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Introduction to Spark - DataFactZ


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We are a company driven by inquisitive data scientists, having developed a pragmatic and interdisciplinary approach, which has evolved over the decades working with over 100 clients across multiple industries. Combining several Data Science techniques from statistics, machine learning, deep learning, decision science, cognitive science, and business intelligence, with our ecosystem of technology platforms, we have produced unprecedented solutions. Welcome to the Data Science Analytics team that can do it all, from architecture to algorithms.

Our practice delivers data driven solutions, including Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. We employ a number of technologies in the area of Big Data and Advanced Analytics such as DataStax (Cassandra), Databricks (Spark), Cloudera, Hortonworks, MapR, R, SAS, Matlab, SPSS and Advanced Data Visualizations.

This presentation is designed for Spark Enthusiasts to get started and details of the course are below.

1. Introduction to Apache Spark
2. Functional Programming + Scala
3. Spark Core
4. Spark SQL + Parquet
5. Advanced Libraries
6. Tips & Tricks
7. Where do I go from here?

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Introduction to Spark - DataFactZ

  1. 1. Introduction to Apache Spark 2
  2. 2. 3 What is Apache Spark?  Architecture  Spark History  Spark vs. Hadoop  Getting Started Scala - A scalable language Spark Core  RDD  Transformations  Actions  Lazy Evaluation - in action Working with KV Pairs  Pair RDDs, Joins Agenda Advanced Spark  Accumulators, Broadcast  Running on a cluster  Standalone Programs Spark SQL  Data Frames (SchemaRDD)  Intro to Parquet  Parquet + Spark Advanced Libraries  Spark Streaming  MLlib
  3. 3. 4 What is Spark? A distributed computing platform designed to be Fast  Fast to develop distributed applications  Fast to run distributed applications General Purpose  A single framework to handle a variety of workloads  Batch, interactive, iterative, streaming, SQL
  4. 4. 5 Fast & General Purpose  Fast/Speed  Computations in memory  Faster than MR even for disk computations  Generality  Designed for a wide range of workloads  Single Engine to combine batch, interactive, iterative, streaming algorithms.  Has rich high-level libraries and simple native APIs in Java, Scala and Python.  Reduces the management burden of maintaining separate tools.
  5. 5. 6 Spark Architecture DataFrame API Packages Sprak Streaming Spark Core Spark SQL MLLib GraphX Standalone Yarn Mesos Datasources
  6. 6. 7 Spark Unified Stack
  7. 7. 8 Cluster Managers Can run on a variety of cluster managers  Hadoop YARN - Yet Another Resource Negotiator is a cluster management technology and one of the key features in Hadoop 2.  Apache Mesos - abstracts CPU, memory, storage, and other compute resources away from machines, enabling fault-tolerant and elastic distributed systems.  Spark Standalone Scheduler – provides an easy way to get started on an empty set of machines.  Spark can leverage existing Hadoop infrastructure
  8. 8. 9 Spark History  Started in 2009 as a research project in UC Berkeley RAD lab which became AMP Lab.  Spark researchers found that Hadoop MapReduce was inefficient for iterative and interactive computing.  Spark was designed from the beginning to be fast for interactive, iterative with support for in-memory storage and fault-tolerance.  Apart from UC Berkeley, Databricks, Yahoo! and Intel are major contributors.  Spark was open sourced in March 2010 and transformed into Apache Foundation project in June 2013.
