Package pyspark :: Module context :: Class SparkContext
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Class SparkContext

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object --+
         |
        SparkContext

Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs and broadcast variables on that cluster.

Instance Methods
 
__init__(self, master, jobName, sparkHome=None, pyFiles=None, environment=None, batchSize=1024)
Create a new SparkContext.
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defaultParallelism(self)
Default level of parallelism to use when not given by user (e.g.
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__del__(self) source code
 
stop(self)
Shut down the SparkContext.
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parallelize(self, c, numSlices=None)
Distribute a local Python collection to form an RDD.
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textFile(self, name, minSplits=None)
Read a text file from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI, and return it as an RDD of Strings.
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union(self, rdds)
Build the union of a list of RDDs.
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broadcast(self, value)
Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions.
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accumulator(self, value, accum_param=None)
Create an Accumulator with the given initial value, using a given AccumulatorParam helper object to define how to add values of the data type if provided.
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addFile(self, path)
Add a file to be downloaded with this Spark job on every node.
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clearFiles(self)
Clear the job's list of files added by addFile or addPyFile so that they do not get downloaded to any new nodes.
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addPyFile(self, path)
Add a .py or .zip dependency for all tasks to be executed on this SparkContext in the future.
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setCheckpointDir(self, dirName, useExisting=False)
Set the directory under which RDDs are going to be checkpointed.
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Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Class Methods
 
setSystemProperty(cls, key, value)
Set a system property, such as spark.executor.memory.
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Properties

Inherited from object: __class__

Method Details

__init__(self, master, jobName, sparkHome=None, pyFiles=None, environment=None, batchSize=1024)
(Constructor)

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Create a new SparkContext.

@param master: Cluster URL to connect to
       (e.g. mesos://host:port, spark://host:port, local[4]).
@param jobName: A name for your job, to display on the cluster web UI
@param sparkHome: Location where Spark is installed on cluster nodes.
@param pyFiles: Collection of .zip or .py files to send to the cluster
       and add to PYTHONPATH.  These can be paths on the local file
       system or HDFS, HTTP, HTTPS, or FTP URLs.
@param environment: A dictionary of environment variables to set on
       worker nodes.
@param batchSize: The number of Python objects represented as a single
       Java object.  Set 1 to disable batching or -1 to use an
       unlimited batch size.


>>> from pyspark.context import SparkContext
>>> sc = SparkContext('local', 'test')

>>> sc2 = SparkContext('local', 'test2') # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
    ...
ValueError:...

Overrides: object.__init__

setSystemProperty(cls, key, value)
Class Method

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Set a system property, such as spark.executor.memory. This must be invoked before instantiating SparkContext.

defaultParallelism(self)

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Default level of parallelism to use when not given by user (e.g. for reduce tasks)

Decorators:
  • @property

parallelize(self, c, numSlices=None)

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Distribute a local Python collection to form an RDD.

>>> sc.parallelize(range(5), 5).glom().collect()
[[0], [1], [2], [3], [4]]

broadcast(self, value)

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Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions. The variable will be sent to each cluster only once.

accumulator(self, value, accum_param=None)

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Create an Accumulator with the given initial value, using a given AccumulatorParam helper object to define how to add values of the data type if provided. Default AccumulatorParams are used for integers and floating-point numbers if you do not provide one. For other types, a custom AccumulatorParam can be used.

addFile(self, path)

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Add a file to be downloaded with this Spark job on every node. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

To access the file in Spark jobs, use SparkFiles.get(path) to find its download location.

>>> from pyspark import SparkFiles
>>> path = os.path.join(tempdir, "test.txt")
>>> with open(path, "w") as testFile:
...    testFile.write("100")
>>> sc.addFile(path)
>>> def func(iterator):
...    with open(SparkFiles.get("test.txt")) as testFile:
...        fileVal = int(testFile.readline())
...        return [x * 100 for x in iterator]
>>> sc.parallelize([1, 2, 3, 4]).mapPartitions(func).collect()
[100, 200, 300, 400]

addPyFile(self, path)

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Add a .py or .zip dependency for all tasks to be executed on this SparkContext in the future. The path passed can be either a local file, a file in HDFS (or other Hadoop-supported filesystems), or an HTTP, HTTPS or FTP URI.

setCheckpointDir(self, dirName, useExisting=False)

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Set the directory under which RDDs are going to be checkpointed. The directory must be a HDFS path if running on a cluster.

If the directory does not exist, it will be created. If the directory exists and useExisting is set to true, then the exisiting directory will be used. Otherwise an exception will be thrown to prevent accidental overriding of checkpoint files in the existing directory.