./bin/spark-shell

Spark’s primary abstraction is a distributed collection of items called a Dataset. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. Let’s make a new Dataset from the text of the README file in the Spark source directory:

scala> val textFile = spark.read.textFile("README.md")
textFile: org.apache.spark.sql.Dataset[String] = [value: string]

You can get values from Dataset directly, by calling some actions, or transform the Dataset to get a new one. For more details, please read the API doc.

scala> textFile.count() // Number of items in this Dataset
res0: Long = 126 // May be different from yours as README.md will change over time, similar to other outputs

scala> textFile.first() // First item in this Dataset
res1: String = # Apache Spark

Now let’s transform this Dataset into a new one. We call filter to return a new Dataset with a subset of the items in the file.

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]

We can chain together transformations and actions:

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

This first maps a line to an integer value, creating a new Dataset. reduce is called on that Dataset to find the largest word count. The arguments to map and reduce are Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We’ll use Math.max() function to make this code easier to understand:

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

One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:

scala> val wordCounts = textFile.flatMap(line => line.split(" ")).groupByKey(identity).count()
wordCounts: org.apache.spark.sql.Dataset[(String, Long)] = [value: string, count(1): bigint]

Here, we call flatMap to transform a Dataset of lines to a Dataset of words, and then combine groupByKey and count to compute the per-word counts in the file as a Dataset of (String, Long) pairs. To collect the word counts in our shell, we can call collect:

scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)

scala> linesWithSpark.cache()
res7: linesWithSpark.type = [value: string]

scala> linesWithSpark.count()
res8: Long = 15

scala> linesWithSpark.count()
res9: Long = 15

It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting bin/spark-shell to a cluster, as described in the RDD programming guide.

We’ll create a very simple Spark application in Scala–so simple, in fact, that it’s named SimpleApp.scala:

/* SimpleApp.scala */
import org.apache.spark.sql.SparkSession

object SimpleApp {
  def main(args: Array[String]) {
    val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
    val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
    val logData = spark.read.textFile(logFile).cache()
    val numAs = logData.filter(line => line.contains("a")).count()
    val numBs = logData.filter(line => line.contains("b")).count()
    println(s"Lines with a: $numAs, Lines with b: $numBs")
    spark.stop()
  }
}

Note that applications should define a main() method instead of extending scala.App. Subclasses of scala.App may not work correctly.

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. Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program.

We call SparkSession.builder to construct a [[SparkSession]], then set the application name, and finally call getOrCreate to get the [[SparkSession]] instance.

Our application depends on the Spark API, so we’ll also include an sbt configuration file, build.sbt, which explains that Spark is a dependency. This file also adds a repository that Spark depends on:

name := "Simple Project"

version := "1.0"

scalaVersion := "2.12.10"

libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.4.5"

For sbt to work correctly, we’ll need to layout SimpleApp.scala and build.sbt according to the typical directory structure. Once that is in place, we can create a JAR package containing the application’s code, then use the spark-submit script to run our program.

# Your directory layout should look like this
$ find .
.
./build.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleApp.scala

# Package a jar containing your application
$ sbt package
...
[info] Packaging {..}/{..}/target/scala-2.12/simple-project_2.12-1.0.jar

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit   --class "SimpleApp"   --master local[4]   target/scala-2.12/simple-project_2.12-1.0.jar
...
Lines with a: 46, Lines with b: 23