Crawley

Pythonic Crawling / Scraping Framework based on Non Blocking I/O operations.

Description

Crawley is a pythonic Scraping / Crawling Framework intended to make easy the way you extract data from web pages into structured storages such as databases.

## Features - High Speed WebCrawler built on Eventlet. - Supports databases engines like Postgre, Mysql, Oracle, Sqlite. - Command line tools. - Extract data using your favourite tool. XPath or Pyquery (A Jquery-like library for python). - Cookie Handlers. - Very easy to use

Programming language: Python

License: GNU General Public License v3.0 only

Tags: Web Crawling     Application Frameworks     Internet    

Latest version: v0.2.1

README

Pythonic Crawling / Scraping Framework Built on Eventlet

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Features

• 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).

Documentation

http://packages.python.org/crawley/

Project WebSite

http://project.crawley-cloud.com/

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To install crawley run

~$ python setup.py install

or from pip

~$ pip install crawley

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To start a new project run

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

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Write your Models

""" models.py """

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))

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Write your Scrapers

""" crawlers.py """

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 = ["http://pypi.python.org/pypi"]

    #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

""" settings.py """

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

#Don't change this if you don't have renamed the project
PROJECT_NAME = "pypi"
PROJECT_ROOT = os.path.join(PATH, PROJECT_NAME)

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

SHOW_DEBUG_INFO = True

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Finally, just run the crawler

~$ crawley run