The RAPIDS Accelerator for Apache Spark provides limited support for Apache Iceberg tables. This document details the Apache Iceberg features that are supported.
The RAPIDS Accelerator supports Apache Iceberg 0.13.x. Earlier versions of Apache Iceberg are not supported.
Note! Apache Iceberg in Databricks is not supported by the RAPIDS Accelerator.
Reads of Apache Iceberg metadata, i.e.: the
snapshots, and other metadata tables associated with a table, will not be GPU-accelerated. The CPU will continue to process these metadata-level queries.
Apache Iceberg supports row-level deletions and updates. Tables that are using a configuration of
write.delete.mode=merge-on-read are not supported.
Columns that are added and removed at the top level of the table schema are supported. Columns that are added or removed within struct columns are not supported.
Apache Iceberg can store data in various formats. Each section below details the levels of support for each of the underlying data formats.
Data stored in Parquet is supported with the same limitations for loading data from raw Parquet files. See the Input/Output documentation for details. The following compression codecs applied to the Parquet data are supported:
- gzip (Apache Iceberg default)
The RAPIDS Accelerator does not support Apache Iceberg tables using the ORC data format.
The RAPIDS Accelerator does not support Apache Iceberg tables using the Avro data format.
The maximum number of bytes to pack into a single partition when reading files on Spark is normally controlled by the config
spark.sql.files.maxPartitionBytes. But on Iceberg that doesn’t apply. Iceberg has its own configs to control the split size. See the read options in the Iceberg Runtime Configuration documentation for details. One example is to use the
split-size reader option like:
The RAPIDS Accelerator for Apache Spark does not accelerate Apache Iceberg writes. Writes to Iceberg tables will be processed by the CPU.