programming-guide.md

layout: global
title: Spark Programming Guide
description: Spark SPARK_VERSION_SHORT programming guide in Java, Scala and Python

• This will become a table of contents (this text will be scraped). {:toc}

Overview

At a high level, every Spark application consists of a driver program that runs the user's main function and executes various parallel operations on a cluster. The main abstraction Spark provides is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to persist an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.

A second abstraction in Spark is shared variables that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: broadcast variables, which can be used to cache a value in memory on all nodes, and accumulators, which are variables that are only "added" to, such as counters and sums.

This guide shows each of these features in each of Spark's supported languages. It is easiest to follow along with if you launch Spark's interactive shell -- either bin/spark-shell for the Scala shell or bin/pyspark for the Python one.

Linking with Spark

Spark {{site.SPARK_VERSION}} is built and distributed to work with Scala {{site.SCALA_BINARY_VERSION}} by default. (Spark can be built to work with other versions of Scala, too.) To write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X).

To write a Spark application, you need to add a Maven dependency on Spark. Spark is available through Maven Central at:

groupId = org.apache.spark
artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
version = {{site.SPARK_VERSION}}

In addition, if you wish to access an HDFS cluster, you need to add a dependency on hadoop-client for your version of HDFS.

groupId = org.apache.hadoop
artifactId = hadoop-client
version = <your-hdfs-version>

Finally, you need to import some Spark classes into your program. Add the following lines:

{% highlight scala %} import org.apache.spark.SparkContext import org.apache.spark.SparkConf {% endhighlight %}

(Before Spark 1.3.0, you need to explicitly import org.apache.spark.SparkContext._ to enable essential implicit conversions.)

Spark {{site.SPARK_VERSION}} works with Java 7 and higher. If you are using Java 8, Spark supports lambda expressions for concisely writing functions, otherwise you can use the classes in the org.apache.spark.api.java.function package.

To write a Spark application in Java, you need to add a dependency on Spark. Spark is available through Maven Central at:

groupId = org.apache.spark
artifactId = spark-core_{{site.SCALA_BINARY_VERSION}}
version = {{site.SPARK_VERSION}}

In addition, if you wish to access an HDFS cluster, you need to add a dependency on hadoop-client for your version of HDFS.

groupId = org.apache.hadoop
artifactId = hadoop-client
version = <your-hdfs-version>

Finally, you need to import some Spark classes into your program. Add the following lines:

{% highlight scala %} import org.apache.spark.api.java.JavaSparkContext import org.apache.spark.api.java.JavaRDD import org.apache.spark.SparkConf {% endhighlight %}

Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter, so C libraries like NumPy can be used. It also works with PyPy 2.3+.

To run Spark applications in Python, use the bin/spark-submit script located in the Spark directory. This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster. You can also use bin/pyspark to launch an interactive Python shell.

If you wish to access HDFS data, you need to use a build of PySpark linking to your version of HDFS. Prebuilt packages are also available on the Spark homepage for common HDFS versions.

Finally, you need to import some Spark classes into your program. Add the following line:

{% highlight python %} from pyspark import SparkContext, SparkConf {% endhighlight %}

PySpark requires the same minor version of Python in both driver and workers. It uses the default python version in PATH, you can specify which version of Python you want to use by PYSPARK_PYTHON, for example:

{% highlight bash %} $ PYSPARK_PYTHON=python3.4 bin/pyspark $ PYSPARK_PYTHON=/opt/pypy-2.5/bin/pypy bin/spark-submit examples/src/main/python/pi.py {% endhighlight %}

Initializing Spark

The first thing a Spark program must do is to create a SparkContext object, which tells Spark how to access a cluster. To create a SparkContext you first need to build a SparkConf object that contains information about your application.

Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one.

{% highlight scala %} val conf = new SparkConf().setAppName(appName).setMaster(master) new SparkContext(conf) {% endhighlight %}

The first thing a Spark program must do is to create a JavaSparkContext object, which tells Spark how to access a cluster. To create a SparkContext you first need to build a SparkConf object that contains information about your application.

