Metadata-Version: 1.1
Name: tornado-slacker
Version: 0.1
Summary: This package provides an easy API for moving the work out of the tornado process / event loop.
Home-page: https://github.com/kmike/tornado-slacker/
Author: Mikhail Korobov
Author-email: kmike84@gmail.com
License: MIT license
Download-URL: https://bitbucket.org/kmike/tornado-slacker/get/tip.zip
Description: ===============
        tornado-slacker
        ===============
        
        This package provides an easy API for moving the work out of
        the tornado process / event loop.
        
        Currently implemented methods are:
        
        * execute the code in another server's http hook
          (django implementation is included);
        * execute the code in a separate thread (thread pool is used);
        * dummy immediate execution.
        
        API example::
        
            from django.contrib.auth.models import User
            from slacker import adisp
            from slacker import Slacker
            from slacker.workers import DjangoWorker
        
            AsyncUser = Slacker(User, DjangoWorker())
        
            @adisp.process
            def process_data():
                # all the django ORM is supported; the query will be executed
                # on remote end, this will not block the IOLoop
        
                users = yield AsyncUser.objects.filter(is_staff=True)[:5]
                print users
        
        (pep-342 syntax and adisp library are optional, callback-style code
        is also supported)
        
        
        Installation
        ============
        
        ::
        
            pip install tornado-slacker
        
        
        Slackers and workers
        ====================
        
        In order to execute some code in non-blocking manner:
        
        1. Create a Slacker (configured with the desired worker) for some python object::
        
               from slacker import Slacker
               from slacker.workers import ThreadWorker
        
               class Foo(object):
                   # ...
        
               worker = ThreadWorker()
               AsyncFoo = Slacker(Foo, worker)
        
        2. build a query (you can access attributes, do calls and slicing)::
        
               query = AsyncFoo('foo').do_blocking_operation(param1, param2)[0]
        
        3. execute the query::
        
               def callback(result):
                   # ...
        
               query.proceed(callback)
        
           or, using pep-342 style::
        
               from slacker import adisp
        
               @adisp.process
               def handler():
                   result = yield query
                   # ...
        
        Slackers
        ========
        
        Slackers are special objects that are collecting operations (attribute
        access, calls, slicing) without actually executing them::
        
            >>> from slacker import Slacker
            >>> class Foo():
            ...     pass
            ...
            >>> FooSlacker = Slacker(Foo)
            >>> FooSlacker.hello.world()
            __main__.Foo: [('hello',), ('world', (), {})]
        
            >>> FooSlacker(name='me').hello.world(1, y=3)[:3]
            __main__.Foo: [(None, (), {'name': 'me'}),
             ('hello',),
             ('world', (1,), {'y': 3}),
             (slice(None, 3, None), None)]
        
        Callables arguments must be picklable. Slackers also provide a
        method to apply the collected operations to a base object.
        
        Any picklable object (including top-level functions and classes) can
        be wrapped into Slacker, e.g.::
        
            from slacker import adisp
            from slacker import Slacker
            from slacker.workers import ThreadWorker
        
            def task(param1, param2):
                # do something blocking and io-bound
                return results
        
            async_task = Slacker(task, ThreadWorker())
        
            # pep-342-style
            @adisp.process
            def process_data():
                results = yield async_task('foo', 'bar')
                print results
        
            # callback style
            def process_data2():
                async_task('foo', 'bar').proceed(on_result)
        
            def on_result(results):
                print results
        
        
        Python modules also can be Slackers::
        
            import shutil
            from slacker import Slacker
            from slacker.workers import ThreadWorker
        
            shutil_async = Slacker(shutil, ThreadWorker())
            op = shutil_async.copy('file1.txt', 'file2.txt')
            op.proceed()
        
        Workers
        =======
        
        Workers are classes that decides how and where the work should be done:
        
        * ``slacker.workers.DummyWorker`` executes code in-place (this
          is blocking);
        
        * ``slacker.workers.ThreadWorker`` executes code in a thread from
          a thread pool;
        
        * ``slacker.workers.HttpWorker`` pickles the slacker, makes an async
          http request with this data to a given server hook and expects it
          to execute the code and return pickled results;
        
          .. note::
        
              IOLoop blocks on any CPU activity and making http requests plus
              unpickling the returned result can cause a significant overhead
              here. So if the query is fast (e.g. database primary key or index
              lookup, say 10ms) then it may be better not to use tornado-slacker
              and call the query in 'blocking' way: the overall blocking time
              may be less than with 'async' approach because of reduced
              computations amount.
        
