Apache Spark Overview
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This document provides an overview of a talk on Apache Spark. It introduces the speaker and their background. It acknowledges inspiration from a previous Spark training. It then outlines the structure of the talk, which will include: a brief history of big data; a tour of Spark including its advantages over MapReduce; and explanations of Spark concepts like RDDs, transformations, and actions. The document serves to introduce the topics that will be covered in the talk.
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Apache Spark Overview
- 1. OVERVIEW Vadim Bichutskiy @vybstat Interface Symposium June 11, 2015 Licensed under: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
- 2. WHO AM I • Computational and Data Sciences, PhD Candidate, George Mason • Independent Data Science Consultant • MS/BS Computer Science, MS Statistics • NOT a Spark expert (yet!)
- 3. ACKNOWLEDGEMENTS • Much of this talk is inspired by SparkCamp at Strata HadoopWorld, San Jose, CA, February 2015 licensed under: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License • Taught by Paco Nathan
- 5. SPARK HELPSYOU BUILD NEWTOOLS 5
- 6. THISTALK… • Part I: Big Data:A Brief History • Part II: A Tour of Spark • Part III: Spark Concepts 6
- 7. PART I: BIG DATA:A BRIEF HISTORY 7
- 8. • Web, e-commerce, marketing, other data explosion • Work no longer fits on a single machine • Move to horizontal scale-out on clusters of commodity hardware • Machine learning, indexing, graph processing use cases at scale DOT COM BUBBLE: 1994-2001 8
- 9. GAME CHANGE: C. 2002-2004 Google File System research.google.com/archive/gfs.html MapReduce: Simplified Data Processing on Large Clusters research.google.com/archive/mapreduce.html 9
- 10. HISTORY: FUNCTIONAL PROGRAMMING FOR BIG DATA 2002 2004 2006 2008 2010 2012 2014 MapReduce @ Google MapReduce Paper Hadoop @ Yahoo! Hadoop Summit Amazon EMR Spark @ Berkeley Spark Paper Databricks Spark Summit Apache Spark takes off Databricks Cloud SparkR KeystoneML c. 1979 - MIT, CMU, Stanford, etc. LISP, Prolog, etc. operations: map, reduce, etc. Slide adapted from SparkCamp, Strata Hadoop World, San Jose, CA, Feb 201510
- 11. MapReduce Limitations • Difficult to program directly in MR • Performance bottlenecks, batch processing only • Streaming, iterative, interactive, graph processing,… MR doesn’t fit modern use cases Specialized systems developed as workarounds… 11
- 12. MapReduce Limitations MR doesn’t fit modern use cases Specialized systems developed as workarounds Slide adapted from SparkCamp, Strata Hadoop World, San Jose, CA, Feb 201512
- 13. PART II: APACHE SPARK TOTHE RESCUE… 13
- 14. Apache Spark • Fast, unified, large-scale data processing engine for modern workflows • Batch, streaming, iterative, interactive • SQL, ML, graph processing • Developed in ’09 at UC Berkeley AMPLab, open sourced in ’10 • Spark is one of the largest Big Data OSS projects “Organizations that are looking at big data challenges – including collection, ETL, storage, exploration and analytics – should consider Spark for its in-memory performance and the breadth of its model. It supports advanced analytics solutions on Hadoop clusters, including the iterative model required for machine learning and graph analysis.” Gartner, Advanced Analytics and Data Science (2014) 14
- 15. Apache Spark Spark’s goal was to generalize MapReduce, supporting modern use cases within same engine! 15
- 16. Spark Research Spark: Cluster Computer withWorking Sets http://people.csail.mit.edu/matei/papers/2010/hotcloud_spark.pdf Resilient Distributed Datasets:A Fault-Tolerant Abstraction for In-Memory Cluster Computing https://www.usenix.org/system/files/conference/nsdi12/nsdi12-final138.pdf 16
- 17. Spark: Key Points • Same engine for batch, streaming and interactive workloads • Scala, Java, Python, and (soon) R APIs • Programming at a higher level of abstraction • More general than MR 17
- 18. WordCount: “Hello World” for Big Data Apps Slide adapted from SparkCamp, Strata Hadoop World, San Jose, CA, Feb 2015 18
- 19. Spark vs. MapReduce • Unified engine for modern workloads • Lazy evaluation of the operator graph • Optimized for modern hardware • Functional programming / ease of use • Reduction in cost to build/maintain enterprise apps • Lower start up overhead • More efficient shuffles 19
