Package: mishka-queue
Mishka Queue - A django task queueMishka queue is a task queue system for Django backed by postgres.It was forked from:https://github.com/SweetProcess/django-pg-queue, which in turn was forked fromhttps://github.com/gavinwahl/django-postgres-queueby Gavin Wahl.Why postgres?I thought you were never supposed to use an RDBMS as a queue? Well,
postgres has some features that make it not as bad as you might think,
it has some compelling advantages.Transactional behavior and reliability.Adding tasks is atomic with respect to other database work. There is
no need to usetransaction.on_commithooks and there is no risk of
a transaction being committed but the tasks it queued being lost.Processing tasks is atomic with respect to other database work.
Database work done by a task will either be committed, or the task
will not be marked as processed, no exceptions. If the task only
does database work, you achieve true exactly-once message
processing.Operational simplicityBy reusing the durable, transactional storage that we're already
using anyway, there's no need to configure, monitor, and backup
another stateful service. For small teams and light workloads, this
is the right trade-off.Easy introspectionSince tasks are stored in a database table, it's easy to query and
monitor the state of the queue.SafetyBy using postgres transactions, there is no possibility of jobs
being left in a locked or ambiguous state if a worker dies. Tasks
immediately become available for another worker to pick up. You can
evenkill -9a worker and be sure your database and queue will be
left in a consistent state.Priority queuesSince ordering is specified explicitly when selecting the next task
to work on, it's easy to ensure high-priority tasks are processed
first.QueuesSimply implemented by allowing filtering by a queue name in the
query.DisadvantagesLower throughput than a dedicated queue server.Harder to scale a relational database than a dedicated queue server.Thundering herd. Postgres will notify all workers who LISTEN for the
same name.With at-least-once delivery, a postgres transaction has to be held
open for the duration of the task. For long running tasks, this can
cause table bloat and performance problems.When a task crashes or raises an exception under at-least-once
delivery, it immediately becomes eligible to be retried. If you want
to implement a retry delay, you must catch exceptions and requeue
the task with a delay. If your task crashes without throwing an
exception (eg SIGKILL), you could end up in an endless retry loop
that prevents other tasks from being processed.How it worksmishka-queue is able to claim, process, and remove a task in a single
(simplified) query.DELETEFROMpgq_jobWHEREid=(SELECTidFROMpgq_jobWHEREexecute_at<=now()ORDERBYpriorityDESC,created_atFORUPDATESKIPLOCKEDLIMIT1)RETURNING*;As soon as this query runs, the task is unable to be claimed by other
workers. When the transaction commits, the task will be deleted. If the
transaction rolls back or the worker crashes, the task will immediately
become available for another worker.To achieve at-least-once delivery, we begin a transaction, process the
task, then commit the transaction. For at-most-once, we claim the task
and immediately commit the transaction, then process the task. For tasks
that don't have any external effects and only do database work, the
at-least-once behavior is actually exactly-once (because both the
claiming of the job and the database work will commit or rollback
together).Comparison to Celerymishka queue fills the same role as Celery. You must use postgres as the
backend and the library is small enough that you can read and understand
all the code.A note on the use ofAtLeastOnceQueueand Django'stransaction.on_commit()A failure in anon_commit()callback will not cause that job to be
retried when using anAtLeastOnceQueue(usually a job in anAtLeastOnceQueuequeue will remain in the queue if the job fails).
This is becauseon_commit()callbacks are executed after the
transaction has been committed and, for mishka-queue, the job is
removed from the queue when the transaction commits.If you require more certainty that the code in anon_commit()callback
is executed successfully, you may need to ensure it is idempotent and
call it from within the job rather than usingon_commit().UsageRequirementsmishka-queue is tested against python 3.9+, at least postgres 9.5 and at least
Django 3.2.InstallationInstall with pip:pip install mishka-queueThen add'pgq'to yourINSTALLED_APPS. Runmanage.py migrateto
create the jobs table.Instantiate a queue object. This can go wherever you like and be named
whatever you like. For example,someapp/queue.py:frompgq.queueimportAtLeastOnceQueuequeue=AtLeastOnceQueue(tasks={# ...},queue='my-queue',notify_channel='my-queue',)You will need to import this queue instance to queue or process tasks.
