Introduction to apache spark
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This document provides an introduction to Apache Spark, including an overview of its components and capabilities. It discusses Spark's history and development at UC Berkeley and the Apache Software Foundation. The document explains the Spark stack and its core abstraction called Resilient Distributed Datasets (RDDs). It provides examples of creating and transforming RDDs in Scala and Java. Finally, it lists some resources for learning more about Spark.
Introduction to apache spark
- 1. INTRODUCTION TO APACHE SPARK JUGBD MEETUP #5.0 MAY 23, 2015 MUKTADIUR RAHMAN TEAM LEAD, M&H INFORMATICS(BD) LTD.
- 2. OVERVIEW • Apache Spark is a cluster computing framework that provide : • fast and general engine for large-scale data processing • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk • Simple API in Scala, Java, Python • This talk will cover : • Components of Spark Stack • Resilient Distributed DataSet(RDD) • Programming with Spark
- 3. A BRIEF HISTORY OF SPARK • Spark started by Matei Zaharia in 2009 as a research project in the UC Berkeley RAD Lab, later to become the AMPLab. • Spark was first open sourced in March 2010 and transferred to the Apache Software Foundation in June 2013 • Spark had over 465 contributors in 2014,making it the most active project in the Apache Software Foundation and among Big Data open source projects • Spark 1.3.1, released on April 17, 2015(http:// www.apache.org/dyn/closer.cgi/spark/spark-1.3.1/ spark-1.3.1-bin-hadoop2.6.tgz)
- 4. SPARK STACK
- 5. Resilient Distributed Datasets (RDD) An RDD in Spark is simply an immutable distributed collection of objects. Each RDD is split into multiple partitions, which may be computed on different nodes of the cluster. RDDs can be created in two ways: • by loading an external dataset •scala> val reads = sc.textFile(“README.md”) • by distributing a collection of objects •scala> val data = sc.parallelize(1 to 100000)
- 6. RDD Once created, RDDs offer two types of operations: • transformations • actions Example : Step 1 : Create a RDD scala> val data = sc.textFile(“README.md") Step 2: Transformation scala> val lines = data.filter(line=>line.contains(“Spark")) Step 3: Action scala> lines.count()
- 7. RDD Persisting an RDD in memory Example : Step 1 : Create a RDD scala> val data = sc.textFile(“README.md") Step 2: Transformation scala> val lines = data.filter(line=>line.contains(“Spark")) Step 3: Persistent in memory scala> lines.cache() or lines.persist() Step 4: Unpersist memory scala> lines.unpersist() Step 5: Action scala> lines.count()
- 8. SPARK EXAMPLE : WORD COUNT Scala>> var data = sc.textFile(“README.md") var counts = data.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.saveAsTextFile("/tmp/output")
- 9. SPARK EXAMPLE : WORD COUNT Java 8>> JavaRDD<String> data = sc.textFile(“README.md"); JavaRDD<String> words = data.flatMap(line -> Arrays.asList(line.split(" “))); JavaPairRDD<String, Integer> counts = words.mapToPair(w -> new Tuple2<String, Integer>(w, 1)) .reduceByKey((x, y) -> x + y); counts.saveAsTextFile(“/tmp/output“);
- 10. RESOURCES • https://spark.apache.org/docs/latest/ • http://shop.oreilly.com/product/0636920028512.do • https://www.edx.org/course/introduction-big-data- apache-spark-uc-berkeleyx-cs100-1x • https://www.edx.org/course/scalable-machine- learning-uc-berkeleyx-cs190-1x • https://www.facebook.com/groups/ 898580040204667/
- 11. Q/A Thank YOU!