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Building a NewsCrawler: pt1


This tutorial/walk through assumes that you are already familiar with python and the terminal. I would also strongly advice anyone remotely interested in understanding scrapy to have a look at their documentation and preferably follow the quick tutorial and setup instruction outlined.

The example in the documentation is a simple spider. Basic spiders are good if we intend on retrieving information from a list of pages that we currently know the URLs of. This is no good in our scenario where a lot of pages need to be scraped. This is where the crawler comes in, crawlers provide a convenient mechanism to follow links, you can refer to the documentation here to refresh your memory.

###Pick a website

We are going to experiment with a Kenyan news website. Please feel free to substitue this website with one that suits your taste. Daily Nation For those of you keen enough, you will realise that I have an app for this news source on the App Store

####Install scrapy Follow the Installation Guide here, but the gist of it all is basically.

pip install Scrapy

####Create a new project

**Have you installed scrapy yet? I struggled to get it to work on osx, if you get libxml issue, just know you are in for a long ride **

scrapy startproject hermes

This will create a project called hermes in the directory you ran it in tutorial in this case. Explore this directory, its pretty boring at first.

	    scrapy.cfg  	#config file for deployment
	    tutorial/    	#project dir/module
	        items.py		#Model
	        settings.py		#project settings
	        spiders/		#spider directory

Scrapy did the burden of work for us. There are only two files we have to be concerned with at the moment, thats the items.py and the spiders. The spider directory will hold all the spiders.

###Define the model.

In scrapy, the models are defined in the items.py file. All items have a Field() type. We create our model named NationmediaItem our items class looks like the following.

	from scrapy.item import Item, Field

	class NationmediaItem(Item):
		title = Field()
		link = Field()
		summary = Field()
		content = Field()
		author = Field()	

###Defining our Spider Spiders are user defined and they are usually quite specific to a particular website, or a group of websites with the same structure.

A spider in its most basic form will have a list or URLS to download, rules that define how links are followed and a method to parse the content.

A spider must subclass scrapy.spider.Spider and define the following:

Navigate into the spider directory and create an empty python file. Call this file nmdspider.py.

Our class looks like the following:

from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from nationmedia.items import NationmediaItem
from scrapy.contrib.spiders import CrawlSpider, Rule
from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor
from scrapy.selector import Selector

class MainParser():
    def __init__(self, response, tags):
        self.response = response
        self.tags = tags

    def scrapNationOnline(self):
        hxs = Selector(self.response)
        item = NationmediaItem()
        item ["link"] = self.response.url
        item ["title"]  = ''.join(hxs.xpath('//div[@class=\"story-view\"]/header/h1/text()').extract()) #this worked by taking the page title, contains tags we dont need/want ''.join(hxs.css('title::text').extract())
        item ["summary"] = ''.join(hxs.xpath('//div/ul/li/text()').extract())
        item ["content"] = '\n'.join(hxs.xpath('//article/section/div/p/text()').extract())

        #retrieve the author. There are numerous formatting issue with this tag

        author = hxs.xpath('//section[@class=\"author\"]/strong/text()').extract()
        if not author:
            author = hxs.xpath('//section[@class=\"author\"]/text()').extract()
        item ["author"] = ''.join(author)

        return item

class PoliticsSpider(CrawlSpider):
    """Scrapes Polics News"""
    name = "politics"
    allowed_domains = ["nation.co.ke"]
    start_urls = ["http://www.nation.co.ke/news/politics/-/1064/1064/-/3hxffhz/-/index.html"]
    rules = (
        Rule(SgmlLinkExtractor(allow=('news\/politics\/',), deny=('\/view\/asFeed\/', '\/-\/1064\/1064\/-\/3hxffhz\/-\/index.html')), callback='parse_page', follow=True),
    def parse_page(self, response):
        mainParser = MainParser(response, ["News", "Politics"])
        item = mainParser.scrapNationOnline()
        return item

Take a look at the code from the repository here Its going to be clearer than the mess above.

###Time to run the crawler

scrapy crawl politics

Its as simple as that. To run the spider and save the results in a json file run

scrapy crawl politics -o politics.json -t json


The output should be a json file of all the scrapped articles. In this case from the politics section of nation.co.ke

Some Explanation

As you can see from the code, we are using xpath to extract information from the html response, this is the part that makes scrapping such a fragile task, if the website you are scrapping updates its html files, then you have to do the same here.

#####PoliticsSpider [Class:] This is our main class, if you are going to have multiple crawlers running, this is the class to duplicate. To use this class, you have to set a few parameters.

  1. Name
  2. Allowed Domains
  3. Start_urls
  4. rules.

All these are self explanatory apart from the last one. These are rules** to follow while crawling links originating from the Start_urls.

Allow: In our example, we are only accepting URLS containing news/politics, as we sure that any article with that URL is a politics item.

Restricted_xpaths: Restricts the link to a certain xpath, we are currently not implementing this.

Deny: We also deny /view/asFeed as this is the extension found in the RSS feed page, there are plenty of other rules we can apply, but these two seem to be of utmost importance.

Callback: This is the method we call back to after parsing is complete, please DO NOT name your method ‘parse’ as this is already in use internally by scrapy, I learnt this the hard way.

Follow: Instructs the program to continue following links as long as they exist, this is how we recursively crawl entire websites.

######Parse_page [Method]

This method is called once we have retrieved the contents of a page. The page is still in its original format, in this case HTML, it will need to be structured.

We delegate the actual parsing to the MainParse class, just trying to observe some OOP principles. To execute the actual parsing, we make the following call. item = mainParser.scrapNationOnline(). It returns an Item object, this item object defines a news article.


That is all that you need to parse a website, I would suggest you read the documentaion for Scrapy as I have left quite a lot unanswered here.

Website scraping is fun, but don’t be a douche bag, don’t run 100 scrapers 24/7 on someone else’s site. Also don’t steal information and claim it as your own, this really pisses some people off.

In the next web scraping tutorial, we will look at how we deploy our scraper to the cloud and automate the entire process.