Simple Python Search Spider, Page Ranker, and Visualizer (Part of Capstone Project on Coursera)

This is an assignment that I did as part of Capstone project of Python for Everybody Specialization on Coursera.

This is a set of programs that emulate some of the functions of a search engine, i.e. it crawls the web pages according to the starting url that the user provides, ranks them according to the number of 'good' incoming links and then visualizes them in a web according to their rank.

Requirements

Python Libraries

• urllib
• ssl
• urlparse
• sqlite3 : You should install the SQLite browser to view and modify the databases from: http://sqlitebrowser.org/

Working

Crawling and Ranking

This program crawls a web site and pulls a series of pages into the database, recording the links between pages. It stores the data in a SQLITE3 database named 'spider.sqlite'. This file can be removed at any time to restart the process if it already exists.

• spider.py

  Enter web url or enter:

  Enter the url from where you wish to start the crawl or you can press enter to start the crawl from www.dr-chuck.com How many pages: 2

  In this sample run, we told it to crawl a website and retrieve two pages. If you restart the program again and tell it to crawl more pages, it will not re-crawl any pages already in the database. Upon restart it goes to a random non-crawled page and starts there. So each successive run of spider.py is additive.

  You can have multiple starting points in the same database - within the program these are called "webs". The spider chooses randomly amongst all non-visited links across all the webs.

  If your code fails complainin about certificate probems, there is some code (SSL) that can be un-commented to work around certificate problems.

• spdump.py

  If you want to dump the contents of the spider.sqlite file, you can run spdump.py. It will show the contents of the database in the following format:

  (count of incoming links , old_rank, new_rank, id, url)

  The spdump.py program only shows pages that have at least one incoming link to them.

• sprank.py

  Once you have a few pages in the database, you can run Page Rank on the pages using the sprank.py program. You simply tell it how many Page Rank iterations to run.

  You can dump the database again to see that page rank has been updated

  You can run sprank.py as many times as you like and it will simply refine the page rank the more times you run it. You can even run sprank.py a few times and then go spider a few more pages with spider.py and then run sprank.py to converge the page ranks.

  For each iteration of the page rank algorithm it prints the average change per page of the page rank. The network initially is quite unbalanced and so the individual page ranks are changeing wildly. But in a few short iterations, the page rank converges. You should run sprank.py long enough that the page ranks converge.

• spreset.py

  If you want to restart the Page Rank calculations without re-spidering the web pages, you can use spreset.py. It sets the ranks of all pages to 1.0

Visualization

If you want to visualize the current top pages in terms of page rank, run spjson.py to write the pages out in JSON format to be viewed in a web browser.

You can view this data by opening the file force.html in your web browser.
This shows an automatic layout of the nodes and links. You can click and drag any node and you can also double click on a node to find the URL that is represented by the node.

This visualization is provided using the force layout from: mbostock.github.com/d3

If you rerun the other utilities and then re-run spjson.py - you merely have to press refresh in the browser to get the new data from spider.js.