The document discusses sitemaps and how they can help search engines crawl websites more efficiently and comprehensively. It presents the results of the first large-scale study of how sitemaps are used in the real world. Key findings include that over 35 million websites publish sitemaps containing billions of URLs, and that sitemaps increase unique page coverage compared to discovery crawls alone. The authors propose metrics to evaluate sitemaps and present a preliminary algorithm to balance sitemaps and discovery crawls to maximize coverage.

1. Sitemaps: Above and Beyond
the Crawl of Duty
Sitemaps! Sitemaps!
Uri Schonfeld (Google and UCLA)
Narayanan Shivakumar (Google)
Copyright Uri Schonfeld, shuri.org April
2009

2. What are we going to talk about?
• The sitemaps protocol:
– Not introduced in this paper
– Friendly web servers publishing URL lists
• Popular and growing in popularity
• First large scale study over real data:
• How it is used by users
• Its Impact
– First look at how it can be used by search engines
– Lots of future work to get excited over
• Let’s start with:
– Underlying problem that sitemaps addresses
Copyright Uri Schonfeld, shuri.org April
2009

3. Dream of the Perfect Crawl
1.Users Have High Expectations:
• Coverage: Every page should be findable
• Freshness: Latest event, viral video,...
• Deep Web: ajax, flash, silverlight,....
1.Search Engines Dream of the perfect crawl:
• Everything the users want
• …but efficient:
– No 404s
– No duplicates
1.Sitemaps to the rescue...
Copyright Uri Schonfeld, shuri.org April
2009

4. Sitemaps
1. Basic idea: The web server
1.Puts a URL list, a sitemaps file, on its site
2.Includes new and changed content
3.Lets the search engines know
2. The URL list may also include:
 URLs
 Last Modification Time
 Expected Change Frequency
 Priority
1. Let the search engine know:
1."Ping" search engines that their sitemaps file has changed
2.Alternatively include sitemaps in robots.txt file (April 2007)
Copyright Uri Schonfeld, shuri.org April
2009

5. Sitemaps: This is how it looks
<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns=
"http://www.sitemaps.org/schemas/sitemap/0.9">
<url>
<loc>http://www.example.com/</loc>
<lastmod>2005-01-01</lastmod>
<changefreq>monthly</changefreq>
<priority>0.8</priority>
</url>
<url>
...
<url>
</urlset>
Copyright Uri Schonfeld, shuri.org April
2009

6. Related Work
1. 1999: "Santa Fe Convention"
1.Lead to OAI-PMH
2."...e-print servers to expose metadata for the papers it
held"
3.Coalition for Networked Information, Digital Library
Federation, Open Archives Initiative (OAI), Herbert Van
de Sompel, Carl Lagoze
2. 2000: Crawler Friendly Web Servers: Brandman, Cho, Garcia-
Molina and Shivakumar
1.Export list of URLs and changed content
3. 2005/6: Sitemaps:
1.Introduced in 2005 by Google
2.2006 Microsoft, Yahoo and Google announced joint
support
Copyright Uri Schonfeld, shuri.org April
2009

7. Our Main Contributions
1. First Study of Sitemaps over real world
data:
a) How it is used
b) It’s impact
2. Define metrics to evaluate Sitemaps feeds.
3. Explore:
a) The challenges of using Sitemaps together
with Discovery Crawl
b) Define a preliminary algorithm combining
the two crawls.Copyright Uri Schonfeld, shuri.org April
2009

8. Inside Google
1. Sitemaps & Discovery
2. Sitemaps:
a) Sitemaps are fetched:
• After they are pinged.
• Several frequencies.
a) Sitemaps discovered URLs are fed to the crawling pipeline.
b) Some sources are fed directly for instant crawling.
3. Discovery:
a) New URLs and URLs of changed content are fed back to the
pipeline
4. Pipeline
Copyright Uri Schonfeld, shuri.org April
2009

9. How Sitemaps Is Used?
1. Approximately 35M websites publish Sitemaps, and
give us metadata for several billions of URLs.
2. Metadata:
1. 61% include a priority field.
2. 58% of URLs include a lastmodification date
3. 7% include a change frequency field
3. Formats Breakdown:
a) XML Sitemap 76.76
b) Url List 3.42
c) Atom 1.61
d) RSS 0.11
e) Unknown 17.51
4. Robots.txt announced April 2007
Copyright Uri Schonfeld, shuri.org April
2009

10. Sitemaps
Case Studies
Copyright Uri Schonfeld, shuri.org April
2009

11. Sitemaps Use Case Studies
1. Looked at three different sites:
a) Amazon: Large.
b) CNN: Dynamic.
c) Pubmedcentral.nih.gov: Archival.
2. Amazon:
a) Huge.
b) Service Oriented Architecture:
• Hard to list valid URLs, when content changes
• Research Opportunity: Auto Generation of Sitemaps
a) 20M URLs published in:
• 10,000 sitemaps files.
• Each file: 20,000-50,000 URLs.
• Log based.
a) Efficiency: URLs crawled vs unique pages
• Discovery 63%, Sitemaps 86%.Copyright Uri Schonfeld, shuri.org April
2009

