Skip to main content

acrilog is a Python library of providing multiprocessing idiom to us in multiprocessing environment

Project description

Overview

acrilog is a python library encapsulating multiprocessing logging into practical use.

acrilog started as Acrisel’s internal utility for programmers.

It included:
  1. Time and size rotating handler.

  2. Multiprocessing logging queue server

We decided to contribute this library to Python community as a token of appreciation to what this community enables us.

We hope that you will find this library useful and helpful as we find it.

If you have comments or insights, please don’t hesitate to contact us at support@acrisel.com

TimedSizedRotatingHandler

Use TimedSizedRotatingHandler is combining TimedRotatingFileHandler with RotatingFileHandler. Usage as handler with logging is as defined in Python’s logging how-to

example

import logging

# create logger
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)

# create console handler and set level to debug
ch = logging.TimedRotatingFileHandler()
ch.setLevel(logging.DEBUG)

# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# add formatter to ch
ch.setFormatter(formatter)

# add ch to logger
logger.addHandler(ch)

# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warn('warn message')
logger.error('error message')
logger.critical('critical message')

MpLogger and LevelBasedFormatter

Multiprocessor logger using QueueListener and QueueHandler It uses TimedSizedRotatingHandler as its logging handler

It also uses acris provided LevelBasedFormatter which facilitate message formats based on record level. LevelBasedFormatter inherent from logging.Formatter and can be used as such in customized logging handlers.

example

Within main process

import time
import random
import logging
from acris import MpLogger
import os
import multiprocessing as mp

def subproc(limit=1, logger_info=None):
    logger=MpLogger.get_logger(logger_info, name="acrilog.subproc", )
        for i in range(limit):
        sleep_time=3/random.randint(1,10)
        time.sleep(sleep_time)
        logger.info("proc [%s]: %s/%s - sleep %4.4ssec" % (os.getpid(), i, limit, sleep_time))

level_formats={logging.DEBUG:"[ %(asctime)s ][ %(levelname)s ][ %(message)s ][ %(module)s.%(funcName)s(%(lineno)d) ]",
                'default':   "[ %(asctime)s ][ %(levelname)s ][ %(message)s ]",
                }

mplogger=MpLogger(logging_level=logging.DEBUG, level_formats=level_formats, datefmt='%Y-%m-%d,%H:%M:%S.%f')
logger=mplogger.start()

logger.debug("starting sub processes")
procs=list()
for limit in [1, 1]:
    proc=mp.Process(target=subproc, args=(limit, mplogger.logger_info(),))
    procs.append(proc)
    proc.start()

for proc in procs:
    if proc:
        proc.join()

logger.debug("sub processes completed")

mplogger.stop()

Example output

[ 2016-12-19,11:39:44.953189 ][ DEBUG ][ starting sub processes ][ mplogger.<module>(45) ]
[ 2016-12-19,11:39:45.258794 ][ INFO ][ proc [932]: 0/1 - sleep  0.3sec ]
[ 2016-12-19,11:39:45.707914 ][ INFO ][ proc [931]: 0/1 - sleep 0.75sec ]
[ 2016-12-19,11:39:45.710487 ][ DEBUG ][ sub processes completed ][ mplogger.<module>(56) ]

Change History

0.9: add ability to pass logger_info to subprocess

Next Steps

  1. Cluster support using TCP/IP

  2. Logging monitor and alert

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

acrilog-0.9.1.tar.gz (9.7 kB view details)

Uploaded Source

File details

Details for the file acrilog-0.9.1.tar.gz.

File metadata

  • Download URL: acrilog-0.9.1.tar.gz
  • Upload date:
  • Size: 9.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for acrilog-0.9.1.tar.gz
Algorithm Hash digest
SHA256 6b0c9ce82387f3199155dd3115e1bec07839bd2626eca9f23910d62353a0c494
MD5 96ecb412463000978add03340a86123e
BLAKE2b-256 c7553f351519be74cfb6ceda3a4b4a758192c6259a01922233bfdc0121bd09fc

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page