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

Jobs

Overview

Jobs and jobboards are a novel concept that taskflow provides to allow for automatic ownership transfer of workflows between capable owners (those owners usually then use engines <engines> to complete the workflow). They provide the necessary semantics to be able to atomically transfer a job from a producer to a consumer in a reliable and fault tolerant manner. They are modeled off the concept used to post and acquire work in the physical world (typically a job listing in a newspaper or online website serves a similar role).

TLDR: It's similar to a queue, but consumers lock items on the queue when claiming them, and only remove them from the queue when they're done with the work. If the consumer fails, the lock is automatically released and the item is back on the queue for further consumption.

Features

• High availability
  • Guarantees workflow forward progress by transfering partially completed work or work that has not been started to entities which can either resume the previously partially completed work or begin initial work to ensure that the workflow as a whole progresses (where progressing implies transitioning through the workflow patterns <patterns> and atoms <atoms> and completing their associated state transitions).
• Atomic transfer and single ownership
  • Ensures that only one workflow is managed (aka owned) by a single owner at a time in an atomic manner (including when the workflow is transferred to a owner that is resuming some other failed owners work). This avoids contention and ensures a workflow is managed by one and only one entity at a time.
  • Note: this does not mean that the owner needs to run the workflow itself but instead said owner could use an engine that runs the work in a distributed manner to ensure that the workflow progresses.
• Separation of workflow construction and execution
  • Jobs can be created with logbooks that contain a specification of the work to be done by a entity (such as an API server). The job then can be completed by a entity that is watching that jobboard (not neccasarily the API server itself). This creates a disconnection between work formation and work completion that is useful for scaling out horizontally.
• Asynchronous completion
  • When for example a API server posts a job for completion to a jobboard that API server can return a tracking identifier to the user calling the API service. This tracking identifier can be used by the user to poll for status (similar in concept to a shipping tracking identifier created by fedex or UPS).

For more information, please see wiki page for more details.

Jobs

A job consists of a unique identifier, name, and a reference to a logbook which contains the details of the work that has been or should be/will be completed to finish the work that has been created for that job.

Jobboards

A jobboard is responsible for managing the posting, ownership, and delivery of jobs. It acts as the location where jobs can be posted, claimed and searched for; typically by iteration or notification. Jobboards may be backed by different capable implementations (each with potentially differing configuration) but all jobboards implement the same interface and semantics so that the backend usage is as transparent as possible. This allows deployers or developers of a service that uses TaskFlow to select a jobboard implementation that fits their setup (and there intended usage) best.

Using Jobboards

All engines are mere classes that implement same interface, and of course it is possible to import them and create their instances just like with any classes in Python. But the easier (and recommended) way for creating jobboards is by using the :pyfetch() <taskflow.jobs.backends.fetch> function which uses entrypoints (internally using stevedore) to fetch and configure your backend

Using this function the typical creation of a jobboard (and an example posting of a job) might look like:

from taskflow.persistence import backends as persistence_backends
from taskflow.jobs import backends as job_backends

...
persistence = persistence_backends.fetch({
    "connection': "mysql",
    "user": ...,
    "password": ...,
})
book = make_and_save_logbook(persistence)
board = job_backends.fetch('my-board', {
    "board": "zookeeper",
}, persistence=persistence)
job = board.post("my-first-job", book)
...

Consumption of jobs is similarly achieved by creating a jobboard and using the iteration functionality to find and claim jobs (and eventually consume them). The typical usage of a joboard for consumption (and work completion) might look like:

import time

from taskflow import exceptions as exc
from taskflow.persistence import backends as persistence_backends
from taskflow.jobs import backends as job_backends

...
my_name = 'worker-1'
coffee_break_time = 60
persistence = persistence_backends.fetch({
    "connection': "mysql",
    "user": ...,
    "password": ...,
})
board = job_backends.fetch('my-board', {
    "board": "zookeeper",
}, persistence=persistence)
while True:
    my_job = None
    for job in board.iterjobs(only_unclaimed=True):
        try:
            board.claim(job, my_name)
        except exc.UnclaimableJob:
            pass
        else:
            my_job = job
            break
    if my_job is not None:
        try:
            perform_job(my_job)
        except Exception:
            LOG.exception("I failed performing job: %s", my_job)
            board.abandon(my_job, my_name)
        else:
            # I finished it, now cleanup.
            board.consume(my_job)
            persistence.destroy_logbook(my_job.book.uuid)
    time.sleep(coffee_break_time)
...

Jobboard Configuration

Known engine types are listed below.

Zookeeper

Board type: 'zookeeper'

Uses zookeeper to provide the jobboard capabilities and semantics by using a zookeeper directory, ephemeral, non-ephemeral nodes and watches.

Additional kwarg parameters:

• client: a class that provides kazoo.client.KazooClient-like interface; it will be used for zookeeper interactions, sharing clients between jobboard instances will likely provide better scalability and can help avoid creating to many open connections to a set of zookeeper servers.
• persistence: a class that provides a persistence <persistence> backend interface; it will be used for loading jobs logbooks for usage at runtime or for usage before a job is claimed for introspection.

Additional configuration parameters:

• path: the root zookeeper path to store job information (defaults to /taskflow/jobs)
• hosts: the list of zookeeper hosts to connect to (defaults to localhost:2181); only used if a client is not provided.
• timeout: the timeout used when performing operations with zookeeper; only used if a client is not provided.
• handler: a class that provides kazoo.handlers-like interface; it will be used internally by kazoo to perform asynchronous operations, useful when your program uses eventlet and you want to instruct kazoo to use an eventlet compatible handler (such as the eventlet handler).

Job Interface

taskflow.jobs.backends

taskflow.jobs.job

Jobboard Interface

taskflow.jobs.jobboard