t be visible in the Spark SQL UI on these platforms. Since the Catalyst optimizer is bypassed, the RAPIDS Accelerator can’t replace the operation with a GPU accelerated version. As a result, DeltaTable merge operations on non-Databricks platforms won’t be GPU accelerated. In those cases the query will need to be modified to use a SQL MERGE INTO statement instead.

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.

Delta Lake delete acceleration is experimental and is disabled by default. To enable acceleration of Delta Lake delete operations, set spark.rapids.sql.command.DeleteCommand=true and also set spark.rapids.sql.command.DeleteCommandEdge=true on Databricks platforms.

Deleting data from Delta Lake tables via the SQL DELETE FROM statement or via the DeltaTable delete API is supported.

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.

Delta Lake update acceleration is experimental and is disabled by default. To enable acceleration of Delta Lake update operations, set spark.rapids.sql.command.UpdateCommand=true and also set spark.rapids.sql.command.UpdateCommandEdge=true on Databricks platforms.

Updating data from Delta Lake tables via the SQL UPDATE statement or via the DeltaTable update API is supported.