Spark 1.1.1 uses Scala 2.10. To write applications in Scala, you will need to use a compatible Scala version (e.g. 2.10.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_2.10
version = 1.1.1

In addition, if you wish to access an HDFS cluster, you need to add a dependency on hadoop-client for your version of HDFS. Some common HDFS version tags are listed on the third party distributions page.

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

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

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf

Spark 1.1.1 works with Java 6 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_2.10
version = 1.1.1

In addition, if you wish to access an HDFS cluster, you need to add a dependency on hadoop-client for your version of HDFS. Some common HDFS version tags are listed on the third party distributions page.

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:

import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.SparkConf

Spark 1.1.1 works with Python 2.6 or higher (but not Python 3). It uses the standard CPython interpreter, so C libraries like NumPy can be used.

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. Some common HDFS version tags are listed on the third party distributions page. 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 lines:

from pyspark import SparkContext, SparkConf

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.

val conf = new SparkConf().setAppName(appName).setMaster(master)
new SparkContext(conf)

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.

SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
JavaSparkContext sc = new JavaSparkContext(conf);

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.

conf = SparkConf().setAppName(appName).setMaster(master)
sc = SparkContext(conf=conf)

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. For example, to run bin/spark-shell on exactly four cores, use:

$ ./bin/spark-shell --master local[4]

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

$ ./bin/spark-shell --master local[4] --jars code.jar

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. For example, to run bin/pyspark on exactly four cores, use:

$ ./bin/pyspark --master local[4]

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

$ ./bin/pyspark --master local[4] --py-files code.py

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 IPYTHON variable to 1 when running bin/pyspark:

$ IPYTHON=1 ./bin/pyspark

You can customize the ipython command by setting IPYTHON_OPTS. For example, to launch the IPython Notebook with PyLab plot support:

$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark

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:

val data = Array(1, 2, 3, 4, 5)
val distData = sc.parallelize(data)

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:

List<Integer> data = Arrays.asList(1, 2, 3, 4, 5);
JavaRDD<Integer> distData = sc.parallelize(data);

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:

data = [1, 2, 3, 4, 5]
distData = sc.parallelize(data)

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.

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:

scala> val distFile = sc.textFile("data.txt")
distFile: RDD[String] = MappedRDD@1d4cee08

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 slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices 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.newHadoopRDD 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:

JavaRDD<String> distFile = sc.textFile("data.txt");

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 slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices 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.newHadoopRDD 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:

>>> distFile = sc.textFile("data.txt")

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 slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices 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 SparkSQL, in which case SparkSQL 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:

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.

>>> 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')]

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:

$ 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})

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.

To illustrate RDD basics, consider the simple program below:

val lines = sc.textFile("data.txt")
val lineLengths = lines.map(s => s.length)
val totalLength = lineLengths.reduce((a, b) => a + b)

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:

lineLengths.persist()

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:

JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(s -> s.length());
int totalLength = lineLengths.reduce((a, b) -> a + b);

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:

lineLengths.persist();

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:

lines = sc.textFile("data.txt")
lineLengths = lines.map(lambda s: len(s))
totalLength = lineLengths.reduce(lambda a, b: a + b)

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:

lineLengths.persist()

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

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:

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

myRdd.map(MyFunctions.func1)

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:

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

Here, if we create a new MyClass 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:

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

is equilvalent 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:

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

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:

JavaRDD<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> 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; }
});

Or, if writing the functions inline is unwieldy:

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<String> lines = sc.textFile("data.txt");
JavaRDD<Integer> lineLengths = lines.map(new GetLength());
int totalLength = lineLengths.reduce(new Sum());

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:

"""MyScript.py"""
if __name__ == "__main__":
    def myFunc(s):
        words = s.split(" ")
        return len(words)

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

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:

class MyClass(object):
    def func(self, s):
        return s
    def doStuff(self, rdd):
        return rdd.map(self.func)

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:

class MyClass(object):
    def __init__(self):
        self.field = "Hello"
    def doStuff(self, rdd):
        return rdd.map(lambda s: self.field + x)

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

def doStuff(self, rdd):
    field = self.field
    return rdd.map(lambda s: field + x)

While most Spark operations work on RDDs containing any type of objects, a few special operations are only available on RDDs of key-value pairs. The most common ones are distributed “shuffle” operations, such as grouping or aggregating the elements by a key.

In Scala, these operations are automatically available on RDDs containing Tuple2 objects (the built-in tuples in the language, created by simply writing (a, b)), as long as you import org.apache.spark.SparkContext._ in your program to enable Spark’s implicit conversions. The key-value pair operations are available in the PairRDDFunctions class, which automatically wraps around an RDD of tuples if you import the conversions.

For example, the following code uses the reduceByKey operation on key-value pairs to count how many times each line of text occurs in a file:

val lines = sc.textFile("data.txt")
val pairs = lines.map(s => (s, 1))
val counts = pairs.reduceByKey((a, b) => a + b)

We could also use counts.sortByKey(), for example, to sort the pairs alphabetically, and finally counts.collect() to bring them back to the driver program as an array of objects.

Note: when using custom objects as the key in key-value pair operations, you must be sure that a custom equals() method is accompanied with a matching hashCode() method. For full details, see the contract outlined in the Object.hashCode() documentation.

While most Spark operations work on RDDs containing any type of objects, a few special operations are only available on RDDs of key-value pairs. The most common ones are distributed “shuffle” operations, such as grouping or aggregating the elements by a key.

In Java, key-value pairs are represented using the scala.Tuple2 class from the Scala standard library. You can simply call new Tuple2(a, b) to create a tuple, and access its fields later with tuple._1() and tuple._2().

