1. Hadoop
&
Cloud
@
Ne.lix:
Taming
the
Social
Data
Firehose
06/13/2012
Mohammad
Sabah
Senior
Data
ScienFst
(@mohammad_sabah
)

3. Algorithms
Everything is personalized 3

4. § Plays
Data / User
§ Behavior
§ Geo-
Information
§ Time
§ Ratings
§ Searches
4

5. Big Data § 25M+ subscribers
@Netflix § Ratings: 4M/day
§ Searches: 3M/day
§ Plays: 30M/day
§ Impressions
§ Device info
§ Metadata
§ Social
5

6. Interesting § 2B hours
Tidbit streamed in Q4
2011
§ 75% select
movies based on
recommendations
§ Moral: We need
to scale
algorithms.
6

7. 7

8. Technology
8

9. Modeling
§ Markov Chains
§ Collaborative Filtering
§ Large-scale Matching
§ LSA
§ Clustering
§ Row Selection
§ Query Categorization
§ Auto-tagging
§ Sentiment Analysis
9

10. Markov Chain: Example I
0.90 0.08
0.80
0.15
Bull Market Bear Market
0.02
0.25 0.25 0.05
Recession
0.50
10

11. Markov Chain: Example II
0.8
0.3
0.3
0.4 0.3
0.2 0.7
11

12. Markov Chain: Formal Definition
§ A Markov chain describes a discrete time
stochastic process over a set of states
S = {s1, s2, … sn}
according to a transition probability matrix P = {Pij}
§ Pij = probability of moving to state j when at
state i
§ Uses temporal ordering to estimated
relatedness
§ The future only depends on today and not the
past
12

13. The Math
§ Time Series Aggregation
<u1, m1, t1>,<u1, m2, t2>,<u2, m3, t3>, …
<u1> => <m1, t1>, <m2, t2>, <m3, t3>, …
§ Co-occurrence
n( ) = 24,000 n( ) = 30,000
§ Transition Probability
p( ) = 0.8
13

14. Baseline Implementation & Inefficiencies
§ RDBMS/DW-based § SQL Limitation
§ Stored procedures § Expensive Copy
§ Once a week § Does not exploit
(weekend) inherent parallelism
§ Does not scale well
(region, models)
§ 4B+ rows – run out of
memory/space
§ Convoluted Joins
(maintenance
nightmare!)
14

15. MapReduce Implementation - I
§ Exploits the inherent parallelism in algorithm.
§ Scale: 25M * 50K (* 50K) ~ 100B+ keys
§ Time Series Aggregation
U1, T1, M1 U1=><T1,M1>
U1 => <T3, M5>, U1 =>
U1 => <T1, M1>, <T1, M1>,
… <T3, M5>,…
U1, T1, M1
U2, T2, M3
U3, T3, M1
U1, T3, M5 U1=><T3,M5> U1=><T1,M1>,…
U2=><T2,M3>,…
U1, T3, M5
U3=><T3,M4> …
U2 => <T2, M3>, U2=>
… <T2, M3>,…
U2, T2, M3 U2=><T2,M3>
Input Shuffle Reduce Result
Split Map
15

16. MapReduce Implementation - II
§ Transition Probability Matrix
U1=>T1,M1, M1,M2=>1
… M1,M3=>1
M1,M3=>1
M1,M3=>1 M1,M3=>3
M1,M3=>1
U1 => T1,M1,…
M1,M3=>1 M1,M2=>.2
U2 => T2,M1,… U2=>T2,M1,
… M2,M3=>1 M1,M3=>.3
U3 => T3,M3,…
M2,M3=>.5
M2,M3>1
M2,M3=>2
M2,M3=>1
U3=>T3,M3 M2,M3=>1
… M1,M3=>1
Input Split Map Shuffle Reduce Result
16

17. In a Nutshell
§ You end up with a N * N matrix
0 0.3 … 0.7
0.3 0 … 0.7
…
0.2 0.1 … 0
17

18. But…there is a catch!
18

19. Solution!
§ Odds Ratio
§ Optimizations
§ Decay
§ Reward
§ In-Window
§ Noise
19

20. Markov Chain Migration Summary
RDBMS/DW Hadoop
Limited by SQL syntax and Can be arbitrarily complex
semantics
Expensive Data copy from Data copy avoided
data source to data center
Does not scale to new Scales beautifully.
models and regions
Maintenance nightmare Easy to maintain (written in
(stored procedures + high-level language e.g.
convoluted joins) Java, Pig)
Resource constraints No special handling
needed.
20

21. Other Algorithms & Challenges
Entity Forms
Star Trek strtrek, startrek, start
trek, star trek, star treck
South Park southpark, sothpark,
south parl, souh park
Doctor Who docter who, doctor wh,
docot who, doctor who:
Prison Break prision break, prison
brake, prison breal
21

22. § Think Parallel!
§ Optimize
§ ML + Hadoop
§ Visualize
§ Experiment
§ Bucket Test
§ Iterative Processing
22

23. Big Data +
Hadoop +
Machine Learning
=>
Great Customer Experience!
23

24. I HAD AN IDEA
I BUILT IT
I PUSHED IT TO TEST
THE TEST WAS POSITIVE
I PUSHED IT LIVE!
We’re hiring!
24

25. @mohammad_sabah
msabah@netflix.com