org.apache.spark

Class SparkContext

Object

org.apache.spark.SparkContext

public class SparkContext
extends Object

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

Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one. This limitation may eventually be removed; see SPARK-2243 for more details.

param: config a Spark Config object describing the application configuration. Any settings in this config overrides the default configs as well as system properties.

Constructor Summary

Constructors

Constructor and Description
SparkContext() Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
SparkContext(SparkConf config)
SparkContext(String master, String appName, SparkConf conf) Alternative constructor that allows setting common Spark properties directly
SparkContext(String master, String appName, String sparkHome, scala.collection.Seq<String> jars, scala.collection.Map<String,String> environment) Alternative constructor that allows setting common Spark properties directly

Method Summary

Methods

Modifier and Type	Method and Description
<R,T> Accumulable<R,T>	accumulable(R initialValue, AccumulableParam<R,T> param) Deprecated. use AccumulatorV2. Since 2.0.0.
<R,T> Accumulable<R,T>	accumulable(R initialValue, String name, AccumulableParam<R,T> param) Deprecated. use AccumulatorV2. Since 2.0.0.
<R,T> Accumulable<R,T>	accumulableCollection(R initialValue, scala.Function1<R,scala.collection.generic.Growable<T>> evidence$9, scala.reflect.ClassTag<R> evidence$10) Deprecated. use AccumulatorV2. Since 2.0.0.
<T> Accumulator<T>	accumulator(T initialValue, AccumulatorParam<T> param) Deprecated. use AccumulatorV2. Since 2.0.0.
<T> Accumulator<T>	accumulator(T initialValue, String name, AccumulatorParam<T> param) Deprecated. use AccumulatorV2. Since 2.0.0.
void	addFile(String path) Add a file to be downloaded with this Spark job on every node.
void	addFile(String path, boolean recursive) Add a file to be downloaded with this Spark job on every node.
void	addJar(String path) Adds a JAR dependency for all tasks to be executed on this SparkContext in the future.
void	addSparkListener(org.apache.spark.scheduler.SparkListenerInterface listener) :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.
scala.Option<String>	applicationAttemptId()
String	applicationId() A unique identifier for the Spark application.
String	appName()
RDD<scala.Tuple2<String,PortableDataStream>>	binaryFiles(String path, int minPartitions) Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)
RDD<byte[]>	binaryRecords(String path, int recordLength, org.apache.hadoop.conf.Configuration conf) Load data from a flat binary file, assuming the length of each record is constant.
<T> Broadcast<T>	broadcast(T value, scala.reflect.ClassTag<T> evidence$11) Broadcast a read-only variable to the cluster, returning a Broadcast object for reading it in distributed functions.
void	cancelAllJobs() Cancel all jobs that have been scheduled or are running.
void	cancelJobGroup(String groupId) Cancel active jobs for the specified group.
void	clearCallSite() Clear the thread-local property for overriding the call sites of actions and RDDs.
void	clearJobGroup() Clear the current thread's job group ID and its description.
<T> CollectionAccumulator<T>	collectionAccumulator() Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.
<T> CollectionAccumulator<T>	collectionAccumulator(String name) Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.
int	defaultMinPartitions() Default min number of partitions for Hadoop RDDs when not given by user Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2.
int	defaultParallelism() Default level of parallelism to use when not given by user (e.g.
String	deployMode()
DoubleAccumulator	doubleAccumulator() Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
DoubleAccumulator	doubleAccumulator(String name) Create and register a double accumulator, which starts with 0 and accumulates inputs by add.
<T> RDD<T>	emptyRDD(scala.reflect.ClassTag<T> evidence$8) Get an RDD that has no partitions or elements.
scala.collection.Seq<String>	files()
scala.collection.Seq<org.apache.spark.scheduler.Schedulable>	getAllPools() :: DeveloperApi :: Return pools for fair scheduler
scala.Option<String>	getCheckpointDir()
SparkConf	getConf() Return a copy of this SparkContext's configuration.
scala.collection.Map<String,scala.Tuple2<Object,Object>>	getExecutorMemoryStatus() Return a map from the slave to the max memory available for caching and the remaining memory available for caching.
StorageStatus[]	getExecutorStorageStatus() :: DeveloperApi :: Return information about blocks stored in all of the slaves
String	getLocalProperty(String key) Get a local property set in this thread, or null if it is missing.
static SparkContext	getOrCreate() This function may be used to get or instantiate a SparkContext and register it as a singleton object.
static SparkContext	getOrCreate(SparkConf config) This function may be used to get or instantiate a SparkContext and register it as a singleton object.
scala.collection.Map<Object,RDD<?>>	getPersistentRDDs() Returns an immutable map of RDDs that have marked themselves as persistent via cache() call.
scala.Option<org.apache.spark.scheduler.Schedulable>	getPoolForName(String pool) :: DeveloperApi :: Return the pool associated with the given name, if one exists
RDDInfo[]	getRDDStorageInfo() :: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.
scala.Enumeration.Value	getSchedulingMode() Return current scheduling mode
org.apache.hadoop.conf.Configuration	hadoopConfiguration() A default Hadoop Configuration for the Hadoop code (e.g.
<K,V> RDD<scala.Tuple2<K,V>>	hadoopFile(String path, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions) Get an RDD for a Hadoop file with an arbitrary InputFormat
<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>	hadoopFile(String path, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm) Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.
<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>	hadoopFile(String path, int minPartitions, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm) Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly.
<K,V> RDD<scala.Tuple2<K,V>>	hadoopRDD(org.apache.hadoop.mapred.JobConf conf, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions) Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g.
boolean	isLocal()
boolean	isStopped()
static scala.Option<String>	jarOfClass(Class<?> cls) Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.
static scala.Option<String>	jarOfObject(Object obj) Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext.
scala.collection.Seq<String>	jars()
