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Add some thoughts on async programming

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File notes/pep_ideas/async_programming.rst

+Some Thoughts on Asynchronous Programming
+Some of the feedback I sent to Guido regarding :pep:`3156` didn't make the
+cut for inclusion in the PEP itself. I still consider it useful background
+and explanatory info, but that PEP's already going to be massive, so it
+makes sense that he'd prefer to keep the PEP text aimed at those that
+already understand the specific problems he is trying to solve.
+As with all essays on these pages, feedback is welcome via the
+`issue tracker`_ or `Twitter`_. If you want to comment on :pep:`3156` itself,
+use the `python-ideas mailing list`_.
+.. _issue tracker:
+.. _Twitter:
+.. _python-ideas mailing list:
+A Bit of Background Info
+The term "Asynchronous I/O" is used to refer to two distinct, but
+related, concepts. The first of these concepts is an execution model
+for network programming, where the scalability of an I/O bound
+application is governed by the number of open socket connections that
+can be handled in a single OS process rather than by the number of
+concurrent OS level threads. This approach can significantly improve
+the scalability of an application, as most POSIX based operating
+systems can effectively manage thousands or tens of thousands of open
+socket connections without any significant tuning of process options, but
+only hundreds of threads (with the default size of the C stack being a
+key culprit - consuming the resources of an entire thread to wait for an
+I/O operation can waste a whole lot of memory). Disk I/O can also be scaled
+in this manner, but it's substantially less common to do so (since disk
+latency is typically orders of magnitude better than network latency).
+The second of these concepts is a programming model based on explicit
+cooperative multi-threading where yield points are visible locally (rather
+than the pre-emptive multi-threading provided by OS level threads or
+implicit cooperative multi-threading where any function call or magic method
+invocation may hide a suspension point). One key goal of this
+explicit programming model is to change the nature of the bugs
+typically seen in an application. Rather than (potentially subtle)
+correctness bugs due to incorrect manipulation of data structures
+shared between preemptively scheduled threads, or inadvertently yielding
+control when a data structure is in a partially modified state, applications
+and libraries using this explicit programming model see performance bugs
+where an erroneous call to a synchronous API blocks the entire application.
+(Note that pre-emptively multi-threaded applications can still see the
+later kind of bug if a blocking call is made while holding a lock on a
+critical data structure). Another perceived benefit is that this model
+better matches the reality of event based programming: every event is
+dealt with immediately, and either translated into a response (whether
+that's a network message or a UI update) based on information already
+available locally or else into waiting for a different event (via some
+kind of callback API).
+The key problem with this explicitly asynchronous programming model, of
+course, is that if an operation starts as synchronous, converting it to
+asynchronous requires modifying every point that calls it to yield
+control appropriately when necessary.
+The Stackless Python project, and the greenlets library it inspired,
+aim to provide the first benefit, while retaining the standard
+synchronous programming model for application level code. This is a
+hugely powerful technique, as it allows the scalability benefits to be
+gained without needing to rewrite the entire application stack.
+Object-Relational-Mappers, for example, usually assume that it is OK
+to query or write to a database as a side effect of attribute
+retrieval or modification. Greenlets can often be used to implicitly
+turn such attribute access operations into asynchronous I/O
+operations, by replacing the underlying database access APIs (if the
+lowest layer is written in Python rather than C, it may even be possible
+to do so via monkey-patching), while switching to explicit asynchronous
+programming would require rewriting the entire ORM. Using explicit
+asynchronous programming also prevents entirely much of the syntactic
+sugar provided by ORMs, such as implicitly loading of data from the
+database when retrieving an attribute from an object.
+:pep:`3156`, however, like the Twisted networking engine and the Tornado
+web server, is aimed at providing *both* benefits: an explicitly
+asynchronous programming model based on cooperative multi-threading where
+the suspension points are clearly marked in the individual functions and
+scalability is limited by the number of concurrent I/O operations supported
+per process rather than the number of OS level threads.
