Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm.

1. Real-time and long-time
Fun with Hadoop + Storm

2. 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

3. This is not a problem.
It’s an opportunity!

4. Hadoop is Not Very Real-time
Unprocessed now
Data
t
Fully Latest full Hadoop job
processed period takes this
long for this
data

5. Need to Plug the Hole in Hadoop
• We have real-time data with limited state
– Exactly what Storm does
– And what Hadoop does not
• Can Storm and Hadoop be combined?

6. Real-time and Long-time together
Blended now
View
view
t
Hadoop works Storm
great back here works
here

7. An Example
• I want to know how many queries I get
– Per second, minute, day, week
• Results should be available
– within <2 seconds 99.9+% of the time
– within 30 seconds almost always
• History should last >3 years
• Should work for 0.001 q/s up to 100,000 q/s
• Failure tolerant, yadda, yadda

8. Rough Design – Data Flow
Search Query Event
Query Event Counter
Counter Logger
Engine Spout
Spout Bolt
Bolt Bolt
Logger
Logger
Bolt Semi Snap
Bolt Agg
Raw Hadoop
Logs Aggregator
Long
agg

9. Counter Bolt Detail
• Input: Labels to count
• Output: Short-term semi-aggregated counts
– (time-window, label, count)
• Non-zero counts emitted if
– event count reaches threshold (typical 100K)
– time since last count reaches threshold (typical 1s)
• Tuples acked when counts emitted
• Double count probability is > 0 but very small

10. Counter Bolt Counterintuitivity
• Counts are emitted for same label, same time
window many times
– these are semi-aggregated
– this is a feature
– tuples can be acked within 1s
– time windows can be much longer than 1s
• No need to send same label to same bolt
– speeds failure recovery

11. Design Flexibility
• Counter can persist short-term transaction log
– counter can recover state on failure
– log is normally burn after write
• Count flush interval can be extended without
extending tuple timeout
– Decreases currency of counts
– System is still real-time at a longer time-scale
• Total bandwidth for log is typically not huge

12. Counter Bolt No-nos
• Cannot accumulate entire period in-memory
– Tuples must be ack’ed much sooner
– State must be persisted before ack’ing
– State can easily grow too large to handle without
disk access
• Cannot persist entire count table at once
– Incremental persistence required

13. 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 no guarantees
– MapRfs is distributed, provides both guarantees

14. Failure Modes
• Bolt failure
– buffered tuples will go un’acked
– after timeout, tuples will be resent
– timeout ≈ 10s
– if failure occurs after persistence, before acking, then
double-counting is possible
• Storage (with MapR)
– most failures invisible
– a few continue within 0-2s, some take 10s
– catastrophic cluster restart can take 2-3 min
– logger can buffer this much easily

15. 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

16. Mobile Network Monitor
Transaction
data
Geo-dispersed
ingest servers Batch aggregation
Retro-analysis
interface
Map
Real-time dashboard
and alerts
16

17. Example 2 – Real-time learning
• My system has to
– learn a response model
and
– select training data
– in real-time
• Data rate up to 100K queries per second

18. Door Number 3
• 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

19. Real-time Constraints
• Selection must happen in <20 ms almost all
the time
• Training events must be handled in <20 ms
• Failover must happen within 5 seconds
• Client should timeout and back-off
– no need for an answer after 500ms
• State persistence required

20. Rough Design
Selector Query Event Counter
DRPC Spout Timed Join Model
Layer Spout Bolt
Conversion Logger
Logger Model
Detector Bolt
Bolt State
Raw
Logs

21. 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?

22. A First Conclusion
• Probability as expressed by humans is
subjective and depends on information and
experience

23. A Second Conclusion
• A single number is a bad way to express
uncertain knowledge
• A distribution of values might be better

24. I Dunno

25. 5 and 5

26. 2 and 10

27. 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

28. And it works!
0.12
0.11
0.1
0.09
0.08
0.07
regret
0.06
ε- greedy, ε = 0.05
0.05
0.04 Bayesian Bandit with Gam m a- Norm al
0.03
0.02
0.01
0
0 100 200 300 400 500 600 700 800 900 1000 1100
n

29. 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

30. • Contact:
– tdunning@maprtech.com
– @ted_dunning
• Slides and such:
– http://info.mapr.com/ted-storm-2012-03