boolean	killExecutor(String executorId) :: DeveloperApi :: Request that the cluster manager kill the specified executor.
boolean	killExecutors(scala.collection.Seq<String> executorIds) :: DeveloperApi :: Request that the cluster manager kill the specified executors.
scala.collection.Seq<String>	listFiles() Returns a list of file paths that are added to resources.
scala.collection.Seq<String>	listJars() Returns a list of jar files that are added to resources.
LongAccumulator	longAccumulator() Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
LongAccumulator	longAccumulator(String name) Create and register a long accumulator, which starts with 0 and accumulates inputs by add.
<T> RDD<T>	makeRDD(scala.collection.Seq<T> seq, int numSlices, scala.reflect.ClassTag<T> evidence$2) Distribute a local Scala collection to form an RDD.
<T> RDD<T>	makeRDD(scala.collection.Seq<scala.Tuple2<T,scala.collection.Seq<String>>> seq, scala.reflect.ClassTag<T> evidence$3) Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object.
String	master()
<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>	newAPIHadoopFile(String path, Class<F> fClass, Class<K> kClass, Class<V> vClass, org.apache.hadoop.conf.Configuration conf) Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>	newAPIHadoopFile(String path, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.reflect.ClassTag<F> fm) Get an RDD for a Hadoop file with an arbitrary new API InputFormat.
<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>>	newAPIHadoopRDD(org.apache.hadoop.conf.Configuration conf, Class<F> fClass, Class<K> kClass, Class<V> vClass) Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.
<T> RDD<T>	objectFile(String path, int minPartitions, scala.reflect.ClassTag<T> evidence$4) Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
<T> RDD<T>	parallelize(scala.collection.Seq<T> seq, int numSlices, scala.reflect.ClassTag<T> evidence$1) Distribute a local Scala collection to form an RDD.
RDD<Object>	range(long start, long end, long step, int numSlices) Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.
void	register(AccumulatorV2<?,?> acc) Register the given accumulator.
void	register(AccumulatorV2<?,?> acc, String name) Register the given accumulator with given name.
boolean	requestExecutors(int numAdditionalExecutors) :: DeveloperApi :: Request an additional number of executors from the cluster manager.
boolean	requestTotalExecutors(int numExecutors, int localityAwareTasks, scala.collection.immutable.Map<String,Object> hostToLocalTaskCount) Update the cluster manager on our scheduling needs.
<T,U,R> PartialResult<R>	runApproximateJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, evaluator, long timeout) :: DeveloperApi :: Run a job that can return approximate results.
<T,U> Object	runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> func, scala.reflect.ClassTag<U> evidence$16) Run a job on all partitions in an RDD and return the results in an array.
<T,U> void	runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> processPartition, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$18) Run a job on all partitions in an RDD and pass the results to a handler function.
<T,U> Object	runJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.reflect.ClassTag<U> evidence$14) Run a job on a given set of partitions of an RDD, but take a function of type Iterator[T] => U instead of (TaskContext, Iterator[T]) => U.
<T,U> Object	runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.reflect.ClassTag<U> evidence$15) Run a job on all partitions in an RDD and return the results in an array.
<T,U> void	runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> processPartition, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$17) Run a job on all partitions in an RDD and pass the results to a handler function.
<T,U> Object	runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.reflect.ClassTag<U> evidence$13) Run a function on a given set of partitions in an RDD and return the results as an array.
<T,U> void	runJob(RDD<T> rdd, scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func, scala.collection.Seq<Object> partitions, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.reflect.ClassTag<U> evidence$12) Run a function on a given set of partitions in an RDD and pass the results to the given handler function.
<K,V> RDD<scala.Tuple2<K,V>>	sequenceFile(String path, Class<K> keyClass, Class<V> valueClass) Get an RDD for a Hadoop SequenceFile with given key and value types.
<K,V> RDD<scala.Tuple2<K,V>>	sequenceFile(String path, Class<K> keyClass, Class<V> valueClass, int minPartitions) Get an RDD for a Hadoop SequenceFile with given key and value types.
<K,V> RDD<scala.Tuple2<K,V>>	sequenceFile(String path, int minPartitions, scala.reflect.ClassTag<K> km, scala.reflect.ClassTag<V> vm, scala.Function0<org.apache.spark.WritableConverter<K>> kcf, scala.Function0<org.apache.spark.WritableConverter<V>> vcf) Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter.
void	setCallSite(String shortCallSite) Set the thread-local property for overriding the call sites of actions and RDDs.
void	setCheckpointDir(String directory) Set the directory under which RDDs are going to be checkpointed.
void	setJobDescription(String value) Set a human readable description of the current job.
void	setJobGroup(String groupId, String description, boolean interruptOnCancel) Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.
void	setLocalProperty(String key, String value) Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool.
void	setLogLevel(String logLevel) Control our logLevel.
String	sparkUser()
long	startTime()
SparkStatusTracker	statusTracker()
void	stop()
<T,U,R> SimpleFutureAction<R>	submitJob(RDD<T> rdd, scala.Function1<scala.collection.Iterator<T>,U> processPartition, scala.collection.Seq<Object> partitions, scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler, scala.Function0<R> resultFunc) Submit a job for execution and return a FutureJob holding the result.
RDD<String>	textFile(String path, int minPartitions) 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.
scala.Option<String>	uiWebUrl()
<T> RDD<T>	union(RDD<T> first, scala.collection.Seq<RDD<T>> rest, scala.reflect.ClassTag<T> evidence$7) Build the union of a list of RDDs passed as variable-length arguments.
<T> RDD<T>	union(scala.collection.Seq<RDD<T>> rdds, scala.reflect.ClassTag<T> evidence$6) Build the union of a list of RDDs.
String	version() The version of Spark on which this application is running.
RDD<scala.Tuple2<String,String>>	wholeTextFiles(String path, int minPartitions) Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SparkContext