+I think this quote from Guido in PEP 343 (the PEP that added the ``with``
+statement) is also relevant to the asynchronous IO PEP:
+    But the final blow came when I read Raymond Chen's rant about
+    `flow-control macros`_.  Raymond argues convincingly that hiding
+    flow control in macros makes your code inscrutable, and I find
+    that his argument applies to Python as well as to C.
+.. _flow-control macros:
+When writing implicitly asynchronous code, you have to assume that you may
+lose control of the execution at any point, since even something as innocous
+as retrieving an attribute from an object may suspend the thread of control.
+By contrast, with explicitly asynchronous code, it is safe to assume that you
+have sole access to shared data structures between suspension points.
+While the inherent duplication between the synchronous programming
+model and the asynchronous programming model is unlikely to ever be
+eliminated, the aim of :pep:`3156` is to help reduce the unnecessary
+duplication between the asynchronous programming frameworks, as well
+as to provide improved asynchronous programming capabilities as part
+of the standard library, with an easier migration path to third party
+projects like Twisted and Tornado. This improved asynchronous
+infrastructure should also benefit greenlets-based
+synchronous-to-asynchronous adapters, as there should be a richer
+asynchronous ecosystem to draw from when implementing the networking
+side of frameworks like ``gevent`` (more on that below).
+Furthermore, Guido's PEP aims to take full advantage of the improved
+support added to the language for using generators as coroutines in
+PEP 342 and PEP 380, as well as aligning with the API for the OS level
+parallel execution techniques supported by the ``concurrent.futures``
+standard library module added in PEP 3148. I've occasionally spoken of
+the changes to Python's generators over the years as "a way to make writing
+Twisted code not suck", and see the new PEP as a natural continuation of
+that effort.
+Gevent and PEP 3156
+If you look at gevent's `monkey patching code`_, you can see that one of
+the key features it provides is the ability to act as a "synchronous to
+asynchronous adapter": taking code that assumes a synchronous blocking model
+and running it based on asynchronous IO instead.
+From an application point of view, that's an amazing capability, and it
+allows some things that are impossible in an explicitly asynchronous model
+(such as implicitly suspending inside a magic method, which is needed for
+features like lazy loading of attributes from the database in an ORM).
+Where even a framework like ``gevent`` can benefit from the transport and
+protocol infrastructure that will be exposed by :pep:`3156` is that, as with
+other projects like Twisted and Tornado, it will become easier to avoid
+reinventing the wheel on the *asynchronous I/O* side of things.
+There will thus be 3 models for integrating asynchronous and synchronous
+* Thread pools: :pep:`3156` will allow operations to be passed to separate
+  threads, allowing blocking operations to be executed without suspending the
+  main thread. This will allows explicitly asynchronous code to take advantage
+  of existing blocking operations without blocking the main loop.
+* Blocking: one of the capabilities anticipated in :pep:`3156` is the
+  ability to effectively block on an asynchronous operation, running the event
+  loop until the operation completes. This won't give any scalability benefits,
+  but should allow synchronous applications to take advantage of at least
+  some asynchronous transport and protocol implementations without needing to
+  rewrite them as synchronous operations.
+* Implicit asynchronous operations: ``gevent`` will be able to share elements
+  of the IO stack with other asynchronous frameworks, while still allowing
+  `gevent`` users to write apparently synchronous code.
+.. _monkey patching code:
+Using Special Methods in Explicitly Asynchronous Code
+One challenge that arises when writing explicitly asynchronous code is
+how to compose it with other elements of Python syntax like operators,
+for loops and with statements. The key to doing this effectively is
+the same as that adopted when designing the
+:func:`concurrent.futures.as_completed` iterator API: these other
+operations should always return a Future or coroutine object, even if the
+result of the operation happens to be available immediately. This allows the
+user code to consistently retrieve the result via ``yield from``. The
+implementation of ``__iter__`` on Future objects and coroutines is such
+that they will return immediately if the result is already available,
+avoiding the overhead of a trip through the event loop.
