Join Apps

Join apps, defined with the @join_app decorator, are a form of app that can launch other pieces of a workflow: for example a Parsl sub-workflow, or a task that runs in some other system.

Parsl sub-workflows

One reason for launching Parsl apps from inside a join app, rather than directly in the main workflow code, is because the definitions of those tasks are not known well enough at the start of the workflow.

For example, a workflow might run an expensive step to detect some objects in an image, and then on each object, run a further expensive step. Because the number of objects is not known at the start of the workflow, but instead only after an expensive step has completed, the subsequent tasks cannot be defined until after that step has completed.

In simple cases, the main workflow script can be stopped using Future.result() and join apps are not necessary, but in more complicated cases, that approach can severely limit concurrency.

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

Using Futures from other components

Sometimes, a workflow might need to incorporate tasks from other systems that run asynchronously but do not need a Parsl worker allocated for their entire run. An example of this is delegating some work into Globus Compute: work can be given to Globus Compute, but Parsl does not need to keep a worker allocated to that task while it runs. Instead, Parsl can be told to wait for the Future returned by Globus Compute to complete.


A join_app looks quite like a python_app, but should return one or more Future objects, rather than a value. Once the Python code has run, the app will wait for those Futures to complete without occuping a Parsl worker, and when those Futures complete, their contents will be the return value of the join_app.

For example:

def some_app():
  return 3

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

assert example.result() == 3

Example of a Parsl sub-workflow

This example workflow shows a preprocessing step, followed by a middle stage that is chosen by the result of the pre-processing step (either option 1 or option 2) followed by a know post-processing step.

def pre_process():
  return 3

def option_one(x):
  return x*2

def option_two(x):
  return (-x) * 2

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

def post_process(x):
  return str(x)

assert post_process(process(pre_process()))).result() == "6"
  • 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: even though process is invoked at the start of the workflow, it will execute later on, when the Future returned by pre_process has a value.

  • 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 choose and invoke the appropriate 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, we want to invoke post_process at submission time near the start of workflow so that Parsl knows about as many tasks as possible. But we don’t want it to execute until the value of the chosen option_{one, two} app is known.

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).

Example of invoking a Futures-driven task from another system

This example shows launching some activity in another system, without occupying a Parsl worker while that activity happens: in this example, work is delegated to Globus Compute, which performs the work elsewhere. When the work is completed, Globus Compute will put the result into the future that it returns, and then (because the Parsl app is a @join_app), that result will be used as the result of the Parsl app.

As above, the motivation for doing this inside an app, rather than in the top level is that sufficient information to launch the Globus Compute task might not be available at start of the workflow.

This workflow will run a first stage, const_five, on a Parsl worker, then using the result of that stage, pass the result as a parameter to a Globus Compute task, getting a Future from that submission. Then, the results of the Globus Compute task will be passed onto a second Parsl local task, times_two.

import parsl
from globus_compute_sdk import Executor

tutorial_endpoint_uuid = '4b116d3c-1703-4f8f-9f6f-39921e5864df'
gce = Executor(endpoint_id=tutorial_endpoint_uuid)

def increment_in_funcx(n):
    return n+1

def increment_in_parsl(n):
    future = gce.submit(increment_in_funcx, n)
    return future

def times_two(n):
    return n*2

def const_five():
    return 5


workflow = times_two(increment_in_parsl(const_five()))

r = workflow.result()

assert r == (5+1)*2


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