public SparkContext(SparkConf config)

SparkContext

public SparkContext()

Create a SparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).

SparkContext

public SparkContext(String master,
            String appName,
            SparkConf conf)

Alternative constructor that allows setting common Spark properties directly

Parameters:

master - Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).

appName - A name for your application, to display on the cluster web UI

conf - a SparkConf object specifying other Spark parameters

SparkContext

public SparkContext(String master,
            String appName,
            String sparkHome,
            scala.collection.Seq<String> jars,
            scala.collection.Map<String,String> environment)

Alternative constructor that allows setting common Spark properties directly

Parameters:

master - Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]).

appName - A name for your application, to display on the cluster web UI.

sparkHome - Location where Spark is installed on cluster nodes.

jars - Collection of JARs to send to the cluster. These can be paths on the local file system or HDFS, HTTP, HTTPS, or FTP URLs.

environment - Environment variables to set on worker nodes.

Method Detail

getOrCreate

public static SparkContext getOrCreate(SparkConf config)

This function may be used to get or instantiate a SparkContext and register it as a singleton object. Because we can only have one active SparkContext per JVM, this is useful when applications may wish to share a SparkContext.

Note: This function cannot be used to create multiple SparkContext instances even if multiple contexts are allowed.

Parameters:

config - (undocumented)

Returns:

(undocumented)

getOrCreate

public static SparkContext getOrCreate()

This function may be used to get or instantiate a SparkContext and register it as a singleton object. Because we can only have one active SparkContext per JVM, this is useful when applications may wish to share a SparkContext.

This method allows not passing a SparkConf (useful if just retrieving).

Note: This function cannot be used to create multiple SparkContext instances even if multiple contexts are allowed.

Returns:

(undocumented)

jarOfClass

public static scala.Option<String> jarOfClass(Class<?> cls)

Find the JAR from which a given class was loaded, to make it easy for users to pass their JARs to SparkContext.

Parameters:

cls - (undocumented)

Returns:

(undocumented)

jarOfObject

public static scala.Option<String> jarOfObject(Object obj)

Find the JAR that contains the class of a particular object, to make it easy for users to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in your driver program.

Parameters:

obj - (undocumented)

Returns:

(undocumented)

startTime

public long startTime()

getConf

public SparkConf getConf()

Return a copy of this SparkContext's configuration. The configuration ''cannot'' be changed at runtime.

Returns:

(undocumented)

jars

public scala.collection.Seq<String> jars()

files

public scala.collection.Seq<String> files()

master

public String master()

deployMode

public String deployMode()

appName

public String appName()

isLocal

public boolean isLocal()

isStopped

public boolean isStopped()

Returns:

true if context is stopped or in the midst of stopping.

statusTracker

public SparkStatusTracker statusTracker()

uiWebUrl

public scala.Option<String> uiWebUrl()

hadoopConfiguration

public org.apache.hadoop.conf.Configuration hadoopConfiguration()

A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.

'''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you plan to set some global configurations for all Hadoop RDDs.

Returns:

(undocumented)

sparkUser

public String sparkUser()

applicationId

public String applicationId()

A unique identifier for the Spark application. Its format depends on the scheduler implementation. (i.e. in case of local spark app something like 'local-1433865536131' in case of YARN something like 'application_1433865536131_34483' )

Returns:

(undocumented)

applicationAttemptId

public scala.Option<String> applicationAttemptId()

setLogLevel

public void setLogLevel(String logLevel)

Control our logLevel. This overrides any user-defined log settings.

Parameters:

logLevel - The desired log level as a string. Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN

setLocalProperty

public void setLocalProperty(String key,
                    String value)

Set a local property that affects jobs submitted from this thread, such as the Spark fair scheduler pool. User-defined properties may also be set here. These properties are propagated through to worker tasks and can be accessed there via TaskContext.getLocalProperty(java.lang.String).