              It is also wise to return as little as possible if HttpWorker is used.
        
        
        * ``slacker.workers.DjangoWorker`` is just a HttpWorker with default
          values for use with bundled django remote server hook implementation
          (``slacker.django_backend``).
        
          In order to enable django hook, include 'slacker.django_backend.urls'
          into urls.py and add SLACKER_SERVER option with server address to
          settings.py.
        
          SLACKER_SERVER is '127.0.0.1:8000' by default so this should work for
          development server out of box.
        
          .. warning::
        
              Do not expose django server hook to public, this is insecure!
              The best way is to configure additional server instance to listen
              some local port (e.g. bind it to the default 127.0.0.1:8000 address).
        
          .. note::
        
              Django's QuerySet arguments like Q, F objects, aggregate and annotate
              functions (e.g. Count) are picklable so tornado-slacker can handle
              them fine::
        
                  AsyncAuthor = Slacker(Author, DjangoWorker())
        
                  # ...
                  qs = AsyncAuthor.objects.filter(
                          Q(name='vasia') or Q(is_great=True)
                       ).values('name').annotate(average_rating=Avg('book__rating'))[:10]
        
                  authors = yield qs
        
              Using slacker.Slacker is better than pickling queryset.query
              (as adviced at http://docs.djangoproject.com/en/dev/ref/models/querysets/#pickling-querysets)
              because this allows to pickle any ORM calls including ones that
              don't return QuerySets (http://docs.djangoproject.com/en/dev/ref/models/querysets/#methods-that-do-not-return-querysets)::
        
                  yield AsyncUser.objects.create_superuser('foo')
        
              Moreover, slacker.Slacker adds transparent support for remote invocation
              of custom managers and model methods, returning just the model instance
              attributes, etc.
        
        
        Parallel execution
        ==================
        
        Parallel task execution is supported by adisp library::
        
            def _task1(param1, param2):
                # do something blocking
                return results
        
            def _task2():
                # do something blocking
                return results
        
            # worker can be reused
            worker = ThreadWorker()
            task1 = Slacker(_task1, worker)
            task2 = Slacker(_task2, worker)
        
            @adisp.process
            def process_data():
                # this will execute task1 and task2 in parallel
                # and return the result after all data is ready
                res1, res2 = yield task1('foo', 'bar'), task2()
                print res1, res2
        
        .. note::
        
            this will fail with ``DjangoWorker`` and django development server
            because django development server is single-threaded
        
        
        Contributing
        ============
        
        If you have any suggestions, bug reports or
        annoyances please report them to the issue tracker:
        
        * https://github.com/kmike/tornado-slacker/issues
        
        Source code:
        
        * https://bitbucket.org/kmike/tornado-slacker/
        * https://github.com/kmike/tornado-slacker/
        
        Both hg and git pull requests are welcome!
        
        Credits
        =======
        
        Inspiration:
        
        * https://github.com/satels/django-async-dbslayer/
        * https://bitbucket.org/david/django-roa/
        * http://tornadogists.org/654157/
        
        Third-party software:
        
        * `adisp <https://code.launchpad.net/adisp>`_ (tornado adisp implementation
          is taken from `brukva <https://github.com/evilkost/brukva>`_);
        * exception serialization utils are from
          `billiard <https://github.com/ask/billiard>`_ by Ask Solem.
        
        License
        =======
        
        The license is MIT.
        
        Bundled adisp library uses Simplified BSD License.
        
        slacker.serialization is under BSD License.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Database
Requires: tornado (>= 1.2)
