A tutorial presentation based on spark.apache.org documentation. I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.

1. Farzad Nozarian
4/18/15 @AUT

2. Purpose
This tutorial provides a quick introduction to using Spark. We will first
introduce the API through Spark’s interactive shell, then show how to write
applications in Scala.
To follow along with this guide, first download a packaged release of Spark
from the Spark website.
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3. Interactive Analysis with the Spark Shell-
Basics
• Spark’s shell provides a simple way to learn the API, as well as a powerful tool
to analyze data interactively.
• It is available in either Scala or Python.
• Start it by running the following in the Spark directory:
• RDDs can be created from Hadoop InputFormats (such as HDFS files) or by
transforming other RDDs.
• Let’s make a new RDD from the text of the README file in the Spark source
directory:
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./bin/spark-shell
scala> val textFile = sc.textFile("README.md")
textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3

4. Interactive Analysis with the Spark Shell-
Basics
• RDDs have actions, which return values, and transformations, which return
pointers to new RDDs. Let’s start with a few actions:
• Now let’s use a transformation:
• We will use the filter transformation to return a new RDD with a subset of the
items in the file.
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scala> textFile.count() // Number of items in this RDD
res0: Long = 126
scala> textFile.first() // First item in this RDD
res1: String = # Apache Spark
scala> val linesWithSpark = textFile.filter(line =>
line.contains("Spark"))
linesWithSpark: spark.RDD[String] = spark.FilteredRDD@7dd4af09

5. Interactive Analysis with the Spark Shell-
More on RDD Operations
• We can chain together transformations and actions:
• RDD actions and transformations can be used for more complex computations.
• Let’s say we want to find the line with the most words:
• The arguments to map and reduce are Scala function literals (closures), and can
use any language feature or Scala/Java library.
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scala> textFile.filter(line => line.contains("Spark")).count()
// How many lines contain "Spark"?
res3: Long = 15
scala> textFile.map(line => line.split(" ").size).reduce((a, b)
=> if (a > b) a else b)
res4: Long = 15

6. Interactive Analysis with the Spark Shell-
More on RDD Operations
• We can easily call functions declared elsewhere.
• We’ll use Math.max() function to make previous code easier to understand:
• One common data flow pattern is MapReduce, as popularized by Hadoop.
• Spark can implement MapReduce flows easily:
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scala> import java.lang.Math
import java.lang.Math
scala> textFile.map(line => line.split(" ").size).reduce((a, b)
=> Math.max(a, b))
res5: Int = 15
scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word =>
(word, 1)).reduceByKey((a, b) => a + b)
wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8

7. Interactive Analysis with the Spark Shell-
More on RDD Operations
• Here, we combined the flatMap, map and reduceByKey transformations to
compute the per-word counts in the file as an RDD of (String, Int) pairs.
• To collect the word counts in our shell, we can use the collect action:
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scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3),
(Because,1), (Python,2), (agree,1), (cluster.,1), ...)

8. Interactive Analysis with the Spark Shell-
Caching
• Spark also supports pulling data sets into a cluster-wide in-memory cache.
• This is very useful when data is accessed repeatedly:
• Querying a small “hot” dataset.
• Running an iterative algorithm like PageRank.
• Let’s mark our linesWithSpark dataset to be cached:
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scala> linesWithSpark.cache()
res7: spark.RDD[String] = spark.FilteredRDD@17e51082
scala> linesWithSpark.count()
res8: Long = 15
scala> linesWithSpark.count()
res9: Long = 15

9. Self-Contained Applications
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/* SimpleApp.scala */
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
object SimpleApp {
def main(args: Array[String]) {
val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
val conf = new SparkConf().setAppName("Simple Application")
val sc = new SparkContext(conf)
val logData = sc.textFile(logFile, 2).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
}

10. Self-Contained Applications (Cont.)
• This program just counts the number of lines containing ‘a’ and the
number containing ‘b’ in the Spark README.
• Note that you’ll need to replace YOUR_SPARK_HOME with the location
where Spark is installed.
• Note that applications should define a main() method instead of
extending scala.App. Subclasses of scala.App may not work correctly.
• Unlike the earlier examples with the Spark shell, which initializes its own
SparkContext, we initialize a SparkContext as part of the program.
• We pass the SparkContext constructor a SparkConf object which
contains information about our application.
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11. Self-Contained Applications (Cont.)
• Our application depends on the Spark API, so we’ll also include an sbt
configuration file, simple.sbt which explains that Spark is a dependency.
• For sbt to work correctly, we’ll need to layout SimpleApp.scala and
simple.sbt according to the typical directory structure.
• Then we can create a JAR package containing the application’s code and
use the spark-submit script to run our program.
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name := "Simple Project"
version := "1.0"
scalaVersion := "2.10.4"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.3.1"

12. Self-Contained Applications (Cont.)
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# Your directory layout should look like this
$ find .
.
./simple.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleApp.scala
# Package a jar containing your application
$ sbt package
...
[info] Packaging {..}/{..}/target/scala-2.10/simple-project_2.10-1.0.jar
# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit
--class "SimpleApp"
--master local[4]
target/scala-2.10/simple-project_2.10-1.0.jar
...
Lines with a: 46, Lines with b: 23