------- Engines ------- Overview ======== Engines are what **really** runs your atoms. An *engine* takes a flow structure (described by :doc:`patterns `) and uses it to decide which :doc:`atom ` 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 use TaskFlow can select an engine that suites their setup best without modifying the code of said service. .. note:: 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 a :py:class:`.linear_flow.Flow` are run one after another, in order, even if the selected 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 their 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 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 `, tasks, flows and the relationships defined there-in and execute these in a well-defined manner (and the engine is responsible for any state manipulation instead). This mix of imperative and declarative (with a stronger emphasis on the declarative model) allows for the following functionality to become 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 workflow is in and how to get back into that state when resumption occurs. * Enhancing scalability: When an 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 very 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 `, to help make it possible to have state and transfer that state via a argument input and output mechanism. * Depending on how much imperative code exists (and state inside that code) there *may* 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 :doc:`examples ` that show how to use these concepts. * 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 ` 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/openstack/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() eng = engines.load(flow, engine='serial', backend=my_persistence_conf) eng.run() ... .. automodule:: taskflow.engines.helpers Usage ===== To select which engine to use and pass parameters to an engine you should use the ``engine`` parameter any engine helper function accepts and for any engine specific options use the ``kwargs`` parameter. Types ===== Serial ------ **Engine type**: ``'serial'`` Runs all tasks on a single thread -- the same thread :py:meth:`~taskflow.engines.base.Engine.run` is called from. .. note:: 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 implicit co-routine system (using greenthreads and monkey patching). See `eventlet `_ and `greenlet `_ for more details. Parallel -------- **Engine type**: ``'parallel'`` A parallel engine schedules tasks onto different threads/processes to allow for running non-dependent tasks simultaneously. See the documentation of :py:class:`~taskflow.engines.action_engine.engine.ParallelActionEngine` for supported arguments that can be used to construct a parallel engine that runs using your desired execution model. .. tip:: Sharing an executor between engine instances provides better scalability by reducing thread/process creation and teardown as well as by reusing existing pools (which is a good practice in general). .. warning:: Running tasks with a `process pool executor`_ is **experimentally** supported. This is mainly due to the `futures backport`_ and the `multiprocessing`_ module that exist in older versions of python not being as up to date (with important fixes such as :pybug:`4892`, :pybug:`6721`, :pybug:`9205`, :pybug:`16284`, :pybug:`22393` and others...) as the most recent python version (which themselves have a variety of ongoing/recent bugs). Workers ------- .. _worker: **Engine type**: ``'worker-based'`` or ``'workers'`` .. note:: Since this engine is significantly more complicated (and different) then the others we thought it appropriate to devote a whole documentation :doc:`section ` to it. How they run ============ To provide a peek into the general process that an 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 ` 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 (see :py:func:`~taskflow.engines.base.Engine.compile`) the flow will be converted into an internal graph representation using a compiler (the default implementation for patterns is the :py:class:`~taskflow.engines.action_engine.compiler.PatternCompiler`). This class compiles/converts the flow objects and contained atoms into a `networkx`_ directed graph (and tree structure) 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 (and tree) are 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). At the finishing of this stage a :py:class:`~taskflow.engines.action_engine.runtime.Runtime` object is created which contains references to all needed runtime components and its :py:func:`~taskflow.engines.action_engine.runtime.Runtime.compile` is called to compile a cache of frequently used execution helper objects. Preparation ----------- This stage (see :py:func:`~taskflow.engines.base.Engine.prepare`) starts by setting up the storage needed for all atoms in the compiled graph, ensuring that corresponding :py:class:`~taskflow.persistence.models.AtomDetail` (or subclass of) objects are created for each node in the graph. Validation ---------- This stage (see :py:func:`~taskflow.engines.base.Engine.validate`) performs any final validation of the compiled (and now storage prepared) engine. It compares the requirements that are needed to start execution and what is currently provided or will be produced in the future. If there are *any* atom requirements that are not satisfied (no known current provider or future producer is found) then execution will **not** be allowed to continue. Execution --------- The graph (and helper objects) previously created are now used for guiding further execution (see :py:func:`~taskflow.engines.base.Engine.run`). The flow is put into the ``RUNNING`` :doc:`state ` and a :py:class:`~taskflow.engines.action_engine.builder.MachineBuilder` state machine object and runner object are built (using the `automaton`_ library). That machine and associated runner then starts to take over and begins going through the stages listed below (for a more visual diagram/representation see the :ref:`engine state diagram `). .. note:: The engine will respect the constraints imposed by the flow. For example, if an engine is executing a :py:class:`~taskflow.patterns.linear_flow.Flow` then it is constrained by the dependency graph which is linear in this case, and hence using a parallel engine may not yield any benefits if one is looking for concurrency. Resumption ^^^^^^^^^^ One of the first stages is to analyze the :doc:`state ` 