Share via

Concurrency and API rate limits for Apache Spark pools in Azure Synapse Analytics

  • Article

The following sections list various numerical limits for Spark pools and APIs to manage jobs in Azure Synapse Analytics.

Resource limits

The following table shows the maximum limits of jobs and cores for individual workspaces and Spark pools.

Important

The limits specified for the Spark pools are irrespective of their node sizes, vCore, and memory configurations and apply to across all created instances of a Spark Pool regardless of the user unless otherwise noted.

Resource Metric Limit Scope Regions Notes
Jobs Running Simultaneously 50 Spark Pool All Limit applies across all users of a Spark Pool definition. For example, if two users are submitting jobs against the same Spark Pool, then the cumulative number of jobs running for the two users cannot exceed 50.
Jobs Queued 200 Spark Pool All Limit applies across all users of a Spark Pool definition.
Jobs Maximum Active Jobs 250 Spark Pool All Limit applies across all users of a Spark Pool definition.
Jobs Maximum Active Jobs 1000 Workspace All
Cores Cores Limit Per User Based on the Pool Definition Spark Pool All For example, if a Spark pool is defined as a 50-core pool, each user can use up to 50 cores within the specific Spark pool, since each user gets its own instance of the pool.
Cores Cores Limit Across All Users Based on Workspace Definition Workspace All For example, if a workspace has a limit of 200 cores, then all users across all pools within the workspace cannot use more than 200 cores combined.
Livy Max Payload size for Livy request 100kBytes Livy All

Note

  • Maximum Active Jobs is the total number of jobs submitted, which includes both Jobs Running Simultaneously and Jobs Queued, i.e., Max Active Jobs = Jobs Running Simultaneously + Jobs Queued

API rate limits

The following table shows the throttling limits for the spark job and session management APIs.

Resource Metric Limit (Queries per Second) Scope Regions
Jobs API Get Spark Session 200 Spark Session All
Jobs API Get Spark Session 200 Spark Pool All
Jobs API Get Spark Statement 200 Spark Session All
Jobs API Get Multiple Spark Statements 200 Spark Session All
Jobs API Create Session 2 Workspace EastUS, EastUS2, WestUS, WestUS2, CentralUS, EastUS2EUAP, West Europe
Jobs API Create Session 2 Workspace All other regions
Jobs API Create Batch Job 2 Workspace All
Jobs API Get Spark Batch Job 200 Workspace All
Jobs API Get Multiple Spark Batch Job 200 Workspace All

Note

The maximum requests limit for all resources and operations is 200 queries per second for all regions.

Tip

If you get an error message or HTTP 429 response that reads

Your request has hit layered throttling rate-limit of 200 requests per 1 second(s) for requests on resource(s) identified by pattern {subscriptionId}. {workspaceName}. {HTTP-Verb}. {operationName} - You are currently hitting at a rate of 282 requests per 1 second(s). Please retry after 1 second(s)

Or

Your request has hit layered throttling rate-limit of 2 requests per 1 second(s) for requests on resource(s) identified by {subscriptionId}. {workspaceName}. {HTTP-Verb}. {operationName} - You are currently hitting at a rate of 24 requests per 1 second(s). Please retry after 1 second(s)

User should use the time period value provided in the "Retry-After" HTTP response header, to wait for that time interval when performing retries. In high traffic scenarios, using a random, constant or exponential time interval for the retries would still result in HTTP 429 failures and will incur in high number of retries,there by increase the overall time taken for the requests to get accepted by the service.

Instead by using the service provided Retry-After value, users would experience higher success rate in job submissions as the value in seconds is computed based on point in time traffic to optimize the number of retries and time taken for client's requests to be accepted by the server

Next steps