I have a job added in apscheduler which loads some data in memory and I am deleting all the objects after the job is complete. Now if I run this job with python it works successfully and memory drop after process exits successfully.But in case of apscheduler the memory usage is not coming down.I am using BackgroundScheduler.Thanks in advance.

I was running quite a few tasks via apscheduler. I suspected this setup led to R14 errors on Heroku, with dyno memory overload, crashes and restarts occurring daily. So I spun up another dyno and scheduled a few jobs to run very frequently.

Watching the metrics tab in Heroku, it immediately became clear that apscheduler was the culprit.

Removing jobs after they're run was recommended to me. But this is of course a bad idea when running cron and interval jobs as they won't run again.

What finally solved it was tweaking the threadpoolexecutioner (lowering max number of workers), see this answer on Stackoverflow and this and this post on Github. I definitely suggest you read the docs on this.

Other diagnostics resources: 1, 2.

Example code:

import logging
from apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor
from apscheduler.schedulers.blocking import BlockingScheduler
from tests import overloadchecklogging.basicConfig()
logging.getLogger('apscheduler').setLevel(logging.DEBUG)sched = BlockingScheduler(executors={'threadpool': ThreadPoolExecutor(max_workers=9),'processpool': ProcessPoolExecutor(max_workers=3)}
)@sched.scheduled_job('interval', minutes=10, executor='threadpool')
def message_overloadcheck():overloadcheck()sched.start()

Or, if you like I do, love to run heavy tasks—try the ProcessPoolExecutor as an alternative, or addition to the ThreadPool, but make sure to call it from specific jobs in such case.

Update: And, you need to import ProcessPoolExecutor as well if you wish to use it, added this to code.