Big Data Analytics with Scala at SCALA.IO 2013
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Big Data Analytics with Scala at SCALA.IO 2013

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Big data analytics on hadoop, spark storm with Scala

Big data analytics on hadoop, spark storm with Scala

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Big Data Analytics with Scala at SCALA.IO 2013 Presentation Transcript

  • 1. Big Data Analytics with Scala Sam BESSALAH @samklr
  • 2. What is Big Data Analytics? It’s about doing aggregations and running complex models on large datasets, offline, in real time or both.
  • 3. Lambda Architecture Blueprint for a Big Data analytics architecture
  • 4. Map Reduce redux map : (Km, Vm)  List (Km, Vm) in Scala : T => List[(K,V)] reduce :(Km, List(Vm))List(Kr, Vr) (K, List[V]) => List[(K,V)]
  • 5. Big data ‘’Hello World’’ : Word count
  • 6. Enters Cascading
  • 7. Word Count Redux (Flat)Map -Reduce
  • 8. SCALDING class WordCount(args : Args) extends Job(args) { TextLine(args("input")) .flatMap ('line -> 'word) { line :String => line.split(“ s+”) } .groupBy('word){ group => group.size } .write(Tsv(args("output"))) }
  • 9. SCALDING : Clustering with Mahout lazy val clust = new StreamingKMeans(new FastProjectionSearch( new EuclideanDistanceMeasure,5,10), args("sloppyclusters").toInt, (10e-6).asInstanceOf[Float]) val count = 0; val sloppyClusters = TextLine(args("input")) .map{ str => val vec = str.split("t").map(_.toDouble) val cent = new Centroid(count, new DenseVector(vec)) count += 1 cent } .unorderedFold [StreamingKMeans,Centroid](clust) {(cl,cent) => cl.cluster(cent); cl } .flatMap(c => c.iterator.asScala.toIterable)
  • 10. SCALDING : Clustering with Mahout val finalClusters = sloppyClusters.groupAll .mapValueStream { centList => lazy val bclusterer = new BallKMeans(new BruteSearch( new EuclideanDistanceMeasure), args("numclusters").toInt, 100) bclusterer.cluster(centList.toList.asJava) bclusterer.iterator.asScala } .values
  • 11. Scalding - Two APIs : Field based API, and Typed API - Field API : project, map, discard , groupBy… - Typed API : TypedPipe[T], works like scala.collection.Iterator[T] - Matrix Library - ALGEBIRD : Abstract Algebra library … we’ll talk about it later
  • 12. STORM
  • 13. - Distributed, fault tolerant, real time stream computation engine. - Four concepts - Streams : infinite sequence of tuples - Spouts : Source of streams - Bolts : Process and produces streams Can do : Filtering, aggregations, Joins, … - Topologies : define a flow or network of spouts and blots.
  • 14. Streaming Word Count
  • 15. Trident TridentTopology topology = new TridentTopology(); TridentState wordCounts = topology.newStream("spout1", spout) .each(new Fields("sentence"), new Split(), new Fields("word")) .groupBy(new Fields("word")) .persistentAggregate(new Factory(), new Count(), new Fields("count")) .parallelismHint(6);
  • 16. ScalaStorm by Evan Chan class SplitSentence extends StormBolt(outputFields = List("word")) { def execute(t: Tuple) = t matchSeq { case Seq(line: String) => line.split(‘’’’).foreach { word => using anchor t emit (word) } t ack } }
  • 17. SummingBird Write your job once and run it on Storm and Hadoop
  • 18. def wordCount[P <: Platform[P]] (source: Producer[P, String], store: P#Store[String, Long]) = source.flatMap { line => line.split(‘’s+’’).map(_ -> 1L) } .sumByKey(store)
  • 19. SummingBird trait Platform[P <: Platform[P]] { type Source[+T] type Store[-K, V] type Sink[-T] type Service[-K, +V] type Plan[T} }
  • 20. On Storm - Source[+T] : Spout[(Long, T)] - Store[-K, V] : StormStore [K, V] - Sink[-T] : (T => Future[Unit]) - Service[-K, +V] : StormService[K,V] - Plan[T] : StormTopology
  • 21. TypeSafety
  • 22. SummingBird dependencies • StoreHaus • Chill • Scalding • Algebird • Tormenta
  • 23. But - Can only aggregate values that are associative : Monoids!!!!!! trait Monoid [V] { def zero : V def aggregate(left : V, right :V): V }
  • 24. Clustering with Mahout redux def StreamClustering(source : Platform[P.String], store : P#Store[_,_]) { lazy val clust = new StreamingKMeans(new FastProjectionSearch( new EuclideanDistanceMeasure,5,10), args("sloppyclusters").toInt, (10e-6).asInstanceOf[Float]) val count = 0; val sloppyClusters = source .map{ str => val vec = str.split("t").map(_.toDouble) val cent = new Centroid(count, new DenseVector(vec)) count += 1 cent } .unorderedFold [StreamingKMeans,Centroid](clust) {(cl,cent) => cl.cluster(cent); cl } .flatMap(c => c.iterator.asScala.toIterable)
  • 25. SCALDING : Clustering with Mahout val finalClusters = sloppyClusters.groupAll .mapValueStream { centList => lazy val bclusterer = new BallKMeans(new BruteSearch( new EuclideanDistanceMeasure), args("numclusters").toInt, 100) bclusterer.cluster(centList.toList.asJava) bclusterer.iterator.asScala } .values .saveTo(store) }
  • 26. APACHE SPARK
  • 27. What is Spark? • • • Fast and expressive cluster computing system compatible with Apache Hadoop, but order of magnitude faster (order of magnitude faster) Improves efficiency through: -General execution graphs -In-memory storage Improves usability through: -Rich APIs in Java, Scala, Python -Interactive shell