{% highlight java %} SparkConf conf = new SparkConf().setAppName(appName).setMaster(master); JavaSparkContext sc = new JavaSparkContext(conf); {% endhighlight %}

The first thing a Spark program must do is to create a SparkContext object, which tells Spark how to access a cluster. To create a SparkContext you first need to build a SparkConf object that contains information about your application.

{% highlight python %} conf = SparkConf().setAppName(appName).setMaster(master) sc = SparkContext(conf=conf) {% endhighlight %}

The appName parameter is a name for your application to show on the cluster UI. master is a Spark, Mesos or YARN cluster URL, or a special "local" string to run in local mode. In practice, when running on a cluster, you will not want to hardcode master in the program, but rather launch the application with spark-submit and receive it there. However, for local testing and unit tests, you can pass "local" to run Spark in-process.

Using the Shell

In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called sc. Making your own SparkContext will not work. You can set which master the context connects to using the --master argument, and you can add JARs to the classpath by passing a comma-separated list to the --jars argument. You can also add dependencies (e.g. Spark Packages) to your shell session by supplying a comma-separated list of maven coordinates to the --packages argument. Any additional repositories where dependencies might exist (e.g. SonaType) can be passed to the --repositories argument. For example, to run bin/spark-shell on exactly four cores, use:

{% highlight bash %} $ ./bin/spark-shell --master local[4] {% endhighlight %}

Or, to also add code.jar to its classpath, use:

{% highlight bash %} $ ./bin/spark-shell --master local[4] --jars code.jar {% endhighlight %}

To include a dependency using maven coordinates:

{% highlight bash %} $ ./bin/spark-shell --master local[4] --packages "org.example:example:0.1" {% endhighlight %}

For a complete list of options, run spark-shell --help. Behind the scenes, spark-shell invokes the more general spark-submit script.

In the PySpark shell, a special interpreter-aware SparkContext is already created for you, in the variable called sc. Making your own SparkContext will not work. You can set which master the context connects to using the --master argument, and you can add Python .zip, .egg or .py files to the runtime path by passing a comma-separated list to --py-files. You can also add dependencies (e.g. Spark Packages) to your shell session by supplying a comma-separated list of maven coordinates to the --packages argument. Any additional repositories where dependencies might exist (e.g. SonaType) can be passed to the --repositories argument. Any python dependencies a Spark Package has (listed in the requirements.txt of that package) must be manually installed using pip when necessary. For example, to run bin/pyspark on exactly four cores, use:

{% highlight bash %} $ ./bin/pyspark --master local[4] {% endhighlight %}

Or, to also add code.py to the search path (in order to later be able to import code), use:

{% highlight bash %} $ ./bin/pyspark --master local[4] --py-files code.py {% endhighlight %}

For a complete list of options, run pyspark --help. Behind the scenes, pyspark invokes the more general spark-submit script.

It is also possible to launch the PySpark shell in IPython, the enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To use IPython, set the PYSPARK_DRIVER_PYTHON variable to ipython when running bin/pyspark:

{% highlight bash %} $ PYSPARK_DRIVER_PYTHON=ipython ./bin/pyspark {% endhighlight %}

To use the Jupyter notebook (previously known as the IPython notebook),

{% highlight bash %} $ PYSPARK_DRIVER_PYTHON=jupyter ./bin/pyspark {% endhighlight %}

You can customize the ipython or jupyter commands by setting PYSPARK_DRIVER_PYTHON_OPTS.

After the Jupyter Notebook server is launched, you can create a new "Python 2" notebook from the "Files" tab. Inside the notebook, you can input the command %pylab inline as part of your notebook before you start to try Spark from the Jupyter notebook.

Resilient Distributed Datasets (RDDs)

Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.

Parallelized Collections

Parallelized collections are created by calling SparkContext's parallelize method on an existing collection in your driver program (a Scala Seq). The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:

{% highlight scala %} val data = Array(1, 2, 3, 4, 5) val distData = sc.parallelize(data) {% endhighlight %}

Once created, the distributed dataset (distData) can be operated on in parallel. For example, we might call distData.reduce((a, b) => a + b) to add up the elements of the array. We describe operations on distributed datasets later on.