RDDs of key-value pairs are represented by the JavaPairRDD class. You can construct JavaPairRDDs from JavaRDDs using special versions of the map operations, like mapToPair and flatMapToPair. The JavaPairRDD will have both standard RDD functions and special key-value ones.

For example, the following code uses the reduceByKey operation on key-value pairs to count how many times each line of text occurs in a file:

JavaRDD<String> lines = sc.textFile("data.txt");
JavaPairRDD<String, Integer> pairs = lines.mapToPair(s -> new Tuple2(s, 1));
JavaPairRDD<String, Integer> counts = pairs.reduceByKey((a, b) -> a + b);

We could also use counts.sortByKey(), for example, to sort the pairs alphabetically, and finally counts.collect() to bring them back to the driver program as an array of objects.

Note: when using custom objects as the key in key-value pair operations, you must be sure that a custom equals() method is accompanied with a matching hashCode() method. For full details, see the contract outlined in the Object.hashCode() documentation.

While most Spark operations work on RDDs containing any type of objects, a few special operations are only available on RDDs of key-value pairs. The most common ones are distributed “shuffle” operations, such as grouping or aggregating the elements by a key.

In Python, these operations work on RDDs containing built-in Python tuples such as (1, 2). Simply create such tuples and then call your desired operation.

For example, the following code uses the reduceByKey operation on key-value pairs to count how many times each line of text occurs in a file:

lines = sc.textFile("data.txt")
pairs = lines.map(lambda s: (s, 1))
counts = pairs.reduceByKey(lambda a, b: a + b)

We could also use counts.sortByKey(), for example, to sort the pairs alphabetically, and finally counts.collect() to bring them back to the driver program as a list of objects.

scala> val accum = sc.accumulator(0, "My Accumulator")
accum: spark.Accumulator[Int] = 0

scala> sc.parallelize(Array(1, 2, 3, 4)).foreach(x => accum += x)
...
10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s

scala> accum.value
res2: Int = 10

While this code used the built-in support for accumulators of type Int, programmers can also create their own types by subclassing AccumulatorParam. The AccumulatorParam interface has two methods: zero for providing a “zero value” for your data type, and addInPlace for adding two values together. For example, supposing we had a Vector class representing mathematical vectors, we could write:

object VectorAccumulatorParam extends AccumulatorParam[Vector] {
  def zero(initialValue: Vector): Vector = {
    Vector.zeros(initialValue.size)
  }
  def addInPlace(v1: Vector, v2: Vector): Vector = {
    v1 += v2
  }
}

// Then, create an Accumulator of this type:
val vecAccum = sc.accumulator(new Vector(...))(VectorAccumulatorParam)

In Scala, Spark also supports the more general Accumulable interface to accumulate data where the resulting type is not the same as the elements added (e.g. build a list by collecting together elements), and the SparkContext.accumulableCollection method for accumulating common Scala collection types.

Accumulator<Integer> accum = sc.accumulator(0);

sc.parallelize(Arrays.asList(1, 2, 3, 4)).foreach(x -> accum.add(x));
// ...
// 10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s

accum.value();
// returns 10

While this code used the built-in support for accumulators of type Integer, programmers can also create their own types by subclassing AccumulatorParam. The AccumulatorParam interface has two methods: zero for providing a “zero value” for your data type, and addInPlace for adding two values together. For example, supposing we had a Vector class representing mathematical vectors, we could write:

class VectorAccumulatorParam implements AccumulatorParam<Vector> {
  public Vector zero(Vector initialValue) {
    return Vector.zeros(initialValue.size());
  }
  public Vector addInPlace(Vector v1, Vector v2) {
    v1.addInPlace(v2); return v1;
  }
}

// Then, create an Accumulator of this type:
Accumulator<Vector> vecAccum = sc.accumulator(new Vector(...), new VectorAccumulatorParam());

In Java, Spark also supports the more general Accumulable interface to accumulate data where the resulting type is not the same as the elements added (e.g. build a list by collecting together elements).

>>> accum = sc.accumulator(0)
Accumulator<id=0, value=0>

>>> sc.parallelize([1, 2, 3, 4]).foreach(lambda x: accum.add(x))
...
10/09/29 18:41:08 INFO SparkContext: Tasks finished in 0.317106 s

scala> accum.value
10

While this code used the built-in support for accumulators of type Int, programmers can also create their own types by subclassing AccumulatorParam. The AccumulatorParam interface has two methods: zero for providing a “zero value” for your data type, and addInPlace for adding two values together. For example, supposing we had a Vector class representing mathematical vectors, we could write:

class VectorAccumulatorParam(AccumulatorParam):
    def zero(self, initialValue):
        return Vector.zeros(initialValue.size)

    def addInPlace(self, v1, v2):
        v1 += v2
        return v1

# Then, create an Accumulator of this type:
vecAccum = sc.accumulator(Vector(...), VectorAccumulatorParam())

Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is not marked “experimental” or “developer API” will be supported in future versions. The only change for Scala users is that the grouping operations, e.g. groupByKey, cogroup and join, have changed from returning (Key, Seq[Value]) pairs to (Key, Iterable[Value]).

Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is not marked “experimental” or “developer API” will be supported in future versions. Several changes were made to the Java API:

• The Function classes in org.apache.spark.api.java.function became interfaces in 1.0, meaning that old code that extends Function should implement Function instead.
• New variants of the map transformations, like mapToPair and mapToDouble, were added to create RDDs of special data types.
• Grouping operations like groupByKey, cogroup and join have changed from returning (Key, List<Value>) pairs to (Key, Iterable<Value>).

Spark 1.0 freezes the API of Spark Core for the 1.X series, in that any API available today that is not marked “experimental” or “developer API” will be supported in future versions. The only change for Python users is that the grouping operations, e.g. groupByKey, cogroup and join, have changed from returning (key, list of values) pairs to (key, iterable of values).