deb-python-taskflow/doc/source/engines.rst

-------
Engines
-------

Overview
========

Engines are what **really** runs your atoms.

An *engine* takes a flow structure (described by :doc:`patterns <patterns>`) and
uses it to decide which :doc:`atom <atoms>` to run and when.

TaskFlow provides different implementations of engines. Some may be easier to
use (ie, require no additional infrastructure setup) and understand; others
might require more complicated setup but provide better scalability. The idea
and *ideal* is that deployers or developers of a service that uses TaskFlow can
select an engine that suites their setup best without modifying the code of
said service.

Engines usually have different capabilities and configuration, but all of them
**must** implement the same interface and preserve the semantics of patterns (e.g.
parts of :py:class:`linear flow <taskflow.patterns.linear_flow.Flow>` are run
one after another, in order, even if engine is *capable* of running tasks in
parallel).

Why they exist
--------------

An engine being the core component which actually makes your flows progress is
likely a new concept for many programmers so let's describe how it operates in
more depth and some of the reasoning behind why it exists. This will hopefully
make it more clear on there value add to the TaskFlow library user.

First though let us discuss something most are familiar already with; the difference
between `declarative`_ and `imperative`_ programming models. The imperative model
involves establishing statements that accomplish a programs action (likely using
conditionals and such other language features to do this). This kind of program embeds
the *how* to accomplish a goal while also defining *what* the goal actually is (and the state
of this is maintained in memory or on the stack while these statements execute). In contrast
there is the the declarative model which instead of combining the *how* to accomplish a goal
along side the *what* is to be accomplished splits these two into only declaring what
the intended goal is and not the *how*. In TaskFlow terminology the *what* is the structure
of your flows and the tasks and other atoms you have inside those flows, but the *how*
is not defined (the line becomes blurred since tasks themselves contain imperative
code, but for now consider a task as more of a *pure* function that executes, reverts and may
require inputs and provide outputs). This is where engines get involved; they do
the execution of the *what* defined via :doc:`atoms <atoms>`, tasks, flows and
the relationships defined there-in and execute these in a well-defined
manner (and the engine is responsible for *most* of the state manipulation
instead).

This mix of imperative and declarative (with a stronger emphasis on the
declarative model) allows for the following functionality to be possible:

* Enhancing reliability: Decoupling of state alterations from what should be accomplished
  allows for a *natural* way of resuming by allowing the engine to track the current state
  and know at which point a flow is in and how to get back into that state when
  resumption occurs.
* Enhancing scalability: When a engine is responsible for executing your desired work
  it becomes possible to alter the *how* in the future by creating new types of execution
  backends (for example the worker model which does not execute locally). Without the decoupling
  of the *what* and the *how* it is not possible to provide such a feature (since by the very
  nature of that coupling this kind of functionality is inherently hard to provide).
* Enhancing consistency: Since the engine is responsible for executing atoms and the
  associated workflow, it can be one (if not the only) of the primary entities
  that is working to keep the execution model in a consistent state. Coupled with atoms
  which *should* be immutable and have have limited (if any) internal state the
  ability to reason about and obtain consistency can be vastly improved.

  * With future features around locking (using `tooz`_ to help) engines can also
    help ensure that resources being accessed by tasks are reliably obtained and
    mutated on. This will help ensure that other processes, threads, or other types
    of entities are also not executing tasks that manipulate those same resources (further
    increasing consistency).

Of course these kind of features can come with some drawbacks:

* The downside of decoupling the *how* and the *what* is that the imperative model
  where functions control & manipulate state must start to be shifted away from
  (and this is likely a mindset change for programmers used to the imperative
  model). We have worked to make this less of a concern by creating and
  encouraging the usage of :doc:`persistence <persistence>`, to help make it possible
  to have some level of provided state transfer mechanism.
* Depending on how much imperative code exists (and state inside that code) there
  can be *significant* rework of that code and converting or refactoring it to these new concepts.
  We have tried to help here by allowing you to have tasks that internally use regular python
  code (and internally can be written in an imperative style) as well as by providing examples
  and these developer docs; helping this process be as seamless as possible.
* Another one of the downsides of decoupling the *what* from the *how*  is that it may become
  harder to use traditional techniques to debug failures (especially if remote workers are
  involved). We try to help here by making it easy to track, monitor and introspect
  the actions & state changes that are occurring inside an engine (see
  :doc:`notifications <notifications>` for how to use some of these capabilities).

