SparkContext (Spark 2.0.0 JavaDoc)

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.
`<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)
 - 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 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 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 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 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 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 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 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)

Class SparkContext

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

SparkContext

SparkContext

SparkContext

SparkContext

Method Detail

getOrCreate

getOrCreate

jarOfClass

jarOfObject

startTime

getConf

jars

files

master

deployMode

appName

isLocal

isStopped

statusTracker

uiWebUrl

hadoopConfiguration

sparkUser

applicationId

applicationAttemptId

setLogLevel

setLocalProperty

getLocalProperty

setJobDescription

setJobGroup

clearJobGroup

parallelize

range

makeRDD

makeRDD

textFile

wholeTextFiles

binaryFiles

binaryRecords

hadoopRDD

hadoopFile

hadoopFile

hadoopFile

newAPIHadoopFile

newAPIHadoopFile

newAPIHadoopRDD

sequenceFile

sequenceFile

sequenceFile

objectFile

union

union

emptyRDD

accumulator

accumulator

accumulable

accumulable

accumulableCollection

register

register

longAccumulator

longAccumulator

doubleAccumulator

doubleAccumulator

collectionAccumulator

collectionAccumulator

broadcast

addFile

listFiles

addFile

addSparkListener

requestExecutors

killExecutors

killExecutor

version

getExecutorMemoryStatus