Introduction to Apache Spark
Brendan DillonJavier Arrieta
Spark Core
Your Applications
The Stack
Spark SQL MLLib GraphX Spark Streaming
Mesos YARN Standalone
sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19").map(t => "Name: " + t(0)).collect().foreach(println)
Distributed Execution
Driver
Spark Context
Worker Node
Executor
Task
Task
Worker Node
Executor
Task
Task
Resilient Distributed Datasets (RDD)
• Immutable: never modified – just transformed to new RDDs
• Distributed: split into multiple partitions and spread across multiple servers in a cluster
• Resilient: can be re-computed if they get destroyed
• Created by:– Loading external data– Distributing a collection of objects in the driver program
RDD Implementation
• Array of partitions• List of dependencies on parent RDDs• Function to compute a partition given its parents
– Returns Iterator over a partition• Preferred locations: list of strings for each
partition (Nil by default)• Partitioner (None by default)
Persistence / Caching
• By default RDDs (and all of their dependencies) are recomputed every time an action is called on them!
• Need to explicitly tell Spark when to persist• Options:
– Default: stored in heap as unserialized objects (pickled objects for Python)
– Memory only: serialized or not– Memory and disk: spills to disk, option to serialize in memory– Disk only
• Tachyon: off-heap distributed caching– Aims to make Spark more resilient– Avoid GC overheads
Dependency Types: Narrow
E.g. map, filter
E.g. unionE.g. join with
co-partitioned input
Each partition of parent is used by at most one partition of the child
Dependency Types: Wide
E.g. groupByKey
E.g. join with inputsnon co-partitioned
Each partition of the parent is used by more than one partition of the child
Transformations
• Return a new RDD• Lazy evaluation• Single RDD transformations: map, flatMap, filter,
distinct• Pair RDDs: keyBy, reduceByKey, groupByKey,
combineByKey, mapValues, flatMapValues, sortByKey
• Two RDD transformations: union, intersection, subtract, cartesian
• Two pair RDDs: join, rightOuterJoin, leftOuterJoin, cogroup
Actions
• Force evaluation of the transformations and return a value to the driver program or write to external storage
• Actions on RDDs:– reduce, fold, aggregate– foreach(func), collect– count, countByValue– top(num)– take(num), takeOrdered(num)(ordering)
• Actions on pair RDDs:– countByKey– collectAsMap– lookup(key)
Single RDD Transformations
map and flatMap
• map takes a function that transforms each element of a collection: map(f: T => U)
• RDD[T] => RDD[U]• flatMap takes a function that transforms a single
element of a collection into a sequence of elements: flatMap(f: T => Seq[U])
• Flattens out the output into a single sequence• RDD[T] => RDD[U]
filter, distinct
• filter takes a (predicate) function that returns true if an element should be in the output collection: map(f: T => Bool)
• distinct removes duplicates from the RDD• Both filter and distinct transform from RDD[T] =>
RDD[T]
Actions
reduce, fold & aggregate
• reduce takes a function that combines pairwise element of a collection: reduce(f: (T, T) => T)
• fold is like reduce except it takes a zero value i.e. fold(zero: T) (f: (T, T) => T)
• reduce and fold: RDD[T] => T• aggregate is the most general form• aggregate(zero: U)(seqOp: (U, T) => U, combOp:
(U, U) => U)• aggregate: RDD[T] => U
Pair RDD Transformations
keyBy, reduceByKey
• keyBy creates tuples of the elements in an RDD by applying a function: keyBy(f: T => K)
• RDD[ T ] => RDD[ (K, T) ]• reduceByKey takes a function that takes a two
values and returns a single value: reduceByKey(f: (V,V) => V)
• RDD[ (K, V) ] => RDD[ (K, V) ]
groupByKey
• Takes a collection of key-value pairs and no parameters
• Returns a sequence of values associated with each key
• RDD[ ( K, V ) ] => RDD[ ( K, Iterable[V] ) ]• Results must fit in memory• Can be slow – use aggregateByKey or
reduceByKey where possible• Ordering of values not guaranteed and can vary
on every evaluation
combineByKey
• def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true, serializer: Serializer = null)• RDD [ (K, V) ] => RDD[ (K, C) ]• createCombiner called per partition when a new key is found• mergeValue combines a new value to an existing accumulator• mergeCombiners with results from different partitions• Sometimes map-size combine not useful e.g. groupByKey• groupByKey, aggregateByKey and reduceByKey all
implemented using combineByKey
