Traits Futures eliminates some potential threading-related pitfalls, but by no means all of them. When using Traits Futures it’s still important to have a good understanding of general concurrency and threading-related issues (deadlocks, race conditions, and so on). This section gives some things to consider. Most of these recommendations are not specific to Traits Futures, but apply more generally any time that you’re writing concurrent (and especially multithreaded) code.
Never execute GUI code off the main thread. With a few documented exceptions, most GUI objects should only ever be manipulated on the main thread. For example, a call to
QLabel.setTextthat occurs on a worker thread may cause a crash or traceback, or may be completely fine until your application is deployed on a customer’s machine. It’s important to be aware that the Traits observation mechanisms together with the way that most TraitsUI editors work make it easy to accidentally make a change that triggers a GUI update from a worker thread.
One possible solution to this problem is to make use of
dispatch="ui"when observing a trait that might be modified on a worker thread, and indeed TraitsUI does use
dispatch="ui"already for some key trait listeners. This solves the issue of making GUI updates off the main thread, but introduces its own complications: because the updates are now asynchronous, the state of the toolkit components can get out of sync with the model.
dispatch="ui", we’re not really addressing the root cause of our issues, which is having a mutable model that’s shared between multiple threads. Traits Futures makes it easier to write code in such a way that the model is kept in the main thread.
Avoid making blocking waits on the main thread. To keep a running GUI responsive, avoid doing anything on the main thread that will block for more than a small amount of time (say 0.1 seconds). Where possible, set up your code to make asynchronous calls and react to the results of those calls, rather than making synchronous blocking calls on the main thread. In brief: reacting is preferable to polling; polling is preferable to blocking. (This is one of the key design principles behind Traits Futures.)
Include a timeout in blocking calls. If you’re making a blocking wait call, consider including a timeout to avoid the possibility of that blocking wait blocking forever. If you’re exposing potentially blocking calls to others in your own API, provide a timeout parameter that clients of your API can use. Having a timeout available is especially important for test suites, where you want to avoid the possibility of a single bad test hanging the entire test suite.
Avoid writes to public traits on worker threads. Public traits may have arbitrary listeners attached to them, and writes to those traits from a worker thread will trigger those listeners on the same thread, meaning that those listeners will have to be thread-safe. In general, people writing listeners for a public trait don’t expect to have to make their listener thread-safe. Writing to a public trait from a worker thread is a common cause of making accidental GUI updates from a worker thread.
Avoid reads from public traits on worker threads. If there’s any chance that a trait value might be modified while a background task is running, then that background task may run into race conditions. Instead of accessing a trait value on an object from a background task, consider retrieving the value at task submission time instead and passing it to the background task.
class SomeView(HasStrictTraits): input = Int() def submit_background_task(self): future = submit_call(self.traits_executor, self.do_square) ... def do_square(self): # BAD: the return value might not even be a square, if the # value of self.input changes between the first attribute # access and the second. return self.input * self.input
class SomeView(HasStrictTraits): input = Int() def submit_background_task(self): future = submit_call(self.traits_executor, self.do_square) ... def do_square(self): # Only access self.input once, and cache and re-use the result # of that access. input = self.input return input * input
# Computation represented by a "pure" function with no access to # shared state. def do_square(input): return input*input class SomeView(HasStrictTraits): input = Int() def submit_background_task(self): # Do the attribute access in the main thread; pass the result # of that access to the worker. Now we're no longer sharing # mutable state between threads. future = submit_call(self.traits_executor, self.do_square, self.input) ...
Make copies of mutable data. This is a generalization of the previous recommendation. If a background task depends on mutable data (for example, a dictionary of configuration values), it may make sense to make a private copy to pass to the background task. That way the background task doesn’t have to worry about those data changing while it’s running.
Protect both reads and writes of shared state. Where sharing of mutable state can’t be avoided, make sure that both writes and reads of that shared state are protected by a suitable lock. Don’t rely on the GIL to do your locking for you: Python makes few guarantees about atomicity of operations. For example,
list.appendmay happen to be atomic in current versions of CPython, but there’s no guarantee that that will remain the case, and you may find that your code is actually working with a subclass of
TraitList) for which
appendis not thread-safe.
