SparkContext (Spark 1.0.2 JavaDoc)

Object
- org.apache.spark.SparkContext

All Implemented Interfaces:

Logging
```
public class SparkContext
extends Object
implements Logging
```
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.

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`SparkContext.DoubleAccumulatorParam$`
`static class`	`SparkContext.FloatAccumulatorParam$`
`static class`	`SparkContext.IntAccumulatorParam$`
`static class`	`SparkContext.LongAccumulatorParam$`

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(SparkConf config, scala.collection.Map<String,scala.collection.Set<SplitInfo>> preferredNodeLocationData)` :: DeveloperApi :: Alternative constructor for setting preferred locations where Spark will create executors.
`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, scala.collection.Map<String,scala.collection.Set<SplitInfo>> preferredNodeLocationData)` Alternative constructor that allows setting common Spark properties directly

Method Summary

Methods
Modifier and Type	Method and Description
`<T,R> Accumulable<T,R>`	`accumulable(T initialValue, AccumulableParam<T,R> param)` Create an `Accumulable` shared variable, to which tasks can add values with `+=`.
`<R,T> Accumulable<R,T>`	`accumulableCollection(R initialValue, scala.Function1<R,scala.collection.generic.Growable<T>> evidence$9, scala.reflect.ClassTag<R> evidence$10)` Create an accumulator from a "mutable collection" type.
`<T> Accumulator<T>`	`accumulator(T initialValue, AccumulatorParam<T> param)` Create an `Accumulator` variable of a given type, which tasks can "add" values to using the `+=` method.
`scala.collection.mutable.HashMap<String,Object>`	`addedFiles()`
`scala.collection.mutable.HashMap<String,Object>`	`addedJars()`
`void`	`addFile(String path)` 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(SparkListener listener)` :: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.
`String`	`appName()`
`static org.apache.spark.WritableConverter<Object>`	`booleanWritableConverter()`
`static org.apache.hadoop.io.BooleanWritable`	`boolToBoolWritable(boolean b)`
`<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.
`static org.apache.hadoop.io.BytesWritable`	`bytesToBytesWritable(byte[] aob)`
`static org.apache.spark.WritableConverter<byte[]>`	`bytesWritableConverter()`
`void`	`cancelAllJobs()` Cancel all jobs that have been scheduled or are running.
`void`	`cancelJobGroup(String groupId)` Cancel active jobs for the specified group.
`scala.Option<String>`	`checkpointDir()`
`scala.Option<org.apache.spark.ContextCleaner>`	`cleaner()`
`void`	`clearCallSite()` Support function for API backtraces.
`void`	`clearFiles()` Clear the job's list of files added by `addFile` so that they do not get downloaded to any new nodes.
`void`	`clearJars()` Clear the job's list of JARs added by `addJar` so that they do not get downloaded to any new nodes.
`void`	`clearJobGroup()` Clear the current thread's job group ID and its description.
`SparkConf`	`conf()`
`org.apache.spark.scheduler.DAGScheduler`	`dagScheduler()`
`int`	`defaultMinPartitions()` Default min number of partitions for Hadoop RDDs when not given by user
`int`	`defaultMinSplits()` Default min number of partitions for Hadoop RDDs when not given by user
`int`	`defaultParallelism()` Default level of parallelism to use when not given by user (e.g.
`static DoubleRDDFunctions`	`doubleRDDToDoubleRDDFunctions(RDD<Object> rdd)`
`static org.apache.hadoop.io.DoubleWritable`	`doubleToDoubleWritable(double d)`
`static org.apache.spark.WritableConverter<Object>`	`doubleWritableConverter()`
`<T>`	`emptyRDD(scala.reflect.ClassTag<T> evidence$8)` Get an RDD that has no partitions or elements.
`SparkEnv`	`env()`
`scala.Option<org.apache.spark.scheduler.EventLoggingListener>`	`eventLogger()`
`scala.collection.mutable.HashMap<String,String>`	`executorEnvs()`
`int`	`executorMemory()`
`scala.collection.Seq<String>`	`files()`
`static org.apache.hadoop.io.FloatWritable`	`floatToFloatWritable(float f)`
`static org.apache.spark.WritableConverter<Object>`	`floatWritableConverter()`
`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.
`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.
`void`	`initLocalProperties()`
`static org.apache.hadoop.io.IntWritable`	`intToIntWritable(int i)`
`static org.apache.spark.WritableConverter<Object>`	`intWritableConverter()`
`boolean`	`isLocal()`
`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()`
`org.apache.spark.scheduler.LiveListenerBus`	`listenerBus()`
`static org.apache.hadoop.io.LongWritable`	`longToLongWritable(long l)`
`static org.apache.spark.WritableConverter<Object>`	`longWritableConverter()`
`<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()`
`org.apache.spark.util.MetadataCleaner`	`metadataCleaner()`
`<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.
`static <T> DoubleRDDFunctions`	`numericRDDToDoubleRDDFunctions(RDD<T> rdd, scala.math.Numeric<T> num)`
`<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.
`org.apache.spark.util.TimeStampedWeakValueHashMap<Object,RDD<?>>`	`persistentRdds()`
`scala.collection.Map<String,scala.collection.Set<SplitInfo>>`	`preferredNodeLocationData()`
`static <T> AsyncRDDActions<T>`	`rddToAsyncRDDActions(RDD<T> rdd, scala.reflect.ClassTag<T> evidence$19)`
`static <K,V> OrderedRDDFunctions<K,V,scala.Tuple2<K,V>>`	`rddToOrderedRDDFunctions(RDD<scala.Tuple2<K,V>> rdd, scala.math.Ordering<K> evidence$24, scala.reflect.ClassTag<K> evidence$25, scala.reflect.ClassTag<V> evidence$26)`
`static <K,V> PairRDDFunctions<K,V>`	`rddToPairRDDFunctions(RDD<scala.Tuple2<K,V>> rdd, scala.reflect.ClassTag<K> kt, scala.reflect.ClassTag<V> vt, scala.math.Ordering<K> ord)`
`static <K,V> SequenceFileRDDFunctions<K,V>`	`rddToSequenceFileRDDFunctions(RDD<scala.Tuple2<K,V>> rdd, scala.Function1<K,org.apache.hadoop.io.Writable> evidence$20, scala.reflect.ClassTag<K> evidence$21, scala.Function1<V,org.apache.hadoop.io.Writable> evidence$22, scala.reflect.ClassTag<V> evidence$23)`
`<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, boolean allowLocal, 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, boolean allowLocal, 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, boolean allowLocal, 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 site)` Support function for API backtraces.
`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.
`static String`	`SPARK_JOB_DESCRIPTION()`
`static String`	`SPARK_JOB_GROUP_ID()`
`static String`	`SPARK_JOB_INTERRUPT_ON_CANCEL()`
`static String`	`SPARK_UNKNOWN_USER()`
`static String`	`SPARK_VERSION()`
`String`	`sparkUser()`
`long`	`startTime()`
`void`	`stop()` Shut down the SparkContext.
`static org.apache.hadoop.io.Text`	`stringToText(String s)`
`static org.apache.spark.WritableConverter<String>`	`stringWritableConverter()`
`<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)` :: Experimental :: Submit a job for execution and return a FutureJob holding the result.
`String`	`tachyonFolderName()`
`org.apache.spark.scheduler.TaskScheduler`	`taskScheduler()`
`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.
`org.apache.spark.ui.SparkUI`	`ui()`
`<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.
`static <T extends org.apache.hadoop.io.Writable> org.apache.spark.WritableConverter<T>`	`writableWritableConverter()`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging
initialized, initializeIfNecessary, initializeLogging, initLock, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logTrace, logTrace, logWarning, logWarning

