What does it do?

Although the Python standard library provides a logging system, you should consider having a look at Logbook for your applications.

We think it will work out for you and be fun to use :)

Logbook leverages some features of Python that are not available in older Python releases. Logbook currently requires Python 2.7 or higher including Python 3 (3.1 or higher, 3.0 is not supported).

Core Features

  • Logbook is based on the concept of loggers that are extensible by the application.
  • Each logger and handler, as well as other parts of the system, may inject additional information into the logging record that improves the usefulness of log entries.
  • Handlers can be set on an application-wide stack as well as a thread-wide stack. Setting a handler does not replace existing handlers, but gives it higher priority. Each handler has the ability to prevent records from propagating to lower-priority handlers.
  • Logbook comes with a quick optional configuration that spits all the information to stderr in a useful manner (by setting the LOGBOOK_INSTALL_DEFAULT_HANDLER environment variable). This is useful for webapps, for example.
  • All of the built-in handlers have a useful default configuration applied with formatters that provide all the available information in a format that makes the most sense for the given handler. For example, a default stream handler will try to put all the required information into one line, whereas an email handler will split it up into nicely formatted ASCII tables that span multiple lines.
  • Logbook has built-in handlers for streams, arbitrary files, files with time and size based rotation, a handler that delivers mails, a handler for the syslog daemon as well as the NT log file.
  • There is also a special “fingers crossed” handler that, in combination with the handler stack, has the ability to accumulate all logging messages and will deliver those in case a severity level was exceeded. For example, it can withhold all logging messages for a specific request to a web application until an error record appears, in which case it will also send all withheld records to the handler it wraps. This way, you can always log lots of debugging records, but only get see them when they can actually tell you something of interest.
  • It is possible to inject a handler for testing that records messages for assertions.
  • Logbook was designed to be fast and with modern Python features in mind. For example, it uses context managers to handle the stack of handlers as well as new-style string formatting for all of the core log calls.
  • Builtin support for ZeroMQ, RabbitMQ, Redis and other means to distribute log messages between heavily distributed systems and multiple processes.
  • The Logbook system does not depend on log levels. In fact, custom log levels are not supported, instead we strongly recommend using logging subclasses or log processors that inject tagged information into the log record for this purpose.
  • PEP 8 naming and code style.

Advantages over Logging

If properly configured, Logbook’s logging calls will be very cheap and provide a great performance improvement over an equivalent configuration of the standard library’s logging module. While for some parts we are not quite at performance we desire, there will be some further performance improvements in the upcoming versions.

It also supports the ability to inject additional information for all logging calls happening in a specific thread or for the whole application. For example, this makes it possible for a web application to add request-specific information to each log record such as remote address, request URL, HTTP method and more.

The logging system is (besides the stack) stateless and makes unit testing it very simple. If context managers are used, it is impossible to corrupt the stack, so each test can easily hook in custom log handlers.


Logbook is an addon library to Python and working in an area where there are already a couple of contestants. First of all there is the standard library’s logging module, secondly there is also the warnings module which is used internally in Python to warn about invalid uses of APIs and more. We know that there are many situations where you want to use either of them. Be it that they are integrated into a legacy system, part of a library outside of your control or just because they are a better choice.

Because of that, Logbook is two-way compatible with logging and one-way compatible with warnings. If you want, you can let all logging calls redirect to the logbook handlers or the other way round, depending on what your desired setup looks like. That way you can enjoy the best of both worlds.

It should be Fun

Logging should be fun. A good log setup makes debugging easier when things go rough. For good results you really have to start using logging before things actually break. Logbook comes with a couple of unusual log handlers to bring the fun back to logging. You can log to your personal twitter feed, you can log to mobile devices, your desktop notification system and more.

Logbook in a Nutshell

This is how easy it is to get started with Logbook:

from logbook import warn, StreamHandler
import sys
warn('This is a warning')


Here a list of things you can expect in upcoming versions:

  • c implementation of the internal stack management and record dispatching for higher performance.
  • a ticketing log handler that creates tickets in trac and redmine.
  • a web frontend for the ticketing database handler.