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.analyzer.Analyzer` 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.models.AtomDetail` object) the :ref:`scheduling ` stage starts. .. _scheduling: Scheduling ^^^^^^^^^^ This stage selects which atoms are eligible to run by using a :py:class:`~taskflow.engines.action_engine.scheduler.Scheduler` implementation (the default implementation looks at their intention, checking if predecessor atoms have ran and so-on, using a :py:class:`~taskflow.engines.action_engine.analyzer.Analyzer` helper object as needed) and submits those atoms to a previously provided compatible `executor`_ for asynchronous execution. This :py:class:`~taskflow.engines.action_engine.scheduler.Scheduler` will return a `future`_ object for each atom scheduled; 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 :ref:`waiting ` stage. .. _waiting: 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 finalized using a :py:class:`~taskflow.engines.action_engine.completer.Completer` implementation. It typically will persist results to a provided persistence backend (saved into the corresponding :py:class:`~taskflow.persistence.models.AtomDetail` and :py:class:`~taskflow.persistence.models.FlowDetail` objects via the :py:class:`~taskflow.storage.Storage` helper) and reflect the new state of the atom. At this point what typically 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 :ref:`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 work will be allowed to finish (see :ref:`suspension `). Finishing --------- At this point the machine (and runner) that was built using the :py:class:`~taskflow.engines.action_engine.builder.MachineBuilder` class 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 ` 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 ` can be used to cleanup any details that were saved for this execution. Special cases ============= .. _suspension: Suspension ---------- Each engine implements a :py:func:`~taskflow.engines.base.Engine.suspend` method that can be used to *externally* (or in the future *internally*) request that the engine stop :ref:`scheduling ` new work. By default what this performs is a transition of the flow state from ``RUNNING`` into a ``SUSPENDING`` state (which will later transition into a ``SUSPENDED`` state). Since an engine may be remotely executing atoms (or locally executing them) and there is currently no preemption what occurs is that the engines :py:class:`~taskflow.engines.action_engine.builder.MachineBuilder` state machine will detect this transition into ``SUSPENDING`` has occurred and the state machine will avoid scheduling new work (it will though let active work continue). After the current work has finished the engine will transition from ``SUSPENDING`` into ``SUSPENDED`` and return from its :py:func:`~taskflow.engines.base.Engine.run` method. .. note:: When :py:func:`~taskflow.engines.base.Engine.run` is returned from at that point there *may* (but does not have to be, depending on what was active when :py:func:`~taskflow.engines.base.Engine.suspend` was called) be unfinished work in the flow that was not finished (but which can be resumed at a later point in time). Scoping ======= During creation of flows it is also important to understand the lookup strategy (also typically known as `scope`_ resolution) that the engine you are using will internally use. For example when a task ``A`` provides result 'a' and a task ``B`` after ``A`` provides a different result 'a' and a task ``C`` after ``A`` and after ``B`` requires 'a' to run, which one will be selected? Default strategy ---------------- When an engine is executing it internally interacts with the :py:class:`~taskflow.storage.Storage` class and that class interacts with the a :py:class:`~taskflow.engines.action_engine.scopes.ScopeWalker` instance and the :py:class:`~taskflow.storage.Storage` class uses the following lookup order to find (or fail) a atoms requirement lookup/request: #. Transient injected atom specific arguments. #. Non-transient injected atom specific arguments. #. Transient injected arguments (flow specific). #. Non-transient injected arguments (flow specific). #. First scope visited provider that produces the named result; note that if multiple providers are found in the same scope the *first* (the scope walkers yielded ordering defines what *first* means) that produced that result *and* can be extracted without raising an error is selected as the provider of the requested requirement. #. Fails with :py:class:`~taskflow.exceptions.NotFound` if unresolved at this point (the ``cause`` attribute of this exception may have more details on why the lookup failed). .. note:: To examine this this information when debugging it is recommended to enable the ``BLATHER`` logging level (level 5). At this level the storage and scope code/layers will log what is being searched for and what is being found. .. _scope: http://en.wikipedia.org/wiki/Scope_%28computer_science%29 Interfaces ========== .. automodule:: taskflow.engines.base Implementations =============== .. automodule:: taskflow.engines.action_engine.engine Components ---------- .. warning:: External usage of internal engine functions, components and modules should be kept to a **minimum** as they may be altered, refactored or moved to other locations **without** notice (and without the typical deprecation cycle). .. automodule:: taskflow.engines.action_engine.analyzer .. automodule:: taskflow.engines.action_engine.builder .. automodule:: taskflow.engines.action_engine.compiler .. automodule:: taskflow.engines.action_engine.completer .. automodule:: taskflow.engines.action_engine.deciders .. automodule:: taskflow.engines.action_engine.executor .. automodule:: taskflow.engines.action_engine.runtime .. automodule:: taskflow.engines.action_engine.scheduler .. autoclass:: taskflow.engines.action_engine.scopes.ScopeWalker :special-members: __iter__ .. automodule:: taskflow.engines.action_engine.traversal Hierarchy ========= .. inheritance-diagram:: taskflow.engines.action_engine.engine.ActionEngine taskflow.engines.base.Engine taskflow.engines.worker_based.engine.WorkerBasedActionEngine :parts: 1 .. _automaton: http://docs.openstack.org/developer/automaton/ .. _multiprocessing: https://docs.python.org/2/library/multiprocessing.html .. _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/ .. _futures backport: https://pypi.python.org/pypi/futures .. _process pool executor: https://docs.python.org/dev/library/concurrent.futures.html#processpoolexecutor