Parallelized collections are created by calling JavaSparkContext's parallelize method on an existing Collection in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:

{% highlight java %} List data = Arrays.asList(1, 2, 3, 4, 5); JavaRDD distData = sc.parallelize(data); {% endhighlight %}

Once created, the distributed dataset (distData) can be operated on in parallel. For example, we might call distData.reduce((a, b) -> a + b) to add up the elements of the list. We describe operations on distributed datasets later on.

Note: In this guide, we'll often use the concise Java 8 lambda syntax to specify Java functions, but in older versions of Java you can implement the interfaces in the org.apache.spark.api.java.function package. We describe passing functions to Spark in more detail below.

Parallelized collections are created by calling SparkContext's parallelize method on an existing iterable or collection in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel. For example, here is how to create a parallelized collection holding the numbers 1 to 5:

{% highlight python %} data = [1, 2, 3, 4, 5] distData = sc.parallelize(data) {% endhighlight %}

Once created, the distributed dataset (distData) can be operated on in parallel. For example, we can call distData.reduce(lambda a, b: a + b) to add up the elements of the list. We describe operations on distributed datasets later on.

One important parameter for parallel collections is the number of partitions to cut the dataset into. Spark will run one task for each partition of the cluster. Typically you want 2-4 partitions for each CPU in your cluster. Normally, Spark tries to set the number of partitions automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to parallelize (e.g. sc.parallelize(data, 10)). Note: some places in the code use the term slices (a synonym for partitions) to maintain backward compatibility.

External Datasets

Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, Amazon S3, etc. Spark supports text files, SequenceFiles, and any other Hadoop InputFormat.

Text file RDDs can be created using SparkContext's textFile method. This method takes an URI for the file (either a local path on the machine, or a hdfs://, s3n://, etc URI) and reads it as a collection of lines. Here is an example invocation:

{% highlight scala %} scala> val distFile = sc.textFile("data.txt") distFile: org.apache.spark.rdd.RDD[String] = data.txt MapPartitionsRDD[10] at textFile at :26 {% endhighlight %}

Once created, distFile can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the map and reduce operations as follows: distFile.map(s => s.length).reduce((a, b) => a + b).

Some notes on reading files with Spark:

• If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.

• All of Spark's file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. For example, you can use textFile("/my/directory"), textFile("/my/directory/*.txt"), and textFile("/my/directory/*.gz").

• The textFile method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.

Apart from text files, Spark's Scala API also supports several other data formats:

• SparkContext.wholeTextFiles lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with textFile, which would return one record per line in each file.

• For SequenceFiles, use SparkContext's sequenceFile[K, V] method where K and V are the types of key and values in the file. These should be subclasses of Hadoop's Writable interface, like IntWritable and Text. In addition, Spark allows you to specify native types for a few common Writables; for example, sequenceFile[Int, String] will automatically read IntWritables and Texts.

• For other Hadoop InputFormats, you can use the SparkContext.hadoopRDD method, which takes an arbitrary JobConf and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use SparkContext.newAPIHadoopRDD for InputFormats based on the "new" MapReduce API (org.apache.hadoop.mapreduce).

• RDD.saveAsObjectFile and SparkContext.objectFile support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.

Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, Amazon S3, etc. Spark supports text files, SequenceFiles, and any other Hadoop InputFormat.

Text file RDDs can be created using SparkContext's textFile method. This method takes an URI for the file (either a local path on the machine, or a hdfs://, s3n://, etc URI) and reads it as a collection of lines. Here is an example invocation:

{% highlight java %} JavaRDD distFile = sc.textFile("data.txt"); {% endhighlight %}

Once created, distFile can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the map and reduce operations as follows: distFile.map(s -> s.length()).reduce((a, b) -> a + b).

Some notes on reading files with Spark:

• If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.

• All of Spark's file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. For example, you can use textFile("/my/directory"), textFile("/my/directory/*.txt"), and textFile("/my/directory/*.gz").

• The textFile method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.

Apart from text files, Spark's Java API also supports several other data formats:

• JavaSparkContext.wholeTextFiles lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with textFile, which would return one record per line in each file.

• For SequenceFiles, use SparkContext's sequenceFile[K, V] method where K and V are the types of key and values in the file. These should be subclasses of Hadoop's Writable interface, like IntWritable and Text.