  9. 9. 10 Spark Vs Hadoop Hadoop MapReduce  Mostly suited for batch jobs  Difficulty to program directly in MR  Batch doesn’t compose well for large apps  Specialized systems needed as a workaround Spark  Handles batch, interactive, and real-time within a single framework  Native integration with Java, Python, Scala  Programming at a higher level of abstraction  More general than MapReduce
  10. 10. 11 Getting Started  Multiple ways of using Spark  Certified Spark Distributions  Datastax Enterprise (Cassandra + Spark)  HortonWorks HDP  MAPR  Local/Standalone  Databricks Cloud  Amazon AWS EC2
  11. 11. 12 Databricks Cloud  A hosted data platform powered by Apache Spark  Features  Exploration and Visualization  Managed Spark Clusters  Production Pipelines  Support for 3rd party apps (Tableau, Pentaho, Qlik View)  Databricks Cloud Trail 
  12. 12. 13 Local Mode  Install Java JDK 6/7 on MacOSX or Windows  Install Python 2.7 using Anaconda (only on Windows)  Download Apache Spark from Databricks, unzip the downloaded file  The provided link is for Spark 1.5.1, however the latest binary can also be obtained from  Connect to the newly created spark-training directory
  13. 13. 14 Exercise The following steps demonstrate how to create a simple spark program in Spark using Scala  Create a collection of 1,000 integers  Use the collection to create a base RDD  Apply a function to filter numbers less than 50  Display the filtered values  Invoke the spark-shell and type the following code $SPARK_HOME/bin/spark-shell val data = 0 to 1000 val distData = sc.parallelize(data) val filteredData = distData.filter(s => s < 50) filteredData.collect()
  14. 14. 15 Functional Programming + Scala
  15. 15. 16 Functional Programming  Functional Programming  Computation as evaluation of mathematical functions.  Avoids changing state and mutable-data.  Functions are treated as values just like integers or literals.  Functions can be passed as arguments and received as results.  Functions can be defined inside other functions.  Functions cannot have side-effects.  Functions communicate with the environment by taking arguments and returning results, they do not maintain state.  In functional programming language operations of a program should map input values to output values rather than change data in place.  Examples: Haskell, Scala
  16. 16. 17 Scala – A Scalable Language  A multi-paradigm programming language with focus on functional programming.  High level language for the JVM  Statically Typed  Object Oriented + Functional  Generates byte code that runs on the top of any JVM  Comparable in speed to Java  Interoperates with Java, can use any Java class  Can be called from Java code  Spark core is completely written in Scala.  Spark SQL, GraphX, Spark Streaming etc. are libraries written in Scala.
  17. 17. 18 Scala – Main Features  What differentiates Scala from Java?  Anonymous functions (Closures/Lambda functions).  Type inference (Statically Typed).  Implicit Conversions.  Pattern Matching.  Higher-order Functions.
  18. 18. 19 Scala – Main Features  Anonymous functions (Closures or Lambda functions) Regular function def containsString( x: String ): Boolean = { x.contains(“mysql”) } Anonymous function x => x.contains(“mysql”) _.contains(“mysql”) //shortcut notation  Type Inference def squareFunc( x: Int ) = { x*x }
  19. 19. 20 Scala – Main Features  Implicit Conversions val a: Int = 1 Val b: Int = 4 val myRange: Range = a to b myRange.foreach(println) OR (1 to 4).foreach(println)  Pattern Matching val pairs = List((1, 2), (2, 3), (3, 4)) val result = pair.filter(s => s._2 != 2) val result = pair.filter{case(x, y) => y != 2}  Higher-order functions messages.filter(x => x.contains(“mysql")) messages.filter(_.contains(“mysql”))
  20. 20. 21 Scala – Exercise 1. Filter strings containing “mysql” from a list. val lines = List("My first Scala program", "My first mysql query") def containsString(x: String) = x.contains("mysql") //regular function lines.filter(containsString) //higher order function lines.filter(s => s.contains("mysql")) //anonymous function lines.filter(_.contains(“mysql")) //shortcut notation 2. From a list of tuples filter tuples that don't have 2 as their second element. val pairs = List((1, 2), (2, 3), (3, 4)) pairs.filter(s => s._2 != 2) //no pattern matching pairs.filter{ case(x, y) => y != 2 } //pattern matching 3. Functional operations map input to output and do not change data in place. val nums = List(1, 2, 3, 4, 5) val numSquares = => s * s) //returns square of each element println(numSquares)
  21. 21. 22 Spark Core
  22. 22. 23 Directed Acyclic Graph (DAG) DAG  A chain of MapReduce jobs  A Pig that script defines a chain of MR jobs  A Spark program is also a DAG Limitations of Hadoop/MapReduce  A graph of MR jobs are schedules to run sequentially, inefficiently  Between each MR job the DAG writes data to disk (HDFS)  In MR the dataset is abstracted as KV pairs called the KV store  MR jobs are batch processes so KV store cannot be queries interactively Advantages of Spark  Spark DAGs don’t run like Hadoop/MR DAGs so much more efficiently  Spark DAGs run in memory as much as possible and spill over to disk only when needed  Spark dataset is called an RDD  The RDD is stored in memory so it can be interactively queried
  23. 23. 24 Resilient Distributed Dataset(RDD) Resilient Distributed Dataset  Spark’s primary abstraction  A distributed collection of items called elements, could be KV pairs or anything else  RDDs are immutable  RDD is a Scala object  Transformations and Actions can be performed on RDDs  RDD can be created from HDFS file, local file, parallelized collection, JSON file etc. Data Lineage (What makes RDD resilient?)  RDD has lineage that keep tracks of where data came from and how it was derived  Lineage is stored in the DAG or the driver program  DAG is logical only because the compiler optimizes the DAG for efficiency
  24. 24. 25 RDD Visualized
  25. 25. 26 RDD Operations Transformations  Operate on an RDD and return a new RDD  Are lazily evaluated Actions  Return a value after running a computation on an RDD Lazy Evaluation  Evaluation happens only when an action is called  Deferring decisions for better runtime optimization
  26. 26. 27 Spark Core Transformations  Operate on an RDD and return a new RDD.  Are Lazily Evaluated Actions  Return a value after running a computation on a RDD.  The DAG is evaluated only when an action takes place. Lazy Evaluation  Only type checking happens when a DAG is compiled.  Evaluation happens only when an action is called.  Deferring decisions will yield more information at runtime to better optimize the program  So a Spark program actually starts executing when an action is called.