- 20. Spark in a nutshell… 20
- 21. Spark Destroys Previous Sort Record Spark: 3x faster with 10x fewer nodes databricks.com/blog/2014/11/05/spark-officially-sets-a-new-record-in-large-scale-sorting.html 21
- 22. Spark is one of the most active Big Data projects… openhub.net/orgs/apache 22
- 23. What does Google say… 23
- 24. Spark on Stack Overflow twitter.com/dberkholz/status/568561792751771648 24
- 25. It pays to Spark… oreilly.com/data/free/2014-data-science-salary-survey.csp 25
- 27. PART III: APACHE SPARK CONCEPTS… 27
- 28. Resilient Distributed Datasets (RDDs) • Spark’s main abstraction - a fault-tolerant collection of elements that can be operated on in parallel • Two ways to create RDDs: I. Parallelized collections val data = Array(1, 2, 3, 4, 5) data: Array[Int] = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) distData: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[24970] II. External Datasets lines = sc.textFile(“s3n://error-logs/error-log.txt”) .map(lambda x: x.split("t")) 28
- 29. RDD Operations • Two types: transformations and actions • Transformations create a new RDD out of existing one, e.g. rdd.map(…) • Actions return a value to the driver program after running a computation on the RDD, e.g., rdd.count() Figure from SparkCamp, Strata Hadoop World, San Jose, CA, Feb 2015 29
- 30. Transformations spark.apache.org/docs/latest/programming-guide.html Transformation Meaning map(func) Return a new distributed dataset formed by passing each element of the source through a function func. filter(func) Return a new dataset formed by selecting those elements of the source on which func returns true. flatMap(func) Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item). mapPartitions(func) Similar to map, but runs separately on each partition (block) of the RDD. mapPartitionsWithIndex(func) Similar to mapPartitions, but also provides func with an integer value representing the index of the partition. sample(withReplacement, fraction, seed) Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed. 30
- 31. Transformations spark.apache.org/docs/latest/programming-guide.html Transformation Meaning union(otherDataset) Return a new dataset that contains the union of the elements in the source dataset and the argument. intersection(otherDataset) Return a new RDD that contains the intersection of elements in the source dataset and the argument. distinct([numTasks])) Return a new dataset that contains the distinct elements of the source dataset. groupByKey([numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs. reduceByKey(func, [numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func. sortByKey([ascending], [numTasks]) When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order 31
- 32. Transformations spark.apache.org/docs/latest/programming-guide.html Transformation Meaning join(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) with all pairs of elements for each key. cogroup(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) tuples. cartesian(otherDataset) When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements). pipe(command, [envVars]) Pipe each partition of the RDD through a shell command. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings. coalesce(numPartitions) Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset. 32
- 33. Ex:Transformations Python >>> x = ['hello world', 'how are you enjoying the conference'] >>> rdd = sc.parallelize(x) >>> rdd.filter(lambda x: 'hello' in x).collect() ['hello world'] >>> rdd.map(lambda x: x.split(" ")).collect() [['hello', 'world'], ['how', 'are', 'you', 'enjoying', 'the', 'conference']] >>> rdd.flatMap(lambda x: x.split(" ")).collect() ['hello', 'world', 'how', 'are', 'you', 'enjoying', 'the', 'conference'] 33
- 34. Ex:Transformations Scala scala> val x = Array(“hello world”, “how are you enjoying the conference”) scala> val rdd = sc.parallelize(x) scala> rdd.filter(x => x contains "hello").collect() res15: Array[String] = Array(hello world) scala> rdd.map(x => x.split(" ")).collect() res19: Array[Array[String]] = Array(Array(hello, world), Array(how, are, you, enjoying, the, conference)) scala> rdd.flatMap(x => x.split(" ")).collect() res20: Array[String] = Array(hello, world, how, are, you, enjoying, the, conference) 34
