Merge pull request #709 from Michael-F-Bryan/speed

Speed

docs/scenarios/speed.rst

@@ -226,13 +226,212 @@ Numba
 -----
 .. todo:: Write about Numba and the autojit compiler for NumPy
 
-Threading
+Concurrency
-:::::::::
+:::::::::::
 
 
+Concurrent.futures
+------------------
+
+The `concurrent.futures`_ module is a module in the standard library that
+provides a "high-level interface for asynchronously executing callables". It
+abstracts away a lot of the more complicated details about using multiple
+threads or processes for concurrency, and allows the user to focus on 
+accomplishing the task at hand.
+
+The `concurrent.futures`_ module exposes two main classes, the
+`ThreadPoolExecutor` and the `ProcessPoolExecutor`. The ThreadPoolExecutor
+will create a pool of worker threads that a user can submit jobs to. These jobs
+will then be executed in another thread when the next worker thread becomes
+available.  
+
+The ProcessPoolExecutor works in the same way, except instead of using multiple
+threads for its workers, it will use multiple processes. This makes it possible
+to side-step the GIL, however because of the way things are passed to worker
+processes, only picklable objects can be executed and returned.
+
+Because of the way the GIL works, a good rule of thumb is to use a
+ThreadPoolExecutor when the task being executed involves a lot of blocking
+(i.e. making requests over the network) and to use a ProcessPoolExecutor
+executor when the task is computationally expensive.
+
+There are two main ways of executing things in parallel using the two
+Executors. One way is with the `map(func, iterables)` method. This works
+almost exactly like the builtin `map()` function, except it will execute
+everything in parallel. :
+
+.. code-block:: python
+
+    from concurrent.futures import ThreadPoolExecutor
+    import requests
+
+    def get_webpage(url):
+        page = requests.get(url)
+        return page
+
+    pool = ThreadPoolExecutor(max_workers=5)
+
+    my_urls = ['http://google.com/']*10  # Create a list of urls
+
+    for page in pool.map(get_webpage, my_urls):
+        # Do something with the result
+        print(page.text)
+
+For even more control, the `submit(func, *args, **kwargs)` method will schedule 
+a callable to be executed ( as `func(*args, **kwargs)`) and returns a `Future`_
+object that represents the execution of the callable.
+
+The Future object provides various methods that can be used to check on the
+progress of the scheduled callable. These include:
+
+cancel()
+    Attempt to cancel the call.
+cancelled()
+    Return True if the call was successfully cancelled.
+running()
+    Return True if the call is currently being executed and cannot be
+    cancelled.
+done()
+    Return True if the call was successfully cancelled or finished running.
+result()
+    Return the value returned by the call. Note that this call will block until
+    the scheduled callable returns by default.
+exception()
+    Return the exception raised by the call. If no exception was raised then
+    this returns `None`. Note that this will block just like `result()`.
+add_done_callback(fn)
+    Attach a callback function that will be executed (as `fn(future)`) when the
+    scheduled callable returns.
+
+
+.. code-block:: python
+
+    from concurrent.futures import ProcessPoolExecutor, as_completed
+
+    def is_prime(n):
+        if n % 2 == 0:
+            return n, False
+
+        sqrt_n = int(n**0.5)
+        for i in range(3, sqrt_n + 1, 2):
+            if n % i == 0:
+                return n, False
+        return n, True
+
+    PRIMES = [
+        112272535095293,
+        112582705942171,
+        112272535095293,
+        115280095190773,
+        115797848077099,
+        1099726899285419]
+
+    futures = []
+    with ProcessPoolExecutor(max_workers=4) as pool:
+        # Schedule the ProcessPoolExecutor to check if a number is prime
+        # and add the returned Future to our list of futures
+        for p in PRIMES:
+            fut = pool.submit(is_prime, p)
+            futures.append(fut)
+
+    # As the jobs are completed, print out the results
+    for number, result in as_completed(futures):
+        if result:
+            print("{} is prime".format(number))
+        else:
+            print("{} is not prime".format(number))
+
+The `concurrent.futures`_ module contains two helper functions for working with
+Futures. The `as_completed(futures)` function returns an iterator over the list
+of futures, yielding the futures as they complete.
+
+The `wait(futures)` function will simply block until all futures in the list of
+futures provided have completed.
+
+For more information, on using the `concurrent.futures`_ module, consult the
+official documentation.
+
 Threading
 ---------
 
+The standard library comes with a `threading`_ module that allows a user to
+work with multiple threads manually.
+
+Running a function in another thread is as simple as passing a callable and
+it's arguments to `Thread`'s constructor and then calling `start()`:
+
+.. code-block:: python
+
+    from threading import Thread
+    import requests
+
+    def get_webpage(url):
+        page = requests.get(url)
+        return page
+
+    some_thread = Thread(get_webpage, 'http://google.com/')
+    some_thread.start()
+
+To wait until the thread has terminated, call `join()`:
+
+.. code-block:: python
+
+    some_thread.join()
+
+After calling `join()`, it is always a good idea to check whether the thread is
+still alive (because the join call timed out):
+
+.. code-block:: python
+
+    if some_thread.is_alive():
+        print("join() must have timed out.")
+    else:
+        print("Our thread has terminated.")
+
+Because multiple threads have access to the same section of memory, sometimes
+there might be situations where two or more threads are trying to write to the
+same resource at the same time or where the output is dependent on the sequence
+or timing of certain events. This is called a `data race`_ or race condition. 
+When this happens, the output will be garbled or you may encounter problems
+which are difficult to debug. A good example is this `stackoverflow post`_.  
+
+The way this can be avoided is by using a `Lock`_ that each thread needs to
+acquire before writing to a shared resource. Locks can be acquired and released
+through either the contextmanager protocol (`with` statement), or by using
+`acquire()` and `release()` directly. Here is a (rather contrived) example:
+
+
+.. code-block:: python
+
+    from threading import Lock, Thread
+
+    file_lock = Lock()
+
+    def log(msg):
+        with file_lock:
+            open('website_changes.log', 'w') as f:
+                f.write(changes)
+
+    def monitor_website(some_website):
+        """
+        Monitor a website and then if there are any changes, 
+        log them to disk.
+        """
+        while True:
+            changes = check_for_changes(some_website)
+            if changes:
+                log(changes)
+
+    websites = ['http://google.com/', ... ]
+    for website in websites:
+        t = Thread(monitor_website, website)
+        t.start()
+
+Here, we have a bunch of threads checking for changes on a list of sites and
+whenever there are any changes, they attempt to write those changes to a file
+by calling `log(changes)`. When `log()` is called, it will wait to acquire
+the lock with `with file_lock:`. This ensures that at any one time, only one
+thread is writing to the file. 
 
 Spawning Processes
 ------------------
@@ -248,3 +447,8 @@ Multiprocessing
 .. _`New GIL`: http://www.dabeaz.com/python/NewGIL.pdf
 .. _`Special care`: http://docs.python.org/c-api/init.html#threads
 .. _`David Beazley's`: http://www.dabeaz.com/GIL/gilvis/measure2.py
+.. _`concurrent.futures`: https://docs.python.org/3/library/concurrent.futures.html
+.. _`Future`: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Future
+.. _`threading`: https://docs.python.org/3/library/threading.html
+.. _`stackoverflow post`: http://stackoverflow.com/questions/26688424/python-threads-are-printing-at-the-same-time-messing-up-the-text-output
+.. _`data race`: https://en.wikipedia.org/wiki/Race_condition