  27. 27. 28 Hello Spark! (Scala) Simple Word Count App  Create a RDD from a text file val lines= sc.textFile("")  Perform a series of transformations to compute the word count val words = lines.flatMap(_.split(" ")) val pairs = => (s, 1)) val wordCounts = pairs.reduceByKey(_ + _)  Action: send word count results back to the driver program wordCounts.collect() wordCounts.take(10)  Action: save word counts to a text file wordCounts.saveAsTextFile("../../WordCount")  How many times does the keyword “Spark” occur?
  28. 28. 29 Hello Spark! (Python) Simple Word Count App (Scala)  Create a RDD from a text file lines = sc.textFile("")  Perform a series of transformations to compute the word count words = lines.flatMap(lambda l: l.split(" ")) pairs = s: (s, 1)) wordCounts = pairs.reduceByKey(lambda x, y: (x + y))  Action: send word count results back to the driver program wordCounts.collect() wordCounts.take(10)  Action: save word counts to a text file wordCounts.saveAsTextFile("WordCount")  How many times does the keyword “Spark” occur?
  29. 29. 30 Working with Key-Value Pairs  Creating Pair RDDs  Many of Spark’s input formats directly return key/value data.  Transformations like map can also be used to create pair RDDs  Creating a pair RDD from csv files that has two columns. val pairs = sc.textFile(“pairsCSV.csv”).map(_.split(“,”)).map(s => (s(0), s(1))  Transforming Pair RDDs  Special transformations exist on pair RDD which are not available for regular RDDs  reduceByKey - combine values with the same key (has a built in map-side reducer)  groupByKey - group values by key  mapValues - apply function to each value of the pair without changing the keys  sort ByKey - returns an RDD sorted by the Keys  Joining Pair RDDs  Two RDDs can be joined using their keys  Only pair RDDs are supported
  30. 30. 31 Broadcast & Accumulator Variables  Broadcast Variable  Read-only variable cached on each node  Useful to keep a moderately large input dataset on each node  Spark uses efficient bit-torrent algorithms to ship broadcast variables to each node  Minimizes network costs while distributing dataset val broadcastVar = sc.broadcast(Array(1, 2, 3)) broadcastVar.value  Accumulators  Implement counters, sums etc. in parallel, supports associative addition  Natively supported type re numeric and standard mutable collections  Only driver can read accumulator value, tasks can't val accum = sc.accumulator(0) sc.parallelize(List(1, 2, 3, 4)).foreach(x => accum++) accum.value
  31. 31. 32 Standalone Apps  Applications must define a “main( )” method  App must create a spark context  Applications can be built using  Java + Maven  Scala + SBT  SBT - Simple Build Tool  Included with Spark download and doesn’t need to be installed separately  Similar to Maven but supports incremental compile and interactive shell  requires a configuration file  IDEs like IntelliJ Idea  have Scala and SBT plugins available  can be configured to build and run Spark programs in Scala
  32. 32. 33 Building with SBT  build.sbt  Should include Scala version and Spark dependencies  Directory Structure ./myapp/src/main/scala/MyApp.scala  Package the jar  from the ./myapp folder run sbt package  a jar file is created in ./myapp/target/scala-2.10/myapp_2.10-1.0.jar  spark-submit, specific master URL or local SPARK_HOME/bin/spark-submit --class "MyApp" --master local[4] target/scala-2.10/myapp_2.10-1.0.jar
  33. 33. 34 Spark Cluster
  34. 34. 35 Spark SQL + Parquet
  35. 35. 36 Spark SQL  Spark’s interface for working with structured and semi-structured data.  Can load data from JSON, Hive, Parquet  Data can be queried internally using SQL, Scala, Python or from external BI tools.  Spark SQL provides a special RDD called Schema RDD. (replaced with data frame since Spark 1.3)  Spark supports UDF  A Schema RDD is an RDD for Row objects.  Spark SQL Components  Catalyst Optimizer  Spark SQL Core  Hive Support
  36. 36. 37 Spark SQL
  37. 37. 38 DataFrames  Extension of RDD API and a Spark SQL abstraction  Distributed collection of data with named columns  Equivalent to RDBMS tables or data frames in R/Pandas  Can be built from a variety of structured data sources  Hive tables, JSON, Databases, RDDs etc.
