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class="title">Quick Start</h1> <ul id="markdown-toc"> <li><a href="#interactive-analysis-with-the-spark-shell" id="markdown-toc-interactive-analysis-with-the-spark-shell">Interactive Analysis with the Spark Shell</a> <ul> <li><a href="#basics" id="markdown-toc-basics">Basics</a></li> <li><a href="#more-on-dataset-operations" id="markdown-toc-more-on-dataset-operations">More on Dataset Operations</a></li> <li><a href="#caching" id="markdown-toc-caching">Caching</a></li> </ul> </li> <li><a href="#self-contained-applications" id="markdown-toc-self-contained-applications">Self-Contained Applications</a></li> <li><a href="#where-to-go-from-here" id="markdown-toc-where-to-go-from-here">Where to Go from Here</a></li> </ul> 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 <a href="https://spark.apache.org/downloads.html">Spark website</a>. 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 <a href="rdd-programming-guide.html">RDD programming guide</a>. However, we highly recommend you to switch to use Dataset, which has better performance than RDD. See the <a href="sql-programming-guide.html">SQL programming guide</a> to get more information about Dataset. <h1 id="interactive-analysis-with-the-spark-shell">Interactive Analysis with the Spark Shell</h1> <h2 id="basics">Basics</h2> 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: <div class="codetabs"> <div data-lang="python"> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/pyspark </code></pre></div> </div> Or if PySpark is installed with pip in your current environment: <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>pyspark </code></pre></div> </div> 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 <code class="language-plaintext highlighter-rouge">DataFrame</code> 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: <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> textFile = spark.read.text("README.md")</code></pre></figure> 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 <a href="api/python/index.html#pyspark.sql.DataFrame">API doc</a>. <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> textFile.count() # Number of rows in this DataFrame 126 >>> textFile.first() # First row in this DataFrame Row(value=u'# Apache Spark')</code></pre></figure> Now let’s transform this DataFrame to a new one. We call <code class="language-plaintext highlighter-rouge">filter</code> to return a new DataFrame with a subset of the lines in the file. <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> linesWithSpark = textFile.filter(textFile.value.contains("Spark"))</code></pre></figure> We can chain together transformations and actions: <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> textFile.filter(textFile.value.contains("Spark")).count() # How many lines contain "Spark"? 15</code></pre></figure> </div> <div data-lang="scala"> <div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>./bin/spark-shell </code></pre></div> </div> 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: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> val textFile = spark.read.textFile("README.md") textFile: org.apache.spark.sql.Dataset[String] = [value: string]</code></pre></figure> 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 <a href="api/scala/org/apache/spark/sql/Dataset.html">API doc</a>. <figure class="highlight"><pre><code class="language-scala" data-lang="scala">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</code></pre></figure> Now let’s transform this Dataset into a new one. We call <code class="language-plaintext highlighter-rouge">filter</code> to return a new Dataset with a subset of the items in the file. <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> val linesWithSpark = textFile.filter(line => line.contains("Spark")) linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]</code></pre></figure> We can chain together transformations and actions: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"? res3: Long = 15</code></pre></figure> </div> </div> <h2 id="more-on-dataset-operations">More on Dataset Operations</h2> Dataset actions and transformations can be used for more complex computations. Let’s say we want to find the line with the most words: <div class="codetabs"> <div data-lang="python"> <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> from pyspark.sql import functions as sf >>> textFile.select(sf.size(sf.split(textFile.value, "\s+")).name("numWords")).agg(sf.max(sf.col("numWords"))).collect() [Row(max(numWords)=15)]</code></pre></figure> This first maps a line to an integer value and aliases it as “numWords”, creating a new DataFrame. <code class="language-plaintext highlighter-rouge">agg</code> is called on that DataFrame to find the largest word count. The arguments to <code class="language-plaintext highlighter-rouge">select</code> and <code class="language-plaintext highlighter-rouge">agg</code> are both <a href="api/python/index.html#pyspark.sql.Column">Column</a>, we can use <code class="language-plaintext highlighter-rouge">df.colName</code> 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: <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> wordCounts = textFile.select(sf.explode(sf.split(textFile.value, "\s+")).alias("word")).groupBy("word").count()</code></pre></figure> Here, we use the <code class="language-plaintext highlighter-rouge">explode</code> function in <code class="language-plaintext highlighter-rouge">select</code>, to transform a Dataset of lines to a Dataset of words, and then combine <code class="language-plaintext highlighter-rouge">groupBy</code> and <code class="language-plaintext highlighter-rouge">count</code> 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 <code class="language-plaintext highlighter-rouge">collect</code>: <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> wordCounts.collect() [Row(word=u'online', count=1), Row(word=u'graphs', count=1), ...]