org.apache.spark.rdd

Class HadoopRDD<K,V>

Object

org.apache.spark.rdd.RDD<scala.Tuple2<K,V>>

org.apache.spark.rdd.HadoopRDD<K,V>

All Implemented Interfaces:

java.io.Serializable, Logging

public class HadoopRDD<K,V>
extends RDD<scala.Tuple2<K,V>>
implements Logging

:: DeveloperApi :: An RDD that provides core functionality for reading data stored in Hadoop (e.g., files in HDFS, sources in HBase, or S3), using the older MapReduce API (org.apache.hadoop.mapred).

Note: Instantiating this class directly is not recommended, please use org.apache.spark.SparkContext.hadoopRDD()

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
HadoopRDD(SparkContext sc, Broadcast<SerializableWritable<org.apache.hadoop.conf.Configuration>> broadcastedConf, scala.Option<scala.Function1<org.apache.hadoop.mapred.JobConf,scala.runtime.BoxedUnit>> initLocalJobConfFuncOpt, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)
HadoopRDD(SparkContext sc, org.apache.hadoop.mapred.JobConf conf, Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass, Class<K> keyClass, Class<V> valueClass, int minPartitions)

Method Summary

Methods

Modifier and Type	Method and Description
static void	addLocalConfiguration(String jobTrackerId, int jobId, int splitId, int attemptId, org.apache.hadoop.mapred.JobConf conf) Add Hadoop configuration specific to a single partition and attempt.
void	checkpoint() Mark this RDD for checkpointing.
InterruptibleIterator<scala.Tuple2<K,V>>	compute(Partition theSplit, TaskContext context) :: DeveloperApi :: Implemented by subclasses to compute a given partition.
static boolean	containsCachedMetadata(String key)
static Object	getCachedMetadata(String key) The three methods below are helpers for accessing the local map, a property of the SparkEnv of the local process.
org.apache.hadoop.conf.Configuration	getConf()
Partition[]	getPartitions() Implemented by subclasses to return the set of partitions in this RDD.
scala.collection.Seq<String>	getPreferredLocations(Partition split) Optionally overridden by subclasses to specify placement preferences.
static Object	putCachedMetadata(String key, Object value)

Methods inherited from class org.apache.spark.rdd.RDD

aggregate, cache, cartesian, checkpointData, coalesce, collect, collect, context, count, countApprox, countApproxDistinct, countByValue, countByValueApprox, creationSiteInfo, dependencies, distinct, distinct, filter, filterWith, first, flatMap, flatMapWith, fold, foreach, foreachPartition, foreachWith, getCheckpointFile, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, iterator, keyBy, map, mapPartitions, mapPartitionsWithContext, mapPartitionsWithIndex, mapPartitionsWithSplit, mapWith, max, min, name, partitioner, partitions, persist, persist, pipe, pipe, pipe, preferredLocations, randomSplit, reduce, repartition, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toArray, toDebugString, toJavaRDD, toLocalIterator, top, toString, union, unpersist, zip, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipPartitions, zipWithIndex, zipWithUniqueId

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, 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

HadoopRDD

public HadoopRDD(SparkContext sc,
         Broadcast<SerializableWritable<org.apache.hadoop.conf.Configuration>> broadcastedConf,
         scala.Option<scala.Function1<org.apache.hadoop.mapred.JobConf,scala.runtime.BoxedUnit>> initLocalJobConfFuncOpt,
         Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
         Class<K> keyClass,
         Class<V> valueClass,
         int minPartitions)

HadoopRDD

public HadoopRDD(SparkContext sc,
         org.apache.hadoop.mapred.JobConf conf,
         Class<? extends org.apache.hadoop.mapred.InputFormat<K,V>> inputFormatClass,
         Class<K> keyClass,
         Class<V> valueClass,
         int minPartitions)

Method Detail

getCachedMetadata

public static Object getCachedMetadata(String key)

The three methods below are helpers for accessing the local map, a property of the SparkEnv of the local process.

containsCachedMetadata

public static boolean containsCachedMetadata(String key)

putCachedMetadata

public static Object putCachedMetadata(String key,
                       Object value)

addLocalConfiguration

public static void addLocalConfiguration(String jobTrackerId,
                         int jobId,
                         int splitId,
                         int attemptId,
                         org.apache.hadoop.mapred.JobConf conf)

Add Hadoop configuration specific to a single partition and attempt.

getPartitions

public Partition[] getPartitions()

Description copied from class: RDD

Implemented by subclasses to return the set of partitions in this RDD. This method will only be called once, so it is safe to implement a time-consuming computation in it.

compute

public InterruptibleIterator<scala.Tuple2<K,V>> compute(Partition theSplit,
                                               TaskContext context)

Description copied from class: RDD

:: DeveloperApi :: Implemented by subclasses to compute a given partition.

Specified by:

compute in class RDD<scala.Tuple2<K,V>>

getPreferredLocations

public scala.collection.Seq<String> getPreferredLocations(Partition split)

Description copied from class: RDD

Optionally overridden by subclasses to specify placement preferences.

checkpoint

public void checkpoint()

Description copied from class: RDD

Mark this RDD for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() and all references to its parent RDDs will be removed. This function must be called before any job has been executed on this RDD. It is strongly recommended that this RDD is persisted in memory, otherwise saving it on a file will require recomputation.

Overrides:

checkpoint in class RDD<scala.Tuple2<K,V>>

getConf

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