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A log parser library packaged with a set of ready to use parsers (DHCPd, Squid, Apache, ...)

Project description

LogsParser
==========

Description
:::::::::::

LogsParser is an opensource python library created by Wallix ( http://www.wallix.org ).
It is used as the core mechanism for logs tagging and normalization by Wallix's LogBox
( http://www.wallix.com/index.php/products/wallix-logbox ).

Logs come in a variety of formats. In order to parse many different types of
logs, a developer used to need to write an engine based on a large list of complex
regular expressions. It can become rapidly unreadable and unmaintainable.

By using LogsParser, a developer can free herself from the burden of writing a
log parsing engine, since the module comes in with "batteries included".
Furthermore, this engine relies upon XML definition files that can be loaded at
runtime. The definition files were designed to be easily readable and need very
little skill in programming or regular expressions, without sacrificing
powerfulness or expressiveness.

Purpose
:::::::

The LogsParser module uses normalization definition files in order to tag
log entries. The definition files are written in XML.

The definition files allow anyone with a basic understanding of regular
expressions and knowledge of a specific log format to create and maintain
a customized pool of parsers.

Basically a definition file will consist of a list of log patterns, each
composed of many keywords. A keyword is a placeholder for a notable and/or
variable part in the described log line, and therefore associated to a tag
name. It is paired to a tag type, e.g. a regular expression matching the
expected value to assign to this tag. If the raw value extracted this way needs
further processing, callback functions can be applied to this value.

This format also allows to add useful meta-data about parsed logs, such as
extensive documentation about expected log patterns and log samples.

Format Description
------------------

A normalization definition file must strictly follow the specifications as
they are detailed in the file normalizer.dtd .

A simple template is provided to help parser writers get started with their
task, called normalizer.template.

Most definition files will include the following sections :

* Some generic documentation about the parsed logs : emitting application,
application version, etc ... (non-mandatory)
* the definition file's author(s) (non-mandatory)
* custom tag types (non-mandatory)
* callback functions (non-mandatory)
* Prerequisites on tag values prior to parsing (non-mandatory)
* Log pattern(s) and how they are to be parsed
* Extra tags with a fixed value that should be added once the parsing is done
(non-mandatory)

Root
....

The definition file's root must hold the following elements :

* the normalizer's name.
* the normalizer's version.
* the flags to apply to the compilation of regular expressions associated with
this parser : unicode support, multiple lines support, and ignore case.
* how to match the regular expression : from the beginning of the log line (match)
or from anywhere in the targeted tag (search)
* the tag value to parse (raw, body...)

Default tag types
-----------------

A few basic tag types are defined in the file common_tagTypes.xml . In order
to use it, it has to be loaded when instantiating the Normalizer class; see the
class documentation for further information.

Here is a list of default tag types shipped with this library.

* Anything : any character chain of any length.
* Integer
* EpochTime : an EPOCH timestamp of arbitrary precision (to the second and below).
* syslogDate : a date as seen in syslog formatted logs (example : Mar 12 20:13:23)
* URL
* MACAddress
* Email
* IP
* ZuluTime : a "Zulu Time"-type timestamp (example : 2012-12-21T13:45:05)


Custom Tag Types
................

It is always possible to define new tag types in a parser definition file, and
to overwrite default ones. To define a new tag type, the following elements are
needed :

* a type name. This will be used as the type reference in log patterns.
* the python type of the expected result : this element is not used yet and can
be safely set to anything.
* a non-mandatory description.
* the regular expression defining this type.

Callback Functions
..................

One might want to transform a raw value after it has been extracted from a pattern:
the syslog normalizer converts the raw log timestamp into a python datetime object,
for example.

In order to do this, the <callback> tag must be used to define a callback function.

<callback> requires a function name as a mandatory attribute. Its text defines the
function body as in python, meaning the PEP8 indentation rules are to be followed.

When writing a callback function, the following rules must be respected :

* Your callback function will take ONLY two arguments: **value** and **log**.
"value" is the raw value extracted from applying the log pattern to the log,
and "log" is the dictionary of the normalized log in its current state (prior
to normalization induced by this parser definition file).
* Your callback function can modify the "log" argument (especially assign
the transformed value to the concerned tag name) and must not return anything.
* Your callback function has a restricted access to the following facilities: ::

"list", "dict", "tuple", "set", "long", "float", "object",
"bool", "callable", "True", "False", "dir",
"frozenset", "getattr", "hasattr", "abs", "cmp", "complex",
"divmod", "id", "pow", "round", "slice", "vars",
"hash", "hex", "int", "isinstance", "issubclass", "len",
"map", "filter", "max", "min", "oct", "chr", "ord", "range",
"reduce", "repr", "str", "unicode", "basestring", "type", "zip", "xrange", "None",
"Exception"

* Importing modules is therefore forbidden and impossible. The *re* and *datetime*
modules are available for use as if the following lines were present: ::

import re
from datetime import datetime


Pattern definition
..................

A definition file can contain as many log patterns as one sees fit. These patterns
are simplified regular expressions and applied in alphabetical order of their names,
so it is important to name them so that the more precise patterns are tried
before the more generic ones.

A pattern is a "meta regular expression", which means that every syntactic rule from
python's regular expressions are to be followed when writing a pattern, especially
escaping special characters. To make the patterns easier to read than an obtuse
regular expression, keywords act as "macros" and correspond to a part of the log
to assign to a tag.

A log pattern has the following components:

* A name.
* A non-mandatory description of the pattern's context.
* The pattern itself.
* The tags as they appear in the pattern, the associated name once the normalization
is over, and the callback functions to eventually call on their raw values
* Non-mandatory log samples

If a tag name starts with __ (double underscore), this tag won't be added to the
final normalized dictionary. This allows to create temporary tags that will
typically be used in conjunction to a series of callback functions, when the
original raw value has no actual interest.

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