Merge "Medium-level docs on engines"

doc/source/engines.rst

@@ -23,9 +23,84 @@ 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
@@ -102,10 +177,131 @@ 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.
+
+.. _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/
+
 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
 =========

doc/source/persistence.rst

@@ -53,12 +53,13 @@ and :py:class:`~taskflow.persistence.backends.base.Backend` objects). As an engi
 initializes it will extract (or create) :py:class:`~taskflow.persistence.logbook.AtomDetail`
 objects for each atom in the workflow the engine will be executing.
 
-**Execution:** When an engine beings to execute it will examine any previously existing
+**Execution:** When an engine beings to execute (see :doc:`engine <engines>` for more
-:py:class:`~taskflow.persistence.logbook.AtomDetail` objects to see if they can be used
+of the details about how an engine goes about this process) it will examine any
-for resuming; see :doc:`resumption <resumption>` for more details on this subject. For atoms which have not
+previously existing :py:class:`~taskflow.persistence.logbook.AtomDetail` objects to
-finished (or did not finish correctly from a previous run) they will begin executing
+see if they can be used for resuming; see :doc:`resumption <resumption>` for more details
-only after any dependent inputs are ready. This is done by analyzing the execution
+on this subject. For atoms which have not finished (or did not finish correctly from a
-graph and looking at predecessor :py:class:`~taskflow.persistence.logbook.AtomDetail`
+previous run) they will begin executing only after any dependent inputs are ready. This
+is done by analyzing the execution graph and looking at predecessor :py:class:`~taskflow.persistence.logbook.AtomDetail`
 outputs and states (which may have been persisted in a past run). This will result
 in either using there previous information or by running those predecessors and
 saving their output to the :py:class:`~taskflow.persistence.logbook.FlowDetail` and

doc/source/utils.rst

@@ -22,3 +22,8 @@ The following classes and modules are *recommended* for external usage:
 .. autofunction:: taskflow.utils.persistence_utils.temporary_flow_detail
 
 .. autofunction:: taskflow.utils.persistence_utils.pformat
+
+Internal usage
+==============
+
+.. automodule:: taskflow.utils.flow_utils