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E-MapReduce:Release notes for EMR Serverless Spark on January 20, 2025

Last Updated:May 22, 2025

This topic describes the release notes for E-MapReduce (EMR) Serverless Spark on January 20, 2025.

Overview

On January 20, 2025, the latest version of EMR Serverless Spark is released, featuring platform improvements, improved performance, and enhanced engine capabilities.

Platform updates

Feature

Description

Sales

Computing resource plans are provided for pay-as-you-go Spark workspaces. This helps reduce costs.

Stability

  • The resource application efficiency and running stability of jobs are improved.

  • Cross-zone high availability is supported.

Integration with other ecosystems

  • Batch and streaming jobs can be used to connect to the external Ranger service.

  • Kyuubi gateways that are compatible with open source Kyuubi can be used to submit SQL jobs.

Data catalog

  • RAM users can be used to access Data Lake Formation (DLF).

  • Access to Hive catalogs created in the DLF 2.0 console is supported.

Resource observation

Monitoring of resource consumption from the workspace and queue dimensions is supported.

Runtime environment

The --conf spark.emr.serverless.environmentId=<Runtime environment id> parameter can be used in Spark Submit to specify a runtime environment.

Engine updates

Engine version

Description

esr-4.0.0 (Spark 3.5.2, Scala 2.12)

esr-3.1.0 (Spark 3.4.3, Scala 2.12)

esr-2.5.0 (Spark 3.3.1, Scala 2.12)

  • Spark 3.5.2 is supported.

  • Fusion acceleration

    • CacheTable is optimized.

    • Tables in the CSV and TEXT formats can be read.

    • Data can be read from and written to files in the complex ORC format.

    • Tables in the Hudi format can be read.

    • The parse_url function is supported.

    • The concat_ws function is supported.

    • Window operators are optimized.

    • Sort operators are optimized.

  • Java Runtime

    • The performance issue caused by downloading JAR files from a driver is fixed to improve the concurrent processing capability of executors.

    • The isolation mechanism for custom JAR packages is enhanced.

    • Empty data can be inserted into an external table.

    • The configurations of memory committers are optimized.

  • Paimon

    • Custom Paimon data paths are supported.

    • Views can be created and used.

    • The CREATE TABLE ... WITH LOCATION syntax is supported.

    • The performance of SHOW TABLES is improved.

Celeborn

Multi-zone high availability is supported.