</code></pre></figure> </div> <div data-lang="scala"> <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b) res4: Int = 15</code></pre></figure> This first maps a line to an integer value, creating a new Dataset. <code class="language-plaintext highlighter-rouge">reduce</code> is called on that Dataset to find the largest word count. The arguments to <code class="language-plaintext highlighter-rouge">map</code> and <code class="language-plaintext highlighter-rouge">reduce</code> 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 <code class="language-plaintext highlighter-rouge">Math.max()</code> function to make this code easier to understand: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">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</code></pre></figure> One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> val wordCounts = textFile.flatMap(line => line.split(" ")).groupByKey(identity).count() wordCounts: org.apache.spark.sql.Dataset[(String, Long)] = [value: string, count(1): bigint]</code></pre></figure> Here, we call <code class="language-plaintext highlighter-rouge">flatMap</code> to transform a Dataset of lines to a Dataset of words, and then combine <code class="language-plaintext highlighter-rouge">groupByKey</code> and <code class="language-plaintext highlighter-rouge">count</code> 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 <code class="language-plaintext highlighter-rouge">collect</code>: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> wordCounts.collect() res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)</code></pre></figure> </div> </div> <h2 id="caching">Caching</h2> 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 <code class="language-plaintext highlighter-rouge">linesWithSpark</code> dataset to be cached: <div class="codetabs"> <div data-lang="python"> <figure class="highlight"><pre><code class="language-python" data-lang="python">>>> linesWithSpark.cache() >>> linesWithSpark.count() 15 >>> linesWithSpark.count() 15</code></pre></figure> 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 <code class="language-plaintext highlighter-rouge">bin/pyspark</code> to a cluster, as described in the <a href="rdd-programming-guide.html#using-the-shell">RDD programming guide</a>. </div> <div data-lang="scala"> <figure class="highlight"><pre><code class="language-scala" data-lang="scala">scala> linesWithSpark.cache() res7: linesWithSpark.type = [value: string] scala> linesWithSpark.count() res8: Long = 15 scala> linesWithSpark.count() res9: Long = 15</code></pre></figure> 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 <code class="language-plaintext highlighter-rouge">bin/spark-shell</code> to a cluster, as described in the <a href="rdd-programming-guide.html#using-the-shell">RDD programming guide</a>. </div> </div> <h1 id="self-contained-applications">Self-Contained Applications</h1> 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). <div class="codetabs"> <div data-lang="python"> 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: <figure class="highlight"><pre><code class="language-python" data-lang="python"> install_requires=[ 'pyspark==3.5.3' ]</code></pre></figure> As an example, we’ll create a simple Spark application, <code class="language-plaintext highlighter-rouge">SimpleApp.py</code>: <figure class="highlight"><pre><code class="language-python" data-lang="python">"""SimpleApp.py""" from pyspark.sql import SparkSession logFile = "YOUR_SPARK_HOME/README.md" # Should be some file on your system spark = SparkSession.builder.appName("SimpleApp").getOrCreate() logData = spark.read.text(logFile).cache() numAs = logData.filter(logData.value.contains('a')).count() numBs = logData.filter(logData.value.contains('b')).count() print("Lines with a: %i, lines with b: %i" % (numAs, numBs)) spark.stop()</code></pre></figure> 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 <code class="language-plaintext highlighter-rouge">spark-submit</code> through its <code class="language-plaintext highlighter-rouge">--py-files</code> argument by packaging them into a .zip file (see <code class="language-plaintext highlighter-rouge">spark-submit --help</code> for details). <code class="language-plaintext highlighter-rouge">SimpleApp</code> is simple enough that we do not need to specify any code dependencies. We can run this application using the <code class="language-plaintext highlighter-rouge">bin/spark-submit</code> script: <figure class="highlight"><pre><code class="language-bash" data-lang="bash"># Use spark-submit to run your application $ YOUR_SPARK_HOME/bin/spark-submit \ --master local[4] \ SimpleApp.py ... Lines with a: 46, Lines with b: 23</code></pre></figure> If you have PySpark pip installed into your environment (e.g., <code class="language-plaintext highlighter-rouge">pip install pyspark</code>), you can run your application with the regular Python interpreter or use the provided ‘spark-submit’ as you prefer. <figure class="highlight"><pre><code class="language-bash" data-lang="bash"># Use the Python interpreter to run your application $ python SimpleApp.py ... Lines with a: 46, Lines with b: 23</code></pre></figure> </div> <div data-lang="scala"> We’ll create a very simple Spark application in Scala–so simple, in fact, that it’s named <code class="language-plaintext highlighter-rouge">SimpleApp.scala</code>: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">/* SimpleApp.scala */ import org.apache.spark.sql.SparkSession object SimpleApp { def main(args: Array[String]): Unit = { 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() } }</code></pre></figure> Note that applications should define a <code class="language-plaintext highlighter-rouge">main()</code> method instead of extending <code class="language-plaintext highlighter-rouge">scala.App</code>. Subclasses of <code