Introduction to Apache Spark
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This document provides an introduction and overview of Apache Spark. It discusses what Spark is, its performance advantages over Hadoop MapReduce, its core abstraction of resilient distributed datasets (RDDs), and how Spark programs are executed. Key features of Spark like its interactive shell, transformations and actions on RDDs, and Spark SQL are explained. Recent new features in Spark like DataFrames, external data sources, and the Tungsten performance optimizer are also covered. The document aims to give attendees an understanding of Spark's capabilities and how it can provide faster performance than Hadoop for certain applications.
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Introduction to Apache Spark
- 1. Introduction to Apache Spark Hubert 范姜 @hubert HadoopCon Taiwan 2015 Sep. 19 , 2015 Taipei Photo from http://quotesgram.com/spark-quotes/
- 2. Who are we? • 亦思科技 • 位於新竹科學園區 • 過去主要客戶為園區各大製造廠 • 2010.7 以研發雲端計算軟體工具之投資計畫獲准進駐新竹科學園區 • 2011 與清華大學資工系鍾葉青教授合作進行產學合作 • 少數獲邀參與國際雲端計算研討會 IEEE CloudCom的專業公司 • 少數已經有實際經驗協助客戶完成建置 Hadoop 系統的資訊廠商 • 2012.01 JackHare (ANSI SQL JDBC Driver) • 2012.11 HareDB Hbase Client • 2013.08 Hare ( High Speed Query in HBase) • 2013.12 榮獲科學園區創新產品獎 • 2014.12 榮獲資訊月創新金質獎
- 3. Hadoop HBase Hive Spark HareDB Core HBase Client HDFS Client Solr Cloud Security KerberosSentry Indexing Restful Service JDBC/ODBC Cluster Monitor HareDB Arch.
- 4. WHAT IS SPARK ?
- 5. What is Apache Spark ? • It is an open source cluster computing framework • In contrast to Hadoop's two-stage disk- based MapReduce paradigm, Spark's multi-stage in-memory primitives provides performance up to 100 faster for certain applications.
- 6. Databricks • Founded in late 2013 • By the creators of Apache Spark • Original team from UC Berkeley AMPLab (Algorithms,Machines,People) • Contributed more than 75% of the code added to Spark in 2014
- 7. World Record From Spark Summit 2015, Matei Zaharia
- 8. Spark is hot ! From Spark Summit 2015, Matei Zaharia
- 9. SPARK 會取代 HADOOP ?
- 10. From Spark Summit 2015, Mike Olson (Cloudera), http://www.slideshare.net/SparkSummit/spark-in-the-hadoop-ecosystem-mike-olson
- 11. From http://hortonworks.com/blog/apache-spark-yarn-ready-hortonworks-data-platform/ && Spark Summit 2015, Arun C. Murthy
- 12. From Spark Summit 2015, Anil Gadre (MapR), http://www.slideshare.net/SparkSummit/spark-in-the-hadoop-ecosystem-mike-olson
- 16. Spark Software Stack Resource Virtualization Storage Processing Engine Access and Interfaces Mesos Hadoop Yarn HDFS,S3 Spark Core Spark Streami ng Spark SQL Spark R GraphX MLlib Splash MLPipelinesBlinkDB
- 17. VS
- 18. 10X ~100X
- 19. 300 MB/s 600 MB/s 10GB/s 1Gb/s = 125MB/s 1Gb/s 125MB/s Nodes in the same rack Nodes in another rack 0.1Gb/s 12.5MB/s 1.資料記憶體化 2.資料在地化 Physical Bottleneck
- 21. R D D
- 22. http://www.cs.berkeley.edu/~matei/papers/2010/hotcloud_spark.pdf “The main abstraction in Spark is that of a resilient dis- tributed dataset (RDD), which represents a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Users can explicitly cache an RDD in memory across machines and reuse it in multiple MapReduce-like parallel operations. RDDs achieve fault tolerance through a notion of lineage: if a partition of an RDD is lost, the RDD has enough information about how it was derived from other RDDs to be able to rebuild just that partition.” What is RDD ?
