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This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python.

To follow along with this guide, first, download a packaged release of Spark from the Spark website. Since we won’t be using HDFS, you can download a package for any version of Hadoop.

Note that, before Spark 2.0, the main programming interface of Spark was the Resilient Distributed Dataset (RDD). After Spark 2.0, RDDs are replaced by Dataset, which is strongly-typed like an RDD, but with richer optimizations under the hood. The RDD interface is still supported, and you can get a more detailed reference at the RDD programming guide. However, we highly recommend you to switch to use Dataset, which has better performance than RDD. See the SQL programming guide to get more information about Dataset.

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 (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python. Start it by running the following in the Spark directory:

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 scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]9 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

Or if PySpark is installed with pip in your current environment:

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. Due to Python’s dynamic nature, we don’t need the Dataset to be strongly-typed in Python. As a result, all Datasets in Python are Dataset[Row], and we call it scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 150 to be consistent with the data frame concept in Pandas and R. Let’s make a new DataFrame from the text of the README file in the Spark source directory:

>>> textFile = spark.read.text("README.md")

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

>>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')

Now let’s transform this DataFrame to a new one. We call scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]9 to return a new DataFrame with a subset of the lines in the file.

>>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))

We can chain together transformations and actions:

>>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 15

More on Dataset Operations

Dataset actions and transformations can be used for more complex computations. Let’s say we want to find the line with the most words:

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. scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 152 is called on that Dataset to find the largest word count. The arguments to scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 153 and scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 152 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 scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 155 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> 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 Spark0

Here, we call scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 156 to transform a Dataset of lines to a Dataset of words, and then combine scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 157 and scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 158 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 scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 159:

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 Spark1

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 Spark2

This first maps a line to an integer value and aliases it as “numWords”, creating a new DataFrame. >>> textFile = spark.read.text("README.md")0 is called on that DataFrame to find the largest word count. The arguments to >>> textFile = spark.read.text("README.md")1 and >>> textFile = spark.read.text("README.md")0 are both , we can use >>> textFile = spark.read.text("README.md")3 to get a column from a DataFrame. We can also import pyspark.sql.functions, which provides a lot of convenient functions to build a new Column from an old one.

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

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 Spark3

Here, we use the >>> textFile = spark.read.text("README.md")4 function in >>> textFile = spark.read.text("README.md")1, to transform a Dataset of lines to a Dataset of words, and then combine >>> textFile = spark.read.text("README.md")6 and scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 158 to compute the per-word counts in the file as a DataFrame of 2 columns: “word” and “count”. To collect the word counts in our shell, we can call scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 159:

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 Spark4

Caching

Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small “hot” dataset or when running an iterative algorithm like PageRank. As a simple example, let’s mark our >>> textFile = spark.read.text("README.md")9 dataset to be cached:

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 Spark5

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 >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')0 to a cluster, as described in the .

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 Spark6

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 >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')1 to a cluster, as described in the .

Suppose we wish to write a self-contained application using the Spark API. We will walk through a simple application in Scala (with sbt), Java (with Maven), and Python (pip).

We’ll create a very simple Spark application in Scala–so simple, in fact, that it’s named >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')2:

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 Spark7

Note that applications should define a >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')3 method instead of extending >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')4. Subclasses of >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')4 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 >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')6 to construct a >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')7, then set the application name, and finally call >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')8 to get the >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')7 instance.

Our application depends on the Spark API, so we’ll also include an sbt configuration file, >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))0, which explains that Spark is a dependency. This file also adds a repository that Spark depends on:

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 Spark8

For sbt to work correctly, we’ll need to layout >>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')2 and >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))0 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 >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))3 script to run our program.

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 Spark9

This example will use Maven to compile an application JAR, but any similar build system will work.

We’ll create a very simple Spark application, >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))4:

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]0

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.

To build the program, we also write a Maven >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))5 file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]1

We lay out these files according to the canonical Maven directory structure:

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]2

Now, we can package the application using Maven and execute it with >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))6.

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]3

Now we will show how to write an application using the Python API (PySpark).

If you are building a packaged PySpark application or library you can add it to your setup.py file as:

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]4

As an example, we’ll create a simple Spark application, >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))7:

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]5

This program just counts the number of lines containing ‘a’ and the number containing ‘b’ in a text file. Note that you’ll need to replace YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala and Java examples, we use a SparkSession to create Datasets. For applications that use custom classes or third-party libraries, we can also add code dependencies to >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))3 through its >>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))9 argument by packaging them into a .zip file (see >>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 150 for details). >>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 151 is simple enough that we do not need to specify any code dependencies.

We can run this application using the >>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 152 script:

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]6

If you have PySpark pip installed into your environment (e.g., >>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 153), you can run your application with the regular Python interpreter or use the provided ‘spark-submit’ as you prefer.

scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]7

Other dependency management tools such as Conda and pip can be also used for custom classes or third-party libraries. See also Python Package Management.