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| Downloads: | http://pypi.python.org/pypi/pulsar-queue |
| Source: | https://github.com/quantmind/pulsar-queue |
| Mailing list: | google user group |
| Design by: | Quantmind and Luca Sbardella |
| Platforms: | Linux, OSX, Windows. Python 3.5 and above |
| Keywords: | server, asynchronous, concurrency, actor, process, queue, tasks, redis |
Asynchronous server for consuming asynchronous IO tasks, green IO tasks, blocking IO tasks and long running CPU bound tasks.
- Fully configurable
- Consumers poll tasks from distributed message brokers (redis broker implemented)
- Publish/subscribe for real-time event and logging (redis pub/sub backend)
- Can schedule tasks when run as a scheduler (
--schedule-periodicflag) - Build on top of pulsar and asyncio
Clone the repository:
git clone git@github.com:quantmind/pulsar-queue.git
Move to the tests/example directory and run the server:
python manage.py
CONTENTS
A simple python file which runs your application:
vim manage.py
from pq.api import PulsarQueue
task_paths = ['sampletasks.*', 'pq.jobs']
def app():
return PulsarQueue(config=__file__)
if __name__ == '__main__':
app().start()Create the modules where Jobs are implemented. It can be a directory containing several submodules.
mkdir sampletasks cd sampletasks vim mytasks.py
import asyncio
import time
from pq import api
@api.job()
def addition(self, a=0, b=0):
return a + b
@api.job()
async def asynchronous(self, lag=1):
start = time.time()
await asyncio.sleep(lag)
return time.time() - startRun the server with two task consumers (pulsar actors).
NOTE: Make sure you have Redis server up and running before you start the server.
python manage.py -w 2
Launch a python shell and play with the api
>>> from manage import app
>>> api = app().api()
>>> task = api.tasks.queue('addition', a=4, b=6)
>>> task
<TaskFuture pending ID=i26ad5c14c5bb422e87b0f7ccbce5ba06>
>>> task = task.wait()
task.addition<i24ab99ddf2744902a375e039790dcbc4><SUCCESS>
>>> task.result
10
>>> task.status_string
'SUCCESS'You can also queue tasks with a delay
>>> task = api.tasks.queue('addition', a=4, b=6, callback=False, delay=2).wait()
>>> task.status_string
'QUEUED'
>>> task.time_queued # timestamp
>>> task = task.done_callback.wait()
>>> task.status_string
'SUCCESS'
>>> task.time_started - task.time_queued
2.00NOTE: The wait method in a task future can only be used on the shell
or when the event loop is not running. In all other cases one should await
for the task future in a coroutine.
The producer API is obtained from the Task application api method:
from pq.api import PusarQueue
api = PusarQueue(...).api()Start listening to events. This method return a coroutine which resolve in the api:
api = await api.start()The start method is used when the api is used by application to queue messages/tasks and listen for events published by distributed consumers.
Add a callback invoked every time an event matching the regular expression event_re
occurs on the message_type channel. The callback has the following signature:
def event_callback(channel, event, message):
# event is string, the event matched
# message is of type message_typeIf the event is a task event (see events) the message is a Task object.
This method is useful when creating applications which needs to respond to the queue server events in real time:
api.on_events('task', 'queued', callback)
api.on_events('task', 'started', callback)
api.on_events('task', 'done', callback)
Remove a previously added event callback. This method is safe.
Queue a message in the message queue, equivalent to:
api.broker.queue(message, callback)This method returns a MessageFuture, a subclass of asyncio Future which
resolves in a message object.
If callback is True (default) the Future is resolved once the message
is delivered (out of the queue), otherwise is is resolved once the message
is queued (entered the queue).
Execute a message without queueing. This is only supported by messages with
a message consumer which execute them (the tasks consumer for example).
If message is a Task, this method is equivalent to:
api.tasks.execute(task)This method returns a MessageFuture, a subclass of asyncio Future which
resolve in a message object.
List of consumers registered with the api.
The tasks producer is obtained vua the tasks property from the producer API instance
tasks = api.tasksThe following methods are available for the tasks producer:
Queue a task and return a TaskFuture which is resolved once the task has finished. It is possible to obtain a task future resolved when the task has been queued, rather than finished, by passing the callback=False parameter:
task = await tasks.queue(..., callback=False)
task.status_string # QUEUEDThe kwargs parameters are used as input parameters for the Job callable with the exception of:
callback: discussed abovedelay: delay execution by a given number of secondsqueue: overrides the Job [default_queue](#job-default-queue)- [timeout](#job-timeout)
meta_params: dictionary of parameters used by the Job callable to override default values of: * [max_retries](#job-max-retries) * [retry_delay](#job-retry-delay) * [max_concurrency](#job-max-concurrency)
Queue a job in the local task queue. The local task queue is processed by the same server instance. It is equivalent to execute:
task = await tasks.queue(..., queue=tasks.node_name)
task.queue # tasks.node_nameExecute a task immediately, it does not put the task in the task queue. This method is useful for debugging and testing. It is equivalent to execute:
task = await tasks.queue(..., queue=False)
task.queue # None
task.status_string # SUCCESSReturn the list of queue names the backend is subscribed. This list is not empty when the backend is a task consumer.
