Managed Service for Apache Spark has API quota limits that are enforced at the project and region level. The quotas reset every 60 seconds (1 minute).

Handling Managed Service for Apache Spark Quota Errors: If you exceed a Managed Service for Apache Spark quota limit, a RESOURCE_EXHAUSTED (HTTP code 429) is generated, and the corresponding Managed Service for Apache Spark API request fails. However, since your project's Managed Service for Apache Spark quota is refreshed every 60 seconds, you can retry your request after 1 minute has elapsed following the failure.

The following table lists the specific and default per-project Managed Service for Apache Spark API quota types, quota limits, and the methods to which they apply.

Quota Type	Limit	API Methods or Description
ClusterOperationRequestsPerMinutePerProjectPerRegion	200	CancelOperation (cancelling batch operation quota is shared with cancelling cluster operation quota).
BatchOperationRequestsPerMinutePerProjectPerRegion	200	CreateBatch, DeleteBatch
SessionOperationRequestsPerMinutePerProjectPerRegion	200	CreateSession, DeleteSession, TerminateSession
DefaultRequestsPerMinutePerProjectPerRegion	7,500	GetBatch, ListBatches, GetSession, ListSessions
ActiveOperationsPerProjectPerRegion	5,000	Limit on the total number of concurrent active operations of all types in a project in a region.

Increasing Resource Quota Limits: Open the Google Cloud Identity and Access Management (IAM) & Admin Quotas page, and select the resources you want to modify. Then click Edit Quotas at the top of the page to start the quota increase process.

Managed Service for Apache Spark serverless DCU quota

Starting with Managed Service for Apache Spark serverless runtime version 4.0, a project's Data Compute Unit (DCU) quota (dataproc.googleapis.com/data_compute_units), instead of Compute Engine quota, manages and limits Managed Service for Apache Spark serverless batch workload and interactive session compute resource usage (underlying Compute Engine quota usage is redirected to a dedicated Managed Service for Apache Spark producer project).

You can view and adjust your DCU quota on the Google Cloud console Quotas page in Dataproc API. You can enable the quota adjuster to increase your DCU quota when your usage approaches its limit.

Note: When onboarding to Managed Service for Apache Spark serverless runtime version `4.0`, please make sure you have sufficient DCU quota for your expected workload size and number of concurrent sessions/batches.

Calculate DCU consumption

DCU usage is calculated per node from the VM shape, using the same formula as Managed Service for Apache Spark serverless DCU pricing:

• 0.6 DCU per vCPU.
• 0.1 DCU per GB of memory, up to 8 GB per vCPU.
• 0.2 DCU per GB of memory above 8 GB per vCPU (extended memory).
• Premium tier nodes count 1.5x the DCUs of the same shape on standard tier.

The DCUs of a node pool are rounded up to the nearest integer:

DCUs = ceiling(nodes × DCUs per node)

Example: A standard tier node with 4 vCPUs and 16 GB of memory uses 4 × 0.6 + 16 × 0.1 = 4 DCUs. A batch with 1 driver and 10 executors of this shape uses 44 DCUs. The same batch on premium tier uses 66 DCUs.

Limitations

The following limitations apply to DCU quotas:

• Runtime versions: DCU quotas apply to Managed Service for Apache Spark serverless runtime version 4.0 and later. Workloads that use runtime versions prior to 4.0 don't consume DCU quota and continue to consume a project's Compute Engine quota.
• Unsupported quotas: GPU, reservation, and global Compute Engine quotas are not supported in the DCU quota model.

Other Google Cloud quotas

Managed Service for Apache Spark batches use other Google Cloud products. These products have project-level quotas, which include quotas that apply to Managed Service for Apache Spark use. Some services are required to use Managed Service for Apache Spark, such as Compute Engine and Cloud Storage. Other services, such as BigQuery and Bigtable, can optionally be used with Managed Service for Apache Spark.

Required services

The following services, which enforce quota limits, are required to create Managed Service for Apache Spark batches.

Compute Engine

Note: The Compute Engine quotas mentioned in this section are not required for runtime version `4.0` and later. For more information, see DCU quota.

Managed Service for Apache Spark batch workloads can select a variety of Compute Engine resources:

• To mitigate obtainability issues with CPUs and disks, Managed Service for Apache Spark supports fallback across multiple machine families and disk types. For example, in the Premium tier, a batch workload can fall back to N2 with persistent SSD, N2D with local SSD, N2D with persistent disk, N4 with hyperdisk balanced, or N4D with hyperdisk balanced.

• A batch workload fails if it has insufficient quota to consume the fallback selection resources.

The following table lists Compute Engine resource quotas that apply to Managed Service for Apache Spark batch workloads.

Compute Tier	Quota
Standard	CPUS
Premium	N2_CPUS N2D_CPUS CPUS_PER_VM_FAMILY with N4 VM Family CPUS_PER_VM_FAMILY with N4D VM Family
Disk Tier	Quota
Standard	DISKS_TOTAL_GB
Premium	DISKS_TOTAL_GB LOCAL_SSD_TOTAL_GB_PER_VM_FAMILY with N2 and N2D VM Families SSD-TOTAL-GB or Persistent Disk SSD (GB) HDB-TOTAL-GB or Hyperdisk Balanced Capacity (GB)
GPU accelerator	Quota
L4	NVIDIA_L4_GPUS
A100 40GB	NVIDIA_A100_GPUS
A100 80GB	NVIDIA_A100_80GB_GPUS

The Compute Engine quotas are split into regional and global limits. These limits apply to batches you create. For example, to run a Spark batch with 4 driver cores (spark.driver.cores=4) and two executors with 4 cores each (spark.executor.cores=4), you use 12 virtual CPUs (4 * 3). This batch usage counts against the regional quota limit of 24 virtual CPUs.

Free trial quotas: If you use the Compute Engine free trial, your project will have a limit of 8 CPUs. To increase this limit, you must enable billing on the project.

Default batch resources

When you create a batch with default settings, the following Compute Engine resources are used:

Item	Resources used
Virtual CPUs	12
Virtual Machine (VM) Instances	3
Persistent disk	1,200 GiB

Logging

Managed Service for Apache Spark saves batch output and logs in Cloud Logging. The Logging quota applies to your Managed Service for Apache Spark batches.

Optional services

The following services, which have quota limits, can optionally be used with Managed Service for Apache Spark batches.

BigQuery

When reading or writing data into BigQuery, the BigQuery quota applies.

Bigtable

When reading or writing data into Bigtable, the Bigtable quota applies.

Identify workloads with quota or IP address limitations

You can use the following Logging queries to identify Managed Service for Apache Spark workloads that reached your quota or were unable to scale due to IP address exhaustion.

Quota query:

jsonPayload.@type="type.googleapis.com/google.cloud.dataproc.logging.AutoscalerLog"