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Stores, Monoids and Dependency Injection

Spark Meetup 01/16/2014 Ryan Weald

@rweald

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What We’re Going to Cover

•What we do and Why we choose Spark

•Common patterns in spark streaming jobs

•Monoids as an abstraction for aggregation

•Abstraction for saving the results of jobs

•Using dependency injection for improved

testability and developer happiness

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What is Sharethrough?

Advertising for the Modern Internet

FunctionForm

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What is Sharethrough?

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Why Spark Streaming?

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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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Great... Now maintain dozens

of streaming jobs

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Common Patterns &

Functional Programming

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Map -> Aggregate ->Store

Common Job Pattern

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Which publisher pages has an ad unit appeared on?

Real World Example

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Mapping DatainputData.map { rawRequest => val params = QueryParams.parse(rawRequest) val pubPage = params.getOrElse( "pub_page_location", "http://example.com") val creative = params.getOrElse( "creative_key", "unknown") val uri = new java.net.URI(pubPage) val cleanPubPage = uri.getHost + "/" + uri.getPath (creative, cleanPubPage)}

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Aggregation

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Basic Aggregation

Add each pub page to a creative’s set

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Basic Aggregation

val sum: (Set[String], Set[String]) => Set[String] = _ ++ _!

creativePubPages.map { case(ckey, pubPage) (ckey, Set(pubPage))}.reduceByKey(sum)

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Way too much memory usage in

production as data size grows

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We need bloom filter to keep memory usage

fixed

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Total code re-write :(

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Monoids to the Rescue

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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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Monoid Example

SetMonoid extends Monoid[Set[String]] { def zero = Set.empty[String] def plus(l: Set[String], r: Set[String]) = l ++ r}!SetMonoid.plus(Set("a"), Set("b"))//returns Set("a", "b")!SetMonoid.plus(Set("a"), Set("a"))//returns Set("a")

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Twitter Algebird !

http://github.com/twitter/algebird

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Algebird Based Aggregation

import com.twitter.algebird._!val bfMonoid = BloomFilter(500000, 0.01)!creativePubPages.map { case(ckey, pubPage) (ckey, bfMonoid.create(pubPage))}.reduceByKey(bfMonoid.plus(_, _))

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Add set of users who have seen creative to

same job

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Algebird Based Aggregationval aggregator = new Monoid[(BF, BF)] { def zero = (bfMonoid.zero, bfMonoid.zero) def plus(l: (BF, BF), r: (BF, BF)) = { (bfMonoid.plus(l._1, r._1), bfMonoid.plus(l._2, r._2)) }}!creativePubPages.map { case(ckey, pubPage, userId) ( ckey, bfMonoid.create(pubPage), bfMonoid.create(userID) )}.reduceByKey(aggregator.plus(_, _))

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Monoids == Reusable Aggregation

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Common Job Pattern

Map -> Aggregate ->Store

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Store

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How do we store the results?

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Storage API Requirements

•Incremental updates (preferably associative)

•Pluggable to support “big data” stores

•Allow for testing jobs

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Storage API

trait MergeableStore[K, V] { def get(key: K): V def put(kv: (K,V)): V /* * Should follow same associative property * as our Monoid from earlier */ def merge(kv: (K,V)): V}

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Twitter Storehaus !

http://github.com/twitter/storehaus

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Storing Spark Results

def saveResults(result: DStream[String, BF], store: HBaseStore[String, BF]) = { result.foreach { rdd => rdd.foreach { element => val (keys, value) = element store.merge(keys, impressions) } } }

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What if we don’t have HBase locally?

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Dependency Injection to the rescue

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Generic storage with environment specific

binding

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Generic Storage Method

def saveResults(result: DStream[String, BF], store: StorageFactory) = { val store = StorageFactory.create result.foreach { rdd => rdd.foreach { element => val (keys, value) = element store.merge(keys, impressions) } } }

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Google Guice !

https://github.com/sptz45/sse-guice

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DI the Store You Need!trait StorageFactory { def create: Store[String, BF]}!class DevModule extends ScalaModule { def configure() { bind[StorageFactory].to[InMemoryStorageFactory] }}!class ProdModule extends ScalaModule { def configure() { bind[StorageFactory].to[HBaseStorageFactory] }}

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Moving Forward

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Potential API additions?

class PairDStreamFunctions[K, V] { def aggregateByKey(aggregator: Monoid[V]) def store(store: MergeableStore[K, V]) }

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Twitter Summingbird !

http://github.com/twitter/summingbird

*https://github.com/twitter/summingbird/issues/387

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Ryan Weald @rweald

Thank You