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(SparkConf config,
            scala.collection.Map<String,scala.collection.Set<SplitInfo>> preferredNodeLocationData)
```
  :: DeveloperApi :: Alternative constructor for setting preferred locations where Spark will create executors.
  
  Parameters:
  preferredNodeLocationData - used in YARN mode to select nodes to launch containers on. Can be generated using org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations from a list of input files or InputFormats for the application.
- 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,
            scala.collection.Map<String,scala.collection.Set<SplitInfo>> preferredNodeLocationData)
```
  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

SPARK_VERSION
```
public static String SPARK_VERSION()
```

SPARK_JOB_DESCRIPTION

public static String SPARK_JOB_DESCRIPTION()

SPARK_JOB_GROUP_ID

public static String SPARK_JOB_GROUP_ID()

SPARK_JOB_INTERRUPT_ON_CANCEL

public static String SPARK_JOB_INTERRUPT_ON_CANCEL()

SPARK_UNKNOWN_USER

public static String SPARK_UNKNOWN_USER()

rddToPairRDDFunctions

public static <K,V> PairRDDFunctions<K,V> rddToPairRDDFunctions(RDD<scala.Tuple2<K,V>> rdd,
                                                scala.reflect.ClassTag<K> kt,
                                                scala.reflect.ClassTag<V> vt,
                                                scala.math.Ordering<K> ord)

rddToAsyncRDDActions

public static <T> AsyncRDDActions<T> rddToAsyncRDDActions(RDD<T> rdd,
                                          scala.reflect.ClassTag<T> evidence$19)