Returns a list of job_name, job_description tuples. The job_name is a string which must be used as the jobname parameter when executing or queing tasks. The job_description is a dictionary containing metadata and documentation for the job. Example:
jobs = dict(tasks.job_lits())
jobs['execute.python']
# {
# 'type': 'regular',
# 'concurrency': 'asyncio',
# 'doc_syntax': 'markdown',
# 'doc': 'Execute arbitrary python code on a subprocess ... '
# }The Job class is how task factories are implemented and added to the tasks backend registry. When writing a new Job one can either subclass:
import asyncio
class AsyncSleep(api.Job):
async def __call__(self, lag=1):
await asyncio.sleep(lag)or use the less verbose job decorator:
@api.job()
async def asyncsleep(self, lag=1):
await asyncio.sleep(lag)In either cases the self parameter is an instance of a Job class and
it has the following useful attributes and methods:
The tasks backend that is processing this Task run
The default queue name where tasks for this job are queued. By default it is None
in which case, if a queue is not given when queueing a task, the first queue
from the queues list taken.
Best possible HTTP session handler for the job concurrency mode.
Python logging handler for this job. The name of this handler
is <app_name>.<job.name>.
Optional positive integer which specify the maximum number of retries when a task fails or is revoked. If not available failing tasks are not re-queued. It can be specified as a class attribute or during initialisation from the task meta parameters.
Optional positive integer which specifies the number of seconds to delay a task retry.
The name of this job. Used to queue tasks
The Task instance associated with this task run
Queue a new job form a task run. It is equivalent to:
meta_params = {'from_task': self.task.id}
self.backend.tasks.queue(..., meta_params=meta_params)Execute a shell command and returns a coroutine:
await self.shell("...")A task contains the metadata information of a job run and it is exchanged between task producers and task consumers via a distributed task queue.
A Task can have one of the following task.status:
QUEUED = 6a task queued but not yet executed.STARTED = 5a task where execution has started.RETRY = 4a task is retrying calculation.REVOKED = 3the task execution has been revoked (or timed-out).FAILURE = 2task execution has finished with failure.SUCCESS = 1task execution has finished with success.
FULL_RUN_STATES
The set of states for which a Task has run: FAILURE and SUCCESS
READY_STATES
The set of states for which a Task has finished: REVOKED, FAILURE and SUCCESS
The task queue broadcast several events during task execution and internal state:
task_queued: a new Task has been queued, the message is a task instancetask_started: a Task has started to be consumed by a task consumer, it is out of the task queuetask_done: a Task is done, the message is a task in a READY_STATES
There are several parameters you can use to twick the way the task queue works. In this list the name in bold is the entry point in the config file and cfg dictionary, while, the value between brackets shows the command line entry with default value.
concurrent_tasks (
--concurrent-tasks 5)The maximum number of concurrent tasks for a given worker in a task consumer server.
data_store (
--data-store redis://127.0.0.1:6379/7)Data store used for publishing and subscribing to messages (redis is the only backend available at the moment)
max_requests (
--max-requests 0)The maximum number of tasks a worker will process before restarting. A 0 value (the default) means no maximum number, workers will process all tasks forever.
message_broker (
--message-broker ...)Data store used as distributed task queue. If not provided (default) the
data_storeis used instead. Redis is the only backend available at the moment.message_serializer (
--message-serializer json)The decoder/encoder for messages and tasks. The default is JSON but Message Pack is also available if msgpack is installed.
schedule_periodic (
--schedule-periodic)When
True, the task application can schedule periodic Jobs. Usually, only one running server is responsible for scheduling tasks.task_pool_timeout (
--task-pool-timeout 2)Timeout in seconds for asynchronously polling tasks from the queues. No need to change this parameter really.
workers (
--workers 4)Number of workers (processes) consuming tasks.
A task can run in one of four concurrency modes.
If not specified by the Job, the concurrency mode is ASYNC_IO.
The asynchronous IO mode is associated with tasks which return an asyncio Future or a coroutine. These tasks run concurrently in the worker event loop. An example can be a Job to scrape web pages and create new tasks to process the html
@api.job()
async def scrape(self, url=None):
assert url, "url is required"
request = await self.http.get(url)
html = request.text()
task = self.queue('process.html', html=html, callback=False)
return task.idThis concurrency mode is best suited for tasks performing
blocking IO operations.
A THREAD_IO job runs its tasks in the event loop executor.
You can use this model for most blocking operation unless
- Long running CPU bound
- The operation does not release the GIL
Example of tasks suitable for thread IO are IO operations on files.
For example the test suite uses this Job for testing THREAD_IO
concurrency (check the tests.example.jobs.standard module
for the full code):
@api.job(concurrency=api.THREAD_IO)
def extract_docx(self, input=None, output=None):
"""
Extract text from a docx document
"""
import docx
assert input and output, "input and output must be given"
document = docx.Document(input)
text = '\n\n'.join(_docx_text(document))
with open(output, 'w') as fp:
fp.write(text)
return {
'thread': threading.get_ident(),
'text': len(text)
}It assumes the task performs blocking CPU bound operations. Jobs with this consurrency mode run their tasks on sub-processeses using asyncio subprocess module.
It is possible to enhance the task queue application by passing
a custom Manager during initialisation.
For example:
from pq import api
class Manager(api.Manager):
async def store_message(self, message):
"""This method is called when a message/task is queued,
started and finished
"""
if message.type == 'task':
# save this task into a db for example
def queues(self):
"""List of queue names for Task consumers
By default it returns the node name and the task_queues
in the config dictionary.
"""
queues = [self.backend.node_name]
queues.extend(self.cfg.task_queues)
return queues
tq = PulsarQueue(Manager, ...)The Manager class is initialised when the backend handler is initialised
(on each consumer and in the scheduler).
This software is licensed under the BSD 3-clause License. See the LICENSE file in the top distribution directory for the full license text. Logo designed by Ralf Holzemer, creative common license.