map vs mapValues
• map takes a function that transforms each element of a collection: map(f: T => U)
• RDD[T] => RDD[U]• When T is a tuple we may want to only act on the
values – not the keys• mapValues takes a function that maps the values
in the inputs to the values in the output: mapValues(f: V => W)
• Where RDD[ (K, V) ] => RDD[ (K, W) ]• NB: use mapValues when you can: avoids
reshuffle when data is partitioned by key
Two RDD Transformations
Pseudo-set: union, intersection, subtract, cartesian
• rdd.union(otherRdd): RRD containing elements from both
• rdd.intersection(otherRdd): RDD containing only elements found in both
• rdd.subtract(otherRdd): remove content of one from the other e.g. removing training data
• rdd.cartesian(otherRdd): Cartesian product of two RDDs e.g. similarity of pairs: RDD[T] RDD[U] => RDD[ (T, U) ]
Two Pair RDD Transformations
join, rightOuterJoin, leftOuterJoin, cogroup
• Join: RDD[ ( K, V) ] and RDD[ (K, W) ] => RDD[ ( K, (V,W) ) ]
• Cogroup: RDD[ ( K, V) ] and RDD[ (K, W) ] => RDD[ ( K, ( Seq[V], Seq[W] ) ) ]
• rightOuterJoin and leftRightJoin when keys must be present in left / right RDD
Partition-specific Transformations and Actions
mapPartitions, mapPartitionsWithIndex, and foreachPartition
• Same as map and foreach except they operate on a per partition basis
• Useful for when you have setup code (DB, RNG etc.) but don’t want to call it for each partition
• You can set preservesPartitioning when you are not altering the keys used for partitioning to avoid unnecessary shuffling– As with mapValues in the last slide
Data Frames
Data Frames & Catalyst Optimizer
DataFrame creation and operations
val sc: SparkContext // An existing SparkContext.val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// Create the DataFrameval df = sqlContext.jsonFile("examples/src/main/resources/people.json”)
// Show the content of the DataFramedf.show()
// Print the schema in a tree formatdf.printSchema()
// Select only the "name" columndf.select("name”)
// Select everybody, but increment the age by 1df.select("name", df("age") + 1)
// Select people older than 21df.filter(df("name") > 21)
// Count people by agedf.groupBy("age").count()
Spark Streaming
Introduction
AlternativesApache Storm
TridentProgramming
ModelMicro-Batch One at a time Micro-batch
Stream Primitive DStream Stream Tuple, Tuple Batch, Partition
Distributed Stream Dataflow
Stream Source ReceiverInputDStream
Container Spouts, Trident Spouts
Data Stream
ComputationMaps/windows/operations on Dstream
StreamTask, Window, join
Filters, functions, aggregations, joins
Maps/windows/operations on Data Stream
Resource mgmt YARN/Mesos YARN YARN/Mesos YARN
ResilienceRequire WAL to DFS (HDFS/S3)
Checkpointing (Kafka)
Nimbus reassigns and failed batch replayed
Lightweight Distributed Snapshots
Scala collections programming model, map, flatMap, window, reduce (fold)
share code between batch and streaming, both share the same programming model (although different semantics)
microbatches allow aggregation on the batches, improved throughput with a latency cost
Why Spark Streaming
Spark Streaming Execution
Driver
Spark Context
Worker NodeExecutor
Task
Task
Worker NodeExecutor
Task
Task
Worker NodeExecutor
Task
TaskStreaming Producer
Example overview
Codeval metaStream = stream.map { case (k, v) => (k, DocumentMetadata.fromMutable(recordDecoder.decode(v).asInstanceOf[GenericRecord])) }
private val pdfFiles = metaStream.filter(_._2.contentType == "application/pdf") .map { case (k, meta) => (meta, fetchFileFromMessage(k, meta)) }val pdfDocs = pdfFiles.map { case (meta, file) => (meta, TextExtractor.parseFile(file)) }val texts = pdfDocs.map { case (meta, doc) => (meta, TextExtractor.extractText(doc)) }.cache()val wordStream = texts.map { case (meta, text) => (meta, text.split("""[\ \n\r\t\u00a0]+""").toList.map(_.replaceAll("""[,;\.]$""", "").trim.toLowerCase()).filter(_.length > 1)) }texts.foreachRDD( rdd => rdd.foreach { case (meta,text) => indexText(meta.id, text) } )
val wordCountStream = wordStream.flatMap(_._2).map(word => (word, 1)).reduceByKey(_ + _)val totalWordCountStream = wordStream.map(_._2.size)val totalWords = totalWordCountStream.reduce(_+_)
val sortedWordCount = wordCountStream.transform(rdd => rdd.sortBy(_._2, ascending = false))
sortedWordCount.foreachRDD(rdd => println(rdd.toDebugString))sortedWordCount.print(30)totalWords.print()
Q & A
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