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Job worker

The Java client's job worker​

The Java client provides a job worker that handles polling for available jobs. This allows you to focus on writing code to handle the activated jobs.

On open, the job worker waits pollInterval milliseconds and then polls for maxJobsActive jobs. It then continues with the following schedule:

  1. If a poll did not activate any jobs, it waits for pollInterval milliseconds and then polls for more jobs.
  2. If a poll activated jobs, the worker submits each job to the job handler.
  3. Every time a job is handled, the worker checks whether the number of unhandled jobs have dropped below 30% (rounded up) of maxJobsActive. The first time that happens, it will poll for more jobs.
  4. If a poll fails with an error response, a backoff strategy is applied. This strategy waits for the delay provided by the backoffSupplier and polls for more jobs.

For example, imagine you have 10 process instances and a single job worker configured with maxJobsActive = 3. The job worker will first pull three jobs and begin executing them. The threshold to poll for new jobs is 1 (30% of 3 rounded up). After two jobs have completed, the threshold is reached and the job worker will poll for up to 2 additional jobs. This process repeats until the jobs from all 10 process instances are completed.

Example usage​

Backoff configuration​

When a poll fails with an error response, the job worker applies a backoff strategy. It waits for some time, after which it polls again for more jobs. This gives a Zeebe cluster some time to recover from a failure. In some cases, you may want to configure this backoff strategy to better fit your situation.

The retry delay (i.e. the time the job worker waits after an error before the next poll for new jobs) is provided by the BackoffSupplier. You can replace it using the .backoffSupplier() method on the JobWorkerBuilder.

By default, the job worker uses an exponential backoff implementation, which you can configure using BackoffSupplier.newBackoffBuilder().

The backoff strategy is especially useful for dealing with the GRPC_STATUS_RESOURCE_EXHAUSTED error response (see gRPC Technical Error Handling).

This error code indicates the Zeebe cluster is currently under too large of a load and has decided to reject this request.

By backing off, the job worker helps Zeebe by reducing the load.

note

Zeebe's backpressure mechanism can also be configured.