12. Case Study: CNN
1. Very Dynamic:
a) Many new URLs added daily
2. Sitemaps:
a) News: 200-400 URLs
b) Weekly:2500-3000 URLs
c) Monthly:5000-10000 URLs
d) The lists don't overlap but complete
e) Additional SitemapsIndex of hub pages
Copyright Uri Schonfeld, shuri.org April
2009

13. Case Study
Pubmedcentral.nih.gov
1. Archival domain:
a) Add and hardly change.
b) Oldest journal published in1809.
2. Thus, can be exhaustive.
3. Sitemap files:
a) 50+ sitemaps files.
b) 30,000 URLs in each.
c) Last modification inaccurate (unlike
CNN and Amazon).Copyright Uri Schonfeld, shuri.org April
2009

14. Pubmedcentral.nih.gov (cont’)
1. URL break down
a) Discovery and Sitemaps 3 million
b) Sitemaps only 1.7 million
c) 1 million due to duplicates
2. Manually examined 3000 sample URLs from the
missing ~300,000
a) 8% errors
b) 10% redirects
c) 11% other duplicate content
d) 51% judgment call needed (should crawl or
not)
Copyright Uri Schonfeld, shuri.org April
2009

15. Pubmedcentral
Copyright Uri Schonfeld, shuri.org April
2009

16. CNN: New URLs Seen Over Time
Copyright Uri Schonfeld, shuri.org April
2009

17. Evaluating Sitemaps
Copyright Uri Schonfeld, shuri.org April
2009

18. Evaluating Sitemaps
1. Coverage and Freshness
2. How should we judge usefulness?
3. How far does a URL get in our pipeline:
1. Seen
2. Crawled
3. Unique
4. Indexed
5. Results
6. Clicked
4. UniqueCoverage = UniqueSitemaps(D) / Unique(D)
5. IndexCoverage = IndexedSitemaps(D) / Indexed(D)
6. PageRankCoverage = RankMassSitemaps(D) / RankMass(D)
Copyright Uri Schonfeld, shuri.org April
2009

19. Coverage
Copyright Uri Schonfeld, shuri.org April
2009

20. Coverage vs UniqueCoverage
Copyright Uri Schonfeld, shuri.org April
2009

21. UniqueCoverage vs Domain Size
• 46% domains
have above 50%
UniqueCoverage
• 12% domains
have 90%
UniqueCoverage.
Copyright Uri Schonfeld, shuri.org April
2009

22. While PageRank Coverage…
Copyright Uri Schonfeld, shuri.org April
2009

23. Bang for the Buck…
Copyright Uri Schonfeld, shuri.org April
2009

24. Pings and Freshness
First Seen by Sitemaps
• Ping: 12.7%
• Non-Ping: 80.3%
First Seen by Discovery
• Ping: 1.5%
• Non-Ping: 5.5%
• 14.2% Discovered through pings.
• But who saw first is independent.
• Doesn't reflect the potential.
Research Opportunity: Detect and ping policy
• Of URLs seen by both Sitemaps and Discovery.
o 78% Seen first by Sitemaps
o 22% Seen first by Discovery
Copyright Uri Schonfeld, shuri.org April
2009

25. Doing Both :
Sitemaps and Discovery
1. New URLs and Refresh: we’ll talk new URLs.
2. You can't fetch it all ⇒ per site quota.
3.What to fetch?
4. Crawl uses some ranking.
5. What should ranking for Sitemaps URLs?
6. How to balance between them?
Copyright Uri Schonfeld, shuri.org April
2009

26. Ranking URLs in Sitemaps
1. Priority:
1.Full autority to the webmaster.
2.Is not available all the time.
2. PageRank:
1. Provenly effective.
2.Not available for the truly new pages.
3.Webmasters don't have a Say at all.
3. PriorityRank:
1.Modify graph to take both into account
2.Add sitemaps as a page implicitly linked to from the root.
3.Links from Sitemaps are weighted by priority if
available
4.Calculate PageRank over this modified graph.
5.Hybrid of the two previous methods .
Copyright Uri Schonfeld, shuri.org April
2009

27. Balancing the Crawl:
Algorithm Simplified
1. for epoch in 0..infinity do
2. kD = kS = 1/2
1.Fetch:
1.Top kD * Quota from Discovery
2.Top kS * Quota from Sitemaps
2.Measure derivative of the utility (IndexCoverage)
3.Adjust kC and KS
Copyright Uri Schonfeld, shuri.org April
2009

28. Conclusion and Future Work
1. Large scale study, real data
2. You cannot stop Discovery… yet.
3. Presented metrics for freshness and coverage.
4. Sitemaps evaluated for coverage and freshness.
5. Presented Algorithm to combine Sitemaps & Discovery
6. To Be Done
1. Good news: tons of future work
2. Duplicates not solved on web-server side either.
3. Better Pings.
4. Ranking Sitemaps URLs can be a challenge.
Copyright Uri Schonfeld, shuri.org April
2009

29. Acks
We wish to thank many Googlers!
thank...
Dennis Geels, Ori Gershony, Laramie, Madhu, Thomal, Alkis,
Peter Dickman, Arup, Charlie, Nish, Rosemary, Ralph, Nikhil.
Copyright Uri Schonfeld, shuri.org April
2009

30. The End
Thank You!
Copyright Uri Schonfeld, shuri.org April
2009