Real-time and long-time      Fun with Hadoop + Storm
The Challenge• Hadoop is great of processing vats of data  – But sucks for real-time (by design!)• Storm is great for real...
This is not a problem.It’s an opportunity!
Hadoop is Not Very Real-time             Unprocessed       now                Data    t           Fully Latest full   Hado...
Need to Plug the Hole in Hadoop• We have real-time data with limited state  – Exactly what Storm does  – And what Hadoop d...
Real-time and Long-time together                  Blended       now                    View                    view      t...
An Example• I want to know how many queries I get  – Per second, minute, day, week• Results should be available  – within ...
Rough Design – Data FlowSearch   Query Event          Query Event   Counter                         Counter   LoggerEngine...
Counter Bolt Detail• Input: Labels to count• Output: Short-term semi-aggregated counts  – (time-window, label, count)• Non...
Counter Bolt Counterintuitivity• Counts are emitted for same label, same time  window many times  – these are semi-aggrega...
Design Flexibility• Counter can persist short-term transaction log  – counter can recover state on failure  – log is norma...
Counter Bolt No-nos• Cannot accumulate entire period in-memory  – Tuples must be ack’ed much sooner  – State must be persi...
Guarantees• Counter output volume is small-ish  – the greater of k tuples per 100K inputs or k tuple/s  – 1 tuple/s/label/...
Failure Modes• Bolt failure   –   buffered tuples will go un’acked   –   after timeout, tuples will be resent   –   timeou...
Presentation Layer• Presentation must  – read recent output of Logger bolt  – read relevant output of Hadoop jobs  – combi...
Mobile Network Monitor                  Transaction                         dataGeo-dispersed ingest servers         Batch...
Example 2 – Real-time learning• My system has to  – learn a response model     and  – select training data  – in real-time...
Door Number 3• I have 15 versions of my landing page• Each visitor is assigned to a version  – Which version?• A conversio...
Real-time Constraints• Selection must happen in <1 ms almost all the  time• Training events must be handled in <20 ms• Fai...
Rough Design Selector                  Query Event   Counter              DRPC Spout    Timed Join    Model  Layer        ...
A Quick Diversion• You see a coin    – What is the probability of heads?    – Could it be larger or smaller than that?•   ...
A First Conclusion• Probability as expressed by humans is  subjective and depends on information and  experience
A Second Conclusion• A single number is a bad way to express  uncertain knowledge• A distribution of values might be better
I Dunno
5 and 5
2 and 10
Bayesian Bandit•   Compute distributions based on data•   Sample p1 and p2 from these distributions•   Put a coin in bandi...
And it works!         0.12         0.11          0.1         0.09         0.08         0.07regret         0.06            ...
The Basic Idea• We can encode a distribution by sampling• After sampling, all code can be deterministic• Sampling allows u...
• Contact:  – tdunning@maprtech.com  – @ted_dunning• Slides and such:  – http://info.mapr.com/ted-chicago-hug-4-23-12.html
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Real-Enough-Time Data Processing - CHUG - 2012024

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Real-Enough-Time Data Processing - CHUG - 2012024

  1. 1. Real-time and long-time Fun with Hadoop + Storm
  2. 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. 3. This is not a problem.It’s an opportunity!
  4. 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. 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. 6. Real-time and Long-time together Blended now View view t Hadoop works Storm great back here works here
  7. 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. 8. Rough Design – Data FlowSearch Query Event Query Event Counter Counter LoggerEngine Spout Spout Bolt Bolt Bolt Logger Logger Bolt Semi Snap Bolt Agg Raw Hadoop Logs Aggregator Long agg
  9. 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. 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. 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 persisted counts – System is still real-time at a longer time-scale• Total bandwidth for log is typically not huge
  12. 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. 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. 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. 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. 16. Mobile Network Monitor Transaction dataGeo-dispersed ingest servers Batch aggregation Retro-analysis interface Map Real-time dashboard and alerts 16
  17. 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. 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. 19. Real-time Constraints• Selection must happen in <1 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. 20. Rough Design Selector Query Event Counter DRPC Spout Timed Join Model Layer Spout BoltConversion Logger Logger Model Detector Bolt Bolt State Raw Logs
  21. 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. 22. A First Conclusion• Probability as expressed by humans is subjective and depends on information and experience
  23. 23. A Second Conclusion• A single number is a bad way to express uncertain knowledge• A distribution of values might be better
  24. 24. I Dunno
  25. 25. 5 and 5
  26. 26. 2 and 10
  27. 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. 28. And it works! 0.12 0.11 0.1 0.09 0.08 0.07regret 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. 29. The Basic Idea• We can encode a distribution by sampling• After sampling, all code can be deterministic• Sampling allows unification of exploration and exploitation• Can be extended to more general response models
  30. 30. • Contact: – tdunning@maprtech.com – @ted_dunning• Slides and such: – http://info.mapr.com/ted-chicago-hug-4-23-12.html

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