Managed Service for Apache Spark release notes

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

• 1.2.86
• 2.2.86
• 2.3.39

Key updates in these runtime versions include:

• OpenLineage updates: In the 2.3 runtime:
  • Upgraded OpenLineage to version 1.49 to support lineage for tables created using the Lakehouse Runtime catalog.
  • Fixed a segmentation fault when OpenLineage parses complex SQL query strings.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine):

A critical bug related to Conda channels has been fixed in-place in image versions 1.4.81, 1.5.92, 2.1.117, and 2.2.85. These image versions were released without pre-configured Conda channels.

Required customer actions: To comply with Google requirements, recreate the following resources if they were created using these image versions on or before August 10, 2026:

• Custom images
• Clusters

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

• 1.2.85
• 2.2.85
• 2.3.38

Notes:

• Apache Spark upgraded to 3.5.3 in 2.2 runtime.

• Apache Gluten upgraded to 1.6 in 2.3 runtime.

Managed Service for Apache Spark latest image and runtime versions:

• Configured spark.scheduler.listenerbus.exitTimeout to 30s.

New Managed Service for Apache Spark on Google Kubernetes Engine (formerly Dataproc on Google Kubernetes Engine) subminor image version:

• 3.5-dataproc-28

Key updates in this image version include:

• Conda channels: The new 3.5-dataproc-28 subminor image version doesn't have preconfigured Conda channels, and is mapped to default aliases (such as 3.5 and latest).
  • Impact: When creating clusters with 3.5-dataproc-28 or using default aliases (3.5, latest), packages cannot be installed using Conda unless channels are manually configured during cluster initialization.
  • Mitigation: If your workloads require preconfigured Conda channels, pin your clusters to the previous image versions before August 25, 2026.
  • Default change schedule: All workloads must transition to image versions without preconfigured Conda channels after August 25, 2026 since the use of prior subminor versions with preconfigured Conda channels will be disallowed.

You may need to delete and replace existing clusters After August 25, 2026, existing clusters created with images that have preconfigured Conda channels (even if cluster jobs don't use Conda to install packages) need to be deleted and replaced with new clusters created or recreated with images that don't have preconfigured Conda channels.

Starting with Managed Service for Apache Spark image version 3.0, clusters that are created without a specified machine type for a node are created with a Flex VM configuration for the node.

Announcing the General Availability (GA) release of Managed Service for Apache Spark cluster image version 3.0, as follows:

• 3.0.0-debian13, 3.0.0-ml-ubuntu24, 3.0.0-rocky9, and 3.0.0-ubuntu24.

  • The 3.0.0-ml-ubuntu24 image extends the 3.0 base image with ML-specific libraries.

Image version 3.0 is a lightweight image that contains the following pre-installed core components, reducing exposure to Common Vulnerabilities and Exposures (CVEs):

• Apache Hadoop 3.5.0.
• Apache Hive 4.2.0
• Apache Spark 4.1.2
• Apache Tez 0.10.5
• Cloud Storage Connector 3.1.13
• Conscrypt 2.6
• Java 21
• Python 3.12
• R 4.5
• Scala 2.13.17
• Spark-BigQuery Connector 0.44.1-preview

You can add listed optional components when you create a 3.0 image version cluster.

Recommendation: Use the 3.0 (and later) image versions to meet security compliance requirements.

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions:

• 2.3.34-debian12, 2.3.34-ml-ubuntu22, 2.3.34-rocky9, 2.3.34-ubuntu22, 2.3.34-ubuntu22-arm
• 3.0.0-debian13, 3.0.0-ml-ubuntu24, 3.0.0-rocky9, 3.0.0-ubuntu24

Managed Service for Apache Spark (formerly Dataproc on Compute Engine):

• The 2.1, 2.2 and 2.3 cluster image versions now support Confidential Compute for the g4-standard-48 GPU machine type.

Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark):

• The 3.0 runtime now uses fewer executors, as follows:

  • 0 min executors for spark.dynamicAllocation.minExecutors property
  • 1 min executor for spark.executor.instances and spark.dynamicAllocation.initialExecutors properties

• All runtimes now configure spark.scheduler.listenerbus.exitTimeout to 30 seconds.

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions:

• 2.1.117-debian11, 2.1.117-rocky8, 2.1.117-ubuntu20, 2.1.117-ubuntu20-arm
• 2.2.85-debian12, 2.2.85-rocky9, 2.2.85-ubuntu22, 2.2.85-ubuntu22-arm
• 2.3.33-debian12, 2.3.33-ml-ubuntu22, 2.3.33-rocky9, 2.3.33-ubuntu22, 2.3.33-ubuntu22-arm

Key updates in these image versions include:

• Conda channels: The new 2.1.117 and 2.2.85 subminor image versions don't have preconfigured Conda channels, and are not mapped to default aliases (such as 2.1-debian11 and 2.2-debian12) until August 25, 2026.
  • Impact: When creating clusters with these image versions, specify the exact subminor version (for example, 2.1.117-debian11 or 2.2.85-debian12). Packages cannot be installed using Conda unless channels are manually configured during cluster initialization.
  • Mitigation: If your workloads require preconfigured Conda channels or default aliases, pin your clusters to the previous image versions (for example, 2.1.116-debian11 or 2.2.84-debian12).
  • Default change schedule: After August 25, 2026, the default aliases for 2.1 and 2.2 will point to image versions without preconfigured Conda channels (the latest subminor versions). All workloads must transition to these new images after August 25, 2026 since the use of prior subminor versions with preconfigured Conda channels will be disallowed.

