In this guide we’ll introduce the key players in the traits_futures package. All classes and data items mentioned here can be imported directly from the traits_futures.api module.

Submitting background tasks

The TraitsExecutor is the main point of entry to traits_futures. Its job is to accept one or more task submissions. For each task submitted, it sends the computation to run in the background on a worker from a worker pool, and returns a corresponding “future” object that allows monitoring of the state of the background computation and retrieval of its results.

We’ll examine the future objects in the next section. This section deals with the executor’s main top-level methods and the task submission functions.

To submit a task, use one of the convenience submission functions available from traits_futures.api:

  • The submit_call function allows submission of a simple Python callable, with given positional and named arguments. For example:

    submit_call(my_executor, int, "10101", base=2)

    will execute int("10101", base=2) in the background. submit_call doesn’t wait for the background task to finish; instead, it immediately returns a CallFuture object. See the next section for more details on the CallFuture and related objects.

  • The submit_iteration function allows submission of an arbitrary iterable. The user provides a callable which, when called, returns an iterable object. For example:

    submit_iteration(my_executor, range, 0, 5)

    It returns an IterationFuture object.

  • The submit_progress function allows submission of a progress-reporting callable, and returns a ProgressFuture object. The callable submitted must have a parameter called “progress”. A value for this parameter will be passed (by name) by the executor machinery. The value passed for the “progress” parameter can then be called to send progress reports to the associated ProgressFuture object.

    For example, your callable might look like this:

    def interruptible_sum_of_squares(n, progress):
        """ Compute the sum of squares of integers smaller than n."""
        total = 0
        for i in range(n):
            # Send a pair of the form (steps_completed, total_steps)
            progress((i, n))
            total += i*i
        progress((n, n))

    The computation consists of n steps: a progress report is sent before each step, and after the end of the computation. The progress callable accepts a single Python object, but of course that Python object can be a compound object like a tuple or a dict. It’s up to you to choose the format of the objects you want to send. They’ll arrive in exactly the same format in the ProgressFuture, and then your application can choose how to interpret them.

Working with future objects

The various submission methods described above are asynchronous: they return a “future” object immediately without waiting for the background task to complete. The returned “future” has three purposes:

  • it provides information about the current state of the background task

  • it provides results from the background task (or exception information in the case of failure)

  • it provides a way to request that a background task be cancelled

In this section we describe these three topics in more detail.

Future states

The CallFuture, IterationFuture and ProgressFuture objects all provide a state trait, of trait type FutureState, that represents the state of the underlying computation. That state has one of six possible different values:


The background task has been scheduled to run, but has not yet started executing (for example, because the worker pool is still busy dealing with previously-submitted tasks.


The background task is currently executing on one of the workers.


The background task has completed without error. For a progress task or a simple call, this implies that a result has been returned and is available via the result property of the future. For an iteration, it means that the iteration has completed.


The background task raised an exception at some point in its execution. Information about the exception is available via the exception property of the future.


Cancellation of the background task has been requested, but the background task has not yet acknowledged that request.


The task has stopped following a cancellation request.

In addition, there are two traits whose values are derived from the state trait: the done trait is True when state is one of COMPLETED, FAILED or CANCELLED, and the cancellable trait is True when state is one of WAITING or EXECUTING.

It’s important to understand that the state trait represents the state of the background task to the best of knowledge of the main thread. For example, when the background task starts executing, it sends a message to the corresponding future telling it to change its state from WAITING to EXECUTING. However, that message won’t necessarily get processed immediately, so there will be a brief interval during which the background task has, in fact, started executing, but the state of the future is still WAITING.

Here’s a diagram showing the possible state transitions. The initial state is WAITING. The final states are CANCELLED, COMPLETED and FAILED. The future expects to receive either the message sequence ["started", "raised"] or the message sequence ["started", "returned"] from the background task: this happens even if cancellation is requested.

digraph FutureStates { WAITING -> EXECUTING [label="started"]; WAITING -> CANCELLING [label="cancel"]; CANCELLING -> CANCELLING [label="started"]; EXECUTING -> FAILED [label="raised"]; EXECUTING -> COMPLETED [label="returned"]; EXECUTING -> CANCELLING [label="cancel"]; CANCELLING -> CANCELLED [label="raised"]; CANCELLING -> CANCELLED [label="returned"]; }

Getting task results

Background task results can be retrieved directly from the corresponding futures.

