.. include:: <s5defs.txt>
=====================================
Web Crawling and Metadata with Python
=====================================
:Author: Andrew Montalenti
:Date: $Date: 2012-10-26 09:00:00 -0500 (Fri, 26 Oct) $
.. This document is copyright Andrew Montalenti and Parsely, Inc.
.. container:: handout
**How this was made**
This document was created using Docutils_/reStructuredText_ and S5_.
Introduction to web crawling (using Scrapy) metadata extraction (using
Schemato).
.. _Docutils: http://docutils.sourceforge.net/
.. _reStructuredText: http://docutils.sourceforge.net/rst.html
.. _S5: http://meyerweb.com/eric/tools/s5/
Meta Information
----------------
**Me**: I've been using Python for 10 years. I use Python full-time, and have for the last 3 years.
**Startup**: I'm co-founder/CTO of Parse.ly_, a tech startup in the digital media space.
**E-mail me**: andrew@parsely.com
**Follow me on Twitter**: amontalenti_
**Connect on LinkedIn**: http://linkedin.com/in/andrewmontalenti
.. _Parse.ly: http://parsely.com
.. _amontalenti: http://twitter.com/amontalenti
Parse.ly
--------
What do we do?
How do we do it?
Crawler
-------
"A computer program that browses the World Wide Web in a methodical, automated
manner or in an orderly fashion."
Open source examples:
* Apache Nutch: built by Doug Cutting, creator of Lucene/Hadoop
* Heritrix: built by the Internet Archive
Web Data
--------
.. class:: incremental
40 billion pages on the Web today (Google)
Growing: size was "just" 15 billion in October 2010
"Deep Web" means it's even bigger
Crawling, Spidering, Scraping
-----------------------------
These terms are almost synonymous, but sometimes have different meaning and connotations.
My take:
.. class:: incremental
* Crawling: downloads and processes web content at one or more URLs
* Spidering: walks links found in web content, either for a single domain or across the web
* Scraping: uses knowledge of HTML pages to convert them into structured data
My first experience with crawlers
---------------------------------
.. class:: incremental
Parse.ly Reader: personalized news reader built in mid 2009
Crawled 500K web sources for content personalized to individual interests
First crawler was really dumb: based on RSS/Atom detection and feed fetching
Seeded from domains appearing on top aggregators like Google News
Technology: multiprocessing, Postgres, Solr
My current experience
---------------------
.. class:: incremental
Parse.ly shifted into web publisher analytics and APIs in 2010/2011
Upgrades: Scrapy, MongoDB, Redis, Solr, Celery
Parse.ly Network Stats
----------------------
.. class:: incremental
>200 top publishing domains (Quantcast top-10,000 sites)
>3B pageviews/month across network
>10M unique URLs in our index
>1TB of hot production data running in memory
Parse.ly Crawl Infrastructure
-----------------------------
.. class:: incremental
Have written 125 custom Scrapy crawlers with >10K of custom crawler code
(Not proud of this fact; more on this later)
Production environment in Rackspace Cloud; several worker nodes
Implementation and QA runs in Scrapy Cloud
Caching and retry strategy implemented atop Redis
Eventual storage in MongoDB, Solr
Implemented as Scrapy Components and Pipelines
Our Business
------------
We're not in the crawling business.
We're in the analytics and APIs business.
Our Strategy
------------
.. class:: incremental
We aim to be the #1 technology partner for large-scale publishers.
Crawling: means to an end.
URLs => Structured Metadata.
Metadata Soup: Schema.org, rNews, OpenGraph, hNews, HTML5, ...
Parse.ly Demo Time!
-------------------
Yay!
Reflections on Scaling Crawlers
-------------------------------
.. class:: incremental
You don't want to write your own crawler infrastructure from scratch.
TRUST ME.
Lots of hidden problems --
Abstractions: asynchronous network I/O (Twisted), data processing pipelines
HTTP/web: retries, throttling, backoff, concurrency, cookie/form handling
Infrastructure: crawling queues, health monitoring
Don't use Nutch, Heritrix
-------------------------
.. class:: incremental
Didier and I tried to understand, and even customize, Nutch in the early days.