+Naming conventions
+The examples below follow Guido's convention in NDB, where it is assumed
+that synchronous and asynchronous versions of operations are offered in the
+same namespace. The synchronous blocking versions are considered the
+"normal" API, and the asynchronous variants are marked with the ``_async``
+If an API is entirely asynchronous (as in :pep:`3156` itself) then the
+suffix may be dispensed with - users should assume that all operations
+are asynchronous. In such an API, marking any synchronous operations API
+with a ``_sync`` suffix may be desirable, but I don't know of any real
+world usage of that convention.
+Asynchronous conditional expressions
+While loops and if statements are a very simple case, as it's merely a
+matter of using an asynchronous expression in place of the normal
+boolean query::
+    while (yield from check_async()):
+        # check_async() always returns a Future or coroutine
+        # The loop will suspend if necessary when evaluating the condition
+Asynchronous Iterators
+Asynchronous iterators work by producing Futures or coroutines at each
+step. These are then waited for explicitly in the body of the loop::
+    for f in iterator_async():
+        # Each iteration step always returns a Future or coroutine immediately
+        # Retrieving the result is then flagged as a possible suspension point
+        x = yield from f
+For example, this approach is useful when executing multiple operations in
+parallel, and you want to process the individual results as they become
+    for f in as_completed(operations):
+        result = yield from f
+        # Process the result
+This is very similar to the way the existing concurrent.futures module
+operates, with the ``f.result()`` call replaced by the explicit
+suspension point ``yield from f``.
+Asynchronous Context Managers
+Asynchronous context managers are able to cope with blocking
+operations on entry to a with statement by implementing them as a
+Future or coroutine that produces a context manager as its result. The
+__enter__ and __exit__ methods on this context manager must themselves
+be non-blocking::
+    with (yield from cm_async) as x:
+        # The potentially blocking operation happens in cm_async.__iter__
+        # The __enter__ and __exit__ methods on the result cannot
+        # suspend execution
+Alternatively, a Future or coroutine may be returned from __enter__,
+similar to the usage of asynchronous iterators::
+    with cm_async as f:
+        # The potentially blocking operation happens in f.__iter__
+        x = yield from f
+        # The __exit__ method on the CM still cannot suspend execution
+For example, either of these models may be used to implement an "asynchronous
+lock" that is used to control shared access to a data structure even across
+operations which require handing control back to the event loop.
+However, it is not currently possible to handle operations (such as
+database transactions) that may need to suspend execution in the
+__exit__ method. In such cases, it is necessary to either adopt a
+synchronous-to-asynchronous adapter framework (such as gevent) or else
+revert to the explicit try statement form::
+    x = yield_from cm.enter_async()
+    try:
+        ...
+    except Exception as ex:
+        cm.handle_error_async(ex)
+    else:
+        cm.handle_success_async()
+Asynchronous Operators
+The approach described above generalises to other operators, such as
+addition or attribute access: rather than returning a result directly,
+an API may be defined as returning a Future or coroutine, to be turned
+into a concrete result with ``yield from``::
+    add_async = objA + objB
+    add_result = yield from add_async
+In practice, it is likely to be clearer to use separate methods for
+potentially asynchronous operations, making it obvious through naming
+conventions (such as the ``_async`` suffix) that the operations return
+a Future or coroutine rather than producing the result directly.
+Synchronous-to-asynchronous adapters also have a role to play here
+in allowing code that relies heavily on operator overloading to
+interact cleanly with asynchronous libraries.

File notes/pep_ideas/index.rst

 .. _tracker issues I am following:,id,activity,nosy,status&@sort=-activity&@group=priority&@filter=nosy,status&@pagesize=50&@startwith=0&status=1&nosy=1309&@dispname=My%20Nosy%20List
+In relation to Guido's async PEP, I had a few suggestions that he felt
+weren't appropriate for the PEP itself. For lack of a better location,
+I've posted them here:
+.. toctree::
+   :maxdepth: 2
+   async_programming.rst
 Tinkering with Ideas