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diff --git a/docs/quick-start.md b/docs/quick-start.md index 60e8b1ba0e..6b4f4ba425 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -99,23 +99,32 @@ scala> linesWithSpark.count() res9: Long = 15 {% endhighlight %} -It may seem silly to use Spark to explore and cache a 30-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 [programming guide](scala-programming-guide.html#initializing-spark). +It may seem silly to use Spark to explore and cache a 30-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 [programming guide](scala-programming-guide.html#initializing-spark). -# A Standalone App in Scala -Now say we wanted to write a standalone application using the Spark API. We will walk through a simple application in both Scala (with SBT), Java (with Maven), and Python. If you are using other build systems, consider using the Spark assembly JAR described in the developer guide. +# A Standalone Application +Now say we wanted to write a standalone application using the Spark API. We will walk through a +simple application in both Scala (with SBT), Java (with Maven), and Python. -We'll create a very simple Spark application in Scala. So simple, in fact, that it's named `SimpleApp.scala`: +<div class="codetabs"> +<div data-lang="scala" markdown="1"> + +We'll create a very simple Spark application in Scala. So simple, in fact, that it's +named `SimpleApp.scala`: {% highlight scala %} /*** 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 sc = new SparkContext("local", "Simple App", "YOUR_SPARK_HOME", - List("target/scala-{{site.SCALA_BINARY_VERSION}}/simple-project_{{site.SCALA_BINARY_VERSION}}-1.0.jar")) + 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() @@ -124,9 +133,17 @@ object SimpleApp { } {% endhighlight %} -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 SparkContext, we initialize a SparkContext as part of the program. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. +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 SparkContext, +we initialize a SparkContext as part of the program. + +We pass the SparkContext constructor a SparkConf object which contains information about our +application. We also call sc.addJar to make sure that when our application is launched in cluster +mode, the jar file containing it will be shipped automatically to worker nodes. -This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on: +This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` +which explains that Spark is a dependency. This file also adds a repository that Spark depends on: {% highlight scala %} name := "Simple Project" @@ -140,15 +157,12 @@ libraryDependencies += "org.apache.spark" %% "spark-core" % "{{site.SPARK_VERSIO resolvers += "Akka Repository" at "http://repo.akka.io/releases/" {% endhighlight %} -If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: - -{% highlight scala %} -libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<your-hdfs-version>" -{% endhighlight %} - -Finally, for sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.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 `sbt/sbt run` to execute our program. +For sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.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. {% highlight bash %} +# Your directory layout should look like this $ find . . ./simple.sbt @@ -157,27 +171,36 @@ $ find . ./src/main/scala ./src/main/scala/SimpleApp.scala -$ sbt/sbt package -$ sbt/sbt run +# 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 target/scala-2.10/simple-project_2.10-1.0.jar \ + --class "SimpleApp" \ + --master local[4] ... Lines with a: 46, Lines with b: 23 {% endhighlight %} -# A Standalone App in Java -Now say we wanted to write a standalone application using the Java API. We will walk through doing this with Maven. If you are using other build systems, consider using the Spark assembly JAR described in the developer guide. +</div> +<div data-lang="java" markdown="1"> +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, `SimpleApp.java`: {% highlight java %} /*** SimpleApp.java ***/ import org.apache.spark.api.java.*; +import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.Function; public class SimpleApp { public static void main(String[] args) { - String logFile = "$YOUR_SPARK_HOME/README.md"; // Should be some file on your system - JavaSparkContext sc = new JavaSparkContext("local", "Simple App", - "$YOUR_SPARK_HOME", new String[]{"target/simple-project-1.0.jar"}); + String logFile = "YOUR_SPARK_HOME/README.md"; // Should be some file on your system + SparkConf conf = new SparkConf().setAppName("Simple Application"); + JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> logData = sc.textFile(logFile).cache(); long numAs = logData.filter(new Function<String, Boolean>() { @@ -193,9 +216,16 @@ public class SimpleApp { } {% endhighlight %} -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 example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail. +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 example, we initialize a SparkContext, though we use the special +`JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by +`JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes +that extend `spark.api.java.function.Function`. The +[Java programming guide](java-programming-guide.html) describes these differences in more detail. -To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version. +To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. +Note that Spark artifacts are tagged with a Scala version. {% highlight xml %} <project> @@ -221,16 +251,6 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a </project> {% endhighlight %} -If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: - -{% highlight xml %} -<dependency> - <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-client</artifactId> - <version>...