CodingCat
diff --git a/‎core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala‎
Lines changed: 129 additions & 0 deletions b/‎core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala‎
Lines changed: 129 additions & 0 deletions
diff --git a/‎core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala‎
Lines changed: 178 additions & 1 deletion b/‎core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala‎
Lines changed: 178 additions & 1 deletion
@@ -0,0 +1,129 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.api.python
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.Logging
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.io._
+import scala.util.{Failure, Success, Try}
+import org.apache.spark.annotation.Experimental
+
+
+/**
+ * :: Experimental ::
+ * A trait for use with reading custom classes in PySpark. Implement this trait and add custom
+ * transformation code by overriding the convert method.
+ */
+@Experimental
+trait Converter[T, U] extends Serializable {
+  def convert(obj: T): U
+}
+
+private[python] object Converter extends Logging {
+
+  def getInstance(converterClass: Option[String]): Converter[Any, Any] = {
+    converterClass.map { cc =>
+      Try {
+        val c = Class.forName(cc).newInstance().asInstanceOf[Converter[Any, Any]]
+        logInfo(s"Loaded converter: $cc")
+        c
+      } match {
+        case Success(c) => c
+        case Failure(err) =>
+          logError(s"Failed to load converter: $cc")
+          throw err
+      }
+    }.getOrElse { new DefaultConverter }
+  }
+}
+
+/**
+ * A converter that handles conversion of common [[org.apache.hadoop.io.Writable]] objects.
+ * Other objects are passed through without conversion.
+ */
+private[python] class DefaultConverter extends Converter[Any, Any] {
+
+  /**
+   * Converts a [[org.apache.hadoop.io.Writable]] to the underlying primitive, String or
+   * object representation
+   */
+  private def convertWritable(writable: Writable): Any = {
+    import collection.JavaConversions._
+    writable match {
+      case iw: IntWritable => iw.get()
+      case dw: DoubleWritable => dw.get()
+      case lw: LongWritable => lw.get()
+      case fw: FloatWritable => fw.get()
+      case t: Text => t.toString
+      case bw: BooleanWritable => bw.get()
+      case byw: BytesWritable => byw.getBytes
+      case n: NullWritable => null
+      case aw: ArrayWritable => aw.get().map(convertWritable(_))
+      case mw: MapWritable => mapAsJavaMap(mw.map { case (k, v) =>
+        (convertWritable(k), convertWritable(v))
+      }.toMap)
+      case other => other
+    }
+  }
+
+  def convert(obj: Any): Any = {
+    obj match {
+      case writable: Writable =>
+        convertWritable(writable)
+      case _ =>
+        obj
+    }
+  }
+}
+
+/** Utilities for working with Python objects <-> Hadoop-related objects */
+private[python] object PythonHadoopUtil {
+
+  /**
+   * Convert a [[java.util.Map]] of properties to a [[org.apache.hadoop.conf.Configuration]]
+   */
+  def mapToConf(map: java.util.Map[String, String]): Configuration = {
+    import collection.JavaConversions._
+    val conf = new Configuration()
+    map.foreach{ case (k, v) => conf.set(k, v) }
+    conf
+  }
+
+  /**
+   * Merges two configurations, returns a copy of left with keys from right overwriting
+   * any matching keys in left
+   */
+  def mergeConfs(left: Configuration, right: Configuration): Configuration = {
+    import collection.JavaConversions._
+    val copy = new Configuration(left)
+    right.iterator().foreach(entry => copy.set(entry.getKey, entry.getValue))
+    copy
+  }
+
+  /**
+   * Converts an RDD of key-value pairs, where key and/or value could be instances of
+   * [[org.apache.hadoop.io.Writable]], into an RDD[(K, V)]
+   */
+  def convertRDD[K, V](rdd: RDD[(K, V)],
+                       keyConverter: Converter[Any, Any],
+                       valueConverter: Converter[Any, Any]): RDD[(Any, Any)] = {
+    rdd.map { case (k, v) => (keyConverter.convert(k), valueConverter.convert(v)) }
+  }
+
+}
@@ -28,6 +28,9 @@ import scala.util.Try
 
 import net.razorvine.pickle.{Pickler, Unpickler}
 
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.mapred.{InputFormat, JobConf}
+import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import org.apache.spark._
 import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
 import org.apache.spark.broadcast.Broadcast
@@ -266,7 +269,7 @@ private object SpecialLengths {
   val TIMING_DATA = -3
 }
 
-private[spark] object PythonRDD {
+private[spark] object PythonRDD extends Logging {
   val UTF8 = Charset.forName("UTF-8")
 