  38. 38. 39 Why DataFrame?  Lots of data formats are structured  Schema-on-read  Data has inherent structure and needed to make sense of it  RDD programming with structured data is not intuitive  DataFrame = RDD(ROW) + Schema + DSL  Write SQLs  Use Domain Specific Language (DSL)
  39. 39. 40 Using Spark SQL  SQLContext  Entry point for all SQL functionality  Extends existing spark context to support SQL  If JSON or Parquet files readily result a DataFrame (schemaRDD)  Register DataFrame as temp table  Tables persist only as long as the program val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext.implicits._ val parquetFile = sqlContext.parquetFile("../spark_training/data/wiki_parquet") parquetFile.registerTempTable("wikiparquet") val teenagers = sqlContext.sql(""" SELECT * FROM wikiparquetlimit 2""") cacheTable("people") teenagers.collect.foreach(println)
  40. 40. 41 Intro to Parquet Business Use Case:  Analytics produce a lot of derived data and statistics  Compression needed for efficient data storage  Compressing is easy but deriving insights is not  Need a new mechanism to store and retrieve data easily and efficiently in Hadoop ecosystem.
  41. 41. 42 Intro to Parquet (Contd.) Solution: Parquet  A columnar storage format for Hadoop eco.  Independent of  Processing Framework (MapReduce, Spark, Cascading, Scalding etc. )  Programming Language (Java, Scala, Python, C++)  Data Model (Avro, Thrift, ProtoBuf, POJO)  Supports Nested data structures  Self-describing data format  Binary packaging for CPU efficiency
  42. 42. 43 Parquet Design Goals Interoperability  Model and Language agnostic  Supports a myriad of frameworks, query engines and data models Space(IO) Efficiency  Columnar Storage  Row layout - encode one value at a time  Column layout - encode an array of values at a time Partitioning  Vertical - for projection pushdown  Horizontal - for predicate pushdown  Read only the blocks that are needed, no need to scan the whole file Query/CPU Efficiency  Binary packaging for CPU efficiency  Right encoding for right data
  43. 43. 44 Parquet File Partitioning When to use Partitioning?  Data too large and takes long time to read  Data always queried with conditions  Columns have reasonable cardinality (not just male vs female)  Choose column combinations that are frequently used together for filtering  Partition pruning helps read only the directories being filtered
  44. 44. 45 Parquet With Spark  Spark fully supports parquet file formats  Spark 1.3 can automatically scan and merge files if data model changes  Spark 1.4 supports partition pruning  Can auto discover partition folders  scans only those folders required by predicate df.write(“year”, “month”, “day”).parquet(“path/to/output”)
  45. 45. 46 SQL Exercise (Twitter Study)old no data frames //create a case class to assign schema to structured data case class Tweet(tweet_id: String, retweet: String, timestamp: String, source: String, text: String) val sqlContext = new org.apache.spark.sql.SQLContext(sc) import sqlContext.implicits._ //sc.textFile("data/tweets.csv").map(s => s.split(",")).map(s => Tweet(s(0), s(3), s(5), s(6), s(7))).take(5).foreach(println) val tweets = sc.textFile("data/tweets.csv").map(s => s.split(",")).map(s => Tweet(s(0), s(3), s(5), s(6), s(7))) tweets.registerTempTable("tweets") //show the top 10 tweets by the number of re-tweets val top10Tweets = sqlContext.sql("""select text, sum(IF(retweet is null, 0, 1)) rtcount from tweets group by text order by rtcount desc limit 10”"") top10Tweets.collect.foreach(println)
  46. 46. 47 SQL Exercise (Twitter Study) import org.apache.spark.sql.types._ import com.databricks.spark.csv._ import sqlContext.implicits._ val csvSchema = StructType(List(StructField("tweet_id",StringType,true), StructField("retweet",StringType,true), StructField("timestamp",StringType,true), StructField("source",DoubleType,true), StructField("text",StringType,true))) val tweets = new CsvParser().withSchema(csvSchema).withDelimiter(',').withUseHeader(false).csvFile(sq lContext, "data/tweets.csv") tweets.registerTempTable("tweets") //show the top 10 tweets by the number of re-tweets val top10Tweets = sqlContext.sql("""select text, sum(IF(retweet is null, 0, 1)) rtcount from tweets where text != "" group by text order by rtcount desc limit 10""") top10Tweets.collect.foreach(println)
  47. 47. 48 Advanced Libraries
  48. 48. 49 Spark Streaming  Big-data apps need to process large data streams in real time  Streaming API similar to that of Spark Core  Scales to 100s of nodes  Fault-tolerant stream processing  Integrates with batch + interactive processing  Stream processing as series of small batch jobs  Divide live stream into batches of X seconds  Each batch is processed as an RDD  Results of RDD ops are returned as batches  Requires additional setup to run 24/7 - checkpointing  Spark 1.2 APIs only in Scala/Java, Python API experimental