class="language-plaintext highlighter-rouge">scala.App</code> 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 <code class="language-plaintext highlighter-rouge">SparkSession.builder</code> to construct a <code class="language-plaintext highlighter-rouge">SparkSession</code>, then set the application name, and finally call <code class="language-plaintext highlighter-rouge">getOrCreate</code> to get the <code class="language-plaintext highlighter-rouge">SparkSession</code> instance. Our application depends on the Spark API, so we’ll also include an sbt configuration file, <code class="language-plaintext highlighter-rouge">build.sbt</code>, which explains that Spark is a dependency. This file also adds a repository that Spark depends on: <figure class="highlight"><pre><code class="language-scala" data-lang="scala">name := "Simple Project" version := "1.0" scalaVersion := "2.12.18" libraryDependencies += "org.apache.spark" %% "spark-sql" % "3.5.3"</code></pre></figure> For sbt to work correctly, we’ll need to layout <code class="language-plaintext highlighter-rouge">SimpleApp.scala</code> and <code class="language-plaintext highlighter-rouge">build.sbt</code> 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 <code class="language-plaintext highlighter-rouge">spark-submit</code> script to run our program. <figure class="highlight"><pre><code class="language-bash" data-lang="bash"># 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</code></pre></figure> </div> <div data-lang="java"> 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, <code class="language-plaintext highlighter-rouge">SimpleApp.java</code>: <figure class="highlight"><pre><code class="language-java" data-lang="java">/* SimpleApp.java */ import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.Dataset; public class SimpleApp { public static void main(String[] args) { String logFile = "YOUR_SPARK_HOME/README.md"; // Should be some file on your system SparkSession spark = SparkSession.builder().appName("Simple Application").getOrCreate(); Dataset<String> logData = spark.read().textFile(logFile).cache(); long numAs = logData.filter(s -> s.contains("a")).count(); long numBs = logData.filter(s -> s.contains("b")).count(); System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs); spark.stop(); } }</code></pre></figure> 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 <code class="language-plaintext highlighter-rouge">pom.xml</code> file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version. <figure class="highlight"><pre><code class="language-xml" data-lang="xml"><project> <groupId>edu.berkeley</groupId> <artifactId>simple-project</artifactId> <modelVersion>4.0.0</modelVersion> <name>Simple Project</name> <packaging>jar</packaging> <version>1.0</version> <dependencies> <dependency>  <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.12</artifactId> <version>3.5.3</version> <scope>provided</scope> </dependency> </dependencies> </project></code></pre></figure> We lay out these files according to the canonical Maven directory structure: <figure class="highlight"><pre><code class="language-bash" data-lang="bash">$ find . ./pom.xml ./src ./src/main ./src/main/java ./src/main/java/SimpleApp.java</code></pre></figure> Now, we can package the application using Maven and execute it with <code class="language-plaintext highlighter-rouge">./bin/spark-submit</code>. <figure class="highlight"><pre><code class="language-bash" data-lang="bash"># Package a JAR containing your application $ mvn package ... [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar # Use spark-submit to run your application $ YOUR_SPARK_HOME/bin/spark-submit \ --class "SimpleApp" \ --master local[4] \ target/simple-project-1.0.jar ... Lines with a: 46, Lines with b: 23</code></pre></figure> </div> </div> Other dependency management tools such as Conda and pip can be also used for custom classes or third-party libraries. See also <a href="api/python/user_guide/python_packaging.html">Python Package Management</a>. <h1 id="where-to-go-from-here">Where to Go from Here</h1> Congratulations on running your first Spark application! <ul> <li>For an in-depth overview of the API, start with the <a href="rdd-programming-guide.html">RDD programming guide</a> and the <a href="sql-programming-guide.html">SQL programming guide</a>, or see “Programming Guides” menu for other components.</li> <li>For running applications on a cluster, head to the <a href="cluster-overview.html">deployment overview</a>.</li> <li>Finally, Spark includes several samples in the <code class="language-plaintext highlighter-rouge">examples</code> directory (<a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples">Scala</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples">Java</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/python">Python</a>, <a href="https://github.com/apache/spark/tree/master/examples/src/main/r">R</a>). You can run them as follows:</li> </ul> <figure class="highlight"><pre><code class="language-bash" data-lang="bash"># For Scala and Java, use run-example: ./bin/run-example SparkPi # For Python examples, use spark-submit directly: ./bin/spark-submit examples/src/main/python/pi.py # For R examples, use spark-submit directly: ./bin/spark-submit examples/src/main/r/dataframe.R</code></pre></figure> </div>  </div> <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/js/bootstrap.bundle.min.js" integrity="sha384-MrcW6ZMFYlzcLA8Nl+NtUVF0sA7MsXsP1UyJoMp4YLEuNSfAP+JcXn/tWtIaxVXM" crossorigin="anonymous"></script> <script src="https://code.jquery.com/jquery.js"></script> <script src="js/vendor/anchor.min.js"></script> <script src="js/main.js"></script> <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/docsearch.js@2/dist/cdn/docsearch.min.js"></script> <script type="text/javascript"> // DocSearch is entirely free and automated. 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