These properties are inherited by child threads spawned from this thread. This may have unexpected consequences when working with thread pools. The standard java implementation of thread pools have worker threads spawn other worker threads. As a result, local properties may propagate unpredictably.

Parameters:

key - (undocumented)

value - (undocumented)

getLocalProperty

public String getLocalProperty(String key)

Get a local property set in this thread, or null if it is missing. See org.apache.spark.SparkContext.setLocalProperty.

Parameters:

key - (undocumented)

Returns:

(undocumented)

setJobDescription

public void setJobDescription(String value)

Set a human readable description of the current job.

setJobGroup

public void setJobGroup(String groupId,
               String description,
               boolean interruptOnCancel)

Assigns a group ID to all the jobs started by this thread until the group ID is set to a different value or cleared.

Often, a unit of execution in an application consists of multiple Spark actions or jobs. Application programmers can use this method to group all those jobs together and give a group description. Once set, the Spark web UI will associate such jobs with this group.

The application can also use org.apache.spark.SparkContext.cancelJobGroup to cancel all running jobs in this group. For example,

 // In the main thread:
 sc.setJobGroup("some_job_to_cancel", "some job description")
 sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count()

 // In a separate thread:
 sc.cancelJobGroup("some_job_to_cancel")
 

If interruptOnCancel is set to true for the job group, then job cancellation will result in Thread.interrupt() being called on the job's executor threads. This is useful to help ensure that the tasks are actually stopped in a timely manner, but is off by default due to HDFS-1208, where HDFS may respond to Thread.interrupt() by marking nodes as dead.

Parameters:

groupId - (undocumented)

description - (undocumented)

interruptOnCancel - (undocumented)

clearJobGroup

public void clearJobGroup()

Clear the current thread's job group ID and its description.

parallelize

public <T> RDD<T> parallelize(scala.collection.Seq<T> seq,
                     int numSlices,
                     scala.reflect.ClassTag<T> evidence$1)

Distribute a local Scala collection to form an RDD.

Parameters:

seq - (undocumented)

numSlices - (undocumented)

evidence$1 - (undocumented)

Returns:

(undocumented)

range

public RDD<Object> range(long start,
                long end,
                long step,
                int numSlices)

Creates a new RDD[Long] containing elements from start to end(exclusive), increased by step every element.

Parameters:

start - the start value.

end - the end value.

step - the incremental step

numSlices - the partition number of the new RDD.

Returns:

makeRDD

public <T> RDD<T> makeRDD(scala.collection.Seq<T> seq,
                 int numSlices,
                 scala.reflect.ClassTag<T> evidence$2)

Distribute a local Scala collection to form an RDD.

This method is identical to parallelize.

Parameters:

seq - (undocumented)

numSlices - (undocumented)

evidence$2 - (undocumented)

Returns:

(undocumented)

makeRDD

public <T> RDD<T> makeRDD(scala.collection.Seq<scala.Tuple2<T,scala.collection.Seq<String>>> seq,
                 scala.reflect.ClassTag<T> evidence$3)

Distribute a local Scala collection to form an RDD, with one or more location preferences (hostnames of Spark nodes) for each object. Create a new partition for each collection item.

Parameters:

seq - (undocumented)

evidence$3 - (undocumented)

Returns:

(undocumented)

textFile

public RDD<String> textFile(String path,
                   int minPartitions)

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.

Parameters:

path - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

wholeTextFiles

public RDD<scala.Tuple2<String,String>> wholeTextFiles(String path,
                                              int minPartitions)

Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file.

For example, if you have the following files:

   hdfs://a-hdfs-path/part-00000
   hdfs://a-hdfs-path/part-00001
   ...
   hdfs://a-hdfs-path/part-nnnnn
 

Do val rdd = sparkContext.wholeTextFile("hdfs://a-hdfs-path"),

then rdd contains

   (a-hdfs-path/part-00000, its content)
   (a-hdfs-path/part-00001, its content)
   ...
   (a-hdfs-path/part-nnnnn, its content)
 

Parameters:

path - Directory to the input data files, the path can be comma separated paths as the list of inputs.

minPartitions - A suggestion value of the minimal splitting number for input data.

Returns:

(undocumented)

binaryFiles

public RDD<scala.Tuple2<String,PortableDataStream>> binaryFiles(String path,
                                                       int minPartitions)

Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file (useful for binary data)

For example, if you have the following files:

   hdfs://a-hdfs-path/part-00000
   hdfs://a-hdfs-path/part-00001
   ...
   hdfs://a-hdfs-path/part-nnnnn
 

Do val rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path"),

then rdd contains

   (a-hdfs-path/part-00000, its content)
   (a-hdfs-path/part-00001, its content)
   ...
   (a-hdfs-path/part-nnnnn, its content)
 

Parameters:

path - Directory to the input data files, the path can be comma separated paths as the list of inputs.

minPartitions - A suggestion value of the minimal splitting number for input data.

