Download - Storm 2012 03-29
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-time processing– But lacks any way to deal with batch processing
• It sounds like there isn’t a solution– Neither fashionable solution handles everything
This is not a problem.
It’s an opportunity!
t
now
Hadoop is Not Very Real-time
UnprocessedData
Fully processed
Latest full period
Hadoop job takes this long for this data
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?
t
now
Hadoop works great back here
Storm workshere
Real-time and Long-time together
Blended view
Blended view
Blended View
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
Rough Design – Data Flow
Search Engine
Query Event Spout
Logger Bolt
Counter Bolt
Raw Logs
LoggerBolt
Semi Agg
Hadoop Aggregator
Snap
Long agg
Query Event Spout
Counter Bolt
Logger Bolt
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
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
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
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
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
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
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 MonitorTransaction
data
Batch aggregation
Map
Real-time dashboard and alerts
Geo-dispersed ingest servers
Retro-analysisinterface
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
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
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
Rough Design
DRPC Spout Query Event Spout
Logger Bolt
Counter Bolt
Raw Logs
Model State
Timed Join Model
Logger Bolt
Conversion Detector
Selector Layer
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?
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 bandit 1 if p1 > p2
• Else, put the coin in bandit 2
And it works!
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
• Contact:– [email protected]– @ted_dunning
• Slides and such:– http://info.mapr.com/ted-storm-2012-03