You may need to delete and replace existing clusters After August 25, 2026, existing clusters created with images that have preconfigured Conda channels (even if cluster jobs don't use Conda to install packages) need to be deleted and replaced with new clusters created or recreated with images that don't have preconfigured Conda channels.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): The following new subminor image versions, 1.3.96, 1.4.81, 1.5.92, 2.0.161, and 2.3.32, don't have preconfigured Conda channels, and are not mapped to default aliases (such as 2.3-debian12 and 2.3-ubuntu22) until August 25, 2026. The new 2.1 and 2.2 subminor images continue to have preconfigured Conda channels, but note the following Default change schedule:, which will impact all supported image versions.

• Impact: When creating clusters with these image versions, specify the exact subminor version (for example, 2.3.32-debian12). Packages cannot be installed using Conda unless channels are manually configured during cluster initialization.
• Mitigation: If your workloads require preconfigured Conda channels or default aliases, pin your clusters to the previous image versions.
• Default change schedule: The subminor versions 1.3.96, 1.4.81, 1.5.92, and 2.0.161 will become default after August 25, 2026. Additionally, newer subminor versions for 2.1, 2.2, and 2.3 released after August 25, 2026 will not have preconfigured Conda channels and will be mapped to default aliases. All workloads must use new images after August 25, 2026 since the use of prior subminor versions with preconfigured Conda channels will be disallowed.

You may need to delete and replace existing clusters After August 25, 2026, existing clusters created with images that have preconfigured Conda channels (even if cluster jobs don't use Conda to install packages) need to be deleted and replaced with new clusters created or recreated with images that don't have preconfigured Conda channels.

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions:

• 1.3.96-debian10, 1.3.96-ubuntu18
• 1.4.81-debian10, 1.4.81-ubuntu18
• 1.5.92-debian10, 1.5.92-rocky8, 1.5.92-ubuntu18
• 2.0.161-debian10, 2.0.161-rocky8, 2.0.161-ubuntu18
• 2.1.115-debian11, 2.1.115-rocky8, 2.1.115-ubuntu20, 2.1.115-ubuntu20-arm
• 2.1.116-debian11, 2.1.116-rocky8, 2.1.116-ubuntu20, 2.1.116-ubuntu20-arm
• 2.2.83-debian12, 2.2.83-rocky9, 2.2.83-ubuntu22, 2.2.83-ubuntu22-arm
• 2.2.84-debian12, 2.2.84-rocky9, 2.2.84-ubuntu22, 2.2.84-ubuntu22-arm
• 2.3.31-debian12, 2.3.31-ml-ubuntu22, 2.3.31-rocky9, 2.3.31-ubuntu22, 2.3.31-ubuntu22-arm
• 2.3.32-debian12, 2.3.32-ml-ubuntu22, 2.3.32-rocky9, 2.3.32-ubuntu22, 2.3.32-ubuntu22-arm

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): Key updates in these image versions include:

• Iceberg support: Added support for Iceberg 1.10 in Dataproc 2.3 images. This change only applies to 2.3 clusters created with the ICEBERG optional component. Users can opt-in to Iceberg 1.10 by setting the dataproc:dataproc.iceberg.version cluster property to 1.10 during cluster creation.
• Spark optimizations: Enabled Spark skewed-join and self-join optimizations by default in new GCE image versions.
• Scheduler exitTimeout: Configured spark.scheduler.listenerbus.exitTimeout to 30s for Compute Engine deployments.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): Rollout of the new sub-minor versions without pre-configured channels will begin on June 22, 2026, delayed from the previously planned date of June 15, 2026 ETA.

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

• 1.2.82
• 2.2.82
• 2.3.35

Key updates in these runtime versions include:

• Spark optimizations: Enabled Spark skewed-join and self-join optimizations by default.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): Added support for selecting specific Confidential Computing technologies (AMD SEV, AMD SEV-SNP, Intel TDX) when creating clusters using the new --confidential-compute-type flag in gcloud and the confidentialInstanceType field in the API. The boolean --enable-confidential-compute flag is now deprecated but will continue to function, defaulting to AMD SEV for backward compatibility.

