containerlog 0.1.0

Optimized, opinionated structured logging for containerized applications.

containerlog

A lightweight, optimized, and opinionated structured logging library for Python, intended for containerized applications.

containerlog was born out of a desire to have high-quality structured logging for containerized applications (e.g. microservices) without having to compromise detailed logging for application/request latency.

structlog is a great general-purpose structured logging library for Python, but being general-purpose means that there is additional overhead when logging messages.

When we updated a microservice to use structured logging, we found that request latency went up, seemingly due to the transition to use structlog.

containerlog is not for everyone. It is highly opinionated, minimally configurable, and intentionally feature-sparse so that it can achieve better performance than the Python standard logger

Not every application needs optimized logging, but where latency and performance matters, containerlog could work for you.

timestamp='2020-07-23T13:11:28.009804Z' logger='my-logger' level='debug' event='loading configuration' path='./config.yaml'
timestamp='2020-07-23T13:11:28.010137Z' logger='my-logger' level='info' event='starting application' 
timestamp='2020-07-23T13:11:28.010158Z' logger='my-logger' level='warn' event='having too much fun' countdown=[3, 2, 1]

Installation

containerlog can be installed with pip:

pip install containerlog

It is only intended to work for Python 3.6+.

Usage

The API for containerlog is sparse, thus simple. There are generally two things you will ever need to do with it:

1. Creating/Configuring a Logger

Similar to the Python standard logger, a logger should in initialized in a module using get_logger

# Logger using module as name
logger = containerlog.get_logger()

# Logger with explicit name set
logger = containerlog.get_logger('my-logger')

If get_logger is not given a name, it will use the name of the module.

There are only a handful of things which can be configured:

• The logger name

  # Set name via `get_logger`
  logger = containerlog.get_logger('my-logger')
  

• The log level

  # Set log level on a single logger
  logger.level = containerlog.INFO
  
  # Set log level for all loggers
  containerlog.set_level(containerlog.INFO)
  

• Where logs are written to. Generally this shouldn't need to be configured, though it may be useful when writing tests. containerlog writes to stdout and stderr by default.

  # Set write target for non-error logs
  out_stream = io.StringIO()
  logger.writeout = out_stream.write
  
  # Set write target for error logs
  err_stream = io.StringIO()
  logger.writerr = err_stream.write
  

By default, containerlog logs at DEBUG level. This is an opinionated decision with the thought that using this out of the box, its better to capture more logs than fewer logs, though the appropriate log level should be set by the application.

2. Logging a message

Once you have a logger, you can log a message at trace, debug, info, warn, error, or critical level.

# Log at trace level
logger.trace('message to log')

# Log at debug level
logger.debug('message to log')

# Log at info level
logger.info('message to log')

# Log at warn level
logger.warn('message to log')
logger.warning('message to log')

# Log at error level
logger.error('message to log')

# Log at critical level
logger.critical('message to log')

Data passed in as keyword arguments to any of the logging methods gets rendered as key-value pairs for the structured log.

logger.info('connected to remote server', ip=server_ip, port=server_port)

Note: Since this library is intended to be used for structured logging, avoid logging formatted message to avoid a performance penalty.

If logging at INFO level,

logger.debug(f'got a new value: {value}')

would not get logged, but the string formatting would still happen since it is being done upfront, not within the log function. Instead, pass it as a kwarg:

logger.debug('got a new value', value=value)

Log levels are modeled as integers internally. Logging can be disabled for a logger either by calling disable on the logger, or by setting the level above containerlog.CRITICAL.

# Disable via method
logger.disable()

# Disable via log level
logger.level = 99

Optimizations

There are numerous sources discussion micro-optimizations in Python. This project probably does not implement them all, so there is room for improvement. Current optimization work has leveraged:

• dis: to disassemble python bytecode for analysis
• timeit: to measure execution time of code snippets

If you wish to contribute optimizations and use other libraries, tools, or sources, open a PR to add them to this list.

Benchmarks

Benchmarking scripts can be found in the benchmarks directory. To run,

$ cd benchmarks
$ ./run.sh

This will run benchmarks the Python standard logger and for containerlog. The latest results can be found below.

Results

Benchmarks were measured using Python 3.8.0 on macOS 10.15.1 with a 2.9 GHz 6-Core Intel Core i9 processor and 16 GB 2400 MHz DDR4 memory.

Benchmark	std logger (ns)	containerlog (ns)
baseline	0.68 +/- 0.02	0.69 +/- 0.02
silent	110.0 +/- 6.0	57.8 +/- 1.4
basic	4720.0 +/- 220.0	1040.0 +/- 50.0
short-simple	5310.0 +/- 170.0	1240.0 +/- 60.0
long-simple	5180.0 +/- 190.0	2029.9999999999998 +/- 70.0
short-complex	5580.0 +/- 270.0	1430.0 +/- 90.0
long-complex	6800.0 +/- 200.0	3140.0 +/- 80.0

Contribute

While containerlog is intentionally feature-sparse, feature requests are welcome. Additionally, if you can find any other ways to micro-optimize the codebase, pull requests are very much appreciated.