org.apache.spark.api.java

Class JavaSparkContext

Object

org.apache.spark.api.java.JavaSparkContext

All Implemented Interfaces:

java.io.Closeable, AutoCloseable

public class JavaSparkContext
extends Object
implements java.io.Closeable

A Java-friendly version of SparkContext that returns JavaRDDs and works with Java collections instead of Scala ones.

Note:

Only one SparkContext should be active per JVM. You must stop() the active SparkContext before creating a new one.

Constructor Summary

Constructors

Constructor and Description
JavaSparkContext() Create a JavaSparkContext that loads settings from system properties (for instance, when launching with ./bin/spark-submit).
JavaSparkContext(SparkConf conf)
JavaSparkContext(SparkContext sc)
JavaSparkContext(String master, String appName)
JavaSparkContext(String master, String appName, SparkConf conf)
JavaSparkContext(String master, String appName, String sparkHome, String jarFile)
JavaSparkContext(String master, String appName, String sparkHome, String[] jars)
JavaSparkContext(String master, String appName, String sparkHome, String[] jars, java.util.Map<String,String> environment)

Method Summary

Modifier and Type	Method and Description
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.
String	appName()
JavaPairRDD<String,PortableDataStream>	binaryFiles(String path) Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array.
JavaPairRDD<String,PortableDataStream>	binaryFiles(String path, int minPartitions) Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array.
JavaRDD<byte[]>	binaryRecords(String path, int recordLength) Load data from a flat binary file, assuming the length of each record is constant.
<T> Broadcast<T>	broadcast(T value) 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() Pass-through to SparkContext.setCallSite.
void	clearJobGroup() Clear the current thread's job group ID and its description.
void	close()
Integer	defaultMinPartitions() Default min number of partitions for Hadoop RDDs when not given by user
Integer	defaultParallelism() Default level of parallelism to use when not given by user (e.g.
<T> JavaRDD<T>	emptyRDD() Get an RDD that has no partitions or elements.
static JavaSparkContext	fromSparkContext(SparkContext sc)
Optional<String>	getCheckpointDir()
SparkConf	getConf() Return a copy of this JavaSparkContext's configuration.
String	getLocalProperty(String key) Get a local property set in this thread, or null if it is missing.
java.util.Map<Integer,JavaRDD<?>>	getPersistentRDDs() Returns a Java map of JavaRDDs that have marked themselves as persistent via cache() call.
Optional<String>	getSparkHome() Get Spark's home location from either a value set through the constructor, or the spark.home Java property, or the SPARK_HOME environment variable (in that order of preference).
org.apache.hadoop.conf.Configuration	hadoopConfiguration() Returns the Hadoop configuration used for the Hadoop code (e.g.
<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> JavaPairRDD<K,V>	hadoopFile(String path, Class<F> inputFormatClass, Class<K> keyClass, Class<V> valueClass) Get an RDD for a Hadoop file with an arbitrary InputFormat
<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> JavaPairRDD<K,V>	hadoopFile(String path, Class<F> 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>> JavaPairRDD<K,V>	hadoopRDD(org.apache.hadoop.mapred.JobConf conf, Class<F> inputFormatClass, Class<K> keyClass, Class<V> valueClass) Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.g.
<K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> JavaPairRDD<K,V>	hadoopRDD(org.apache.hadoop.mapred.JobConf conf, Class<F> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions) Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.g.
Boolean	isLocal()
static 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 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.
java.util.List<String>	jars()
String	master()
<K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> JavaPairRDD<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>> JavaPairRDD<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> JavaRDD<T>	objectFile(String path) Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
<T> JavaRDD<T>	objectFile(String path, int minPartitions) Load an RDD saved as a SequenceFile containing serialized objects, with NullWritable keys and BytesWritable values that contain a serialized partition.
<T> JavaRDD<T>	parallelize(java.util.List<T> list) Distribute a local Scala collection to form an RDD.
<T> JavaRDD<T>	parallelize(java.util.List<T> list, int numSlices) Distribute a local Scala collection to form an RDD.
JavaDoubleRDD	parallelizeDoubles(java.util.List<Double> list) Distribute a local Scala collection to form an RDD.
JavaDoubleRDD	parallelizeDoubles(java.util.List<Double> list, int numSlices) Distribute a local Scala collection to form an RDD.
<K,V> JavaPairRDD<K,V>	parallelizePairs(java.util.List<scala.Tuple2<K,V>> list) Distribute a local Scala collection to form an RDD.
<K,V> JavaPairRDD<K,V>	parallelizePairs(java.util.List<scala.Tuple2<K,V>> list, int numSlices) Distribute a local Scala collection to form an RDD.
java.util.Map<String,ResourceInformation>	resources()
SparkContext	sc()
<K,V> JavaPairRDD<K,V>	sequenceFile(String path, Class<K> keyClass, Class<V> valueClass) Get an RDD for a Hadoop SequenceFile.
<K,V> JavaPairRDD<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.
void	setCallSite(String site) Pass-through to SparkContext.setCallSite.
void	setCheckpointDir(String dir) 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) 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	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, and all child threads, such as the Spark fair scheduler pool.
void	setLogLevel(String logLevel) Control our logLevel.
String	sparkUser()
Long	startTime()
JavaSparkStatusTracker	statusTracker()
void	stop() Shut down the SparkContext.
JavaRDD<String>	textFile(String path) 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.
JavaRDD<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.
static SparkContext	toSparkContext(JavaSparkContext jsc)
JavaDoubleRDD	union(JavaDoubleRDD... rdds) Build the union of JavaDoubleRDDs.
<K,V> JavaPairRDD<K,V>	union(JavaPairRDD<K,V>... rdds) Build the union of JavaPairRDDs.
<T> JavaRDD<T>	union(JavaRDD<T>... rdds) Build the union of JavaRDDs.
JavaDoubleRDD	union(scala.collection.Seq<JavaDoubleRDD> rdds) Build the union of JavaDoubleRDDs.
<K,V> JavaPairRDD<K,V>	union(scala.collection.Seq<JavaPairRDD<K,V>> rdds) Build the union of JavaPairRDDs.
<T> JavaRDD<T>	union(scala.collection.Seq<JavaRDD<T>> rdds) Build the union of JavaRDDs.
String	version() The version of Spark on which this application is running.
JavaPairRDD<String,String>	wholeTextFiles(String path) Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI.
JavaPairRDD<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

