Facebook's data analysis team uses Hadoop for managing and analyzing the large amounts of data Facebook collects. This includes social graph data on users and connections, social behavior data on interactions, and social content data. The team conducts business intelligence analysis to support growth strategies and product decisions. They also perform "artificial intelligence" analysis to predict user behavior and recommend content and applications. Special challenges include the scale of data, high-dimensional and noisy features, and building models that can make predictions on millions of samples quickly and accurately.

2. Data Analysis at Facebook
Jeff Hammerbacher, Ding Zhou*
Facebook Inc.

3. Outline
• How does Facebook work
• Managing Big Data
• Data Analysis for Business Intelligence
• Data Analysis for “Artificial Intelligence”
• Questions

4. How does Facebook work?

5. Profile page - content generation portal

6. Newsfeed page - content consumption portal

7. Friends page - social graph portal

8. App page - social app platform

9. Facebook Data
▪ Social Graph Data
▪ The Nodes:
▪
100m+ users; 100+ dimensions each user (numerical, text, categorical);
▪
350k registrations daily;
▪ The Edges:
▪
200+ friends each user (median);
▪
20 categories of edges (fb friends, co-workers, family, etc);
▪ Social Behavior Data
▪ Social Interactions: interactions among users, via 100+ interaction types;
▪ Social Actions: between users and 33k+ facebook apps, via 200+ action types;
▪ Social Content Data
▪ Content of Posts, Notes, Photos, Video, etc

10. Managing Big Data
▪ Data scale [backend]:
▪ Over 1.3 PB raw capacity in largest cluster;
▪ Nearly 2 TB uncompressed data per day;
▪ Over 20 TB read/write per day;
▪ Distributed Data management:
▪ HDFS/Hadoop (MapReduce in Java);
▪ MetaStore (MetaData management);
▪ Hive QL (Query language on Hadoop+MetaStore);
▪ Usage:
▪
at least 50 engineers have run hadoop jobs
▪
3,514 Jobs weekly
▪
821 Projections,152 Joins, 800 Aggregates, 600 Loaders weekly

11. Hadoop - MapReduce in Java
facebook:1
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team:1 data:1
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facebook data team uses: 1 data:2
uses hadoop for hadoop: 1 facebook:1
data analysis for: 1 for:1
hadoop:1
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uses: 1
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MapReduce Execution Flow
[Dean, J and Ghemawat, S, 2004]

12. Data Analysis for Business Intelligence

13. Data for Business Intelligence
▪ General Goal:
▪ support growth and monetization strategies, and product decisions
▪ User Behavior Studies
▪ NUX: Longitudinal study using LARS and recursive partitioning to identify features predictive
of engagement;
▪ Identity*: Unsupervised learning over user session data to identify common usage patterns.
Techniques employed include K-Means, PageRank, dimension reduction methods;
▪ Experimentation Platform
▪ Columbus*: Top-level site health metrics; drill down by user groups (country, age, gender...);
▪
Columbus++*: A/B testing for impact of site change on site health metrics;;
▪ Reporting System
▪ ad-hoc analysis done by Hive queries
* - underlined are projects that Ding Zhou participates in;

14. Columbus
Geographical bird-view of
growth by country
Comparison between
user groups

15. Data Analysis for “Artificial Intelligence”
-- predicting user social behavior

16. who the user will
interact with
• predict interactions between friends
• features are user profile and browsing history
• tried linear models and tree models
• applied for search, newsfeed, etc

17. who the user hasn’t
found yet
• missing edge prediction problem
• observations are friend/non-friend pairs
• features include profile and local graph info
• profile info more informative
• graph info supplemental if profile incomplete

18. what applications the
user may like*
• 33k apps, only 0.1% of them used;
• a different recommendation problem;
• prediction model not applicable,
user preference unavailable;
• build a prediction model to infer “user ratings”;
• user-based + item-based recommendation
• how to combine profile, social graph, ratings?
* projects that Ding Zhou participates in;

19. what content is
interesting*
• newsfeed as the main content distribution channel
• stories generated by 100s of social actions:
on the site, platform, or the Web
• <0.1% of possible stories are shown
• predictions built on story features, and user
browsing history
* projects that Ding Zhou participates in;

20. Challenges in Data
- 100s of TBs of meaningful data available
- 1,000s of non-trivial features
- sampling not always applicable (e.g. small app has no user data)
- prediction requirements
▪ models regularly applied for 10 billion novel samples
▪ models used on-the-fly for 100k samples in 50 ms

21. Special Machine Learning Problems
- use machine learning to predict user behavior
▪ labels: insufficient; inferred implicitly; imbalanced;
▪ features: high-dimensional; strongly correlated; noisy;
- scale requires distributed algorithms
▪ in-house implementation of tree ensemble methods (bagging predictors)
▪ larger training sets grant performance improvements
- speed and accuracy improvements underway

22. tip of the iceberg
Questions?

23. (c) 2004-2008 Facebook, Inc. or its licensors. quot;Facebookquot; is a registered trademark of Facebook, Inc.. All rights reserved. 1.0