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Regular expression module for forensics and big data

Project description

This is a general purpose regular expression matching engine, with particularly good performance for searching large byte buffers, for example large files or raw disk images, using multiple encodings. It was writted to solve perfomance issues in big data extraction tasks including artefact discovery for digital forensics.

jsre is:

  • Fast: When matching complex patterns or a large number of keywords on large input buffers it is substantially faster than current regular expression engines. jsre is designed to scale well in the face of complexity: its relative performance improves with increasing pattern complexity.
  • Unicode Encoding Neutral: A regular expression is written as a string, the user separately specifies what encodings are to be searched when the expression is compiled. All Python codecs are supported and the capability provided is compilant with Unicode regular expression level 1 requirements.
  • Deployable: The compiled matching engine has a small memory footprint limited to below 10MByte, allowing processing to be easily distributed across multiple CPUs.
  • Portable: The software uses a single Python type extension and only standard C and Python libraries. Installs with pip on Windows or Linux.

jsre includes additional functions that are specific to its intended application, they include alternative expression indexing, the processing of overlapped buffers and the specification of stride and offset for search anchors (e.g. for searching at fixed positions in disk sectors).

Version 1.1 is based on a new library for managing character classes and associated set operations. This has significantly improved compilation speeds, allowing the use of this module in more geneal purpos re applications.

As far as possible jsre provides a similar interface to the standard Python re module. See documentation examples for an introduction to the module and its application-specific features. This documentation assumes that the reader is familiar with regular expressions and their use; newcomers may find it easier to first read the Python re documentation and tutorials.


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