This document provides a quick-and-dirty look at Spark's API. See the [programming guide](scala-programming-guide.html) for a complete reference. To follow along with this guide, you only need to have successfully built Spark on one machine. Building Spark is as simple as running
Start the Spark shell by executing `./spark-shell` in the Spark directory.
Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDD's can be created from Hadoop InputFormat's (such as HDFS files) or by transforming other RDD's. Let's make a new RDD derived from the text of the README file in the Spark source directory:
RDD's have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDD's. Let's start with a few actions:
Now let's use a transformation. We will use the [filter](scala-programming-guide.html#transformations)() transformation to return a new RDD with a subset of the items in the file.
This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to [map](scala-programming-guide.html#transformations)() and [reduce](scala-programming-guide.html#actions)() are scala closures. We can easily include functions declared elsewhere, or include existing functions in our anonymous closures. For instance, we can use `Math.max()` to make this code easier to understand.
Here, we combined the [flatMap](scala-programming-guide.html#transformations)(), [map](scala-programming-guide.html#transformations)() and [reduceByKey](scala-programming-guide.html#transformations)() transformations to create per-word counts in the file. To collect the word counts in our shell, we can use the [collect](scala-programming-guide.html#actions)() action:
Spark also supports pulling data sets into a cluster-wide cache. This is very useful when data is accessed iteratively, such as in machine learning jobs, or repeatedly, such as when small "hot data" is queried repeatedly. As a simple example, let's pull part of our file into memory:
{% highlight scala %}
scala> val linesWithSparkCached = linesWithSpark.cache()
It may seem silly to use a 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.
Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. 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 job, the directory where Spark is installed, and a name for the jar file containing the job's sources. 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.
Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a jar package containing the job's code, then use `sbt run` to execute our example job.
This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.
Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
{% highlight bash %}
$ sbt/sbt publish-local
{% endhighlight %}
Next, we'll create a very simple Spark job in Scala. So simple, in fact, that it's named `SimpleJob.java`:
This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. 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 job, the directory where Spark is installed, and a name for the jar file containing the job's sources. 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 example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.