timber 1.0.3

timber.io client API library

Timber - Master your Python app with structured logging

Timber for Python is a logging handler that sends your log statements to Timber, making them easier to search, use, and read. In particular, timber makes it easier to add metadata and context to your log statements.

:point_right: Timber python is under development and is in beta testing, if interested in joining, please email us at [e-mail]

Installation

To install the library and get started logging to Timber:

pip install timber

Usage

Basic Logging

timber provides a TimberHandler that works with the built-in logging library. Just like any other handler, all that you need to do to set it up is add it to a logger:

import logging
import timber

logger = logging.getLogger(__name__)

timber_handler = timber.TimberHandler(api_key='...')
logger.addHandler(timber_handler)

Then, make logging calls just like usual:

logger.debug('Debug message')
logger.info('Info message')
logger.warning('Warning message')
logger.critical('Critical message')
logger.error('Error message')

Logging Events (structured data)

Log structured data by providing named events as part of the extra parameter to any logging call:

logger.debug('Debug message', extra={
  'payment_rejected': {
    'customer_id': 'abcd1234',
    'amount': 1000,
    'reason': 'Card Expired',
  }
})

Any top-level dict on the extra argument will be sent as an event to the Timber console. All other types will be ignored.

Setting Context

Add shared structured data across multiple logging statements:

with timber.context(job={'id': 123}):
  logger.info('Background job execution started')
  # ... code here
  logger.info('Background job execution completed')

Contexts nest and merge naturally:

with timber.context(job={'id': 123, 'count': 1}):
  # Sends a context {'job': {'id': 123, 'count': 1}}
  logger.info('Background job execution started')
  # ... code here
  with timber.context(job={'count': 2}):
    # Sends a context {'job': {'id': 123, 'count': 2}}
    logger.info('Background job in progress')
  # ... code here
  # Sends a context {'job': {'id': 123, 'count': 1}}
  logger.info('Background job execution completed')

Configuration

The TimberHandler takes a variety of parameters that allow for fine-grained control over its behavior.

level

Like any other logger.Handler, the TimberHandler can be configured to only respond to log events of a specific level:

# Only respond to events as least as important as `warning`
timber_handler = timber.TimberHandler(api_key='...', level=logging.WARNING)

buffer_capacity and flush_interval

Timber buffers log events and sends them in the background for maximum performance. All outstanding log events are sent when the buffer is full or a certain amount of time has passed since any events were sent. To control the size of the buffer, pass the buffer_capacity argument:

# Never allow more than 50 outstanding log events
timber_handler = timber.TimberHandler(api_key='...', buffer_capacity=50)

To control the maximum amount of time between buffer flushes, pass the flush_interval argument:

# Send any outstanding log events at most every 60 seconds
timber_handler = timber.TimberHandler(api_key='...', flush_interval=60)

raise_exceptions

Logging should never break your application, which is why the TimberHandler suppresses all internal exceptions by default. To change this behavior:

# Allow exceptions from internal log handling to propagate to the application,
# instead of suppressing them.
timber_handler = timber.TimberHandler(api_key='...', raise_exceptions=True)

drop_extra_events

As soon as the internal log event buffer is full, Timber flushes all of the events to the server, but while that occurs any incoming log events are dropped by default. To make your application block in this case to ensure that all log statements are sent to Timber:

# Make log statements block until the internal log event buffer is no longer full.
timber_handler = timber.TimberHandler(api_key='...', drop_extra_events=False)

context

By default all TimberHandler instances use the same context object (timber.context), but if you'd like to use multiple loggers and multiple handlers, each with a different context, it is possible to explicitly create and pass your own:

import logging
import timber

logger = logging.getLogger(__name__)

context = timber.TimberContext()
timber_handler = timber.TimberHandler(api_key='...', context=context)
logger.addHandler(timber_handler)

with context(job={'id': 123}):
  logger.critical('Background job execution started')
  # ... code here
  logger.critical('Background job execution completed')

Testing

1. Install the packages required for development and testing:

pip install -r requirements.txt
pip install -r test-requirements.txt

2. Run the tests:

nosetests

To see test coverage, run the following and then open ./htmlcov/index.html in your browser:

nosetests --with-coverage --cover-branches --cover-package=timber tests
coverage html --include='timber*'