Returns:

(undocumented)

binaryRecords

public RDD<byte[]> binaryRecords(String path,
                        int recordLength,
                        org.apache.hadoop.conf.Configuration conf)

Load data from a flat binary file, assuming the length of each record is constant.

'''Note:''' We ensure that the byte array for each record in the resulting RDD has the provided record length.

Parameters:

path - Directory to the input data files, the path can be comma separated paths as the list of inputs.

recordLength - The length at which to split the records

conf - Configuration for setting up the dataset.

Returns:

An RDD of data with values, represented as byte arrays

hadoopRDD

public <K,V> RDD<scala.Tuple2<K,V>> hadoopRDD(org.apache.hadoop.mapred.JobConf conf,
                                     Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
                                     Class<K> keyClass,
                                     Class<V> valueClass,
                                     int minPartitions)

Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf given its InputFormat and other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable), using the older MapReduce API (org.apache.hadoop.mapred).

Parameters:

conf - JobConf for setting up the dataset. Note: This will be put into a Broadcast. Therefore if you plan to reuse this conf to create multiple RDDs, you need to make sure you won't modify the conf. A safe approach is always creating a new conf for a new RDD.

inputFormatClass - Class of the InputFormat

keyClass - Class of the keys

valueClass - Class of the values

minPartitions - Minimum number of Hadoop Splits to generate.

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Returns:

(undocumented)

hadoopFile

public <K,V> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
                                      Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
                                      Class<K> keyClass,
                                      Class<V> valueClass,
                                      int minPartitions)

Get an RDD for a Hadoop file with an arbitrary InputFormat

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

inputFormatClass - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

hadoopFile

public <K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
                                                                                          int minPartitions,
                                                                                          scala.reflect.ClassTag<K> km,
                                                                                          scala.reflect.ClassTag<V> vm,
                                                                                          scala.reflect.ClassTag<F> fm)

Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly. Instead, callers can just write, for example,

 val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path, minPartitions)
 

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

minPartitions - (undocumented)

km - (undocumented)

vm - (undocumented)

fm - (undocumented)

Returns:

(undocumented)

hadoopFile

public <K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> hadoopFile(String path,
                                                                                          scala.reflect.ClassTag<K> km,
                                                                                          scala.reflect.ClassTag<V> vm,
                                                                                          scala.reflect.ClassTag<F> fm)

Smarter version of hadoopFile() that uses class tags to figure out the classes of keys, values and the InputFormat so that users don't need to pass them directly. Instead, callers can just write, for example,

 val file = sparkContext.hadoopFile[LongWritable, Text, TextInputFormat](path)
 

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

km - (undocumented)

vm - (undocumented)

fm - (undocumented)

Returns:

(undocumented)

newAPIHadoopFile

public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopFile(String path,
                                                                                                   scala.reflect.ClassTag<K> km,
                                                                                                   scala.reflect.ClassTag<V> vm,
                                                                                                   scala.reflect.ClassTag<F> fm)

Get an RDD for a Hadoop file with an arbitrary new API InputFormat.

newAPIHadoopFile

public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopFile(String path,
                                                                                                   Class<F> fClass,
                                                                                                   Class<K> kClass,
                                                                                                   Class<V> vClass,
                                                                                                   org.apache.hadoop.conf.Configuration conf)

Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

fClass - (undocumented)

kClass - (undocumented)

vClass - (undocumented)

conf - (undocumented)

Returns:

(undocumented)

newAPIHadoopRDD

public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> RDD<scala.Tuple2<K,V>> newAPIHadoopRDD(org.apache.hadoop.conf.Configuration conf,
                                                                                                  Class<F> fClass,
                                                                                                  Class<K> kClass,
                                                                                                  Class<V> vClass)

Get an RDD for a given Hadoop file with an arbitrary new API InputFormat and extra configuration options to pass to the input format.

Parameters:

conf - Configuration for setting up the dataset. Note: This will be put into a Broadcast. Therefore if you plan to reuse this conf to create multiple RDDs, you need to make sure you won't modify the conf. A safe approach is always creating a new conf for a new RDD.

fClass - Class of the InputFormat

kClass - Class of the keys

vClass - Class of the values

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Returns:

(undocumented)

sequenceFile

public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
                                        Class<K> keyClass,
                                        Class<V> valueClass,
                                        int minPartitions)

Get an RDD for a Hadoop SequenceFile with given key and value types.

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

sequenceFile

public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
                                        Class<K> keyClass,
                                        Class<V> valueClass)

Get an RDD for a Hadoop SequenceFile with given key and value types.

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

Returns:

(undocumented)

sequenceFile

public <K,V> RDD<scala.Tuple2<K,V>> sequenceFile(String path,
                                        int minPartitions,
                                        scala.reflect.ClassTag<K> km,
                                        scala.reflect.ClassTag<V> vm,
                                        scala.Function0<org.apache.spark.WritableConverter<K>> kcf,
                                        scala.Function0<org.apache.spark.WritableConverter<V>> vcf)

Version of sequenceFile() for types implicitly convertible to Writables through a WritableConverter. For example, to access a SequenceFile where the keys are Text and the values are IntWritable, you could simply write

 sparkContext.sequenceFile[String, Int](path, ...)
 