• For other Hadoop InputFormats, you can use the JavaSparkContext.hadoopRDD method, which takes an arbitrary JobConf and input format class, key class and value class. Set these the same way you would for a Hadoop job with your input source. You can also use JavaSparkContext.newAPIHadoopRDD for InputFormats based on the "new" MapReduce API (org.apache.hadoop.mapreduce).

• JavaRDD.saveAsObjectFile and JavaSparkContext.objectFile support saving an RDD in a simple format consisting of serialized Java objects. While this is not as efficient as specialized formats like Avro, it offers an easy way to save any RDD.

PySpark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, Amazon S3, etc. Spark supports text files, SequenceFiles, and any other Hadoop InputFormat.

Text file RDDs can be created using SparkContext's textFile method. This method takes an URI for the file (either a local path on the machine, or a hdfs://, s3n://, etc URI) and reads it as a collection of lines. Here is an example invocation:

{% highlight python %}

distFile = sc.textFile("data.txt") {% endhighlight %}

Once created, distFile can be acted on by dataset operations. For example, we can add up the sizes of all the lines using the map and reduce operations as follows: distFile.map(lambda s: len(s)).reduce(lambda a, b: a + b).

Some notes on reading files with Spark:

• If using a path on the local filesystem, the file must also be accessible at the same path on worker nodes. Either copy the file to all workers or use a network-mounted shared file system.

• All of Spark's file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. For example, you can use textFile("/my/directory"), textFile("/my/directory/*.txt"), and textFile("/my/directory/*.gz").

• The textFile method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.

Apart from text files, Spark's Python API also supports several other data formats:

• SparkContext.wholeTextFiles lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with textFile, which would return one record per line in each file.

• RDD.saveAsPickleFile and SparkContext.pickleFile support saving an RDD in a simple format consisting of pickled Python objects. Batching is used on pickle serialization, with default batch size 10.

• SequenceFile and Hadoop Input/Output Formats

Note this feature is currently marked Experimental and is intended for advanced users. It may be replaced in future with read/write support based on Spark SQL, in which case Spark SQL is the preferred approach.

Writable Support

PySpark SequenceFile support loads an RDD of key-value pairs within Java, converts Writables to base Java types, and pickles the resulting Java objects using Pyrolite. When saving an RDD of key-value pairs to SequenceFile, PySpark does the reverse. It unpickles Python objects into Java objects and then converts them to Writables. The following Writables are automatically converted:

Writable Type	Python Type
Text	unicode str
IntWritable	int
FloatWritable	float
DoubleWritable	float
BooleanWritable	bool
BytesWritable	bytearray
NullWritable	None
MapWritable	dict

Arrays are not handled out-of-the-box. Users need to specify custom ArrayWritable subtypes when reading or writing. When writing, users also need to specify custom converters that convert arrays to custom ArrayWritable subtypes. When reading, the default converter will convert custom ArrayWritable subtypes to Java Object[], which then get pickled to Python tuples. To get Python array.array for arrays of primitive types, users need to specify custom converters.

Saving and Loading SequenceFiles

Similarly to text files, SequenceFiles can be saved and loaded by specifying the path. The key and value classes can be specified, but for standard Writables this is not required.

{% highlight python %}

rdd = sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x )) rdd.saveAsSequenceFile("path/to/file") sorted(sc.sequenceFile("path/to/file").collect()) [(1, u'a'), (2, u'aa'), (3, u'aaa')] {% endhighlight %}

Saving and Loading Other Hadoop Input/Output Formats

PySpark can also read any Hadoop InputFormat or write any Hadoop OutputFormat, for both 'new' and 'old' Hadoop MapReduce APIs. If required, a Hadoop configuration can be passed in as a Python dict. Here is an example using the Elasticsearch ESInputFormat:

{% highlight python %} $ SPARK_CLASSPATH=/path/to/elasticsearch-hadoop.jar ./bin/pyspark

conf = {"es.resource" : "index/type"} # assume Elasticsearch is running on localhost defaults rdd = sc.newAPIHadoopRDD("org.elasticsearch.hadoop.mr.EsInputFormat",
"org.apache.hadoop.io.NullWritable", "org.elasticsearch.hadoop.mr.LinkedMapWritable", conf=conf) rdd.first() # the result is a MapWritable that is converted to a Python dict (u'Elasticsearch ID', {u'field1': True, u'field2': u'Some Text', u'field3': 12345}) {% endhighlight %}

Note that, if the InputFormat simply depends on a Hadoop configuration and/or input path, and the key and value classes can easily be converted according to the above table, then this approach should work well for such cases.