.. _declarative: http://en.wikipedia.org/wiki/Declarative_programming
.. _imperative: http://en.wikipedia.org/wiki/Imperative_programming
.. _tooz: https://github.com/stackforge/tooz

Creating
========

.. _creating engines:

All engines are mere classes that implement the same interface, and of course
it is possible to import them and create instances just like with any classes
in Python. But the easier (and recommended) way for creating an engine is using
the engine helper functions. All of these functions are imported into the
`taskflow.engines` module namespace, so the typical usage of these functions
might look like::

    from taskflow import engines

    ...
    flow = make_flow()
    engine = engines.load(flow, engine_conf=my_conf, backend=my_persistence_conf)
    engine.run


.. automodule:: taskflow.engines.helpers

Usage
=====

To select which engine to use and pass parameters to an engine you should use
the ``engine_conf`` parameter any helper factory function accepts. It may be:

* a string, naming engine type;
* a dictionary, holding engine type with key ``'engine'`` and possibly
  type-specific engine configuration parameters.

Single-Threaded
---------------

**Engine type**: ``'serial'``

Runs all tasks on the single thread -- the same thread `engine.run()` is called
on. This engine is used by default.

.. tip::

    If eventlet is used then this engine will not block other threads
    from running as eventlet automatically creates a co-routine system (using
    greenthreads and monkey patching). See `eventlet <http://eventlet.net/>`_
    and `greenlet <http://greenlet.readthedocs.org/>`_ for more details.

Parallel
--------

**Engine type**: ``'parallel'``

Parallel engine schedules tasks onto different threads to run them in parallel.

Additional supported keyword arguments:

* ``executor``: a object that implements a :pep:`3148` compatible `executor`_
  interface; it will be used for scheduling tasks. You can use instances
  of a `thread pool executor`_ or a
  :py:class:`green executor <taskflow.utils.eventlet_utils.GreenExecutor>`
  (which internally uses `eventlet <http://eventlet.net/>`_ and greenthread pools).

.. tip::

    Sharing executor between engine instances provides better
    scalability by reducing thread creation and teardown as well as by reusing
    existing pools (which is a good practice in general).

.. note::

    Running tasks with a `process pool executor`_ is not currently supported.

Worker-Based
------------

**Engine type**: ``'worker-based'``

For more information, please see :doc:`workers <workers>` for more details on
how the worker based engine operates (and the design decisions behind it).

How they run
============

To provide a peek into the general process that a engine goes through when
running lets break it apart a little and describe what one of the engine types
does while executing (for this we will look into the
:py:class:`~taskflow.engines.action_engine.engine.ActionEngine` engine type).

Creation
--------

The first thing that occurs is that the user creates an engine for a given
flow, providing a flow detail (where results will be saved into a provided
:doc:`persistence <persistence>` backend). This is typically accomplished via
the methods described above in `creating engines`_. The engine at this point now will
have references to your flow and backends and other internal variables are
setup.

Compiling
---------

During this stage the flow will be converted into an internal graph representation
using a flow :py:func:`~taskflow.utils.flow_utils.flatten` function. This function
converts the flow objects and contained atoms into a `networkx`_ directed graph that
contains the equivalent atoms defined in the flow and any nested flows & atoms as
well as the constraints that are created by the application of the different flow
patterns. This graph is then what will be analyzed & traversed during the engines
execution. At this point a few helper object are also created and saved to
internal engine variables (these object help in execution of atoms, analyzing
the graph and performing other internal engine activities).

Preparation
-----------

This stage starts by setting up the storage needed for all atoms in the
previously created graph, ensuring that corresponding
:py:class:`~taskflow.persistence.logbook.AtomDetail` (or subclass of) objects
are created for each node in the graph. Once this is done final validation occurs
on the requirements that are needed to start execution and what storage provides.
If there is any atom or flow requirements not satisfied then execution will not be
allowed to continue.