Beware Traits defaults! Idiomatic Traits-based code makes frequent use of lazy instantiation and defaults. For example, if your
HasTraitsclass needs a lock to protect some piece of shared state, you might consider writing code like this:
class MyModel(HasStrictTraits): #: State shared by multiple threads _results = Dict(Str, AnalysisResult) #: Lock used to protect access to results _results_lock = Any() def __results_lock_default(self): return threading.Lock() def add_result(self, experiment_id, analysis_result): with self._results_lock: self._results[experiment_id] = analysis_result
But this is dangerous! The
__results_lock_defaultmethod will be invoked lazily on first use, and can be invoked simultaneously (or near-simultaneously) on two different threads. We then temporarily have two different locks, allowing
_resultsto be simultaneously accessed from multiple threads and defeating the point of the lock.
In this case, it’s better to create the
_results_lockexplicitly in the main thread when
MyModelis instantiated (e.g., by adding an
__init__method). Better still, rework the design to avoid needing to access
_resultsfrom multiple threads in the first place.
Have a clear, documented thread-ownership model. The organization and documentation of your code should make it clear which pieces of code are intended for possible execution by a worker thread, which pieces of code might be executed simultaneously by multiple threads, and which pieces of code are required to be thread-safe. Ideally, the portion of the codebase that needs to be thread-safe should be small, isolated, and clearly identifiable. (Writing, reasoning about, maintaining and testing thread-safe code is difficult and error-prone. We want to do as little of it as we possibly can.)
Keep task-coordination logic in the main thread. Sometimes you want to execute additional tasks depending on the results of an earlier task. In that case it may be tempting to try to launch those additional tasks directly within the code for the earlier task, but the logic is likely to be more manageable if it’s all kept in the main thread: fire off the first task, then add a trait listener for its completion that inspects the results and fires off additional tasks as necessary. Traits Futures currently encourages this model by forbidding submission of new tasks from a background thread, though that restriction may be lifted in the future.
Avoid having too many Python threads. CPython’s GIL logic can have limiting effects when there are too many Python threads, in some cases causing non-CPU-bound threads not to have a chance to run at all. Avoid creating too many Python threads in your process. The reasonable upper bound will be context dependent, but as a rule of thumb, if you have more than 20 Python threads, consider whether there’s a way of reducing the total number. For more about the problems caused by the GIL, see David Beazley’s talk Understanding the Python GIL (especially Part 5).
Always join your threads. At application shutdown time, or on exit from a script, or in a test’s
tearDownmethod, explicitly join any threads that you created directly. Similarly, explicitly shut down worker pools and executors. Clean shutdown helps to avoid odd side-effects at Python process exit time, and to avoid hard-to-debug interactions between tests in a test suite.
In particular, you should avoid completely the use of
dispatch="new"in Traits listeners. This creates a new thread with no easy way to shut that thread down again, and while it may be an attractive solution in simple cases it generally creates more problems than it solves for more complicated code.
Watch your references. Each Qt
QObjectis “owned” by a particular thread (usually the thread that the
QObjectwas created on, which for most objects will be the main thread). From the Qt documentation on Threads and QObjects, a
QObjectmust not be deleted on a thread other than the one which owns that
QObject. Python’s garbage collection semantics can make conforming to this rule challenging. With good model-view separation, it’s usually simple to ensure that worker threads don’t hold references to any part of the GUI. However, this isn’t enough: Python’s cyclic garbage collector can kick in unpredictably at any time and on any thread (even on a thread that’s completely unrelated to the objects being collected), so if a GUI object is part of a reference cycle, or is merely reachable from a reference cycle, then it may be deleted at a moment out of your control, on an arbitrary thread, potentially causing a segmentation fault. Being disciplined about cleanup and shutdown of GUI components (including explicitly breaking cycles during that cleanup) helps avoid these situations by ensuring that objects are deallocated at a time and on a thread of your choosing.
If you suspect you may be running into issues with GUI objects being collected off the main thread, consider turning off the cyclic garbage collection (
import gc; gc.disable()) as a diagnosis step.
Use thread pools. Use thread pools in preference to creating your own worker threads. This makes it easy to shut down worker threads, and to avoid an explosion of Python threads (see the last two items).