WritableConverters are provided in a somewhat strange way (by an implicit function) to support both subclasses of Writable and types for which we define a converter (e.g. Int to IntWritable). The most natural thing would've been to have implicit objects for the converters, but then we couldn't have an object for every subclass of Writable (you can't have a parameterized singleton object). We use functions instead to create a new converter for the appropriate type. In addition, we pass the converter a ClassTag of its type to allow it to figure out the Writable class to use in the subclass case.

'''Note:''' Because Hadoop's RecordReader class re-uses the same Writable object for each record, directly caching the returned RDD or directly passing it to an aggregation or shuffle operation will create many references to the same object. If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first copy them using a map function.

Parameters:

path - (undocumented)

minPartitions - (undocumented)

km - (undocumented)

vm - (undocumented)

kcf - (undocumented)

vcf - (undocumented)

Returns:

(undocumented)

objectFile

public <T> RDD<T> objectFile(String path,
                    int minPartitions,
                    scala.reflect.ClassTag<T> evidence$4)

Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition. This is still an experimental storage format and may not be supported exactly as is in future Spark releases. It will also be pretty slow if you use the default serializer (Java serialization), though the nice thing about it is that there's very little effort required to save arbitrary objects.

Parameters:

path - (undocumented)

minPartitions - (undocumented)

evidence$4 - (undocumented)

Returns:

(undocumented)

union

public <T> RDD<T> union(scala.collection.Seq<RDD<T>> rdds,
               scala.reflect.ClassTag<T> evidence$6)

Build the union of a list of RDDs.

union

public <T> RDD<T> union(RDD<T> first,
               scala.collection.Seq<RDD<T>> rest,
               scala.reflect.ClassTag<T> evidence$7)

Build the union of a list of RDDs passed as variable-length arguments.

emptyRDD

public <T> RDD<T> emptyRDD(scala.reflect.ClassTag<T> evidence$8)

Get an RDD that has no partitions or elements.

accumulator

public <T> Accumulator<T> accumulator(T initialValue,
                             AccumulatorParam<T> param)

Deprecated. use AccumulatorV2. Since 2.0.0.

Create an Accumulator variable of a given type, which tasks can "add" values to using the += method. Only the driver can access the accumulator's value.

Parameters:

initialValue - (undocumented)

param - (undocumented)

Returns:

(undocumented)

accumulator

public <T> Accumulator<T> accumulator(T initialValue,
                             String name,
                             AccumulatorParam<T> param)

Deprecated. use AccumulatorV2. Since 2.0.0.

Create an Accumulator variable of a given type, with a name for display in the Spark UI. Tasks can "add" values to the accumulator using the += method. Only the driver can access the accumulator's value.

Parameters:

initialValue - (undocumented)

name - (undocumented)

param - (undocumented)

Returns:

(undocumented)

accumulable

public <R,T> Accumulable<R,T> accumulable(R initialValue,
                                 AccumulableParam<R,T> param)

Deprecated. use AccumulatorV2. Since 2.0.0.

Create an Accumulable shared variable, to which tasks can add values with +=. Only the driver can access the accumulable's value.

Parameters:

initialValue - (undocumented)

param - (undocumented)

Returns:

(undocumented)

accumulable

public <R,T> Accumulable<R,T> accumulable(R initialValue,
                                 String name,
                                 AccumulableParam<R,T> param)

Deprecated. use AccumulatorV2. Since 2.0.0.

Create an Accumulable shared variable, with a name for display in the Spark UI. Tasks can add values to the accumulable using the += operator. Only the driver can access the accumulable's value.

Parameters:

initialValue - (undocumented)

name - (undocumented)

param - (undocumented)

Returns:

(undocumented)

accumulableCollection

public <R,T> Accumulable<R,T> accumulableCollection(R initialValue,
                                           scala.Function1<R,scala.collection.generic.Growable<T>> evidence$9,
                                           scala.reflect.ClassTag<R> evidence$10)

Deprecated. use AccumulatorV2. Since 2.0.0.

Create an accumulator from a "mutable collection" type.

Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by standard mutable collections. So you can use this with mutable Map, Set, etc.

Parameters:

initialValue - (undocumented)

evidence$9 - (undocumented)

evidence$10 - (undocumented)

Returns:

(undocumented)

register

public void register(AccumulatorV2<?,?> acc)

Register the given accumulator. Note that accumulators must be registered before use, or it will throw exception.

Parameters:

acc - (undocumented)

register

public void register(AccumulatorV2<?,?> acc,
            String name)

Register the given accumulator with given name. Note that accumulators must be registered before use, or it will throw exception.

Parameters:

acc - (undocumented)

name - (undocumented)

longAccumulator

public LongAccumulator longAccumulator()

Create and register a long accumulator, which starts with 0 and accumulates inputs by add.

Returns:

(undocumented)

longAccumulator

public LongAccumulator longAccumulator(String name)

Create and register a long accumulator, which starts with 0 and accumulates inputs by add.

Parameters:

name - (undocumented)

Returns:

(undocumented)

doubleAccumulator

public DoubleAccumulator doubleAccumulator()

Create and register a double accumulator, which starts with 0 and accumulates inputs by add.

