More Threads! Simpler and faster threading.
The main benefits over Python's threading library is:
- Multi-threaded queues do not use serialization - Serialization is great in the general case, where you may also be communicating between processes, but it is a needless overhead for single-process multi-threading. It is left to the programmer to ensure the messages put on the queue are not changed, which is not ominous demand.
- Shutdown order is deterministic and explicit - Python's threading
library is missing strict conventions for controlled and orderly shutdown.
These conventions eliminate the need for
abort(), both of which are unstable idioms when using resources. Each thread can shutdown on its own terms, but is expected to do so expediently.
- All threads are required to accept a
please_stopsignal; are expected to test it in a timely manner; and expected to exit when signalled.
- All threads have a parent - The parent is responsible for ensuring their
children get the
please_stopsignal, and are dead, before stopping themselves. This responsibility is baked into the thread spawning process, so you need not deal with it unless you want.
- Uses Signals (much like Events) to simplify logical dependencies among multiple threads, events, and timeouts.
- Logging and Profiling is Integrated - Logging and exception handling is seamlessly integrated: This means logs are centrally handled, and thread safe. Parent threads have access to uncaught child thread exceptions, and the cProfiler properly aggregates results from the multiple threads.
What's it used for
A good amount of time is spent waiting for underlying C libraries and OS services to respond to network and file access requests. Multiple threads can make your code faster, despite the GIL, when dealing with those requests. For example, by moving logging off the main thread, we can get up to 15% increase in overall speed because we no longer have the main thread waiting for disk writes or remote logging posts. Please note, this level of speed improvement can only be realized if there is no serialization happening at the multi-threaded queue.
Do not use Async
- calling styles between synchronous and asynchronous methods can be easily confused
- actors can use blocking methods, async can not
- there is no way to manage resource priority with co-routines.
- stack traces are lost with co-routines
- async scope easily escapes lexical scope, which promotes bugs
Python's async efforts are still immature; a re-invention of threading functionality by another name. Expect to experience a decade of problems that are already solved by threading; here is an example.
- Fibers were an async experiment using a stack, as opposed to the state-machine-based async Python uses now. It does not apply to my argument, but is an interesting read: [Fibers are] not an appropriate solution for writing scalable concurrent software
Most threads will be declared and run in a single line. It is much like Python's threading library, except it demands a name for the thread:
thread = Thread.run("name", function, p1, p2, ...)
Sometimes you want to separate creation from starting:
thread = Thread("name", function, p1, p2, ...) thread.start()
Once a thread is created, one of two actions can be performed.
join()- Join on
threadwill make the caller thread wait until
threadhas stopped. Then, return the resulting value or to re-raise
thread's exception in the caller.
result = thread.join() # may raise exception
release()- Will ignore any return value, and post any exception to logging. Tracking is still performed; released threads are still properly stopped. You may still
join()on a released thread, but you risk being too late: The thread will have already completed and logged it's failure.
thread.release() # release thread resources asap
Threads created without this module can call your code; You want to ensure these "alien" threads have finished their work, released the locks, and exited your code before stopping. If you register alien threads, then
mo-threads will ensure the alien work is done for a clean stop.
def my_method(): with RegisterThread(): t = Thread.current() # we can now use this library on this thread print(t.name) # a name is always given to the alien thread
There are three major aspects of a synchronization primitive:
- Resource - Monitors and locks can only be owned by one thread at a time
- Binary - The primitive has only two states
- Irreversible - The state of the primitive can only be set, or advanced, never reversed
The last, irreversibility is very useful, but ignored in many threading libraries. The irreversibility allows us to model progression; and we can allow threads to poll for progress, or be notified of progress.
These three aspects can be combined to give us 8 synchronization primitives:
- - -- Semaphore
- B -- Event
R - -- Monitor
R B -- Lock
- - I- Iterator/generator
- B I- Signal (or Promise)
R - I- Private Iterator
R B I- Private Signal (best implemented as
Locks are identical to threading monitors, except for two differences:
wait()method will always acquire the lock before returning. This is an important feature, it ensures every line inside a
withblock has lock acquisition, and is easier to reason about.
- Exiting a lock via
__exit__()will always signal a waiting thread to resume. This ensures no signals are missed, and every thread gets an opportunity to react to possible change.
Lockis not reentrant! This is a feature to ensure locks are not held for long periods of time.
lock = Lock() while not please_stop: with lock: while not todo: lock.wait(seconds=1) # DO SOME WORK
In this example, we look for stuff
todo, and if there is none, we wait for a second. During that time others can acquire the
lock and add
todo items. Upon releasing the the
lock, our example code will immediately resume to see what's available, waiting again if nothing is found.
Signal class is a binary semaphore that can be signalled only once; subsequent signals have no effect. It can be signalled by any thread; any thread can wait on a
Signal; and once signalled, all waiting threads are unblocked, including all subsequent waiting threads. A Signal's current state can be accessed by any thread without blocking.
Signal is used to model thread-safe state advancement. It initializes to
False, and when signalled (with
True. It can not be reversed.
Signals are like a Promise, but more explicit
|s & t||Promise.all(s, t)|
|s | t||Promise.race(s, t)|
is_done = Signal() yield is_done # give signal to another that wants to know when done # DO WORK is_done.go()
You can attach methods to a
Signal, which will be run, just once, upon
go(). If already signalled, then the method is run immediately.
is_done = Signal() is_done.then(lambda: print("done")) return is_done
You may also wait on a
Signal, which will block the current thread until the
Signal is a go
is_done = worker_thread.stopped.wait() print("worker thread is done")
Waiting on the
stoppedsignal is different than
join(); the latter will return the thread state (or throw an exception)
Signals are first class, they can be passed around and combined with other Signals. For example, using the
__or__ operator (
either = lhs | rhs;
either will be triggered when
rhs is triggered.
def worker(please_stop): while not please_stop: #DO WORK user_cancel = get_user_cancel_signal() worker(user_cancel | Till(seconds=360))
Signals can also be combined using logical and (
both = lhs & rhs;
both is triggered only when both
rhs are triggered:
(workerA.stopped & workerB.stopped).wait() print("both threads are done")
Till class is a special
Signal used to represent timeouts.
Till(seconds=20).wait() Till(till=Date("21 Jan 2016").unix).wait()
Till rather than
sleep() because you can combine
Till objects with other
Beware that all
Till objects will be triggered before expiry when the main thread is asked to shutdown
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