If you have custom serialized binary data (such as loading data from Cassandra / HBase), then you will first need to transform that data on the Scala/Java side to something which can be handled by Pyrolite's pickler. A Converter trait is provided for this. Simply extend this trait and implement your transformation code in the convert method. Remember to ensure that this class, along with any dependencies required to access your InputFormat, are packaged into your Spark job jar and included on the PySpark classpath.

See the Python examples and the Converter examples for examples of using Cassandra / HBase InputFormat and OutputFormat with custom converters.

RDD Operations

RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, map is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, reduce is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel reduceByKey that returns a distributed dataset).

All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently. For example, we can realize that a dataset created through map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.

By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist (or cache) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.

Basics

To illustrate RDD basics, consider the simple program below:

{% highlight scala %} val lines = sc.textFile("data.txt") val lineLengths = lines.map(s => s.length) val totalLength = lineLengths.reduce((a, b) => a + b) {% endhighlight %}

The first line defines a base RDD from an external file. This dataset is not loaded in memory or otherwise acted on: lines is merely a pointer to the file. The second line defines lineLengths as the result of a map transformation. Again, lineLengths is not immediately computed, due to laziness. Finally, we run reduce, which is an action. At this point Spark breaks the computation into tasks to run on separate machines, and each machine runs both its part of the map and a local reduction, returning only its answer to the driver program.

If we also wanted to use lineLengths again later, we could add:

{% highlight scala %} lineLengths.persist() {% endhighlight %}

before the reduce, which would cause lineLengths to be saved in memory after the first time it is computed.

To illustrate RDD basics, consider the simple program below:

{% highlight java %} JavaRDD lines = sc.textFile("data.txt"); JavaRDD lineLengths = lines.map(s -> s.length()); int totalLength = lineLengths.reduce((a, b) -> a + b); {% endhighlight %}

The first line defines a base RDD from an external file. This dataset is not loaded in memory or otherwise acted on: lines is merely a pointer to the file. The second line defines lineLengths as the result of a map transformation. Again, lineLengths is not immediately computed, due to laziness. Finally, we run reduce, which is an action. At this point Spark breaks the computation into tasks to run on separate machines, and each machine runs both its part of the map and a local reduction, returning only its answer to the driver program.

If we also wanted to use lineLengths again later, we could add:

{% highlight java %} lineLengths.persist(StorageLevel.MEMORY_ONLY()); {% endhighlight %}

before the reduce, which would cause lineLengths to be saved in memory after the first time it is computed.

To illustrate RDD basics, consider the simple program below:

{% highlight python %} lines = sc.textFile("data.txt") lineLengths = lines.map(lambda s: len(s)) totalLength = lineLengths.reduce(lambda a, b: a + b) {% endhighlight %}

The first line defines a base RDD from an external file. This dataset is not loaded in memory or otherwise acted on: lines is merely a pointer to the file. The second line defines lineLengths as the result of a map transformation. Again, lineLengths is not immediately computed, due to laziness. Finally, we run reduce, which is an action. At this point Spark breaks the computation into tasks to run on separate machines, and each machine runs both its part of the map and a local reduction, returning only its answer to the driver program.

If we also wanted to use lineLengths again later, we could add:

{% highlight python %} lineLengths.persist() {% endhighlight %}

before the reduce, which would cause lineLengths to be saved in memory after the first time it is computed.

Passing Functions to Spark

Spark's API relies heavily on passing functions in the driver program to run on the cluster. There are two recommended ways to do this:

• Anonymous function syntax, which can be used for short pieces of code.
• Static methods in a global singleton object. For example, you can define object MyFunctions and then pass MyFunctions.func1, as follows:

{% highlight scala %} object MyFunctions { def func1(s: String): String = { ... } }

myRdd.map(MyFunctions.func1) {% endhighlight %}

Note that while it is also possible to pass a reference to a method in a class instance (as opposed to a singleton object), this requires sending the object that contains that class along with the method. For example, consider:

{% highlight scala %} class MyClass { def func1(s: String): String = { ... } def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(func1) } } {% endhighlight %}

Here, if we create a new MyClass instance and call doStuff on it, the map inside there references the func1 method of that MyClass instance, so the whole object needs to be sent to the cluster. It is similar to writing rdd.map(x => this.func1(x)).

