scheduler 0.8.5

A simple in-process python scheduler library with asyncio, threading and timezone support. Use datetime standard library objects for planning of Jobs depending on time cycles, fixed times, weekdays, dates, weights, offsets and execution counts.

A simple in-process python scheduler library with asyncio, threading and timezone support. Schedule tasks by their time cycles, fixed times, weekdays, dates, weights, offsets and execution counts and automate Jobs.

Features

• Easy and user friendly in-process Job scheduling (Quick Start)
• Asyncio scheduler (Example)
• Threading scheduler (Example)
• Timezone compatibility (Example)
• Passing of parameters (Example)
• Job prioritization
  • Default linear prioritization (Example)
  • User definable prioritization functions (Guide)
• Job tagging (Example)
• Job batching (Example)
• Job metadata (Example)
• Lightweight
• High test coverage
• Online documentation

Installation

pip

scheduler can be installed directly from the PyPI repositories with:

pip install scheduler

Alternatively install scheduler from the git repository with:

git clone https://gitlab.com/DigonIO/scheduler.git
cd scheduler
pip install .

Arch Linux

The PKGBUILD file can be utilized from the Arch Build System. Download the PKGBUILD file and from within the containing folder run

makepkg -i

Example: How to schedule Jobs

The following example shows how the Scheduler is instantiated and how basic Jobs are created. For advanced scheduling examples please visit the online documentation.

import datetime as dt

from scheduler import Scheduler
from scheduler.trigger import Monday, Tuesday

def foo():
    print("foo")

schedule = Scheduler()

schedule.cyclic(dt.timedelta(minutes=10), foo)

schedule.minutely(dt.time(second=15), foo)
schedule.hourly(dt.time(minute=30, second=15), foo)
schedule.daily(dt.time(hour=16, minute=30), foo)
schedule.weekly(Monday(), foo)
schedule.weekly(Monday(dt.time(hour=16, minute=30)), foo)

schedule.once(dt.timedelta(minutes=10), foo)
schedule.once(Tuesday(), foo)
schedule.once(dt.datetime(year=2022, month=2, day=15, minute=45), foo)

A human readable overview of the scheduled jobs can be created with a simple print statement:

print(schedule)

max_exec=inf, tzinfo=None, priority_function=linear_priority_function, #jobs=9

type     function / alias due at                 due in      attempts weight
-------- ---------------- ------------------- --------- ------------- ------
MINUTELY foo()            2021-05-26 03:55:15   0:00:14         0/inf      1
CYCLIC   foo()            2021-05-26 04:05:00   0:09:59         0/inf      1
ONCE     foo()            2021-05-26 04:05:00   0:09:59           0/1      1
HOURLY   foo()            2021-05-26 04:30:15   0:35:14         0/inf      1
DAILY    foo()            2021-05-26 16:30:00  12:34:59         0/inf      1
WEEKLY   foo()            2021-05-31 00:00:00    4 days         0/inf      1
WEEKLY   foo()            2021-05-31 16:30:00    5 days         0/inf      1
ONCE     foo()            2021-06-01 00:00:00    5 days           0/1      1
ONCE     foo()            2022-02-15 00:45:00  264 days           0/1      1

Executing pending Jobs periodically can be achieved with a simple loop:

import time

while True:
    schedule.exec_jobs()
    time.sleep(1)

Documentation

View the API documentation online.

Sponsor

We would like to thank Digon.IO for sponsoring the development of this library. Digon.IO is building bridges between data science and software development. They enable companies to automate and accelerate their data-driven processes. Please visit their website: digon.io

License

This free and open source software (FOSS) is published under the LGPLv3 license.