</version> -</dependency> -{% endhighlight %} - We lay out these files according to the canonical Maven directory structure: {% highlight bash %} $ find . @@ -241,16 +261,25 @@ $ find . ./src/main/java/SimpleApp.java {% endhighlight %} -Now, we can execute the application using Maven: +Now, we can package the application using Maven and execute it with `./bin/spark-submit`. {% highlight bash %} +# Package a jar containing your application $ mvn package -$ mvn exec:java -Dexec.mainClass="SimpleApp" +... +[INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar + +# Use spark-submit to run your application +$ YOUR_SPARK_HOME/bin/spark-submit target/simple-project-1.0.jar \ + --class "SimpleApp" \ + --master local[4] ... Lines with a: 46, Lines with b: 23 {% endhighlight %} -# A Standalone App in Python +</div> +<div data-lang="python" markdown="1"> + Now we will show how to write a standalone application using the Python API (PySpark). As an example, we'll create a simple Spark application, `SimpleApp.py`: @@ -259,7 +288,7 @@ As an example, we'll create a simple Spark application, `SimpleApp.py`: """SimpleApp.py""" from pyspark import SparkContext -logFile = "$YOUR_SPARK_HOME/README.md" # Should be some file on your system +logFile = "YOUR_SPARK_HOME/README.md" # Should be some file on your system sc = SparkContext("local", "Simple App") logData = sc.textFile(logFile).cache() @@ -270,11 +299,15 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs) {% endhighlight %} -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. +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 SparkContext to create RDDs. -We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference. -For applications that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html). +We can pass Python functions to Spark, which are automatically serialized along with any variables +that they reference. +For applications that use custom classes or third-party libraries, we can add those code +dependencies to SparkContext to ensure that they will be available on remote machines; this is +described in more detail in the [Python programming guide](python-programming-guide.html). `SimpleApp` is simple enough that we do not need to specify any code dependencies. We can run this application using the `bin/pyspark` script: @@ -286,57 +319,12 @@ $ ./bin/pyspark SimpleApp.py Lines with a: 46, Lines with b: 23 {% endhighlight python %} -# Running on a Cluster - -There are a few additional considerations when running applicaitons on a -[Spark](spark-standalone.html), [YARN](running-on-yarn.html), or -[Mesos](running-on-mesos.html) cluster. - -### Including Your Dependencies -If your code depends on other projects, you will need to ensure they are also -present on the slave nodes. A popular approach is to create an -assembly jar (or "uber" jar) containing your code and its dependencies. Both -[sbt](https://github.com/sbt/sbt-assembly) and -[Maven](http://maven.apache.org/plugins/maven-assembly-plugin/) -have assembly plugins. When creating assembly jars, list Spark -itself as a `provided` dependency; it need not be bundled since it is -already present on the slaves. Once you have an assembled jar, -add it to the SparkContext as shown here. It is also possible to add -your dependent jars one-by-one using the `addJar` method of `SparkContext`. - -For Python, you can use the `pyFiles` argument of SparkContext -or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed. - -### Setting Configuration Options -Spark includes several [configuration options](configuration.html#spark-properties) -that influence the behavior of your application. -These should be set by building a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) -object and passing it to the SparkContext constructor. -For example, in Java and Scala, you can do: - -{% highlight scala %} -import org.apache.spark.{SparkConf, SparkContext} -val conf = new SparkConf() - .setMaster("local") - .setAppName("My application") - .set("spark.executor.memory", "1g") -val sc = new SparkContext(conf) -{% endhighlight %} - -Or in Python: - -{% highlight scala %} -from pyspark import SparkConf, SparkContext -conf = SparkConf() -conf.setMaster("local") -conf.setAppName("My application") -conf.set("spark.executor.memory", "1g")) -sc = SparkContext(conf = conf) -{% endhighlight %} +</div> +</div> -### Accessing Hadoop Filesystems +# Where to go from here +Congratulations on running your first Spark application! -The examples here access a local file. To read data from a distributed -filesystem, such as HDFS, include -[Hadoop version information](index.html#a-note-about-hadoop-versions) -in your build file. By default, Spark builds against HDFS 1.0.4. +* For an in-depth overview of the API see "Programming Guides" menu section. +* For running applications on a cluster head to the [deployment overview](cluster-overview.html). +* For configuration options available to Spark applications see the [configuration page](configuration.html).
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