fast-logging 1.0.2

A faster replacement of the standard logging module.

The fastlogging module is a faster replacement of the standard logging module with a mostly compatible API.

It comes with the following features:

• (colored, if colorama is installed) logging to console

• logging to file (maximum file size with rotating/history feature can be configured)

• old log files can be compressed (the compression algorithm can be configured)

• count same successive messages within a 30s time frame and log only once the message with the counted value.

• log domains

• log to different files

• writing to log files is done in (per file) background threads, if configured

• configure callback function for custom detection of same successive log messages

• configure callback function for custom message formatter

• configure callback function for custom log writer

The API is described here.

Installation

Simply run

python setup.py install --user

or create a wheel and install it.

python setup.py bdist_wheel

An optimized version of fastlogging will be installed if package cython is installed. If you need a pure python version of the fastlogging module then add option nocython.

Usage

from fastlogging import LogInit

logger = LogInit(pathName="/tmp/example1.log", console=True, colors=True)
logger.debug("This is a debug message.")
logger.info("This is an info message.")
logger.warning("This is a warning message.")
logger.rotate()
logger.fatal("This is a fatal message.")
logger.shutdown()

The example above writes all messages to a file and to the console. On the console the messages are printed with colors. With the rotate call the log file is renamed to example1.log.1 and a new log file is created.

The second example creates a server socket on localhost and writes all messages to a log file for 15 seconds.

import os
import time

from fastlogging import LogInit

addr = "127.0.0.1"
port = 12345
pathName = "C:/temp/server.log" if os.name == 'nt' else "/tmp/server.log"
logger = LogInit(pathName=pathName, server=(addr, port))
logger.info("Logging started.")
logger.debug("This is a debug message.")
logger.info("This is an info message.")
logger.warning("This is a warning message.")
time.sleep(15)
logger.info("Shutdown logging.")
logger.shutdown()

And now the third example connects to the log server and sends 300000 messages.

import os
import time

from fastlogging import LogInit

addr = "127.0.0.1"
port = 12345
logger = LogInit(connect=(addr, port, "HELLO%d" % os.getpid()))
for i in range(100000):
    logger.debug("This is a DBG message %d." % i)
    logger.info("This is an INF message %d." % i)
    logger.warning("This is a WRN message %d." % i)
time.sleep(10.0)
logger.shutdown()

The messages are sent in blocks to improve speed.

Optimizing for speed

As you can see in the charts below fastlogging is much faster than the default logging module which comes with Python (red bar).

You also can see that using threads can be slower than writing logs directly to the file, because of additional overhead. So threads should only be used if you’ve got a slow disk and lot’s of messages to log.

There are 3 more bars which show even better performance. To understand the optimizations a deeper look into a logging line has to be done.

Let’s analyze what is going on when the following code line is executed:

logger.debug("This is a debug message.")

The Python interpreter first creates a tuple for the positioned arguments and a dictionary for the named arguments. Then it calls method info. In method info the log level is checked against the severity. Only if the severity is high enough the message will be logged.

Now what if we set a if before the above line?

if logger.level <= DEBUG:
    logger.debug("This is a debug message.")

Running benchmarks will show us that the code runs faster now if the log level is higher than DEBUG. Normally we need debug messages only in case of development or bugfixing. So it makes sense to optimize such lines. But doing this manually is awkward and bloats the code.

To simplify this task the fastlogging module comes with an AST optimizer which does the work for you.

Benchmarks

The following benchmarks were measured on Ubuntu 18.10 with a Ryzen 7 CPU and an SSD.

You can see that fastlogging is ~5x faster when rotating is disabled and >13x faster in case of log rotating.

Benchmark results with a single log files

Benchmark results with rotating log files