Package 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:
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. param: ignoreCorruptFiles Whether to ignore corrupt files. param: ignoreMissingFiles Whether to ignore missing files.

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

  • Constructor Details

    • 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, boolean ignoreCorruptFiles, boolean ignoreMissingFiles)

    • 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 Details

    • 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)

    • 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)

    • LogStringContext

      public static org.apache.spark.internal.Logging.LogStringContext LogStringContext(scala.StringContext sc)

    • getPartitions

      public Partition[] getPartitions()

    • 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.immutable.Seq<String> getPreferredLocations(Partition split)

    • 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.

      The data is only checkpointed when doCheckpoint() is called, and this only happens at the end of the first action execution on this RDD. The final data that is checkpointed after the first action may be different from the data that was used during the action, due to non-determinism of the underlying operation and retries. If the purpose of the checkpoint is to achieve saving a deterministic snapshot of the data, an eager action may need to be called first on the RDD to trigger the checkpoint.

      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()