Execution
---------

The graph (and helper objects) previously created are now used for guiding further
execution. The flow is put into the ``RUNNING`` :doc:`state <states>` and a
:py:class:`~taskflow.engines.action_engine.graph_action.FutureGraphAction`
object starts to take over and begins going through the stages listed below.

Resumption
^^^^^^^^^^

One of the first stages is to analyze the :doc:`state <states>` of the tasks in the graph,
determining which ones have failed, which one were previously running and
determining what the intention of that task should now be (typically an
intention can be that it should ``REVERT``, or that it should ``EXECUTE`` or
that it should be ``IGNORED``). This intention is determined by analyzing the
current state of the task; which is determined by looking at the state in the task
detail object for that task and analyzing edges of the graph for things like
retry atom which can influence what a tasks intention should be (this is aided
by the usage of the :py:class:`~taskflow.engines.action_engine.graph_analyzer.GraphAnalyzer`
helper object which was designed to provide helper methods for this analysis). Once
these intentions are determined and associated with each task (the intention is
also stored in the :py:class:`~taskflow.persistence.logbook.AtomDetail` object) the
scheduling stage starts.

Scheduling
^^^^^^^^^^

This stage selects which atoms are eligible to run (looking at there intention,
checking if predecessor atoms have ran and so-on, again using the
:py:class:`~taskflow.engines.action_engine.graph_analyzer.GraphAnalyzer` helper
object) and submits those atoms to a previously provided  compatible
`executor`_ for asynchronous execution. This executor will return a `future`_ object
for each atom submitted; all of which are collected into a list of not done
futures. This will end the initial round of scheduling and at this point the
engine enters the waiting stage.

Waiting
^^^^^^^

In this stage the engine waits for any of the future objects previously submitted
to complete. Once one of the future objects completes (or fails) that atoms result
will be examined and persisted to the persistence backend (saved into the
corresponding :py:class:`~taskflow.persistence.logbook.AtomDetail` object) and
the state of the atom is changed. At this point what happens falls into two categories,
one for if that atom failed and one for if it did not. If the atom failed it may
be set to a new intention such as ``RETRY`` or ``REVERT`` (other atoms that were
predecessors of this failing atom may also have there intention altered). Once this
intention adjustment has happened a new round of scheduling occurs and this process
repeats until the engine succeeds or fails (if the process running the engine
dies the above stages will be restarted and resuming will occur).

.. note::

    If the engine is suspended while the engine is going through the above
    stages this will stop any further scheduling stages from occurring and
    all currently executing atoms will be allowed to finish (and there results
    will be saved).

Finishing
---------

At this point the :py:class:`~taskflow.engines.action_engine.graph_action.FutureGraphAction`
has now finished successfully, failed, or the execution was suspended. Depending
on which one of these occurs will cause the flow to enter a new state (typically one
of ``FAILURE``, ``SUSPENDED``, ``SUCCESS`` or ``REVERTED``). :doc:`Notifications <notifications>`
will be sent out about this final state change (other state changes also send out notifications)
and any failures that occurred will be reraised (the failure objects are wrapped
exceptions). If no failures have occurred then the engine will have finished and
if so desired the :doc:`persistence <persistence>` can be used to cleanup any
details that were saved for this execution.

Interfaces
==========

.. automodule:: taskflow.engines.base
.. automodule:: taskflow.engines.action_engine.engine
.. automodule:: taskflow.engines.action_engine.graph_action
.. automodule:: taskflow.engines.action_engine.graph_analyzer

Hierarchy
=========

.. inheritance-diagram::
    taskflow.engines.base
    taskflow.engines.action_engine.engine
    taskflow.engines.worker_based.engine
    :parts: 1

.. _future: https://docs.python.org/dev/library/concurrent.futures.html#future-objects
.. _executor: https://docs.python.org/dev/library/concurrent.futures.html#concurrent.futures.Executor
.. _networkx: https://networkx.github.io/
.. _thread pool executor: https://docs.python.org/dev/library/concurrent.futures.html#threadpoolexecutor
.. _process pool executor: https://docs.python.org/dev/library/concurrent.futures.html#processpoolexecutor