The submit_call and submit_progress functions run callables that eventually expect to return a result. Once the state of the corresponding future reaches COMPLETED, the result of the call is available via the result attribute. Assuming that your calculation future is stored in a trait called future, you might use this as follows:

def _update_result(self, future, name, done):

Any attempt to access future.result before the future completes successfully raises an AttributeError. This includes the cases where the background task was cancelled, or failed with an exception, as well as the cases where the task is still executing or has yet to start running.

A ProgressFuture object also receives progress information send by the background task via its progress event trait. You might use that trait like this:

def _report_progress(self, progress_info):
    current_step, max_steps, matches = progress_info
    self.message = "{} of {} chunks processed. {} matches so far".format(
        current_step, max_steps, matches)

The submit_iteration function is a little bit different: it produces a result on each iteration, but doesn’t necessarily give a final result. Its result_event trait is an Event that you can hook listeners up to in order to receive the iteration results. For example:

def _record_result(self, result):

If a background task fails with an exception, then the corresponding future future eventually reaches FAILED state. In that case, information about the exception that occurred is available in the future.exception attribute. This information takes the form of a tuple of length 3, containing stringified versions of the exception type, the exception value and the exception traceback.

As with future.result, an attempt to access future.exception for a future that’s not in FAILED state will give an AttributeError.

Cancelling the background task

The CallFuture, IterationFuture and ProgressFuture classes all have a cancel method that allows the user to request cancellation of the corresponding background task. That request gets interpreted a little differently depending on the type of task.

For CallFuture, the cancel method either tells a waiting task not to execute, or tells an already executing task that the user is no longer interested in the result. It doesn’t interrupt an already executing background task.

For IterationFuture, the cancel method causes a running background task to abort on the next iteration. No further results are received after calling cancel.

For ProgressFuture, the cancel method causes a running task to abort the next time that task calls progress. No further progress results are received after calling cancel.

In all cases, a future may only be cancelled if its state is one of WAITING or EXECUTING. Attempting to cancel a future in another state will raise a RuntimeError. Calling cancel immediately puts the future into CANCELLING state, and the state is updated to CANCELLED once the future has finished executing. No results or exception information are received from a future in CANCELLING state. A cancelled future will never reach FAILED state, and will never record information from a background task exception that occurs after the cancel call.

Stopping the executor

Like the various future classes, a TraitsExecutor also has a state trait, of type ExecutorState. This state is one of the following:


The executor is running and accepting task submissions.


The user has requested that the executor stop, but there are still running futures associated with this executor. An executor in STOPPING state will not accept new task submissions.


The executor has stopped, and all futures associated with this executor have finished. An executor in this state cannot be used to submit new tasks, and cannot be restarted.

Once a TraitsExecutor object is no longer needed (for example at application shutdown time), its stop method may be called. This cancels all current executing or waiting futures, puts the executor into STOPPING state and then returns.

Once all futures reach CANCELLED state, an executor in STOPPING state moves into STOPPED state. If the executor owns its worker pool, that worker pool is shut down immediately before moving into STOPPED state.

It’s advisable to stop the executor explicitly and wait for it to reach STOPPING state before exiting an application using it.

Using a shared worker pool

By default, the TraitsExecutor creates its own worker pool, and shuts that worker pool down when its stop method is called. In a large multithreaded application, you might want to use a shared worker pool for multiple different application components. In that case, you can instantiate the TraitsExecutor with an existing worker pool, which should be an instance of concurrent.futures.ThreadPoolExecutor:

worker_pool = concurrent.futures.ThreadPoolExecutor(max_workers=24)
executor = TraitsExecutor(worker_pool=worker_pool)

It’s then your responsibility to shut down the worker pool once it’s no longer needed.