Pythonic Crawling / Scraping Framework based on Non Blocking I/O operations.
Python Tcl Shell
Latest commit 3953dca Jun 13, 2015 @jmg Merge pull request #17 from crawley-project/master
Add Travis CI integration and fixed newer version dependencies
Failed to load latest commit information.
.code_swarm Offline crawler added. Browser is now using it Oct 17, 2011
crawley Fixed building issues Jun 12, 2015
doc Fixed some typo in documentation Nov 14, 2011
examples Fixed building issues Jun 12, 2015
.gitignore Fixed building issues Jun 12, 2015
.travis.yml fixed templates dir Oct 28, 2011 build_deb script Nov 14, 2011 Fixed building issues Jun 12, 2015
requirements.txt removed whitespaces Nov 14, 2011
setup.cfg documentation config file Sep 26, 2011 Adds custom headers and fixes dependencies Mar 14, 2014 we're on pip Sep 14, 2011

Pythonic Crawling / Scraping Framework Built on Eventlet

Build Status Code Climate Stories in Ready


  • High Speed WebCrawler built on Eventlet.
  • Supports relational databases engines like Postgre, Mysql, Oracle, Sqlite.
  • Supports NoSQL databased like Mongodb and Couchdb. New!
  • Export your data into Json, XML or CSV formats. New!
  • Command line tools.
  • Extract data using your favourite tool. XPath or Pyquery (A Jquery-like library for python).
  • Cookie Handlers.
  • Very easy to use (see the example).


Project WebSite

To install crawley run

~$ python install

or from pip

~$ pip install crawley

To start a new project run

~$ crawley startproject [project_name]
~$ cd [project_name]

Write your Models

""" """

from crawley.persistance import Entity, UrlEntity, Field, Unicode

class Package(Entity):

    #add your table fields here
    updated = Field(Unicode(255))    
    package = Field(Unicode(255))
    description = Field(Unicode(255))

Write your Scrapers

""" """

from crawley.crawlers import BaseCrawler
from crawley.scrapers import BaseScraper
from crawley.extractors import XPathExtractor
from models import *

class pypiScraper(BaseScraper):

    #specify the urls that can be scraped by this class
    matching_urls = ["%"]

    def scrape(self, response):

        #getting the current document's url.
        current_url = response.url        
        #getting the html table.
        table = response.html.xpath("/html/body/div[5]/div/div/div[3]/table")[0]

        #for rows 1 to n-1
        for tr in table[1:-1]:

            #obtaining the searched html inside the rows
            td_updated = tr[0]
            td_package = tr[1]
            package_link = td_package[0]
            td_description = tr[2]

            #storing data in Packages table
            Package(updated=td_updated.text, package=package_link.text, description=td_description.text)

class pypiCrawler(BaseCrawler):

    #add your starting urls here
    start_urls = [""]

    #add your scraper classes here    
    scrapers = [pypiScraper]

    #specify you maximum crawling depth level    
    max_depth = 0

    #select your favourite HTML parsing tool
    extractor = XPathExtractor

Configure your settings

""" """

import os 
PATH = os.path.dirname(os.path.abspath(__file__))

#Don't change this if you don't have renamed the project

DATABASE_ENGINE = 'sqlite'     
DATABASE_NAME = 'pypi'  
DATABASE_USER = ''             
DATABASE_HOST = ''             


Finally, just run the crawler

~$ crawley run