rddToSequenceFileRDDFunctions

public static <K,V> SequenceFileRDDFunctions<K,V> rddToSequenceFileRDDFunctions(RDD<scala.Tuple2<K,V>> rdd,
                                                                scala.Function1<K,org.apache.hadoop.io.Writable> evidence$20,
                                                                scala.reflect.ClassTag<K> evidence$21,
                                                                scala.Function1<V,org.apache.hadoop.io.Writable> evidence$22,
                                                                scala.reflect.ClassTag<V> evidence$23)

rddToOrderedRDDFunctions

public static <K,V> OrderedRDDFunctions<K,V,scala.Tuple2<K,V>> rddToOrderedRDDFunctions(RDD<scala.Tuple2<K,V>> rdd,
                                                                        scala.math.Ordering<K> evidence$24,
                                                                        scala.reflect.ClassTag<K> evidence$25,
                                                                        scala.reflect.ClassTag<V> evidence$26)

doubleRDDToDoubleRDDFunctions

public static DoubleRDDFunctions doubleRDDToDoubleRDDFunctions(RDD<Object> rdd)

numericRDDToDoubleRDDFunctions

public static <T> DoubleRDDFunctions numericRDDToDoubleRDDFunctions(RDD<T> rdd,
                                                    scala.math.Numeric<T> num)

intToIntWritable

public static org.apache.hadoop.io.IntWritable intToIntWritable(int i)

longToLongWritable

public static org.apache.hadoop.io.LongWritable longToLongWritable(long l)

floatToFloatWritable

public static org.apache.hadoop.io.FloatWritable floatToFloatWritable(float f)

doubleToDoubleWritable

public static org.apache.hadoop.io.DoubleWritable doubleToDoubleWritable(double d)

boolToBoolWritable

public static org.apache.hadoop.io.BooleanWritable boolToBoolWritable(boolean b)

bytesToBytesWritable

public static org.apache.hadoop.io.BytesWritable bytesToBytesWritable(byte[] aob)

stringToText

public static org.apache.hadoop.io.Text stringToText(String s)

intWritableConverter

public static org.apache.spark.WritableConverter<Object> intWritableConverter()

longWritableConverter

public static org.apache.spark.WritableConverter<Object> longWritableConverter()

doubleWritableConverter

public static org.apache.spark.WritableConverter<Object> doubleWritableConverter()

floatWritableConverter

public static org.apache.spark.WritableConverter<Object> floatWritableConverter()

booleanWritableConverter

public static org.apache.spark.WritableConverter<Object> booleanWritableConverter()

bytesWritableConverter

public static org.apache.spark.WritableConverter<byte[]> bytesWritableConverter()

stringWritableConverter

public static org.apache.spark.WritableConverter<String> stringWritableConverter()

writableWritableConverter

public static <T extends org.apache.hadoop.io.Writable> org.apache.spark.WritableConverter<T> writableWritableConverter()

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.

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.

preferredNodeLocationData

public scala.collection.Map<String,scala.collection.Set<SplitInfo>> preferredNodeLocationData()

conf
```
public SparkConf conf()
```

getConf
```
public SparkConf getConf()
```
Return a copy of this SparkContext's configuration. The configuration ''cannot'' be changed at runtime.

jars

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

files

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

master
```
public String master()
```

appName
```
public String appName()
```

tachyonFolderName
```
public String tachyonFolderName()
```

isLocal
```
public boolean isLocal()
```

listenerBus

public org.apache.spark.scheduler.LiveListenerBus listenerBus()

env
```
public SparkEnv env()
```

addedFiles

public scala.collection.mutable.HashMap<String,Object> addedFiles()

addedJars

public scala.collection.mutable.HashMap<String,Object> addedJars()

persistentRdds

public org.apache.spark.util.TimeStampedWeakValueHashMap<Object,RDD<?>> persistentRdds()

metadataCleaner

public org.apache.spark.util.MetadataCleaner metadataCleaner()

ui

public org.apache.spark.ui.SparkUI ui()

hadoopConfiguration
```
public org.apache.hadoop.conf.Configuration hadoopConfiguration()
```
A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse.

eventLogger

public scala.Option<org.apache.spark.scheduler.EventLoggingListener> eventLogger()

startTime
```
public long startTime()
```

executorMemory
```
public int executorMemory()
```

executorEnvs

public scala.collection.mutable.HashMap<String,String> executorEnvs()

sparkUser
```
public String sparkUser()
```

taskScheduler

public org.apache.spark.scheduler.TaskScheduler taskScheduler()

dagScheduler

public org.apache.spark.scheduler.DAGScheduler dagScheduler()

cleaner

public scala.Option<org.apache.spark.ContextCleaner> cleaner()

checkpointDir

public scala.Option<String> checkpointDir()

initLocalProperties
```
public void initLocalProperties()
```

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.

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.

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.

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.

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.

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.

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.