UseAtLeastOnceQueuefor at-least-once delivery, orAtMostOnceQueuefor at-most-once delivery.mishka-queue comes with a management command base class that you can
use to consume your tasks. It can be called whatever you like, for
example in asomeapp/managment/commands/worker.py:frompgq.commandsimportWorkerfromsomeapp.queueimportqueueclassCommand(Worker):queue=queueThen you can runmanage.py workerto start your worker.A task function takes two arguments -- the queue instance in use, and
the Job instance for this task. The function can be defined anywhere and
called whatever you like. Here's an example:frompgq.decoratorsimporttaskfrom.queuesimportqueue@task(queue)defdebug_task(queue,job,args,meta):print(args)Instead of using the task decorator, you can manually register it as a
task. Add it to your queue instance when it is being created:queue=AtLeastOnceQueue(tasks={'debug_task':debug_task,},queue='my-queue')The key is the task name, used to queue the task. It doesn't have to
match the function name.To queue the task, if you used the task decorator you may:debug_task.enqueue({'some_args':0})To manually queue the task, use theenqueuemethod on your queue
instance:queue.enqueue('debug_task',{'some_args':0})Assuming you have a worker running for this queue, the task will be run
immediately. The second argument must be a single json-serializeable
value and will be available to the task asjob.args.Tasks registered using the@taskdecorator will only be available on
the queue if the file in which the task is defined has been imported. If
your worker doesn't import the file containing the@taskdecorators
somewhere, the tasks will not be available for dispatch. Importing files
in theapps.pyAppConfig.ready()method will ensure that the tasks
are always available on the queue without having to import them in your
worker just for the import side effects.# Contents of someapp/apps.pyfromdjango.appsimportAppConfigclassSomeAppAppConfig(AppConfig):defready(self):# Tasks registered with @task are defined in this importimportsomeapp.tasksMultiple QueuesYou may run multiple queues and workers may each listen to a queue. You
can have multiple workers listening to the same queue too. A queue is
implemented as a CharField in the database. The queue would simply
filter for jobs matching its queue name.Bulk EnqueueMany jobs can be efficiently created usingbulk_enqueue()which
accepts one task name for all the jobs being created and a list of
dictionaries containingargsfor the task to execute with and,
optionally,priorityandexecute_atfor that particular job.queue.bulk_enqueue('debug_task',[{'args':{'some_args':0}},{'args':{'some_args':10}'priority':10,'execute_at':timezone.now()+timedelta(days=1),},])MonitoringTasks are just database rows stored in thepgq_jobtable, so you can
monitor the system with SQL.To get a count of current tasks:SELECTqueue,count(*)FROMpgq_jobWHEREexecute_at<=now()GROUPBYqueueThis will include both tasks ready to process and tasks currently being
processed. To see tasks currently being processed, we need visibility
into postgres row locks. This can be provided by thepgrowlocks
extension.
Once installed, this query will count currently-running tasks:SELECTqueue,count(*)FROMpgrowlocks('pgq_job')WHERE'For Update'=ANY(modes)GROUPBYqueue;You could join the results ofpgrowlockswithpgq_jobto get the
full list of tasks in progress if you want.Loggingmishka-queue logs through Python's logging framework, so can be
configured with theLOGGINGdict in your Django settings. It will not
log anything under the default config, so be sure to configure some form
of logging. Everything is logged under thepgqnamespace. Here is an
example configuration that will log INFO level messages to stdout:LOGGING={'version':1,'root':{'level':'DEBUG','handlers':['console'],},'formatters':{'verbose':{'format':'%(levelname)s%(asctime)s%(module)s%(process)d%(thread)d%(message)s',},},'handlers':{'console':{'level':'INFO','class':'logging.StreamHandler','formatter':'verbose',},},'loggers':{'pgq':{'handlers':['console'],'level':'INFO','propagate':False,},}}It would also be sensible to log WARNING and higher messages to
something like Sentry:LOGGING={'version':1,'root':{'level':'INFO','handlers':['sentry','console'],},'formatters':{'verbose':{'format':'%(levelname)s%(asctime)s%(module)s%(process)d%(thread)d%(message)s',},},'handlers':{'console':{'level':'INFO','class':'logging.StreamHandler','formatter':'verbose',},'sentry':{'level':'WARNING','class':'raven.contrib.django.handlers.SentryHandler',},},'loggers':{'pgq':{'level':'INFO','handlers':['console','sentry'],'propagate':False,},},}You could also log to a file by using the built-inlogging.FileHandler.Useful RecipesThese recipes aren't officially supported features ofmishka-queue. We provide them so that you
can mimick some of the common features in other task queues.QUEUE_ALWAYS_EAGERThe queues in this library allow you to use theQUEUE_ALWAYS_EAGERsetting to run a task immediately, without queueing it for a worker. It
could be used during tests, and while debugging in a development
environment with any workers turned off.It is similar in behaviour toCELERY_ALWAYS_EAGERsetting in Celery.