This sample is a Python implementation of the TypeScript "Sticky Workers" example, full credit for the design to the authors of that sample. A sticky execution is a job distribution design pattern where all workflow computational tasks are executed on a single worker. In the Go and Java SDKs this is explicitly supported via the Session option, but in other SDKs a different approach is required.

Typical use cases for sticky executions include tasks where interaction with a filesystem is required, such as data processing or interacting with legacy access structures. This example will write text files to folders corresponding to each worker, located in the demo_fs folder. In production, these folders would typically be independent machines in a worker cluster.

This strategy is:

• Create a get_available_task_queue activity that generates a unique task queue name, unique_worker_task_queue.
• For activities intended to be "sticky", only register them in one Worker, and have that be the only Worker listening on that unique_worker_task_queue. This will be run on a series of FileProcessing workflows.
• Execute workflows from the Client like normal. Check the Temporal Web UI to confirm tasks were staying with their respective worker.

It doesn't matter where the get_available_task_queue activity is run, so it can be "non sticky" as per Temporal default behavior. In this demo, unique_worker_task_queue is simply a uuid initialized in the Worker, but you can inject smart logic here to uniquely identify the Worker, as Netflix did.

Activities have been artificially slowed with time.sleep(3) to simulate slow activities.

To run, first see README.md for prerequisites. Then, run the following from this directory to start the worker:

poetry run python worker.py

This will start the worker. Then, in another terminal, run the following to execute the workflow:

poetry run python starter.py

(temporalio-samples-py3.10) user@machine:~/samples-python/activities_sticky_queues$ poetry run python starter.py
Output checksums:
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