Returns:

(undocumented)

doubleAccumulator

public DoubleAccumulator doubleAccumulator(String name)

Create and register a double accumulator, which starts with 0 and accumulates inputs by add.

Parameters:

name - (undocumented)

Returns:

(undocumented)

collectionAccumulator

public <T> CollectionAccumulator<T> collectionAccumulator()

Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.

Returns:

(undocumented)

collectionAccumulator

public <T> CollectionAccumulator<T> collectionAccumulator(String name)

Create and register a CollectionAccumulator, which starts with empty list and accumulates inputs by adding them into the list.

Parameters:

name - (undocumented)

Returns:

(undocumented)

broadcast

public <T> Broadcast<T> broadcast(T value,
                         scala.reflect.ClassTag<T> evidence$11)

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.

Parameters:

value - (undocumented)

evidence$11 - (undocumented)

Returns:

(undocumented)

addFile

public void addFile(String path)

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(fileName) to find its download location.

Parameters:

path - (undocumented)

listFiles

public scala.collection.Seq<String> listFiles()

Returns a list of file paths that are added to resources.

Returns:

(undocumented)

addFile

public void addFile(String path,
           boolean recursive)

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(fileName) to find its download location.

A directory can be given if the recursive option is set to true. Currently directories are only supported for Hadoop-supported filesystems.

Parameters:

path - (undocumented)

recursive - (undocumented)

addSparkListener

public void addSparkListener(org.apache.spark.scheduler.SparkListenerInterface listener)

:: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.

Parameters:

listener - (undocumented)

requestTotalExecutors

public boolean requestTotalExecutors(int numExecutors,
                            int localityAwareTasks,
                            scala.collection.immutable.Map<String,Object> hostToLocalTaskCount)

Update the cluster manager on our scheduling needs. Three bits of information are included to help it make decisions.

Parameters:

numExecutors - The total number of executors we'd like to have. The cluster manager shouldn't kill any running executor to reach this number, but, if all existing executors were to die, this is the number of executors we'd want to be allocated.

localityAwareTasks - The number of tasks in all active stages that have a locality preferences. This includes running, pending, and completed tasks.

hostToLocalTaskCount - A map of hosts to the number of tasks from all active stages that would like to like to run on that host. This includes running, pending, and completed tasks.

Returns:

whether the request is acknowledged by the cluster manager.

requestExecutors

public boolean requestExecutors(int numAdditionalExecutors)

:: DeveloperApi :: Request an additional number of executors from the cluster manager.

Parameters:

numAdditionalExecutors - (undocumented)

Returns:

whether the request is received.

killExecutors

public boolean killExecutors(scala.collection.Seq<String> executorIds)

:: DeveloperApi :: Request that the cluster manager kill the specified executors.

Note: This is an indication to the cluster manager that the application wishes to adjust its resource usage downwards. If the application wishes to replace the executors it kills through this method with new ones, it should follow up explicitly with a call to {{SparkContext#requestExecutors}}.

Parameters:

executorIds - (undocumented)

Returns:

whether the request is received.

killExecutor

public boolean killExecutor(String executorId)

:: DeveloperApi :: Request that the cluster manager kill the specified executor.

Note: This is an indication to the cluster manager that the application wishes to adjust its resource usage downwards. If the application wishes to replace the executor it kills through this method with a new one, it should follow up explicitly with a call to {{SparkContext#requestExecutors}}.

Parameters:

executorId - (undocumented)

Returns:

whether the request is received.

version

public String version()

The version of Spark on which this application is running.

getExecutorMemoryStatus

public scala.collection.Map<String,scala.Tuple2<Object,Object>> getExecutorMemoryStatus()

Return a map from the slave to the max memory available for caching and the remaining memory available for caching.

Returns:

(undocumented)

getRDDStorageInfo

public RDDInfo[] getRDDStorageInfo()

:: DeveloperApi :: Return information about what RDDs are cached, if they are in mem or on disk, how much space they take, etc.

Returns:

(undocumented)

getPersistentRDDs

public scala.collection.Map<Object,RDD<?>> getPersistentRDDs()

Returns an immutable map of RDDs that have marked themselves as persistent via cache() call. Note that this does not necessarily mean the caching or computation was successful.

Returns:

(undocumented)

getExecutorStorageStatus

public StorageStatus[] getExecutorStorageStatus()

:: DeveloperApi :: Return information about blocks stored in all of the slaves

Returns:

(undocumented)

getAllPools

public scala.collection.Seq<org.apache.spark.scheduler.Schedulable> getAllPools()

:: DeveloperApi :: Return pools for fair scheduler

Returns:

(undocumented)

getPoolForName

public scala.Option<org.apache.spark.scheduler.Schedulable> getPoolForName(String pool)

:: DeveloperApi :: Return the pool associated with the given name, if one exists

Parameters:

pool - (undocumented)

Returns:

(undocumented)

getSchedulingMode

public scala.Enumeration.Value getSchedulingMode()

Return current scheduling mode

Returns:

(undocumented)

addJar

public void addJar(String path)

Adds a JAR 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), an HTTP, HTTPS or FTP URI, or local:/path for a file on every worker node.

