Big Data Analytics with Scala at SCALA.IO 2013
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This document provides an overview of big data analytics with Scala, including common frameworks and techniques. It discusses Lambda architecture, MapReduce, word counting examples, Scalding for batch and streaming jobs, Apache Storm, Trident, SummingBird for unified batch and streaming, and Apache Spark for fast cluster computing with resilient distributed datasets. It also covers clustering with Mahout, streaming word counting, and analytics platforms that combine batch and stream processing.
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Big Data Analytics with Scala at SCALA.IO 2013
- 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
- 8. 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)]
- 10. Big data ‘’Hello World’’ : Word count
- 11. Enters Cascading
- 13. Word Count Redux (Flat)Map -Reduce
- 14. 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"))) }
- 15. 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)
- 16. 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
- 17. 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
- 19. STORM
- 20. - 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.
- 23. 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);
- 24. 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 } }
- 26. SummingBird Write your job once and run it on Storm and Hadoop
- 27. def wordCount[P <: Platform[P]] (source: Producer[P, String], store: P#Store[String, Long]) = source.flatMap { line => line.split(‘’s+’’).map(_ -> 1L) } .sumByKey(store)
- 28. SummingBird trait Platform[P <: Platform[P]] { type Source[+T] type Store[-K, V] type Sink[-T] type Service[-K, +V] type Plan[T} }
- 29. 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
- 30. TypeSafety
- 31. SummingBird dependencies • StoreHaus • Chill • Scalding • Algebird • Tormenta
- 32. But - Can only aggregate values that are associative : Monoids!!!!!! trait Monoid [V] { def zero : V def aggregate(left : V, right :V): V }
- 34. 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)
- 35. 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) }
- 36. APACHE SPARK
- 37. 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
- 38. 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)
- 40. Other RDD Operators • • • • • • • • map filter groupBy sort union join leftOuterJoin rightOuterJoin
- 41. 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
- 42. 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(...))
- 43. 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
- 44. 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
- 45. 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
- 46. 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)
- 47. 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
- 48. 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
- 49. 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
- 50. 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
- 51. 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(_ ++ _)
- 52. 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("[", ",", "]"))) } })
- 53. Multi purpose analytics stack MLBASE TACHYON Stream Processing Spark + Shark + Spark Streaming Batch Processing Ad-hoc Queries GraphX BLINK DB
- 54. SPARK SPARK STREAMING - Almost Similar API for batch or Streaming Single¨Platform with fewer moving parts Order of magnitude faster
- 55. 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