- 35. Actions spark.apache.org/docs/latest/programming-guide.html Action Meaning reduce(func) Aggregate the elements of the dataset using a function func (which takes two arguments and returns one), func should be commutative and associative so it can be computed correctly in parallel. collect() Return all elements of the dataset as array at the driver program. Usually useful after a filter or other operation that returns sufficiently small data. count() Return the number of elements in the dataset. first() Return the first element of the dataset (similar to take(1)). take(n) Return an array with the first n elements of the dataset. takeSample(withReplacement, num, [seed]) Return an array with a random sample of num elements of the dataset, with or without replacement, with optional random number generator seed. takeOrdered(n, [ordering]) Return the first n elements of the RDD using either their natural order or a custom comparator. 35
- 36. Actions spark.apache.org/docs/latest/programming-guide.html Action Meaning saveAsTextFile(path) Write the dataset as a text file (or set of text files) in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. saveAsSequenceFile(path) (Java and Scala) Write the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. saveAsObjectFile(path) (Java and Scala) Write the dataset in a simple format using Java serialization, which can then be loaded using SparkContext.objectFile(). countByKey() For RDD of type (K, V), returns a hashmap of (K, Int) pairs with the count of each key. foreach(func) Run a function func on each element of the dataset. This is usually done for side effects such as updating an accumulator variable or interacting with external storage systems. 36
- 37. Ex:Actions Python >>> x = [“hello world", "hello there", "hello again”] >>> rdd = sc.parallelize(x) >>> wordsCounts = rdd.flatMap(lamdba x: x.split(" “)).map(lambda w: (w, 1)) .reduceByKey(add) >>> wordCounts.saveAsTextFile("/Users/vb/wordcounts") >>> wordCounts.collect() [(again,1), (hello,3), (world,1), (there,1)] >>> from operator import add 37
- 38. Ex:Actions Scala scala> val x = Array("hello world", "hello there", "hello again") scala> val rdd = sc.parallelize(x) scala> val wordsCounts = rdd.flatMap(x => x.split(" ")).map(word => (word, 1)) .reduceByKey(_ + _) scala> wordCounts.saveAsTextFile("/Users/vb/wordcounts") scala> wordCounts.collect() res43: Array[(String, Int)] = Array((again,1), (hello,3), (world,1), (there,1)) 38
- 39. RDD Persistence • Unlike MapReduce, Spark can persist (or cache) a dataset in memory across operations • Each node stores any partitions of it that it computes in memory and reuses them in other transformations/actions on that RDD • 10x increase in speed • One of the most important Spark features >>> wordCounts = rdd.flatMap(lamdba x: x.split(“ “)) .map(lambda w: (w, 1)) .reduceByKey(add) .cache() 39
- 40. RDD Persistence Storage Levels Storage Level Meaning MEMORY_ONLY Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, some partitions will not be cached and will be recomputed on the fly each time they're needed. This is the default level. MEMORY_AND_DISK Store RDD as deserialized Java objects in the JVM. If the RDD does not fit in memory, store the partitions that don't fit on disk, and read them from there when they're needed. MEMORY_ONLY_SER Store RDD as serialized Java objects (one byte array per partition). This is generally more space-efficient than deserialized objects, especially when using a fast serializer, but more CPU-intensive to read. http://spark.apache.org/docs/latest/programming-guide.html 40
- 41. More RDD Persistence Storage Levels Storage Level Meaning MEMORY_AND_DISK_SER Similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them on the fly each time they're needed. DISK_ONLY Store RDD partitions only on disk. MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc. Same as the levels above, but replicate each partition on two cluster nodes. OFF_HEAP (experimental) Store RDD in serialized format in Tachyon. Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors to be smaller and to share a pool of memory, making it attractive in environments with large heaps or multiple concurrent applications. http://spark.apache.org/docs/latest/programming-guide.html 41
- 43. And so much more… • DataFrames and SQL • Spark Streaming • MLlib • GraphX spark.apache.org/docs/latest/ 43