• Introduced confidentialInstanceType enum in the API.
• The --enable-confidential-compute flag and enableConfidentialCompute field are deprecated in favor of the new type-specific flag/field.
• Clusters created with the deprecated boolean flag will default to SEV.
• Added validation for machine type compatibility for SEV, SEV-SNP, and TDX.
• Updated live migration logic to support compatible machine types and CPU platforms for each technology, including N2D and C3D for SEV.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): The following subminor image versions announced on May 19, 2026 have been rolled back:

• 2.2.82-debian12, 2.2.82-rocky9, 2.2.82-ubuntu22, 2.2.82-ubuntu22-arm

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions:

• 2.1.114-debian11, 2.1.114-rocky8, 2.1.114-ubuntu20, 2.1.114-ubuntu20-arm
• 2.2.82-debian12, 2.2.82-rocky9, 2.2.82-ubuntu22, 2.2.82-ubuntu22-arm
• 2.3.30-debian12, 2.3.30-ml-ubuntu22, 2.3.30-rocky9, 2.3.30-ubuntu22, 2.3.30-ubuntu22-arm

Rollback Notice: The 2.2.82 image versions were rolled back on May 25, 2026.

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): The configuration for Spark shuffle partitions (spark.sql.shuffle.partitions) has changed from an integer to a string type.

This change impacts image versions 2.3.30 and later in version 2.3, and 2.2.82 and later in version 2.2.

• Impact: This change only affects users who are programmatically setting the configuration in code using spark.conf.set() with an integer literal.
  • Impacted example: spark.conf.set("spark.sql.shuffle.partitions", 100)
• User action: Update your code to pass a string literal instead of an integer.
  • Example fix: spark.conf.set("spark.sql.shuffle.partitions", "100")
• Not impacted: Setting the configuration via command-line arguments (e.g., spark-submit --conf spark.sql.shuffle.partitions=100), properties files, or Spark SQL commands (spark.sql("SET spark.sql.shuffle.partitions=100")) remains unaffected, as these methods naturally parse the input as strings.

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

• 1.2.80
• 2.2.80
• 2.3.33

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions for 3.0 (Preview):

• 3.0.0-RC2-debian13, 3.0.0-RC2-ml-ubuntu24, 3.0.0-RC2-rocky9, 3.0.0-RC2-ubuntu24

Key updates in this release:

• The debian image for 3.0 is upgraded to Debian 13.
• The default Java runtime is upgraded to Java 21.
• Apache Hadoop upgraded to 3.5.0.
• Apache Spark upgraded to 4.1.1.
• Apache Hive upgraded to 4.2.0.
• Trino upgraded to 480.
• Apache Solr upgraded to 9.10.1.
• Apache Kafka upgraded to 3.9.2.
• Docker upgraded to 28.1.
• Apache Flink upgraded to 2.2.0.
• Scala upgraded to 2.13.17.
• Cloud Storage Connector upgraded to 3.1.13.
• Apache Zookeeper upgraded to 3.9.5.
• BigQuery Connector upgraded to 0.44.1-Preview.
• Zeppelin Notebook upgraded to 0.12.0.
• JupyterLab upgraded to 4.5.7.
• Pixi is used as python package manager instead of conda.

New Managed Service for Apache Spark (formerly Google Cloud Serverless for Apache Spark) subminor runtime versions:

• 1.2.79
• 2.2.79
• 2.3.32

Key updates in this release:

• Upgraded Metastore Proxy version to v0.0.79.
• Upgraded Spark RAPIDS to version 26.04.0 version in the Managed Service for Apache Spark version 3.0 serverless runtime.

New Managed Service for Apache Spark (formerly Dataproc on Compute Engine) subminor cluster image versions:

• 2.1.113-debian11, 2.1.113-rocky8, 2.1.113-ubuntu20, 2.1.113-ubuntu20-arm
• 2.2.81-debian12, 2.2.81-rocky9, 2.2.81-ubuntu22, 2.2.81-ubuntu22-arm
• 2.3.29-debian12, 2.3.29-ml-ubuntu22, 2.3.29-rocky9, 2.3.29-ubuntu22, 2.3.29-ubuntu22-arm

Key updates in this release:

• The native runtime engine is now supported on base Ubuntu cluster images starting from image version 2.3.29-ubuntu22 (not supported on Arm and ML images).

Managed Service for Apache Spark (formerly Dataproc on Compute Engine): Announcing the following cluster features:

• Flexible VMs: Minimize stockouts and improve machine obtainability by defining prioritized lists for your master, primary, and secondary worker VM types. Managed Spark for Apache Spark utilizes these lists to create your cluster, and selects the optimal VM type based on current capacity, quotas, and existing reservations.
• Cluster Scheduled Stop: Optimize cost and maintain your cluster configuration by stopping clusters after a specified idle period, at a specified future time, or after a specified period from cluster creation or a cluster update request. All custom cluster configurations are restored once you restart the cluster.
• Zero-scale clusters: Reduce costs by creating only secondary workers, scaling them down to zero when they are not in use.