- 23. (Scala & Python only) Interactive Shell
- 24. # Read a local txt file in Python linesRDD = sc.textFile("/path/to/README.md") // Read a local txt file in Scala val linesRDD = sc.textFile("/path/to/README.md") // Read a local txt file in Java JavaRDD<String> lines = sc.textFile("/path/to/README.md"); Read From TextFile
- 25. item-1 item-2 item-3 item-4 item-5 item-6 item-7 item-8 item-9 item-10 item-11 item-12 item-13 item-14 item-15 item-16 item-17 item-18 item-19 item-20 item-21 item-22 item-23 item-24 item-25 RDD Ex RDD W RDD Ex RDD W RDD Ex RDD W more partitions = more parallelism Where is RDD ? http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 26. Error, ts, msg1 Warn, ts, msg2 Error, ts, msg1 Info, ts, msg8 Warn, ts, msg2 Info, ts, msg8 Error, ts, msg3 Info, ts, msg5 Info, ts, msg5 Error, ts, msg4 Warn, ts, msg9 Error, ts, msg1 logLinesRDD Error, ts, msg1 Error, ts, msg1 Error, ts, msg3 Error, ts, msg4 Error, ts, msg1 errorsRDD .filter( ) (input/base RDD) http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 27. errorsRDD .coalesce( 2 ) Error, ts, msg1 Error, ts, msg3 Error, ts, msg1 Error, ts, msg4 Error, ts, msg1 cleanedRDD Error, ts, msg1 Error, ts, msg1 Error, ts, msg3 Error, ts, msg4 Error, ts, msg1 .collect( ) Driver http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 29. .collect( ) Driver logLinesRD D http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 30. .collect( ) logLinesRD D errorsRDD cleanedRDD .filter( ) .coalesce( 2 ) Driver Error, ts, msg1 Error, ts, msg3 Error, ts, msg1 Error, ts, msg4 Error, ts, msg1 http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 31. .collect( ) Driver logLinesRDD errorsRDD cleanedRDD data .filter( ) .coalesce( 2, shuffle= False) http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 34. logLinesRD D errorsRDD Error, ts, msg1 Error, ts, msg3 Error, ts, msg1 Error, ts, msg4 Error, ts, msg1 cleanedRDD .filter( ) Error, ts, msg1 Error, ts, msg1 Error, ts, msg1 errorMsg1RD D .collect( ) .saveToCassandra( ) .count( ) 5 http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 35. logLinesRD D errorsRDD Error, ts, msg1 Error, ts, msg3 Error, ts, msg1 Error, ts, msg4 Error, ts, msg1 cleanedRDD .filter( ) Error, ts, msg1 Error, ts, msg1 Error, ts, msg1 errorMsg1RDD .collect( ) .count( ) .saveToCassandra( ) 5 http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 36. Lifecycle of a Spark program 1. Create some input RDDs from external data or parallelize a collection in your driver program. 2. Lazily transform them to define new RDDs using transformations like filter() or map() 3. Ask Spark to cache() any intermediate RDDs that will need to be reused. 4. Launch actions such as count() and collect() to kick off a parallel computation, which is then optimized and executed by Spark. http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 37. map() intersection() cartesion() flatMap() distinct() pipe() filter() groupByKey() coalesce() mapPartitions() reduceByKey() repartition() mapPartitionsWithIndex() sortByKey() partitionBy() sample() join() ... union() cogroup() ... Transformations http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 38. reduce() takeOrdered() collect() saveAsTextFile() count() saveAsSequenceFile() first() saveAsObjectFile() take() countByKey() takeSample() foreach() ... ... Actions http://www.slideshare.net/SparkSummit/intro-to-spark-development
- 39. SPARK SQL
- 40. sqlCtx = new HiveContext(sc) results = sqlCtx.sql( "SELECT * FROM people") names = results.map(lambda p: p.name) What is Spark SQL
- 41. NEW FEATURES IN 1.4 AND 1.5
- 49. Spark R 49
- 50. Spark R 50
- 59. Spark 1.5 • A large part of Spark 1.5, on the other hand, focuses on under-the-hood changes to improve Spark’s performance, usability, and operational stability. • Spark 1.5 delivers the first phase of Project Tungsten Reference: https://databricks.com/blog/2015/08/18/spark-1-5-preview-now-available-in-databricks.html
- 60. Thank you
Notes de l'éditeur
- Mike Olson : Chief Strategy Officer at Cloudera
- Senior Vice President, Product Management
- This RDD has 5 partitions. An RDD is simply a distributed collection of elements. You can think of the distributed collections like of like an array or list in your single machine program, except that it’s spread out across multiple nodes in the cluster. In Spark all work is expressed as either creating new RDDs, transforming existing RDDs, or calling operations on RDDs to compute a result. Under the hood, Spark automatically distributes the data contained in RDDs across your cluster and parallelizes the operations you perform on them. So, Spark gives you APIs and functions that lets you do something on the whole collection in parallel using all the nodes.
- Introduce that Spark has Operations which can be transformations or actions. Those are 4 green unique blocks in a single HDFS file Here we are filtering out the warnings and info messages so we are left with just errors in the RDD. This doesn’t actually read the file from HDFS just yet… we’re just building out a lineage graph
- directed acyclic graph. That is, it is formed by a collection of vertices and directed edges, each edge connecting one vertex to another, such that there is no way to start at some vertex v and follow a sequence of edges that eventually loops back to v again. A collection of tasks that must be ordered into a sequence, subject to constraints that certain tasks must be performed earlier than others, may be represented as a DAG with a vertex for each task and an edge for each constraint https://en.wikipedia.org/wiki/Directed_acyclic_graph
- ----- Meeting Notes (6/15/15 16:02) ----- This is a stage (which we'll talk about later).
- Now the RDDs dissapear and get destroyed
- It’s okay if only part of the RDD actually fits in memory Talk about lineage: parent RDD and child RDD
- ----- Meeting Notes (6/15/15 16:08) ----- Also note that an application can have many such 1 through 4 procedures.
- Actions force the evaluation of the transformations required for the RDD they are called on, since they are required to actually produce output.