cherrypy server for Flask + task scheduler and monitor
Project description
Cherrypy prod server for Flask + parallel task scheduler
Installation
pip install flask_production
Usage example
CherryFlask
Cherrypy server on top of Flask app
CherryFlask(app, scheduler=None, silent=False)
Parameters:
app (Flask): Flask application
scheduler (TaskScheduler): task scheduler to run in parallel with Flask app
- silent (bool): don’t print logs
default False
from flask import Flask
from flask_production import CherryFlask
app = Flask(__name__)
...
cherry = CherryFlask(app)
cherry.run(host="0.0.0.0", port=8080, threads=5, debug=False)
TaskScheduler
TaskScheduler(check_interval=5,
holidays_calendar=None,
tzname=None,
on_job_error=None,
log_filepath=None,
log_maxsize=5*1024*1024, # 5 MB
log_backups=1,
startup_grace_mins=0, # minutes
persist_states=True,
state_handler=None)
Parameters:
- check_interval (int): how often to check for pending jobs
default 5 seconds
- holidays_calendar (holidays.HolidayBase): calendar to use for intervals like businessday
default US holidays
tzname (str): name of timezone as supported by dateutil.tz
on_job_error (func(e)): function to call if any job fails
log_filepath (path): file to write logs to
- log_maxsize (int): byte limit per log file
default 5 mb (only effective if log_filepath is provided)
- log_backups (int): number of backups of logs to retain
default 1 (only effective if log_filepath is provided)
- startup_grace_mins (int): grace period for tasks in case a schedule was missed because of app restart
default 0
- persist_states (bool): store job logs and read back on app restart
default True (logs will be stored)
- state_handler (.state.BaseStateHandler): different handler backends to store job logs
default .state.FileSystemState (logs will be stored in a unique data directory)
Standalone usage
from flask_production import TaskScheduler
sched = TaskScheduler(check_interval=2)
# Run every minute
sched.every(60).do(foo)
# Run on end of every month (with strict_date False)
sched.every("31st").strict_date(False).at("08:00").do(foo)
# Run every weekday
sched.every("weekday").at("08:00").do(lambda:bar())
sched.every("weekday").at("08:00").timezone("Europe/London").do(lambda:bar())
# catch() will run on job error
example_job = sched.every("weekday").at("09:00").do(lambda:failing()).catch(lambda e: print(e))
# access job information and status as dict
print(example_job.to_dict())
print(sched.jobs[-1].to_dict()) # same job
sched.start() # starts the task scheduler and blocks
Instead of sched.start(), TaskScheduler can be run in parallel with a Flask application using CherryFlask
from flask import Flask
from flask_production import TaskScheduler, CherryFlask
app = Flask(__name__)
...
sched = TaskScheduler()
...
cherry = CherryFlask(app, scheduler=sched)
cherry.run(host="0.0.0.0", port=8080, threads=5, debug=False)
TaskMonitor
TaskMonitor(
app,
sched,
display_name=None,
endpoint="@taskmonitor",
homepage_refresh=30,
taskpage_refresh=5,
can_rerun=True,
can_disable=True)
Parameters:
app (int): Flask application
sched (TaskScheduler): task scheduler with task definitions
- display_name (str): name of the application to be displayed
default app.name
- endpoint (str): URL endpoint where the taskmonitor can be viewed
default “@taskmonitor”
- homepage_refresh (int): home page auto refresh interval (in seconds)
default 30
- taskpage_refresh (int): task page auto refresh interval (in seconds)
default 5
- can_rerun (bool): if True adds rerun button to job page
default True
- can_disable (bool): if True adds disable button to job page
default True
from flask import Flask
from flask_production import CherryFlask, TaskScheduler
from flask_production.plugins import TaskMonitor
app = Flask(__name__)
sched = TaskScheduler(check_interval=2)
monitor = TaskMonitor(app, sched)
print(monitor._endpoint) # /@taskmonitor
# Run every minute
sched.every(60).do(foo)
cherry = CherryFlask(app, scheduler=sched)
cherry.run(host="0.0.0.0", port=8080) # localhost:8080/@taskmonitor
TODO:
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