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Hadoop at Yahoo! Eric Baldeschwieler VP Hadoop Software Development Yahoo!
What we want you to know about Yahoo! and Hadoop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The Largest Hadoop Contributor All Patches Core Patches
The Largest Hadoop Tester ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Largest Hadoop User 2006 now Hardware Internal Hadoop Users building  new datacenter PB Disk, >82PB Today Nodes, >25,000 Today
Collaborations around the globe ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Usage of Hadoop
Why Hadoop @ Yahoo! ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Yahoo! front page - Case Study
Yahoo! front page - Case Study Ads Optimization Search  Index
Yahoo! front page - Case Study Ads Optimization Content Optimization Search  Index Machine Learned  Spam filters RSS Feeds Content Optimization
Large Applications 2008 2009 Webmap ~70 hours runtime ~300 TB shuffling ~200 TB output ~73 hours runtime ~490 TB shuffling ~280 TB output +55% Hardware Sort benchmarks (Jim Gray contest) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Largest cluster ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tremendous Impact on Productivity
Example: Search Assist TM ,[object Object],[object Object],[object Object],Before Hadoop After Hadoop Time 26 days 20 minutes Language C++ Python Development Time 2-3 weeks 2-3 days
Futures
Current Yahoo! Development ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions? Eric Baldeschwieler VP Hadoop Software Development Yahoo! For more information: http://hadoop.apache.org/  http://hadoop.yahoo.com/  (including job openings)

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Hadoop at Yahoo! -- Hadoop World NY 2009

Editor's Notes

  1. Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection
  2. Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection
  3. Load Balancing : Brooklyn (DNS) directs users to their local datacenter RSS Feeds : Feed-norm leverages Yahoo Traffic Server to normalize, cache, and proxy site feeds for Auto Apps Image and Video Delivery : All images and thumbnails displayed on the page Substantial part the 20-25 billion objects YCS serves a day Stats Coming Site thumbnails (Auto-apps) These are the Metro applications generated from web sites that are added to the left column Metro is currently storing about 220K thumbnails replicated on both US coasts Usage is currently about 55K/second (heavily cached by YCS) growing 100% month over month Attachment Store Mail uses YMDB (MObStor pre-cursor) to store 10TB of attachments Search Index : Data mining to obtain the top-n user search queries Ads Optimization: On-going refreshes to the Ad ranking model for revenue optimization Content Optimization: Computation of Content centric user profiles to get user segmentation Models generation refresh for content categorization User centric recommendation module Machine Learning: Model creation for various purposes at Yahoo Spam Filters: Utilizing Co-occurrence and other data intensive techniques for mail spam detection