Join Apps

Join apps allows an app to define a sub-workflow: the app can launch other apps and incorporate them into the main task graph. They can be specified using the join_app decorator.

Join apps allow more naunced dependencies to be expressed that can help with:

  • increased concurrency - helping with strong scaling

  • more focused error propagation - allowing more of an ultimately failing workflow to complete

  • more useful monitoring information


A join_app looks quite like a python_app, but should return a Future, rather than a value. After the python code has run, the app invocation will not complete until that future has completed, and the return value of the join_app will be the return value (or exception) from the returned future.

For example:

def some_app():
  return 3

def example():
  x: Future = some_app()
  return x  # note that x is a Future, not a value

# example.result() == 3

What/why/how can you do with a join app

join apps are useful when a workflow needs to launch some apps, but it doesn’t know what those apps are until some earlier apps are completed.

For example, a pre-processing stage might be followed by n middle stages, but the value of n is not known until pre-processing is complete; or the choice of app to run might depend on the output of pre-processing.

In the following example, a pre-processing stage is followed by a choice of option 1 or option 2 apps, with a post-processing stage afterwards. All of the example apps are toy apps that are intended to demonstrate control/data flow but they are based on a real use case.

Here is the implementation using join apps. Afterwards, there are some examples of the problems that arise trying to implement this without join apps.

def pre_process():
  return 3

def option_one(x):
  # do some stuff
  return x*2

def option_two(x):
  # do some more stuff
  return (-x) * 2

def process(x):
  if x > 0:
    return option_one(x)
    return option_two(x)

def post_process(x):
  return str(x) # convert x to a string

# here is a simple workflow using these apps:
# post_process(process(pre_process()))).result() == "6"
# pre_process gives the number 3, process turns it into 6,
# and post_process stringifys it to "6"

So why do we need process to be a @join_app for this to work?

  • Why can’t process be a regular python function?

process needs to inspect the value of x to make a decision about what app to launch. So it needs to defer execution until after the pre-processing stage has completed. In parsl, the way to defer that is using apps: the execution of process will happen when the future returned by pre_process has completed.

  • Why can’t process be a @python_app?

A python app, if run in a parsl.executors.ThreadPoolExecutor, can launch more parsl apps; so a python app implementation of process() would be able to inspect x and launch option_{one, two}.

From launching the option_{one, two} app, the app body python code would get a Future[int] - a Future that will eventually contain int.

But now, we want to (at submission time) invoke post_process, and have it wait until the relevant option_{one, two} app has completed.

If we don’t have join apps, how can we do this?

We could make process wait for option_{one, two} to complete, before returning, like this:

def process(x):
  if x > 0:
    f = option_one(x)
    f = option_two(x)
  return f.result()

but this will block the worker running process until option_{one, two} has completed. If there aren’t enough workers to run option_{one, two} this can even deadlock. (principle: apps should not wait on completion of other apps and should always allow parsl to handle this through dependencies)

We could make process return the Future to the main workflow thread:

def process(x):
  if x > 0:
    f = option_one(x)
    f = option_two(x)
  return f  # f is a Future[int]

# process(3) is a Future[Future[int]]

What comes out of invoking process(x) now is a nested Future[Future[int]] - it’s a promise that eventually process will give you a promise (from option_one, two}) that will eventually give you an int.

We can’t pass that future into post_process… because post_process wants the final int, and that future will complete before the int is ready, and that (outer) future will have as its value the inner future (which won’t be complete yet).

So we could wait for the result in the main workflow thread:

f_outer = process(pre_process())  # Future[Future[int]]
f_inner = f_outer.result  # Future[int]
result = post_process(f_inner)
# result == "6"

But this now blocks the main workflow thread. If we really only need to run these three lines, that’s fine, but what about if we are in a for loop that sets up 1000 parametrised iterations:

for x in [1..1000]:
  f_outer = process(pre_process(x))  # Future[Future[int]]
  f_inner = f_outer.result()  # Future[int]
  result = post_process(f_inner)

The for loop can only iterate after pre_processing is done for each iteration - it is unnecessarily serialised by the .result() call, so that pre_processing cannot run in parallel.

So, the rule about not calling .result() applies in the main workflow thread too.

What join apps add is the ability for parsl to unwrap that Future[Future[int]] into a Future[int] in a “sensible” way (eg it doesn’t need to block a worker).


The term “join” comes from use of monads in functional programming, especially Haskell.