taskflow/doc/source/engines.rst

Engines

Overview

Engines are what really runs your atoms.

An engine takes a flow structure (described by patterns <patterns>) and uses it to decide which 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 use 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 a :py.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 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 atoms <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 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 persistence <persistence>, to help make it possible to have state and tranfer 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 examples <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 notifications <notifications> for how to use some of these capabilities).

Creating

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

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 the engine type.
• A dictionary, naming engine type with key 'engine' and possibly type-specific engine configuration parameters.

Types

Serial

Engine type: 'serial'

Runs all tasks on a single thread -- the same thread 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'

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

Additional supported keyword arguments:

• executor: a object that implements a 3148 compatible executor interface; it will be used for scheduling tasks. You can use instances of a thread pool executor or a :pygreen executor <taskflow.utils.eventlet_utils.GreenExecutor> (which internally uses eventlet 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 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~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 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 :py~taskflow.engines.action_engine.compiler.Compiler (the default implementation for patterns is the :py~taskflow.engines.action_engine.compiler.PatternCompiler). This class compiles/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). At the finishing of this stage a :py~taskflow.engines.action_engine.runtime.Runtime object is created which contains references to all needed runtime components.

Preparation

This stage starts by setting up the storage needed for all atoms in the previously created graph, ensuring that corresponding :py~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 state <states> and a :py~taskflow.engines.action_engine.runner.Runner implementation object starts to take over and begins going through the stages listed below (for a more visual diagram/representation see the engine state diagram <engine states>).

Resumption

One of the first stages is to analyze the 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~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~taskflow.persistence.logbook.AtomDetail object) the scheduling <scheduling> stage starts.

Scheduling

This stage selects which atoms are eligible to run by using a :py~taskflow.engines.action_engine.runtime.Scheduler implementation (the default implementation looks at there intention, checking if predecessor atoms have ran and so-on, using a :py~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~taskflow.engines.action_engine.runtime.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 waiting <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 finalized using a :py~taskflow.engines.action_engine.runtime.Completer implementation. It typically will persist results to a provided persistence backend (saved into the corresponding :py~taskflow.persistence.logbook.AtomDetail and :py~taskflow.persistence.logbook.FlowDetail objects) 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 scheduling <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~taskflow.engines.action_engine.runner.Runner 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). 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 persistence <persistence> can be used to cleanup any details that were saved for this execution.

Interfaces

taskflow.engines.action_engine.analyzer

taskflow.engines.action_engine.compiler

taskflow.engines.action_engine.engine

taskflow.engines.action_engine.runner

taskflow.engines.action_engine.runtime

taskflow.engines.base

Hierarchy

taskflow.engines.base taskflow.engines.action_engine.engine taskflow.engines.worker_based.engine