   /**
@@ -346,6 +349,180 @@ private[spark] object PythonRDD {
     }
   }
 
+  /**
+   * Create an RDD from a path using [[org.apache.hadoop.mapred.SequenceFileInputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def sequenceFile[K, V](
+      sc: JavaSparkContext,
+      path: String,
+      keyClassMaybeNull: String,
+      valueClassMaybeNull: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      minSplits: Int) = {
+    val keyClass = Option(keyClassMaybeNull).getOrElse("org.apache.hadoop.io.Text")
+    val valueClass = Option(valueClassMaybeNull).getOrElse("org.apache.hadoop.io.Text")
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+
+    val rdd = sc.sc.sequenceFile[K, V](path, kc, vc, minSplits)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a file path, using an arbitrary [[org.apache.hadoop.mapreduce.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: String,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val baseConf = sc.hadoopConfiguration()
+    val mergedConf = PythonHadoopUtil.mergeConfs(baseConf, conf)
+    val rdd =
+      newAPIHadoopRDDFromClassNames[K, V, F](sc,
+        Some(path), inputFormatClass, keyClass, valueClass, mergedConf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a [[org.apache.hadoop.conf.Configuration]] converted from a map that is
+   * passed in from Python, using an arbitrary [[org.apache.hadoop.mapreduce.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def newAPIHadoopRDD[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val rdd =
+      newAPIHadoopRDDFromClassNames[K, V, F](sc,
+        None, inputFormatClass, keyClass, valueClass, conf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  private def newAPIHadoopRDDFromClassNames[K, V, F <: NewInputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: Option[String] = None,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      conf: Configuration) = {
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    implicit val fcm = ClassTag(Class.forName(inputFormatClass)).asInstanceOf[ClassTag[F]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+    val fc = fcm.runtimeClass.asInstanceOf[Class[F]]
+    val rdd = if (path.isDefined) {
+      sc.sc.newAPIHadoopFile[K, V, F](path.get, fc, kc, vc, conf)
+    } else {
+      sc.sc.newAPIHadoopRDD[K, V, F](conf, fc, kc, vc)
+    }
+    rdd
+  }
+
+  /**
+   * Create an RDD from a file path, using an arbitrary [[org.apache.hadoop.mapred.InputFormat]],
+   * key and value class.
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def hadoopFile[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: String,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val baseConf = sc.hadoopConfiguration()
+    val mergedConf = PythonHadoopUtil.mergeConfs(baseConf, conf)
+    val rdd =
+      hadoopRDDFromClassNames[K, V, F](sc,
+        Some(path), inputFormatClass, keyClass, valueClass, mergedConf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  /**
+   * Create an RDD from a [[org.apache.hadoop.conf.Configuration]] converted from a map
+   * that is passed in from Python, using an arbitrary [[org.apache.hadoop.mapred.InputFormat]],
+   * key and value class
+   * A key and/or value converter class can optionally be passed in
+   * (see [[org.apache.spark.api.python.Converter]])
+   */
+  def hadoopRDD[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      keyConverterClass: String,
+      valueConverterClass: String,
+      confAsMap: java.util.HashMap[String, String]) = {
+    val conf = PythonHadoopUtil.mapToConf(confAsMap)
+    val rdd =
+      hadoopRDDFromClassNames[K, V, F](sc,
+        None, inputFormatClass, keyClass, valueClass, conf)
+    val keyConverter = Converter.getInstance(Option(keyConverterClass))
+    val valueConverter = Converter.getInstance(Option(valueConverterClass))
+    val converted = PythonHadoopUtil.convertRDD[K, V](rdd, keyConverter, valueConverter)
+    JavaRDD.fromRDD(SerDeUtil.rddToPython(converted))
+  }
+
+  private def hadoopRDDFromClassNames[K, V, F <: InputFormat[K, V]](
+      sc: JavaSparkContext,
+      path: Option[String] = None,
+      inputFormatClass: String,
+      keyClass: String,
+      valueClass: String,
+      conf: Configuration) = {
+    implicit val kcm = ClassTag(Class.forName(keyClass)).asInstanceOf[ClassTag[K]]
+    implicit val vcm = ClassTag(Class.forName(valueClass)).asInstanceOf[ClassTag[V]]
+    implicit val fcm = ClassTag(Class.forName(inputFormatClass)).asInstanceOf[ClassTag[F]]
+    val kc = kcm.runtimeClass.asInstanceOf[Class[K]]
+    val vc = vcm.runtimeClass.asInstanceOf[Class[V]]
+    val fc = fcm.runtimeClass.asInstanceOf[Class[F]]
+    val rdd = if (path.isDefined) {
+      sc.sc.hadoopFile(path.get, fc, kc, vc)
+    } else {
+      sc.sc.hadoopRDD(new JobConf(conf), fc, kc, vc)
+    }
+    rdd
+  }
+
   def writeUTF(str: String, dataOut: DataOutputStream) {
     val bytes = str.getBytes(UTF8)
     dataOut.writeInt(bytes.length)