We love Lucene/Solr, so we figured it'd be a good fit.
But no -- it's a WORLD OF PAIN.
(They are for building search engines and archives -- not structured metadata.)
Use Scrapy
----------
It's really Pythonic.
It's built on proven tools, like Twisted, w3lib, and lxml.
It's getting better and better.
Just trust me: use Scrapy.
Scrapy Overview
---------------
.. sourcecode:: sh
$ git clone git://github.com/scrapy/dirbot.git
$ cd dirbot
$ mkvirtualenv dirbot
$ pip install scrapy
$ pip install ipython
$ scrapy list
dmoz
$ scrapy crawl dmoz
[scrapy] INFO: Scrapy 0.16.0 started (bot: dirbot)
...
Example Output
--------------
.. sourcecode:: sh
[dmoz] DEBUG: Crawled (200) <GET http://dmoz.org/Comp.../Python/Resources/>
[dmoz] DEBUG: Crawled (200) <GET http://dmoz.org/Comp.../Python/Books/>
[dmoz] DEBUG: Scraped from <200 http://dmoz.org/Comp.../Python/Resources/>
Website: name=[u'Top'] url=[u'/']
[dmoz] DEBUG: Scraped from <200 http://dmoz.org/Comp.../Python/Resources/>
Website: name=[u'Computers'] url=[u'/Computers/']
[dmoz] DEBUG: Scraped from <200 http://dmoz.org/Comp.../Python/Resources/>
Website: name=[u'Programming'] url=[u'/Computers/Programming/']
...
[dmoz] DEBUG: Scraped from <200 http://dmoz.org/.../Python/Books/>
Website: name=[u'Text Processing in Python'] url=[u'http://gnosis.cx/TPiP/']
[dmoz] INFO: Spider closed (finished)
Links:
* http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/
* http://www.dmoz.org/Computers/Programming/Languages/Python/Books/
Spider Example
--------------
.. sourcecode:: python
class DmozSpider(BaseSpider):
name = "dmoz"
allowed_domains = ["dmoz.org"]
start_urls = [
"http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
"http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/",
]
def parse(self, response):
hxs = HtmlXPathSelector(response)
sites = hxs.select('//ul/li')
items = []
for site in sites:
item = Website()
item['name'] = site.select('a/text()').extract()
item['url'] = site.select('a/@href').extract()
item['description'] = site.select('text()').extract()
items.append(item)
return items
Live Demos!
-----------
Examples: DailyCaller.com, ArsTechnica.com
* https://www.stypi.com/pixelmonkey/dailycaller.py
* https://www.stypi.com/pixelmonkey/arstechnica.py
Item
----
.. sourcecode:: python
from scrapy.item import Item, Field
class DbItem(Item):
title = Field()
link = Field()
DailyCaller: imperative style
-----------------------------
.. sourcecode:: python
from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from dirbot.items import DbItem
class DailycallerSpider(BaseSpider):
name = "dailycaller.com"
allowed_domains = ["dailycaller.com"]
start_urls = ["http://dailycaller.com"]
def parse(self, response):
hxs = HtmlXPathSelector(response)
item = DbItem()
item["title"] = hxs.select("//h1/text()").extract()[0]
item["link"] = hxs.select("//link[@rel='canonical']/@href").extract()[0]
return item
ArsTechnica: declarative style
------------------------------
.. sourcecode:: python
from scrapy.spider import BaseSpider
from scrapy.selector import HtmlXPathSelector
from dirbot.items import DbItem
from scrapy.contrib.loader import XPathItemLoader
from scrapy.contrib.loader.processor import TakeFirst
class ArstechnicaSpider(BaseSpider):
name = "arstechnica.com"
allowed_domains = ["arstechnica.com"]
start_urls = ["http://arstechnica.com"]
def parse(self, response):
loader = XPathItemLoader(item=DbItem(), response=response)
loader.add_xpath("title", "//meta/[@property='og:title']/@content")
loader.add_xpath("link", "//link[@rel='canonical']/@href")
item = loader.load_item()
item["title"] = loader.get_value(item["title"], TakeFirst(), unicode.title)
item["link"] = loader.get_value(item["link"], TakeFirst())
return item
Live Spider Shell
-----------------
.. sourcecode:: python
>>> fetch("http://dailycaller.com/2012...-most-rallies/")
[dailycaller.com] INFO: Spider opened
[dailycaller.com] DEBUG: Crawled (200) <GET http://dailycaller.com/...ies/>
[s] Available Scrapy objects:
[s] hxs <HtmlXPathSelector xpath...>
[s] item Website: name=None url=None
[s] request <GET http://dailycaller.com/...es/>
[s] response <200 http://dailycaller.com/...es/>
[s] settings <CrawlerSettings module=<module 'dirbot.settings'>
[s] spider <DailycallerSpider 'dailycaller.com' at 0x2484d90>
[s] Useful shortcuts:
[s] shelp() Shell help
[s] fetch(req_or_url) Fetch request (or URL) and update local objects
[s] view(response) View response in a browser
>>> hxs.select("//title/text()")
Scrapy Cloud Demo
-----------------
How we host, test, and QA our spiders across millions of pages.
