Buzzwords 2012 Real time & long time

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This is the talk that Ted Dunning gave at Buzzwords. it is a heavily condensed version of the real-time learning talk I have given previously. The associated video is available at …

This is the talk that Ted Dunning gave at Buzzwords. it is a heavily condensed version of the real-time learning talk I have given previously. The associated video is available at http://www.youtube.com/watch?v=1nQBnyaI6s0

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  • 1. 1©MapR Technologies - Confidential Real-time and Long-time with Storm and Hadoop
  • 2. 2©MapR Technologies - Confidential Real-time and Long-time with Storm and Hadoop MapR
  • 3. 3©MapR Technologies - Confidential The Challenge  Hadoop is great of processing vats of data – But sucks for real-time (by design!)  Storm is great for real-time processing – But lacks any way to deal with batch processing  It sounds like there isn’t a solution – Neither fashionable solution handles everything
  • 4. 4©MapR Technologies - Confidential This is not a problem. It’s an opportunity!
  • 5. 5©MapR Technologies - Confidential t now Hadoop is Not Very Real-time Unprocessed Data Fully processed Latest full period Hadoop job takes this long for this data
  • 6. 6©MapR Technologies - Confidential t now Hadoop works great back here Storm works here Real-time and Long-time together Blended view Blended view Blended View
  • 7. 7©MapR Technologies - Confidential Rough Design – Data Flow Search Engine Query Event Spout Logger Bolt Counter Bolt Raw Logs Logger Bolt Semi Agg Hadoop Aggregator Snap Long agg Query Event Spout Counter Bolt Logger Bolt
  • 8. 8©MapR Technologies - Confidential Guarantees  Counter output volume is small-ish – the greater of k tuples per 100K inputs or k tuple/s – 1 tuple/s/label/bolt for this exercise  Persistence layer must provide guarantees – distributed against node failure – must have either readable flush or closed-append  HDFS is distributed, but provides no guarantees and strange semantics  MapRfs is distributed, provides all necessary guarantees
  • 9. 9©MapR Technologies - Confidential Presentation Layer  Presentation must – read recent output of Logger bolt – read relevant output of Hadoop jobs – combine semi-aggregated records  User will see – counts that increment within 0-2 s of events – seamless meld of short and long-term data
  • 10. 10©MapR Technologies - Confidential Example 2 – AB testing in real-time  I have 15 versions of my landing page  Each visitor is assigned to a version – Which version?  A conversion or sale or whatever can happen – How long to wait?  Some versions of the landing page are horrible – Don’t want to give them traffic
  • 11. 11©MapR Technologies - Confidential A Quick Diversion  You see a coin – What is the probability of heads? – Could it be larger or smaller than that?  I flip the coin and while it is in the air ask again  I catch the coin and ask again  I look at the coin (and you don’t) and ask again  Why does the answer change? – And did it ever have a single value?
  • 12. 12©MapR Technologies - Confidential A Philosophical Conclusion  Probability as expressed by humans is subjective and depends on information and experience
  • 13. 13©MapR Technologies - Confidential I Dunno
  • 14. 14©MapR Technologies - Confidential 5 heads out of 10 throws
  • 15. 15©MapR Technologies - Confidential 2 heads out of 12 throws
  • 16. 16©MapR Technologies - Confidential Bayesian Bandit  Compute distributions based on data  Sample p1 and p2 from these distributions  Put a coin in bandit 1 if p1 > p2  Else, put the coin in bandit 2
  • 17. 17©MapR Technologies - Confidential And it works! 11000 100 200 300 400 500 600 700 800 900 1000 0.12 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 n regret ε- greedy, ε = 0.05 Bayesian Bandit with Gamma- Normal
  • 18. 18©MapR Technologies - Confidential Video Demo
  • 19. 19©MapR Technologies - Confidential The Code  Select an alternative  Select and learn  But we already know how to count! n = dim(k)[1] p0 = rep(0, length.out=n) for (i in 1:n) { p0[i] = rbeta(1, k[i,2]+1, k[i,1]+1) } return (which(p0 == max(p0))) for (z in 1:steps) { i = select(k) j = test(i) k[i,j] = k[i,j]+1 } return (k)
  • 20. 20©MapR Technologies - Confidential The Basic Idea  We can encode a distribution by sampling  Sampling allows unification of exploration and exploitation  Can be extended to more general response models
  • 21. 21©MapR Technologies - Confidential  Contact: – tdunning@maprtech.com – @ted_dunning  Slides and such (available late tonight): – http://info.mapr.com/ted-bbuzz-2012  Hash tags: #mapr #bbuzz Collective notes: http://bit.ly/JDCRhc
  • 22. 22©MapR Technologies - Confidential Thank You