In a similar way, accessing fields of the outer object will reference the whole object:

{% highlight scala %} class MyClass { val field = "Hello" def doStuff(rdd: RDD[String]): RDD[String] = { rdd.map(x => field + x) } } {% endhighlight %}

is equivalent to writing rdd.map(x => this.field + x), which references all of this. To avoid this issue, the simplest way is to copy field into a local variable instead of accessing it externally:

{% highlight scala %} def doStuff(rdd: RDD[String]): RDD[String] = { val field_ = this.field rdd.map(x => field_ + x) } {% endhighlight %}

Spark's API relies heavily on passing functions in the driver program to run on the cluster. In Java, functions are represented by classes implementing the interfaces in the org.apache.spark.api.java.function package. There are two ways to create such functions:

• Implement the Function interfaces in your own class, either as an anonymous inner class or a named one, and pass an instance of it to Spark.
• In Java 8, use lambda expressions to concisely define an implementation.

While much of this guide uses lambda syntax for conciseness, it is easy to use all the same APIs in long-form. For example, we could have written our code above as follows:

{% highlight java %} JavaRDD lines = sc.textFile("data.txt"); JavaRDD lineLengths = lines.map(new Function<String, Integer>() { public Integer call(String s) { return s.length(); } }); int totalLength = lineLengths.reduce(new Function2<Integer, Integer, Integer>() { public Integer call(Integer a, Integer b) { return a + b; } }); {% endhighlight %}

Or, if writing the functions inline is unwieldy:

{% highlight java %} class GetLength implements Function<String, Integer> { public Integer call(String s) { return s.length(); } } class Sum implements Function2<Integer, Integer, Integer> { public Integer call(Integer a, Integer b) { return a + b; } }

JavaRDD lines = sc.textFile("data.txt"); JavaRDD lineLengths = lines.map(new GetLength()); int totalLength = lineLengths.reduce(new Sum()); {% endhighlight %}

Note that anonymous inner classes in Java can also access variables in the enclosing scope as long as they are marked final. Spark will ship copies of these variables to each worker node as it does for other languages.

Spark's API relies heavily on passing functions in the driver program to run on the cluster. There are three recommended ways to do this:

• Lambda expressions, for simple functions that can be written as an expression. (Lambdas do not support multi-statement functions or statements that do not return a value.)
• Local defs inside the function calling into Spark, for longer code.
• Top-level functions in a module.

For example, to pass a longer function than can be supported using a lambda, consider the code below:

{% highlight python %} """MyScript.py""" if name == "main": def myFunc(s): words = s.split(" ") return len(words)

sc = SparkContext(...)
sc.textFile("file.txt").map(myFunc)

{% endhighlight %}

Note that while it is also possible to pass a reference to a method in a class instance (as opposed to a singleton object), this requires sending the object that contains that class along with the method. For example, consider:

{% highlight python %} class MyClass(object): def func(self, s): return s def doStuff(self, rdd): return rdd.map(self.func) {% endhighlight %}

Here, if we create a new MyClass and call doStuff on it, the map inside there references the func method of that MyClass instance, so the whole object needs to be sent to the cluster.

In a similar way, accessing fields of the outer object will reference the whole object:

{% highlight python %} class MyClass(object): def init(self): self.field = "Hello" def doStuff(self, rdd): return rdd.map(lambda s: self.field + s) {% endhighlight %}

To avoid this issue, the simplest way is to copy field into a local variable instead of accessing it externally:

{% highlight python %} def doStuff(self, rdd): field = self.field return rdd.map(lambda s: field + s) {% endhighlight %}

Understanding closures

One of the harder things about Spark is understanding the scope and life cycle of variables and methods when executing code across a cluster. RDD operations that modify variables outside of their scope can be a frequent source of confusion. In the example below we'll look at code that uses foreach() to increment a counter, but similar issues can occur for other operations as well.