Parameters:

path - (undocumented)

listJars

public scala.collection.Seq<String> listJars()

Returns a list of jar files that are added to resources.

Returns:

(undocumented)

stop

public void stop()

setCallSite

public void setCallSite(String shortCallSite)

Set the thread-local property for overriding the call sites of actions and RDDs.

Parameters:

shortCallSite - (undocumented)

clearCallSite

public void clearCallSite()

Clear the thread-local property for overriding the call sites of actions and RDDs.

runJob

public <T,U> void runJob(RDD<T> rdd,
                scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                scala.collection.Seq<Object> partitions,
                scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
                scala.reflect.ClassTag<U> evidence$12)

Run a function on a given set of partitions in an RDD and pass the results to the given handler function. This is the main entry point for all actions in Spark.

Parameters:

rdd - (undocumented)

func - (undocumented)

partitions - (undocumented)

resultHandler - (undocumented)

evidence$12 - (undocumented)

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                  scala.collection.Seq<Object> partitions,
                  scala.reflect.ClassTag<U> evidence$13)

Run a function on a given set of partitions in an RDD and return the results as an array.

Parameters:

rdd - (undocumented)

func - (undocumented)

partitions - (undocumented)

evidence$13 - (undocumented)

Returns:

(undocumented)

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function1<scala.collection.Iterator<T>,U> func,
                  scala.collection.Seq<Object> partitions,
                  scala.reflect.ClassTag<U> evidence$14)

Run a job on a given set of partitions of an RDD, but take a function of type Iterator[T] => U instead of (TaskContext, Iterator[T]) => U.

Parameters:

rdd - (undocumented)

func - (undocumented)

partitions - (undocumented)

evidence$14 - (undocumented)

Returns:

(undocumented)

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                  scala.reflect.ClassTag<U> evidence$15)

Run a job on all partitions in an RDD and return the results in an array.

Parameters:

rdd - (undocumented)

func - (undocumented)

evidence$15 - (undocumented)

Returns:

(undocumented)

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function1<scala.collection.Iterator<T>,U> func,
                  scala.reflect.ClassTag<U> evidence$16)

Run a job on all partitions in an RDD and return the results in an array.

Parameters:

rdd - (undocumented)

func - (undocumented)

evidence$16 - (undocumented)

Returns:

(undocumented)

runJob

public <T,U> void runJob(RDD<T> rdd,
                scala.Function2<TaskContext,scala.collection.Iterator<T>,U> processPartition,
                scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
                scala.reflect.ClassTag<U> evidence$17)

Run a job on all partitions in an RDD and pass the results to a handler function.

Parameters:

rdd - (undocumented)

processPartition - (undocumented)

resultHandler - (undocumented)

evidence$17 - (undocumented)

runJob

public <T,U> void runJob(RDD<T> rdd,
                scala.Function1<scala.collection.Iterator<T>,U> processPartition,
                scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
                scala.reflect.ClassTag<U> evidence$18)

Run a job on all partitions in an RDD and pass the results to a handler function.

Parameters:

rdd - (undocumented)

processPartition - (undocumented)

resultHandler - (undocumented)

evidence$18 - (undocumented)

runApproximateJob

public <T,U,R> PartialResult<R> runApproximateJob(RDD<T> rdd,
                                         scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                                          evaluator,
                                         long timeout)

:: DeveloperApi :: Run a job that can return approximate results.

Parameters:

rdd - (undocumented)

func - (undocumented)

evaluator - (undocumented)

timeout - (undocumented)

Returns:

(undocumented)

submitJob

public <T,U,R> SimpleFutureAction<R> submitJob(RDD<T> rdd,
                                      scala.Function1<scala.collection.Iterator<T>,U> processPartition,
                                      scala.collection.Seq<Object> partitions,
                                      scala.Function2<Object,U,scala.runtime.BoxedUnit> resultHandler,
                                      scala.Function0<R> resultFunc)

Submit a job for execution and return a FutureJob holding the result.

Parameters:

rdd - (undocumented)

processPartition - (undocumented)

partitions - (undocumented)

resultHandler - (undocumented)

resultFunc - (undocumented)

Returns:

(undocumented)

cancelJobGroup

public void cancelJobGroup(String groupId)

Cancel active jobs for the specified group. See org.apache.spark.SparkContext.setJobGroup for more information.

Parameters:

groupId - (undocumented)

cancelAllJobs

public void cancelAllJobs()

Cancel all jobs that have been scheduled or are running.

setCheckpointDir

public void setCheckpointDir(String directory)

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

Parameters:

directory - (undocumented)

getCheckpointDir

public scala.Option<String> getCheckpointDir()

defaultParallelism

public int defaultParallelism()

Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD).

defaultMinPartitions

public int defaultMinPartitions()

Default min number of partitions for Hadoop RDDs when not given by user Notice that we use math.min so the "defaultMinPartitions" cannot be higher than 2. The reasons for this are discussed in https://github.com/mesos/spark/pull/718

Returns:

(undocumented)