Spark Summit 2013 Talk: At Sharethrough we have deployed Spark to our production environment to support several user facing product features. While building these features we uncovered a consistent set of challenges across multiple streaming jobs. By addressing these challenges you can speed up development of future streaming jobs. In this talk we will discuss the 3 major challenges we encountered while developing production streaming jobs and how we overcame them. First we will look at how to write jobs to ensure fault tolerance since streaming jobs need to run 24/7 even under failure conditions. Second we will look at the programming abstractions we created using functional programming and existing libraries. Finally we will look at the way we test all the pieces of a job –from manipulating data through writing to external databases– to give us confidence in our code before we deploy to production

1. Productionalizing
Spark Streaming
Spark Summit 2013
Ryan Weald
@rweald
@rweald
Monday, December 2, 13

2. What We’re Going to Cover
•What we do and Why we choose Spark
•Fault tolerance for long lived streaming jobs
•Common patterns and functional abstractions
•Testing before we “do it live”
@rweald
Monday, December 2, 13

3. Special focus on
common patterns and
their solutions
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Monday, December 2, 13

4. What is Sharethrough?
Advertising for the Modern Internet
Form
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Monday, December 2, 13
Function

5. What is Sharethrough?
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Monday, December 2, 13

6. Why Spark Streaming?
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Monday, December 2, 13

7. Why Spark Streaming
•Liked theoretical foundation of mini-batch
•Scala codebase + functional API
•Young project with opportunities to contribute
•Batch model for iterative ML algorithms
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Monday, December 2, 13

8. Great...
Now productionalize it
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Monday, December 2, 13

9. Fault Tolerance
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Monday, December 2, 13

10. Keys to Fault Tolerance
1.Receiver fault tolerance
2.Monitoring job progress
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Monday, December 2, 13

11. Receiver Fault Tolerance
•Use Actors with supervisors
•Use self healing connection pools
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Monday, December 2, 13

12. Use Actors
class RabbitMQStreamReceiver (uri:String, exchangeName: String,
routingKey: String) extends Actor with Receiver with Logging {
implicit val system = ActorSystem()
override def preStart() = {
//Your code to setup connections and actors
//Include inner class to process messages
}
def receive: Receive = {
case _
=> logInfo("unknown message")
}
}
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Monday, December 2, 13

13. Track All Outputs
•Low watermarks - Google MillWheel
•Database updated_at
•Expected output file size alerting
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Monday, December 2, 13

14. Common Patterns
&
Functional
Programming
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Monday, December 2, 13

15. Common Job Pattern
Map -> Aggregate ->Store
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Monday, December 2, 13

16. Mapping Data
inputData.map { rawRequest =>
val params = QueryParams.parse(rawRequest)
(params.getOrElse("beaconType", "unknown"), 1L)
}
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Monday, December 2, 13

17. Aggregation
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Monday, December 2, 13

18. Basic Aggregation
//beacons is DStream[String, Long]
//example Seq(("click", 1L), ("click", 1L))
val sum: (Long, Long) => Long = _ + _
beacons.reduceByKey(sum)
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Monday, December 2, 13

19. What Happens when
we want to sum
multiple things?
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Monday, December 2, 13

20. Long Basic Aggregation
val inputData = Seq(
("user_1",(1L, 1L, 1L)),
("user_1",(2L, 2L, 2L))
)
def sum(l: (Long, Long, Long),
r: (Long, Long, Long)) = {
(l._1 + r._1, l._2 + r._2, l._3 + r._3)
}
inputData.reduceByKey(sum)
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Monday, December 2, 13

21. Now Sum 4 Ints
instead
(ﾉಥ益ಥ）ﾉ ┻━┻
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Monday, December 2, 13

22. Monoids to the Rescue
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Monday, December 2, 13

23. WTF is a Monoid?
trait Monoid[T] {
def zero: T
def plus(r: T, l: T): T
}
* Just need to make sure plus is associative.
(1+ 5) + 2 == (2 + 1) + 5
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Monday, December 2, 13

24. Monoid Based Aggregation
object LongMonoid extends Monoid[(Long, Long, Long)]
{
def zero = (0, 0, 0)
def plus(r: (Long, Long, Long),
l: (Long, Long, Long)) = {
(l._1 + r._1, l._2 + r._2, l._3 + r._3)
}
}
inputData.reduceByKey(LongMonid.plus(_, _))
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Monday, December 2, 13

25. Twitter Algebird
http://github.com/twitter/algebird
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Monday, December 2, 13

26. Algebird Based Aggregation
import com.twitter.algebird._
val aggregator = implicitly[Monoid[(Long,Long, Long)]]
inputData.reduceByKey(aggregator.plus(_, _))
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Monday, December 2, 13

27. How many unique
users per publisher?
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28. Too big for memory
based naive Map
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Monday, December 2, 13

29. HyperLogLog FTW
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Monday, December 2, 13

30. HLL Aggregation
import com.twitter.algebird._
val aggregator = new HyperLogLogMonoid(12)
inputData.reduceByKey(aggregator.plus(_, _))
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Monday, December 2, 13

31. Monoids == Reusable
Aggregation
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32. Common Job Pattern
Map -> Aggregate ->Store
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33. Store
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34. How do we store the
results?
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Monday, December 2, 13

35. Storage API Requirements
•Incremental updates (preferably associative)
•Pluggable to support “big data” stores
•Allow for testing jobs
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Monday, December 2, 13

36. Storage API
trait MergeableStore[K,
def get(key: K): V
def put(kv: (K,V)): V
/*
* Should follow same
* as our Monoid from
*/
def merge(kv: (K,V)):
}
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Monday, December 2, 13
V] {
associative property
earlier
V

37. Twitter Storehaus
http://github.com/twitter/storehaus
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Monday, December 2, 13

38. Storing Spark Results
def saveResults(result: DStream[String, Long],
store: RedisStore[String, Long]) = {
result.foreach { rdd =>
rdd.foreach { element =>
val (keys, value) = element
store.merge(keys, impressions)
}
}
}
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Monday, December 2, 13

39. Everyone can benefit
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Monday, December 2, 13

40. Potential API additions?
class PairDStreamFunctions[K, V] {
def aggregateByKey(aggregator: Monoid[V])
def store(store: MergeableStore[K, V])
}
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Monday, December 2, 13

41. Twitter Summingbird
http://github.com/twitter/summingbird
*https://github.com/twitter/summingbird/issues/387
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Monday, December 2, 13

42. Testing Your Jobs
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Monday, December 2, 13

43. Testing best Practices
•Try and avoid full integration tests
•Use in-memory stores for testing
•Keep logic outside of Spark
•Use Summingbird in memory platform???
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Monday, December 2, 13

44. Thank You
Ryan Weald
@rweald
@rweald
Monday, December 2, 13