  49. 49. 50 DStreams - Discretized Streams  Abstraction provided by Streaming API  Sequence of data arriving over time  Represented as a sequence of RDDs  Can be created from various sources  Flume  Kafka  HDFS  Offer two types of operations  Transformations - yield new DStreams  Output operations - write data to external systems  New time related operations like sliding window are also offered
  50. 50. 51 DStream Transformations Stateless  Processing of one batch doesn’t depend on previous batch  Similar to any RDD transformation  map, filter, reduceByKey  Transformations are applied to each individual RDD of the DStream  Can join data with the same batch using join, cogroup etc.  Combine data from multiple DStreams using union  transform can be applied to RDDs within DStreams individually Stateful  Uses intermediate results from previous batches  Require check pointing to enable fault tolerance  Two types  Windowed operations - Transformations based on sliding window of time  updateStateByKey - track state across events for each key (key, event) -> (key, state)
  51. 51. 52 DStream Output Operations  Specify what needs to be done to the final transformed data  If no output operation is specified the DStream is not evaluated  If there is no output operation in the entire streaming context then the context will not start  Common Output Operations  print( ) - prints first 10 elements from each batch of the DStream  saveAsTextFile( ) - saves the output to a file  foreachRDD( ) - run arbitrary operation on each RDD of the DStream  foreachPartition( ) - write each partition to an external database
  52. 52. 53 Machine Learning - MLlib  Spark’s machine learning library designed to run in parallel on clusters  Consists of a variety of learning algorithms accessible from all of Spark’s APIs  A set of functions to call on RDDs but introduces a few new data types  Vectors  LabeledPoints A typical machine learning task consists of the following steps  Data Preparation  Start with an RDD of raw data (text etc.)  Perform data preparation to clean up the data  Feature Extraction  Convert text to numerical features and create an RDD of vectors  Model Training  Apply learning algorithm to the RDD of vectors resulting in a model object  Model Evaluation  Evaluate the model using the test dataset  Tune the model and its parameters  Apply model to real data to perform predictions
  53. 53. 54 Tips & Tricks
  54. 54. 55 Performance Tuning Shuffle in Spark  Performance issues Code on Driver vs Workers  Cause of Errors Serialization  Task not serializable error
  55. 55. 56 Shuffle in Spark  reduceByKey vs groupByKey  Can solve the same problem  groupByKey can cause out of disk error  Prefer reduceByKey, combineByKey, foldByKey over groupByKey
  56. 56. 57 Execution on Driver vs. Workers What is the Driver program?  Programs that declares transformations and actions on RDDs  Program that submits requests to the Spark master  Program that creates the SparkContext  Main program is executed on the Driver  Transformations are executed on the Workers  Actions may transfer data from workers to Driver  Collect sends all the partitions to the driver  Collect on large RDDs can cause Out of Memory  Instead use saveAsText( ) or count( ) or take(N)
  57. 57. 58 Serializations Errors  Serialization Error  org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable:  Happens when…  Initialize variable on driver/master and use on workers  Spark will try to serialize the object and send to workers  Will error out is the object is not serializable  Try to create DB connection on driver and use on workers  Some available fixes  Make the class serializable  Declare instance with in the lambda function  Make NotSerializable object as static and create once per worker using rdd.forEachPartition  Create db connection on each worker
  58. 58. 59 Where do I go from here?
  59. 59. 60 Community   Worldwide events:  Video, presentation archives:  Dev resources:  Workshops:
  60. 60. 61 Books  Learning Spark - Holden Karau, Andy Konwinski, Matei Zaharia, Patrick Wendell  Fast Data Processing with Spark - Holden Karau  Spark in Action - Chris Fregly
  61. 61. 62 Where can I find all the code and examples?  All the code presented in this class and the assignments + data can be found on my github:  Instructions on how to download, compile and run are also given there.  I will keep adding new code and examples so keep checking it!
  62. 62. 63