Schemato Overview
-----------------
Domo arigato, Mr. Schemato!
Schemato Distilling
-------------------
.. sourcecode:: python
from distillers import Distill, Distiller
class NewsDistiller(Distiller):
title = Distill("s:headline", "og:title")
image_url = Distill("s:associatedMedia.ImageObject/url", "og:image")
pub_date = Distill("s:datePublished")
author = Distill("s:author", "s:creator.Person/name")
section = Distill("s:articleSection")
description = Distill("s:description", "og:description")
link = Distill("s:url", "og:url")
site = Distill("og:site_id")
id = Distill("s:identifier")
Schemato Distilling in Action
-----------------------------
.. sourcecode:: python
>>> from distillery import NewsDistiller
>>> from schemato import Schemato
>>> lnk = "http://www.cnn.com/2012/10/26/world/europe/italy-berlusconi-convicted/index.html"
>>> cnn = Schemato(lnk)
>>> distiller = NewsDistiller(cnn)
>>> distiller.distill()
{'author': "Ben Wedeman",
'id': None,
'image_url': 'http://i2.cdn.turner.com/cnn/...-video-tease.jpg',
'link': 'http://www.cnn.com/2012/10/26/world/europe/italy-berlusconi-convicted/index.html',
'pub_date': '2012-10-26T14:36:35Z',
'section': 'world',
'title': 'Ex-Italian PM Berlusconi handed 4-year prison term for tax fraud',
'site': 'CNN',
'description': 'Flamboyant former Italian Prime Minister...'}
Schemato: Bridging Gaps Between Standards
-----------------------------------------
Facebook OpenGraph provided image_url and link.
Schema.org NewsArticle provided the rest.
.. sourcecode:: python
>>> distiller.sources
{'author': 's:author',
'id': None,
'image_url': 'og:image',
'link': 'og:url',
'pub_date': 's:datePublished',
'section': 's:articleSection',
'title': 's:headline',
'site': 'og:site_id',
'description': 's:description'}
Data sets to get started
------------------------
Are you interested in tackling some of these web crawling problems on your own project?
If so, you may want some data to get started.
I currently sell a few news data sets that help with this:
* 30M news headlines and 500K web sources, 30gb of JSON data ($300)
* 15K news domains that are the most popular in US market ($100)
You could use either of these to build your own Google News, for example.
Interested? Find me after or tweet me: amontalenti_
Schemato: A Call to Action
--------------------------
The time is ripe for the semantic web.
Want to build the ultimate web metadata validator, distiller, and extractor?
Want to work on getting Schemato to run across millions of URLs?
Want your contributions open source on Github?
Find me at the sprints on Sunday!
Baby Turtles
------------
Use your powers wisely, and always remember...
.. image:: img/babyturtles.png
:align: center
Magic Turtles!
--------------
It's turtles all the way down!
.. image:: img/magicturtle.jpg
:align: center