Comment by solidasparagus

Great feedback, thank you. We'll certainly be working on adding more examples to illustrate more complex use cases.

One thing I'd mention is that we don't really imagine Pyper as a whole observability and orchestration platform. It's really a package for writing Python functions and executing them concurrently, in a flexible pattern that can be integrated with other tools.

For example, I'm personally a fan of Prefect as an observability platform-- you could define pipelines in Pyper then wrap it in a Prefect flow for orchestration logic.

Exception handling and logging can also be handled by orchestration tools (or in the business logic if appropriate, literally using try... except...)

For a simple progress bar, tqdm is probably the first thing to try. As it wraps anything iterable, applying it to a pipeline might look like:

  import time
  from pyper import task
  from tqdm import tqdm


  @task(branch=True)
  def func(limit: int):
      for i in range(limit):
          time.sleep(0.1)
          yield i


  def main():
      for _ in tqdm(func(limit=20), total=20):
          pass


  if __name__ == "__main__":
      main()

> I don't want to pickle and re-instantiate a cloud client every time I download some bytes for instance

Have you tried multiprocessing.shared_memory to address this?

Do you really need to reinvent the wheel every time for parallel workloads? Just learn GNU parallel and write single-threaded code.

Concurrency in general isn't about parallelism. It's just about doing multiple things at the same time.