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

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
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 will create many references to the same object. If you plan to directly cache Hadoop writable objects, you should first copy them using a map function.

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 will create many references to the same object. If you plan to directly cache Hadoop writable objects, you should first copy them using a map function.

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)

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)


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

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.

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.

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 will create many references to the same object. If you plan to directly cache Hadoop writable objects, you should first copy them using a map function.

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 will create many references to the same object. If you plan to directly cache Hadoop writable objects, you should first copy them using a map function.

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 will create many references to the same object. If you plan to directly cache Hadoop writable objects, you should first copy them using a map function.

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.

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>  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)
```
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.

accumulable

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

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

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)

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.

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.

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(path) to find its download location.

addSparkListener
```
public void addSparkListener(SparkListener listener)
```
:: DeveloperApi :: Register a listener to receive up-calls from events that happen during execution.

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.

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.

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.

getExecutorStorageStatus
```
public StorageStatus[] getExecutorStorageStatus()
```
:: DeveloperApi :: Return information about blocks stored in all of the slaves

getAllPools

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

:: DeveloperApi :: Return pools for fair scheduler

getPoolForName
```
public scala.Option<org.apache.spark.scheduler.Schedulable> getPoolForName(String pool)
```
:: DeveloperApi :: Return the pool associated with the given name, if one exists

getSchedulingMode

public scala.Enumeration.Value getSchedulingMode()

Return current scheduling mode

clearFiles
```
public void clearFiles()
```
Clear the job's list of files added by addFile so that they do not get downloaded to any new nodes.

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.

clearJars
```
public void clearJars()
```
Clear the job's list of JARs added by addJar so that they do not get downloaded to any new nodes.

stop
```
public void stop()
```
Shut down the SparkContext.

setCallSite
```
public void setCallSite(String site)
```
Support function for API backtraces.

clearCallSite
```
public void clearCallSite()
```
Support function for API backtraces.

runJob

public <T,U> void runJob(RDD<T> rdd,
                scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                scala.collection.Seq<Object> partitions,
                boolean allowLocal,
                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. The allowLocal flag specifies whether the scheduler can run the computation on the driver rather than shipping it out to the cluster, for short actions like first().

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function2<TaskContext,scala.collection.Iterator<T>,U> func,
                  scala.collection.Seq<Object> partitions,
                  boolean allowLocal,
                  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. The allowLocal flag specifies whether the scheduler can run the computation on the driver rather than shipping it out to the cluster, for short actions like first().

runJob

public <T,U> Object runJob(RDD<T> rdd,
                  scala.Function1<scala.collection.Iterator<T>,U> func,
                  scala.collection.Seq<Object> partitions,
                  boolean allowLocal,
                  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.

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.

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.

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.

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.

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.

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)

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

cancelJobGroup
```
public void cancelJobGroup(String groupId)
```
Cancel active jobs for the specified group. See org.apache.spark.SparkContext.setJobGroup for more information.

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.

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).

defaultMinSplits
```
public int defaultMinSplits()
```
Default min number of partitions for Hadoop RDDs when not given by user

defaultMinPartitions
```
public int defaultMinPartitions()
```
Default min number of partitions for Hadoop RDDs when not given by user

Class SparkContext

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class Object

Methods inherited from interface org.apache.spark.Logging

Constructor Detail

SparkContext

SparkContext

SparkContext

SparkContext

SparkContext

Method Detail

SPARK_VERSION

SPARK_JOB_DESCRIPTION

SPARK_JOB_GROUP_ID

SPARK_JOB_INTERRUPT_ON_CANCEL

SPARK_UNKNOWN_USER

rddToPairRDDFunctions

rddToAsyncRDDActions

rddToSequenceFileRDDFunctions

rddToOrderedRDDFunctions

doubleRDDToDoubleRDDFunctions

numericRDDToDoubleRDDFunctions

intToIntWritable

longToLongWritable

floatToFloatWritable

doubleToDoubleWritable

boolToBoolWritable

bytesToBytesWritable

stringToText

intWritableConverter

longWritableConverter

doubleWritableConverter

floatWritableConverter

booleanWritableConverter

bytesWritableConverter

stringWritableConverter

writableWritableConverter

jarOfClass

jarOfObject

preferredNodeLocationData

conf

getConf

jars

files

master

appName

tachyonFolderName

isLocal

listenerBus

env

addedFiles

addedJars

persistentRdds

metadataCleaner

ui

hadoopConfiguration

eventLogger

startTime

executorMemory

executorEnvs

sparkUser

taskScheduler

dagScheduler

cleaner

checkpointDir

initLocalProperties

setLocalProperty

getLocalProperty

setJobDescription

setJobGroup

clearJobGroup

parallelize

makeRDD

makeRDD

textFile

wholeTextFiles

hadoopRDD

hadoopFile