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, org.apache.spark.internal.Logging

public class HadoopRDD<K,V>
extends RDD<scala.Tuple2<K,V>>
implements org.apache.spark.internal.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).

param: sc The SparkContext to associate the RDD with. param: broadcastedConf A general Hadoop Configuration, or a subclass of it. If the enclosed variable references an instance of JobConf, then that JobConf will be used for the Hadoop job. Otherwise, a new JobConf will be created on each executor using the enclosed Configuration. param: initLocalJobConfFuncOpt Optional closure used to initialize any JobConf that HadoopRDD creates. param: inputFormatClass Storage format of the data to be read. param: keyClass Class of the key associated with the inputFormatClass. param: valueClass Class of the value associated with the inputFormatClass. param: minPartitions Minimum number of HadoopRDD partitions (Hadoop Splits) to generate.

See Also:

Serialized Form

Note:

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

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HadoopRDD.HadoopMapPartitionsWithSplitRDD$

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
HadoopRDD(SparkContext sc, Broadcast<SerializableConfiguration> 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

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 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$1) Maps over a partition, providing the InputSplit that was used as the base of the partition.
static void	org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
static org.slf4j.Logger	org$apache$spark$internal$Logging$$log_()
HadoopRDD<K,V>	persist(StorageLevel storageLevel) Set this RDD's storage level to persist its values across operations after the first time it is computed.
static int	RECORDS_BETWEEN_BYTES_READ_METRIC_UPDATES() Update the input bytes read metric each time this number of records has been read

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

aggregate, barrier, cache, cartesian, cleanShuffleDependencies, coalesce, collect, collect, context, count, countApprox, countApproxDistinct, countApproxDistinct, countByValue, countByValueApprox, dependencies, distinct, distinct, doubleRDDToDoubleRDDFunctions, filter, first, flatMap, fold, foreach, foreachPartition, getCheckpointFile, getNumPartitions, getResourceProfile, getStorageLevel, glom, groupBy, groupBy, groupBy, id, intersection, intersection, intersection, isCheckpointed, isEmpty, iterator, keyBy, localCheckpoint, map, mapPartitions, mapPartitionsWithIndex, max, min, name, numericRDDToDoubleRDDFunctions, partitioner, partitions, persist, pipe, pipe, pipe, preferredLocations, randomSplit, rddToAsyncRDDActions, rddToOrderedRDDFunctions, rddToPairRDDFunctions, rddToSequenceFileRDDFunctions, reduce, repartition, sample, saveAsObjectFile, saveAsTextFile, saveAsTextFile, setName, sortBy, sparkContext, subtract, subtract, subtract, take, takeOrdered, takeSample, toDebugString, toJavaRDD, toLocalIterator, top, toString, treeAggregate, treeAggregate, treeReduce, union, unpersist, withResources, 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.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

HadoopRDD

public HadoopRDD(SparkContext sc,
                 Broadcast<SerializableConfiguration> 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().

Returns:

(undocumented)

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.

Parameters:

key - (undocumented)

Returns:

(undocumented)

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.

org$apache$spark$internal$Logging$$log_

public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

org$apache$spark$internal$Logging$$log__$eq

public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

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.

The partitions in this array must satisfy the following property: rdd.partitions.zipWithIndex.forall { case (partition, index) => partition.index == index }

Returns:

(undocumented)

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>>

Parameters:

theSplit - (undocumented)

context - (undocumented)

Returns:

(undocumented)

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.

Parameters:

split - (undocumented)

Returns:

(undocumented)

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>>

persist

public HadoopRDD<K,V> persist(StorageLevel storageLevel)

Description copied from class: RDD

Set this RDD's storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. Local checkpointing is an exception.

Overrides:

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

Parameters:

storageLevel - (undocumented)

Returns:

(undocumented)

getConf

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