JavaSparkContext

public JavaSparkContext(SparkContext sc)

JavaSparkContext

public JavaSparkContext()

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

JavaSparkContext

public JavaSparkContext(SparkConf conf)

Parameters:

conf - a SparkConf object specifying Spark parameters

JavaSparkContext

public JavaSparkContext(String master,
                        String appName)

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

JavaSparkContext

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

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

JavaSparkContext

public JavaSparkContext(String master,
                        String appName,
                        String sparkHome,
                        String jarFile)

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 - The SPARK_HOME directory on the worker nodes

jarFile - JAR file to send to the cluster. This can be a path on the local file system or an HDFS, HTTP, HTTPS, or FTP URL.

JavaSparkContext

public JavaSparkContext(String master,
                        String appName,
                        String sparkHome,
                        String[] jars)

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 - The SPARK_HOME directory on the worker 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.

JavaSparkContext

public JavaSparkContext(String master,
                        String appName,
                        String sparkHome,
                        String[] jars,
                        java.util.Map<String,String> environment)

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 - The SPARK_HOME directory on the worker 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

fromSparkContext

public static JavaSparkContext fromSparkContext(SparkContext sc)

toSparkContext

public static SparkContext toSparkContext(JavaSparkContext jsc)

jarOfClass

public static 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 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)

union

public <T> JavaRDD<T> union(JavaRDD<T>... rdds)

Build the union of JavaRDDs.

union

public <K,V> JavaPairRDD<K,V> union(JavaPairRDD<K,V>... rdds)

Build the union of JavaPairRDDs.

union

public JavaDoubleRDD union(JavaDoubleRDD... rdds)

Build the union of JavaDoubleRDDs.

sc

public SparkContext sc()

statusTracker

public JavaSparkStatusTracker statusTracker()

isLocal

public Boolean isLocal()

sparkUser

public String sparkUser()

master

public String master()

appName

public String appName()

resources

public java.util.Map<String,ResourceInformation> resources()

jars

public java.util.List<String> jars()

startTime

public Long startTime()

version

public String version()

The version of Spark on which this application is running.

defaultParallelism

public Integer defaultParallelism()

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

defaultMinPartitions

public Integer defaultMinPartitions()

Default min number of partitions for Hadoop RDDs when not given by user

parallelize

public <T> JavaRDD<T> parallelize(java.util.List<T> list,
                                  int numSlices)

