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Hadoop at Yahoo! Eric Baldeschwieler VP Hadoop Software Development Yahoo!
Agenda ,[object Object],[object Object],[object Object],[object Object]
Introduction to Hadoop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MapReduce ,[object Object]
Hadoop and 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]
The Largest Hadoop User 2006 now Hardware Internal Hadoop Users building  new datacenter PB Disk, >82PB Today Nodes, >25,000 Today
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]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The Largest Hadoop Contributor Core Patches
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 Content Management 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],[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
Pig – Making Hadoop Easy! 1/20 the lines of code 1/16 the development time Performance within 2x
Pig – Making Hadoop Easy! Users =  load   ‘users’   as  (name, age); Fltrd =  filter  Users  by     age >= 18  and  age <= 25;  Pages =  load  ‘pages’  as  (user, url); Jnd =  join  Fltrd  by  name, Pages  by  user; Grpd =  group  Jnd  by  url; Smmd =  foreach  Grpd  generate  group,   COUNT(Jnd)  as  clicks; Srtd =  order  Smmd  by  clicks desc; Top5 =  limit  Srtd 5; store  Top5  into   ‘top5sites’ ;
Hadoop Improvements
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 @ Yahoo! - Internet Scale Data Processing

  • 1. Hadoop at Yahoo! Eric Baldeschwieler VP Hadoop Software Development Yahoo!
  • 2.
  • 3.
  • 4.
  • 6.
  • 7. The Largest Hadoop User 2006 now Hardware Internal Hadoop Users building new datacenter PB Disk, >82PB Today Nodes, >25,000 Today
  • 8.
  • 9.
  • 11.
  • 12. Yahoo! front page - Case Study
  • 13. Yahoo! front page - Case Study Ads Optimization Search Index
  • 14. Yahoo! front page - Case Study Ads Optimization Content Optimization Search Index Machine Learned Spam filters Content Management Content Optimization
  • 15.
  • 16.
  • 17.
  • 18. Pig – Making Hadoop Easy! 1/20 the lines of code 1/16 the development time Performance within 2x
  • 19. Pig – Making Hadoop Easy! Users = load ‘users’ as (name, age); Fltrd = filter Users by age >= 18 and age <= 25; Pages = load ‘pages’ as (user, url); Jnd = join Fltrd by name, Pages by user; Grpd = group Jnd by url; Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks; Srtd = order Smmd by clicks desc; Top5 = limit Srtd 5; store Top5 into ‘top5sites’ ;
  • 21.
  • 22. Questions? Eric Baldeschwieler VP Hadoop Software Development Yahoo! For more information: http://hadoop.apache.org/ http://hadoop.yahoo.com/ (including job openings)

Hinweis der Redaktion

  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