Tornado IO loop process pool with message passing
Project description
Looppool
Looppool is a Python 3 package for running worker process pool of Tornado IO loops. It’s useful for a heavy asynchronous application which doesn’t fit into single process due to increasing CPU usage and suffers from IO loop blocking (see set_blocking_log_threshold [1]).
It was developed as a part of performance optimisation of a Tornado data extraction application. The application mixed IO-bound and CPU-bound tasks. Moreover, the CPU-bound tasks were highly coupled with IO loop. Because of such coupling much simpler approach of concurrent.futures [3] wouldn’t have helped.
Design
A picture worth a thousand words.
A few observations and notes:
Messages are off-loaded to IO loops immediately. Thus there’s no much sense in setting max_size to the queues. And if your workers don’t timely process them memory usage will quickly grow.
Without proper synchronisation, Barrier [4], task listener threads would greedily take message from the queue which would make “winner” busy while others idle.
add_callback [2] is safe (and only safe) method to pass control from other thread to IO loop’s thread.
Because queue message handlers (fn1 and fn2) are called from IO loop they can be coroutines.
Pool stops its workers by sending PoisonPill task message per worker.
Worker
There are two workers in the package: Worker and SafeWorker. Both require override of _process_message(self, message) in a subclass. The difference between them is that on receipt of poison pill SafeWorker will wait until its task are completed, while Worker won’t. The latter is sufficient when tasks are not critical, which is typically true in a data extraction application. Also it will make application process supervision simpler, because Pool will quickly stop.
Subclassing
WorkerSubclass._process_message may be plain function or coroutine. It processes task messages and puts them in result queue, like self._result_queue.put_nowait('some result'). SafeWorkerSubclass._process_message must also call self._task_queue.task_done() in try-finally clause, like:
def _process_message(self, url):
try:
result = 'some processing'
self._result_queue.put_nowait(result)
finally:
self._task_queue.task_done()
Or use task_done decorator (see example below).
More details are available in the package’s unit test module [6].
Stateful worker
If you want to run stateful workers, for instance, use some periodically calculated lookup table, but don’t want to calculate it in every workers (e.g. burden of maintaining database connection), you can rely on Barrier synchronisation. Sending 2n - 1 messages guarantees receipt by every workers at least once, where n is number of workers. On start of pool, n messages are sufficient because there’s no overlap over the Barrier.
Installation
pip install LooppoolUsage
#!/usr/bin/env python3
import looppool
from tornado import gen, ioloop, httpclient
class FetchWorker(looppool.SafeWorker):
_http_client = None
'''Tornado asynchronous HTTP client'''
def _initialise(self):
self._http_client = httpclient.AsyncHTTPClient()
@looppool.task_done
@gen.coroutine
def _process_message(self, url):
response = yield self._http_client.fetch(url)
self._result_queue.put_nowait((url, response.headers.get('server')))
@gen.coroutine
def main():
loop = ioloop.IOLoop.instance()
pool = looppool.Pool(loop, pool_size = 4, worker_class = FetchWorker)
pool.process_message = print
pool.start()
urls = [
'https://python.org/',
'http://tornadoweb.org/',
'https://google.com/',
'https://stackoverflow.com/',
]
list(map(pool.put_nowait, urls))
pool.stop()
if __name__ == '__main__':
ioloop.IOLoop.instance().run_sync(main)Maintenance
Maintaining a process group instead of one process is more tricky thing to do. Initially, you may want to see if your pool instance has actually spawned any processes. Here’s what you can do visualise your process tree, which has main process, one or two (depending on start method) multiprocessing helper processes, and processes of your looppool pools (you can use different pools for different tasks):
htop -p $(pgrep -d"," -g $(pgrep -f "main-process-name-or-its-start-args"))
top will also work but is limited to 20 PIDs. Enforcing the process tree stop is also different. If something goes wrong process tree should be killed like:
kill -9 -- -$(pgrep -f "main-process-name-or-its-start-args")
Killing only may process will leave helper and worker processes running.
You can improve the names of your worker processes by setting them in worker’s initialiser with setproctitle [7] (see example below).
Monitoring
Generally it’s very important to know how well your application behaves. Even more important it is for single-threaded (asynchronous) and multi-process applications. For the former is critical to know that the process doesn’t use 100% CPU except for rare peaks, which would otherwise impair IO loop’s ability to schedule tasks. For the latter CPU usage shows how well current number of workers handle the load. This is being said about application metrics.
looppool comes with build-in loopppol.utility.ResourceReporter which periodically (10 seconds by default) sends metrics as CPU usage, memory usage (RSS) and length of IO loop backlogs (ioloop._handlers, ioloop._callbacks and ioloop._timeouts) to statsd-compatible server [9].
Logging
Multi-process logging is complicated. Most important part of logging is error reporting. Sentry [8] goes a great solution to error reporting problem. It seamlessly integrates with logging and is suggested tool to know what errors occur in your workers.
Instrumentated worker
For the following code you will need to run pip install raven statsd setproctitle.
#!/usr/bin/env python3
import logging
import looppool
from looppool.utility import ResourceReporter
from statsd import StatsClient
from raven import Client
from raven.exceptions import InvalidDsn
from raven.handlers.logging import SentryHandler
from setproctitle import setproctitle
class InstrumentatedWorker(looppool.Worker):
_resource_reporter = None
'''CPU, RSS and IO loop stats reporter'''
def _initialise(self):
setproctitle('python APP_NAME POOL_NAME pool worker')
statsd = StatsClient('localhost', 8125, 'APP_PREFIX')
self._resource_reporter = ResourceReporter(self._ioloop, statsd,
'worker.instrumentated.process.{}'.format(self._number))
try:
handler = SentryHandler(Client('SENTRY_DSN'))
handler.setLevel(logging.WARNING)
except InvalidDsn:
logging.exception('Cannot configure Sentry handler')
else:
logging.basicConfig(handlers=[handler], level=logging.WARNING)
def _process_message(self, message):
self._result_queue.put_nowait((message, self._number))
def start(self):
super().start()
self._resource_reporter.start()
def join(self):
self._resource_reporter.stop()
super().join()
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