Distribute a local Scala collection to form an RDD.

emptyRDD

public <T> JavaRDD<T> emptyRDD()

Get an RDD that has no partitions or elements.

parallelize

public <T> JavaRDD<T> parallelize(java.util.List<T> list)

Distribute a local Scala collection to form an RDD.

parallelizePairs

public <K,V> JavaPairRDD<K,V> parallelizePairs(java.util.List<scala.Tuple2<K,V>> list,
                                               int numSlices)

Distribute a local Scala collection to form an RDD.

parallelizePairs

public <K,V> JavaPairRDD<K,V> parallelizePairs(java.util.List<scala.Tuple2<K,V>> list)

Distribute a local Scala collection to form an RDD.

parallelizeDoubles

public JavaDoubleRDD parallelizeDoubles(java.util.List<Double> list,
                                        int numSlices)

Distribute a local Scala collection to form an RDD.

parallelizeDoubles

public JavaDoubleRDD parallelizeDoubles(java.util.List<Double> list)

Distribute a local Scala collection to form an RDD.

textFile

public JavaRDD<String> textFile(String path)

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. The text files must be encoded as UTF-8.

Parameters:

path - (undocumented)

Returns:

(undocumented)

textFile

public JavaRDD<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. The text files must be encoded as UTF-8.

Parameters:

path - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

wholeTextFiles

public JavaPairRDD<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. The text files must be encoded as UTF-8.

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

   JavaPairRDD<String, String> rdd = sparkContext.wholeTextFiles("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.

path - (undocumented)

Returns:

(undocumented)

Note:

Small files are preferred, large file is also allowable, but may cause bad performance.

wholeTextFiles

public JavaPairRDD<String,String> wholeTextFiles(String path)

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. The text files must be encoded as UTF-8.

Parameters:

path - (undocumented)

Returns:

(undocumented)

See Also:

wholeTextFiles(path: String, minPartitions: Int).

binaryFiles

public JavaPairRDD<String,PortableDataStream> binaryFiles(String path,
                                                          int minPartitions)

Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. 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

   JavaPairRDD<String, byte[]> rdd = sparkContext.dataStreamFiles("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.

path - (undocumented)

Returns:

(undocumented)

Note:

Small files are preferred; very large files but may cause bad performance.

binaryFiles

public JavaPairRDD<String,PortableDataStream> binaryFiles(String path)

Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. 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

   JavaPairRDD<String, byte[]> rdd = sparkContext.dataStreamFiles("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 - (undocumented)

Returns:

(undocumented)

Note:

Small files are preferred; very large files but may cause bad performance.

binaryRecords

public JavaRDD<byte[]> binaryRecords(String path,
                                     int recordLength)

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

Parameters:

path - Directory to the input data files

recordLength - (undocumented)

Returns:

An RDD of data with values, represented as byte arrays

sequenceFile

public <K,V> JavaPairRDD<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.

Parameters:

path - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

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> JavaPairRDD<K,V> sequenceFile(String path,
                                           Class<K> keyClass,
                                           Class<V> valueClass)

Get an RDD for a Hadoop SequenceFile.

Parameters:

path - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

Returns:

(undocumented)

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> JavaRDD<T> objectFile(String path,
                                 int minPartitions)

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)

Returns:

(undocumented)

objectFile

public <T> JavaRDD<T> objectFile(String path)

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)

Returns:

(undocumented)

hadoopRDD

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

Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable, etc).

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.

Returns:

(undocumented)

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.

hadoopRDD

public <K,V,F extends org.apache.hadoop.mapred.InputFormat<K,V>> JavaPairRDD<K,V> hadoopRDD(org.apache.hadoop.mapred.JobConf conf,
                                                                                            Class<F> inputFormatClass,
                                                                                            Class<K> keyClass,
                                                                                            Class<V> valueClass)

Get an RDD for a Hadoop-readable dataset from a Hadoop JobConf giving its InputFormat and any other necessary info (e.g. file name for a filesystem-based dataset, table name for HyperTable,

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

Returns:

(undocumented)

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>> JavaPairRDD<K,V> hadoopFile(String path,
                                                                                             Class<F> inputFormatClass,
                                                                                             Class<K> keyClass,
                                                                                             Class<V> valueClass,
                                                                                             int minPartitions)

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

Parameters:

path - (undocumented)

inputFormatClass - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

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>> JavaPairRDD<K,V> hadoopFile(String path,
                                                                                             Class<F> inputFormatClass,
                                                                                             Class<K> keyClass,
                                                                                             Class<V> valueClass)

Get an RDD for a Hadoop file with an arbitrary InputFormat

Parameters:

path - (undocumented)

inputFormatClass - (undocumented)

keyClass - (undocumented)

valueClass - (undocumented)

Returns:

(undocumented)

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.

newAPIHadoopFile

public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> JavaPairRDD<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.

