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diff --git a/docs/quick-start.md b/docs/quick-start.md index 8f782db5b8..9b9261cfff 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -12,7 +12,7 @@ See the [programming guide](scala-programming-guide.html) for a more complete re To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run: {% highlight bash %} -$ sbt/sbt assembly +$ sbt assembly {% endhighlight %} # Interactive Analysis with the Spark Shell @@ -20,7 +20,7 @@ $ sbt/sbt assembly ## Basics Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively. -Start the shell by running `./spark-shell` in the Spark directory. +Start the shell by running `./bin/spark-shell` in the Spark directory. Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). 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: @@ -99,7 +99,7 @@ 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 `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. @@ -124,7 +124,7 @@ object SimpleApp { } {% endhighlight %} -This program simply 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 proogram. 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 proogram. 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 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: @@ -193,7 +193,7 @@ public class SimpleApp { } {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log 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. @@ -224,11 +224,11 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a 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> +<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: @@ -270,18 +270,18 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs) {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. +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). `SimpleApp` is simple enough that we do not need to specify any code dependencies. -We can run this application using the `pyspark` script: +We can run this application using the `bin/pyspark` script: {% highlight python %} $ cd $SPARK_HOME -$ ./pyspark SimpleApp.py +$ ./bin/pyspark SimpleApp.py ... Lines with a: 46, Lines with b: 23 {% endhighlight python %} @@ -301,14 +301,38 @@ assembly jar (or "uber" jar) containing your code and its dependencies. Both 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 submit -your dependent jars one-by-one when creating a SparkContext. +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 which influence the behavior -of your application. These should be set as -[JVM system properties](configuration.html#system-properties) in your -program. The options will be captured and shipped to all slave nodes. +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 %} ### Accessing Hadoop Filesystems |