taskflow/doc/source/user/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.

Note

For more information, please visit the paradigm shift page for more details.

Definitions

Jobs

A :pyjob <taskflow.jobs.base.Job> consists of a unique identifier, name, and a reference to a :pylogbook <taskflow.persistence.models.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 :pyjobboard <taskflow.jobs.base.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 their intended usage) best.

High level architecture

Note: This diagram shows the high-level diagram (and further parts of this documentation also refer to it as well) of the zookeeper implementation (other implementations will typically have different architectures).

Features

• High availability
  • Guarantees workflow forward progress by transferring partially complete 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 states <states> 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 necessarily 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).

Usage

All jobboards are mere classes that implement same interface, and of course it is possible to import them and create instances of them just like with any other class 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 jobboard 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.get_connection().destroy_logbook(my_job.book.uuid)
    time.sleep(coffee_break_time)
...

There are a few ways to provide arguments to the flow. The first option is to add a store to the flowdetail object in the :pylogbook <taskflow.persistence.models.LogBook>.

You can also provide a store in the :pyjob <taskflow.jobs.base.Job> itself when posting it to the job board. If both store values are found, they will be combined, with the :pyjob <taskflow.jobs.base.Job> store overriding the :pylogbook <taskflow.persistence.models.LogBook> store.

from oslo_utils import uuidutils

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


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

book = models.LogBook('my-book', uuidutils.generate_uuid())

flow_detail = models.FlowDetail('my-job', uuidutils.generate_uuid())
book.add(flow_detail)

connection = persistence.get_connection()
connection.save_logbook(book)

flow_detail.meta['store'] = {'a': 1, 'c': 3}

job_details = {
    "flow_uuid": flow_detail.uuid,
    "store": {'a': 2, 'b': 1}
}

engines.save_factory_details(flow_detail, flow_factory,
                             factory_args=[],
                             factory_kwargs={},
                             backend=persistence)

jobboard = get_jobboard(zk_client)
jobboard.connect()
job = jobboard.post('my-job', book=book, details=job_details)

# the flow global parameters are now the combined store values
# {'a': 2, 'b': 1', 'c': 3}
...

Types

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.

Note

See :py~taskflow.jobs.backends.impl_zookeeper.ZookeeperJobBoard for implementation details.

Redis

Board type: 'redis'

Uses redis to provide the jobboard capabilities and semantics by using a redis hash data structure and individual job ownership keys (that can optionally expire after a given amount of time).

Note

See :py~taskflow.jobs.backends.impl_redis.RedisJobBoard for implementation details.

Considerations

Some usage considerations should be used when using a jobboard to make sure it's used in a safe and reliable manner. Eventually we hope to make these non-issues but for now they are worth mentioning.

Dual-engine jobs

What: Since atoms and engines are not currently preemptable we can not force an engine (or the threads/remote workers... it is using to run) to stop working on an atom (it is general bad behavior to force code to stop without its consent anyway) if it has already started working on an atom (short of doing a kill -9 on the running interpreter). This could cause problems since the points an engine can notice that it no longer owns a claim is at any state <states> change that occurs (transitioning to a new atom or recording a result for example), where upon noticing the claim has been lost the engine can immediately stop doing further work. The effect that this causes is that when a claim is lost another engine can immediately attempt to acquire the claim that was previously lost and it could begin working on the unfinished tasks that the later engine may also still be executing (since that engine is not yet aware that it has lost the claim).

TLDR: not preemptable, possible to become aware of losing a claim after the fact (at the next state change), another engine could have acquired the claim by then, therefore both would be working on a job.

Alleviate by:

1. Ensure your atoms are idempotent, this will cause an engine that may be executing the same atom to be able to continue executing without causing any conflicts/problems (idempotency guarantees this).
2. On claiming jobs that have been claimed previously enforce a policy that happens before the jobs workflow begins to execute (possibly prior to an engine beginning the jobs work) that ensures that any prior work has been rolled back before continuing rolling forward. For example:
   • Rolling back the last atom/set of atoms that finished.
   • Rolling back the last state change that occurred.
3. Delay claiming partially completed work by adding a wait period (to allow the previous engine to coalesce) before working on a partially completed job (combine this with the prior suggestions and most dual-engine issues should be avoided).

Interfaces

taskflow.jobs.base

taskflow.jobs.backends

Implementations

Zookeeper

taskflow.jobs.backends.impl_zookeeper

Redis

taskflow.jobs.backends.impl_redis

Hierarchy

taskflow.jobs.base taskflow.jobs.backends.impl_redis taskflow.jobs.backends.impl_zookeeper