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About the developer

alephdata
245 Stars 44 Forks MIT License 762 Commits 22 Opened issues

Description

Distributed crawling framework for documents and structured data.

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Memorious

The solitary and lucid spectator of a multiform, instantaneous and almost intolerably precise world.

-- Funes the Memorious <http:>_, Jorge Luis Borges

.. image:: https://github.com/alephdata/memorious/workflows/memorious/badge.svg

memorious
is a distributed web scraping toolkit. It is a light-weight tool that schedules, monitors and supports scrapers that collect structured or un-structured data. This includes the following use cases:
  • Maintain an overview of a fleet of crawlers
  • Schedule crawler execution in regular intervals
  • Store execution information and error messages
  • Distribute scraping tasks across multiple machines
  • Make crawlers modular and simple tasks re-usable
  • Get out of your way as much as possible

.. image:: docs/memorious-ui.png

Design

When writing a scraper, you often need to paginate through through an index page, then download an HTML page for each result and finally parse that page and insert or update a record in a database.

memorious
handles this by managing a set of
crawlers
, each of which can be composed of multiple
stages
. Each
stage
is implemented using a Python function, which can be re-used across different
crawlers
.

The basic steps of writing a Memorious crawler:

  1. Make YAML crawler configuration file
  2. Add different stages
  3. Write code for stage operations (optional)
  4. Test, rinse, repeat

Documentation

The documentation for Memorious is available at

memorious.readthedocs.io 
_. Feel free to edit the source files in the
docs
folder and send pull requests for improvements.

To build the documentation, inside the

docs
folder run
make html

You'll find the resulting HTML files in /docs/_build/html.

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