  • 28. Key idea • • Write programs in terms of transformations on distributed datasets Concept: resilient distributed datasets (RDDs) - Collections of objects spread across a cluster - Built through parallel transformations (map, filter, etc) - Automatically rebuilt on failure - Controllable persistence (e.g. caching in RAM)
  • 29. Example: Word Count
  • 30. Other RDD Operators • • • • • • • • map filter groupBy sort union join leftOuterJoin rightOuterJoin
  • 31. Example: Log Mining Load error messages from a log into memory, then interactively search for various patterns lines = spark.textFile(“hdfs://...”) Base Transformed RDD RDD results errors = lines.filter(s => s.startswith(“ERROR”)) messages = errors.map(s => s.split(“t”)) messages.cache() messages.filter(s=> s.contains(“foo”)).count() Cache 1 Driver Worker tasks Block 1 Action messages.filter(s=> s.contains(“bar”)).count() Cache 2 Worker . . . Cache 3 Worker Result: full-text search scaled to 1 TBin 0.5 in 5 (vs 20 s for on-disk Result: of Wikipedia data sec sec (vs 180 sec for on-disk data) data) Block 3 Block 2
  • 32. Fault Recovery RDDs track lineage information that can be used to efficiently recompute lost data Ex: msgs = textFile.filter(-=> _.startsWith(“ERROR”)) .map(_ => _.split(“t”)) HDFS File Filtered RDD filter (func = _.contains(...)) Mapped RDD map (func = _.split(...))
  • 33. Spark Streaming - Extends Spark capabilities to large scale stream processing. - Scales to 100s of nodes and achieves second scale latencies -Efficient and fault-tolerant stateful stream processing - Simple batch-like API for implementing complex algorithms
  • 34. Discretized Stream Processing live data stream  Chop up the live stream into batches of X seconds  Spark treats each batch of data as RDDs and processes them using RDD operations  Finally, the processed results of the RDD operations are returned in batches Spark Streaming batches of X seconds Spark processed results 44
  • 35. Discretized Stream Processing live data stream  Batch sizes as low as ½ second, latency of about 1 second  Potential for combining batch processing and streaming processing in the same system Spark Streaming batches of X seconds Spark processed results 45
  • 36. Example – Get hashtags from Twitter val tweets = ssc.twitterStream() DStream: a sequence of RDDs representing a stream of data Twitter Streaming API batch @ t batch @ t+1 batch @ t+2 tweets DStream stored in memory as an RDD (immutable, distributed)
  • 37. Example – Get hashtags from Twitter val tweets = ssc.twitterStream() val hashTags = tweets.flatMap (status => getTags(status)) new DStream transformation: modify data in one DStream to create another DStream batch @ t batch @ t+1 batch @ t+2 tweets DStream hashTags Dstream [#cat, #dog, … ] flatMap flatMap … flatMap new RDDs created for every batch
  • 38. Example – Get hashtags from Twitter val tweets = ssc.twitterStream() val hashTags = tweets.flatMap (status => getTags(status)) hashTags.foreach(hashTagRDD => { ... }) foreach: do whatever you want with the processed data batch @ t batch @ t+1 batch @ t+2 tweets DStream flatMap hashTags DStream flatMap flatMap foreach foreach foreach Write to database, update analytics UI, do whatever you want
  • 39. Example – Get hashtags from Twitter val tweets = ssc.twitterStream() val hashTags = tweets.flatMap (status => getTags(status)) hashTags.saveAsHadoopFiles("hdfs://...") output operation: to push data to external storage batch @ t batch @ t+1 batch @ t+2 tweets DStream flatMap flatMap flatMap save save save hashTags DStream every batch saved to HDFS
  • 40. Window-based Transformations val tweets = ssc.twitterStream() val hashTags = tweets.flatMap (status => getTags(status)) val tagCounts = hashTags.window(Minutes(1), Seconds(5)).countByValue() sliding window operation window length sliding interval window length DStream of data sliding interval
  • 41. Compute TopK Ip addresses val ssc = new StreamingContext(master, "AlgebirdCMS", Seconds(10), …) val stream = ssc.KafkaStream(None, filters, StorageLevel.MEMORY, ..) val addresses = stream.map(ipAddress => ipAddress.getText) val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC) val globalCMS = cms.zero val mm = new MapMonoid[Long, Int]() //init val topAddresses = adresses.mapPartitions(ids => { ids.map(id => cms.create(id)) }) .reduce(_ ++ _)
  • 42. topAddresses.foreach(rdd => { if (rdd.count() != 0) { val partial = rdd.first() val partialTopK = partial.heavyHitters.map(id => (id, partial.frequency(id).estimate)) .toSeq.sortBy(_._2).reverse.slice(0, TOPK) globalCMS ++= partial val globalTopK = globalCMS.heavyHitters.map(id => (id, globalCMS.frequency(id).estimate)) .toSeq.sortBy(_._2).reverse.slice(0, TOPK) globalTopK.mkString("[", ",", "]"))) } })
  • 43. Multi purpose analytics stack MLBASE TACHYON Stream Processing Spark + Shark + Spark Streaming Batch Processing Ad-hoc Queries GraphX BLINK DB
  • 44. SPARK SPARK STREAMING - Almost Similar API for batch or Streaming Single¨Platform with fewer moving parts Order of magnitude faster
  • 45. References Sam Ritchie : SummingBird https://speakerdeck.com/sritchie/summingbird-streaming-mapreduce-attwitter Chris Severs, Vitaly Gordon : Scalable Machine Learning with Scala http://slideshare.net/VitalyGordon/scalable-and-flexible-machine-learningwith-scala-linkedin Apache Spark : http://spark.incubator.apache.org Matei Zaharia : Parallel Programming with Spark