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

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HadoopRDD.HadoopMapPartitionsWithSplitRDD<U,T> Analogous to MapPartitionsRDD, but passes in an InputSplit to the given function rather than the index of the partition.
static class	HadoopRDD.HadoopMapPartitionsWithSplitRDD$
static class	HadoopRDD.SplitInfoReflections

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 Object	CONFIGURATION_INSTANTIATION_LOCK() Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456).
static boolean	containsCachedMetadata(String key)
static scala.collection.Seq<String>	convertSplitLocationInfo(Object[] infos)
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.
<U> RDD<U>	mapPartitionsWithInputSplit(scala.Function2<org.apache.hadoop.mapred.InputSplit,scala.collection.Iterator<scala.Tuple2<K,V>>,scala.collection.Iterator<U>> f, boolean preservesPartitioning, scala.reflect.ClassTag<U> evidence$1) Maps over a partition, providing the InputSplit that was used as the base of the partition.
static Object	putCachedMetadata(String key, Object value)
static int	RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES() Update the input bytes read metric each time this number of records has been read
static scala.Option<HadoopRDD.SplitInfoReflections>	SPLIT_INFO_REFLECTIONS()

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

aggregate, cache, cartesian, checkpointData, coalesce, collect, collect, collectPartitions, computeOrReadCheckpoint, conf, context, count, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, creationSite, dependencies, distinct, distinct, doCheckpoint, elementClassTag, filter, filterWith, first, flatMap, flatMapWith, fold, foreach, foreachPartition, foreachWith, getCheckpointFile, getCreationSite, getNarrowAncestors, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, iterator, keyBy, map, mapPartitions, mapPartitionsWithContext, mapPartitionsWithIndex, mapPartitionsWithSplit, mapWith, markCheckpointed, max, min, name, partitioner, partitions, persist, persist, pipe, pipe, pipe, preferredLocations, randomSplit, reduce, repartition, retag, retag, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sortBy, 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

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, 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

CONFIGURATION_INSTANTIATION_LOCK

public static Object CONFIGURATION_INSTANTIATION_LOCK()

Configuration's constructor is not threadsafe (see SPARK-1097 and HADOOP-10456). Therefore, we synchronize on this lock before calling new JobConf() or new Configuration().

RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES

public static int RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES()

Update the input bytes read metric each time this number of records has been read

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.

SPLIT_INFO_REFLECTIONS

public static scala.Option<HadoopRDD.SplitInfoReflections> SPLIT_INFO_REFLECTIONS()

convertSplitLocationInfo

public static scala.collection.Seq<String> convertSplitLocationInfo(Object[] infos)

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>>

mapPartitionsWithInputSplit

public <U> RDD<U> mapPartitionsWithInputSplit(scala.Function2<org.apache.hadoop.mapred.InputSplit,scala.collection.Iterator<scala.Tuple2<K,V>>,scala.collection.Iterator<U>> f,
                                     boolean preservesPartitioning,
                                     scala.reflect.ClassTag<U> evidence$1)

Maps over a partition, providing the InputSplit that was used as the base of the partition.

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()