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Below are archived releases for RAPIDS Accelerator for Apache Spark.
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## Release v25.12.0
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### Hardware Requirements:
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The plugin is designed to work on NVIDIA Volta, Turing, Ampere, Ada Lovelace, Hopper and Blackwell generation datacenter GPUs. The plugin jar is tested on the following GPUs:
OS: Spark RAPIDS is compatible with any Linux distribution with glibc >= 2.28 (Please check ldd --version output). glibc 2.28 was released August 1, 2018.
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Tested on Ubuntu 22.04, Ubuntu 24.04, Rocky Linux 8 and Rocky Linux 9
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NVIDIA Driver*: R525+
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Runtime:
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Scala 2.12, 2.13
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Python, Java Virtual Machine (JVM) compatible with your spark-version.
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* Check the Spark documentation for Python and Java version compatibility with your specific
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Spark version. For instance, visit `https://spark.apache.org/docs/3.4.1` for Spark 3.4.1.
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Supported Spark versions:
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Scala 2.12: Spark 3.3.x to Spark 3.5.x
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Scala 2.13: Spark 3.5.0+, 4.0.0, 4.0.1
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Supported Databricks runtime versions for Azure and AWS:
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Databricks 12.2 ML LTS (GPU, Scala 2.12, Spark 3.3.2)
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Databricks 13.3 ML LTS (GPU, Scala 2.12, Spark 3.4.1)
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Databricks 14.3 ML LTS (GPU, Scala 2.12, Spark 3.5.0)
gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <sw-spark@nvidia.com>"
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### Release Notes
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* Iceberg enhancements including DML operations (delete, update, merge) for merge-on-read tables, partition transforms (year/month/day/hour/truncate), and write operations enabled by default.
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* Delta Lake clustered tables DML support including update, merge, and delete operations with deletion vector enabled GPU by default.
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* Join improvements including support for left-outer joins with no columns, new join strategies with logging and heuristic configurations, and improved gather map ordering.
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* CSV support for GBK encoded data.
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* Refine GpuTaskMetrics over the spill framework.
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* Fix race condition due to premature disk handle exposure.
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Note: There is a known issue in the 25.12.0 release when decompressing gzip files on H100 GPUs.
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Please find more details in [issue-16661](https://github.com/rapidsai/cudf/issues/16661).
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that uses the RAPIDS Accelerator For Apache Spark. See the [getting-started
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guide](https://docs.nvidia.com/spark-rapids/user-guide/latest/getting-started/overview.html) for more details.
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## Release v25.12.0
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## Release v26.02.0
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### Hardware Requirements:
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The plugin is designed to work on NVIDIA Volta, Turing, Ampere, Ada Lovelace, Hopper and Blackwell generation datacenter GPUs. The plugin jar is tested on the following GPUs:
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Supported Spark versions:
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Scala 2.12: Spark 3.3.x to Spark 3.5.x
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Scala 2.13: Spark 3.5.0+, 4.0.0, 4.0.1
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Scala 2.13: Spark 3.5.0+, 4.0.0, 4.0.1, 4.1.1
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Supported Databricks runtime versions for Azure and AWS:
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Databricks 12.2 ML LTS (GPU, Scala 2.12, Spark 3.3.2)
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### RAPIDS Accelerator's Support Policy for Apache Spark
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The RAPIDS Accelerator maintains support for Apache Spark versions available for download from [Apache Spark](https://spark.apache.org/downloads.html)
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### Download RAPIDS Accelerator for Apache Spark v25.12.0
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### Download RAPIDS Accelerator for Apache Spark v26.02.0
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| Processor | Scala Version | Download Jar | Download Signature | Download From Maven |
gpg: Good signature from "NVIDIA Spark (For the signature of spark-rapids release jars) <sw-spark@nvidia.com>"
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### Release Notes
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* Iceberg enhancements including DML operations (delete, update, merge) for merge-on-read tables, partition transforms (year/month/day/hour/truncate), and write operations enabled by default.
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* Delta Lake clustered tables DML support including update, merge, and delete operations with deletion vector enabled GPU by default.
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* Join improvements including support for left-outer joins with no columns, new join strategies with logging and heuristic configurations, and improved gather map ordering.
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* CSV support for GBK encoded data.
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* Refine GpuTaskMetrics over the spill framework.
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* Fix race condition due to premature disk handle exposure.
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Note: There is a known issue in the 25.12.0 release when decompressing gzip files on H100 GPUs.
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Please find more details in [issue-16661](https://github.com/rapidsai/cudf/issues/16661).
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* Add support for Spark 4.1.1 ([#14120](https://github.com/NVIDIA/spark-rapids/pull/14120))
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* Iceberg enhancements: identity partitioning support ([#14183](https://github.com/NVIDIA/spark-rapids/pull/14183)), all types for bucket transform ([#14001](https://github.com/NVIDIA/spark-rapids/pull/14001)), and Iceberg 1.9.2 support ([#13986](https://github.com/NVIDIA/spark-rapids/pull/13986))
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* SHA-2 hash support ([#14038](https://github.com/NVIDIA/spark-rapids/pull/14038))
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