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Developer's guide - Worker performance

Note: All metrics in this article are prepended with the “temporal_” prefix. The prefix is omitted in this article to make the names more descriptive.


Performance tuning involves three important SDK metric groups:

  1. worker_task_slots_available gauges tagged worker_type=WorkflowWorker and worker_type=ActivityWorker for Workflow Task and Activity Workers correspondingly. These gauges report how many executor “slots” are currently available (unoccupied) for each Worker type.
  2. workflow_task_schedule_to_start_latency and activity_schedule_to_start_latency timers for Workflow Tasks and Activities correspondingly. For more information about schedule_to_start timeout and latency, see Schedule-To-Start Timeout.
  3. sticky_cache_size and workflow_active_thread_count report the size of the Workflow cache and the number of cached Workflow threads.

Note: To have access to all the metrics mentioned above in the JavaSDK, version ≥ 1.8.0 is required.


The following options are defined on WorkerOptions and are applicable for each Worker separately:

  1. maxConcurrentWorkflowTaskExecutionSize and maxConcurrentActivityExecutionSize define the number of total available slots for that Worker.
  2. maxConcurrentWorkflowTaskPollers (JavaSDK: workflowPollThreadCount) and maxConcurrentActivityTaskPollers (JavaSDK: activityPollThreadCount) define the number of pollers performing poll requests waiting on Workflow / Activity task queue and delivering the tasks to the executors.

The Workflow Cache is created and shared between all the workers. It's designed to limit the amount of resources used by the cache for the whole host/process. So the options are defined on WorkerFactoryOptions in JavaSDK and in worker package in GoSDK:

  1. WorkerFactoryOptions#workflowCacheSize (GoSDK: worker.setStickyWorkflowCacheSize) defines the maximum number of cached Workflows Executions. Each cached Workflow contains at least one Workflow thread and its resources (memory, etc).
  2. maxWorkflowThreadCount defines the maximum number of Workflow threads.

These options limit the resource consumption of the in-memory Workflow cache. Workflow cache options are shared between all Workers, because the Workflow cache is something that has to do with the resource consumption of the whole host, like memory and the total amount of threads, and should be limited per JVM.

Task Queues Processing Tuning

These steps are intended to make sure that there are no delays in the processing of Task Queues because of the under-provisioning of Workers or their unbalanced configuration. You should revisit these steps if you observe elevated schedule_to_start metrics. The steps are arranged in the recommended order of execution.

Hosts and Resources provisioning

If currently provisioned Worker hosts are fully utilized (near full CPU usage, high load average, etc), additional Workers hosts have to be provisioned to increase the capacity of the Workers pool.

It's possible to have too many Workers

Monitor the poll success (poll_success/poll_success_sync) and poll timeout poll_timeouts Server metric counters.

Poll Success Rate = (poll_success + poll_success_sync) / (poll_success + poll_success_sync + poll_timeouts)

Poll Success Rate should be >90% in most cases of systems with a steady load. For high volume and low latency, try to target >95%.

If you see

  1. low Poll Success Rate, and
  2. low schedule_to_start_latency, and
  3. low Worker hosts resource utilization at the same time,

then you might have too many workers, consider sizing down.

Worker Executor Slots sizing

The main area to focus on when tuning is the number of Worker Executor Slots. Increase the maximum number of working slots by adjusting maxConcurrentWorkflowTaskExecutionSize or maxConcurrentActivityExecutionSize if both of the following conditions are met:

  1. The Worker hosts are underutilized (no bottlenecks on CPU, load average, etc.).
  2. The worker_task_slots_available metric from the corresponding Worker type frequently shows a depleted number of available Worker slots.

Poller count


Adjustments to pollers are rarely needed and rarely make a difference. Please consider this step only after adjusting Worker slots in the previous step. The only scenario in which the pollers' adjustment makes sense is when there is a significant network latency between the Workers and Temporal Server.


  1. the schedule_to_start metric is abnormally long, and
  2. the Worker hosts are underutilized (there are no bottlenecks on CPU, load average, etc), and
  3. worker_task_slots_available metric from the corresponding Worker type shows that a significant percentage of Worker slots are available on a regular basis,

then consider increasing the number of pollers by adjusting maxConcurrentWorkflowTaskPollers or maxConcurrentActivityTaskPollers, depending on which type of schedule_to_start metric is elevated.

Rate Limiting

If, after adjusting the poller and executors count as specified earlier, you still observe an elevated schedule_to_start, underutilized Worker hosts, or high worker_task_slots_available, you might want to check the following:

  • If server-side rate limiting per Task Queue is set by WorkerOptions#maxTaskQueueActivitiesPerSecond, remove the limit or adjust the value up. (See Go and Java.)
  • If Worker-side rate limiting per Worker is set by WorkerOptions#maxWorkerActivitiesPerSecond, remove the limit. (See Go, TypeScript, and Java.)

Workflow Cache Tuning

When the number of cached Workflow Executions reported by sticky_cache_size hits workflowCacheSize or the number of their threads reported by workflow_active_thread_count metrics gauge hits maxWorkflowThreadCount, Workflow Executions start to get evicted from the cache. An evicted Workflow Execution will need to be replayed when it gets any action that may advance it.


  1. The Workflow Cache limits described above are hit, and
  2. Worker hosts have enough free RAM and are not close to reasonable thread limits,

workflowCacheSize and maxWorkflowThreadCount limits may be increased to decrease the overall latency and cost of the replays in the system. If the opposite occurs, consider decreasing the limits.


In CoreSDK based SDKs, like TypeScript, this metric works differently and should be monitored and adjusted on a per Worker and Task Queue basis.


These properties should always be true for a Worker's configuration.

These are applicable to JavaSDK only.

Perform this sanity check after the adjustments to Worker settings.

  1. workflowCacheSize should be ≤ maxWorkflowThreadCount. Each Workflow has at least one Workflow thread.
  2. maxConcurrentWorkflowTaskExecutionSize should be ≤ maxWorkflowThreadCount. Having more Worker slots than the Workflow cache size will lead to resource contention/stealing between executors and unpredictable delays. It's recommended that maxWorkflowThreadCount be at least 2x of maxConcurrentWorkflowTaskExecutionSize.
  3. maxConcurrentWorkflowTaskPollers should be significantly ≤ maxConcurrentWorkflowTaskExecutionSize. And maxConcurrentActivityTaskPollers should be significantly ≤ maxConcurrentActivityExecutionSize. The number of pollers should always be lower than the number of executors.

Drawbacks of putting just "large values everywhere"

As with any multithreading system, specifying too large values without monitoring with the SDK and system metrics will lead to constant resource contention/stealing, which decreases the total throughput and increases latency jitter of the system.