Parameters:

path - (undocumented)

fClass - (undocumented)

kClass - (undocumented)

vClass - (undocumented)

conf - (undocumented)

Returns:

(undocumented)

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.

newAPIHadoopRDD

public <K,V,F extends org.apache.hadoop.mapreduce.InputFormat<K,V>> JavaPairRDD<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

Returns:

(undocumented)

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.

union

public <T> JavaRDD<T> union(scala.collection.Seq<JavaRDD<T>> rdds)

Build the union of JavaRDDs.

union

public <K,V> JavaPairRDD<K,V> union(scala.collection.Seq<JavaPairRDD<K,V>> rdds)

Build the union of JavaPairRDDs.

union

public JavaDoubleRDD union(scala.collection.Seq<JavaDoubleRDD> rdds)

Build the union of JavaDoubleRDDs.

broadcast

public <T> Broadcast<T> broadcast(T value)

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)

Returns:

(undocumented)

stop

public void stop()

Shut down the SparkContext.

close

public void close()

Specified by:

close in interface java.io.Closeable

Specified by:

close in interface AutoCloseable

getSparkHome

public Optional<String> getSparkHome()

Get Spark's home location from either a value set through the constructor, or the spark.home Java property, or the SPARK_HOME environment variable (in that order of preference). If neither of these is set, return None.

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)

Note:

A path can be added only once. Subsequent additions of the same path are ignored.

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)

Note:

A path can be added only once. Subsequent additions of the same path are ignored.

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), or an HTTP, HTTPS or FTP URI.

Parameters:

path - (undocumented)

Note:

A path can be added only once. Subsequent additions of the same path are ignored.

hadoopConfiguration

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

Returns the Hadoop configuration used for the Hadoop code (e.g. file systems) we reuse.

Returns:

(undocumented)

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.

setCheckpointDir

public void setCheckpointDir(String dir)

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

Parameters:

dir - (undocumented)

getCheckpointDir

public Optional<String> getCheckpointDir()

getConf

public SparkConf getConf()

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

Returns:

(undocumented)

setCallSite

public void setCallSite(String site)

Pass-through to SparkContext.setCallSite. For API support only.

Parameters:

site - (undocumented)

clearCallSite

public void clearCallSite()

Pass-through to SparkContext.setCallSite. For API support only.

setLocalProperty

public void setLocalProperty(String key,
                             String value)

Set a local property that affects jobs submitted from this thread, and all child threads, such as the Spark fair scheduler pool.

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.api.java.JavaSparkContext.setLocalProperty.

Parameters:

key - (undocumented)

Returns:

(undocumented)

setJobDescription

public void setJobDescription(String value)

Set a human readable description of the current job.

Parameters:

value - (undocumented)

Since:

2.3.0

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

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.api.java.JavaSparkContext.cancelJobGroup to cancel all running jobs in this group. For example,

 // In the main thread:
 sc.setJobGroup("some_job_to_cancel", "some job description");
 rdd.map(...).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)

setJobGroup

public void setJobGroup(String groupId,
                        String description)

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

Parameters:

groupId - (undocumented)

description - (undocumented)

See Also:

setJobGroup(groupId: String, description: String, interruptThread: Boolean). This method sets interruptOnCancel to false.

clearJobGroup

public void clearJobGroup()

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

cancelJobGroup

public void cancelJobGroup(String groupId)

Cancel active jobs for the specified group. See org.apache.spark.api.java.JavaSparkContext.setJobGroup for more information.

Parameters:

groupId - (undocumented)

cancelAllJobs

public void cancelAllJobs()

Cancel all jobs that have been scheduled or are running.

getPersistentRDDs

public java.util.Map<Integer,JavaRDD<?>> getPersistentRDDs()

Returns a Java map of JavaRDDs that have marked themselves as persistent via cache() call.

Returns:

(undocumented)

Note:

This does not necessarily mean the caching or computation was successful.