Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/455#discussion_r13518669
--- Diff:
core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala ---
@@ -0,0 +1,156 @@
+/*
+ * 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, SparkContext}
+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] {
+ def convert(obj: T): U
+}
+
+/**
+ * A converter that handles conversion of common
[[org.apache.hadoop.io.Writable]] objects.
+ * Other objects are passed through without conversion.
+ */
+private[python] object 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 =>
SparkContext.intWritableConverter().convert(iw)
+ case dw: DoubleWritable =>
SparkContext.doubleWritableConverter().convert(dw)
+ case lw: LongWritable =>
SparkContext.longWritableConverter().convert(lw)
+ case fw: FloatWritable =>
SparkContext.floatWritableConverter().convert(fw)
+ case t: Text => SparkContext.stringWritableConverter().convert(t)
+ case bw: BooleanWritable =>
SparkContext.booleanWritableConverter().convert(bw)
+ case byw: BytesWritable =>
SparkContext.bytesWritableConverter().convert(byw)
+ 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
+ }
+ }
+}
+
+/**
+ * The converter registry holds a key and value converter, so that they
are only instantiated
+ * once per RDD partition.
+ */
+private[python] class ConverterRegistry extends Logging {
+
+ var keyConverter: Converter[Any, Any] = DefaultConverter
+ var valueConverter: Converter[Any, Any] = DefaultConverter
+
+ def convertKey(obj: Any): Any = keyConverter.convert(obj)
+
+ def convertValue(obj: Any): Any = valueConverter.convert(obj)
+
+ def registerKeyConverter(converterClass: String) = {
+ keyConverter = register(converterClass)
+ logInfo(s"Loaded and registered key converter ($converterClass)")
+ }
+
+ def registerValueConverter(converterClass: String) = {
+ valueConverter = register(converterClass)
+ logInfo(s"Loaded and registered value converter ($converterClass)")
+ }
+
+ private def register(converterClass: String): Converter[Any, Any] = {
+ Try {
+ val converter =
Class.forName(converterClass).newInstance().asInstanceOf[Converter[Any, Any]]
+ converter
+ } match {
+ case Success(s) => s
+ case Failure(err) =>
+ logError(s"Failed to register converter: $converterClass")
+ throw err
+ }
+ }
+}
+
+/** 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)],
+ keyClass: String,
+ keyConverter: Option[String],
+ valueClass: String,
+ valueConverter: Option[String]) = {
+ rdd.mapPartitions { case iter =>
+ val registry = new ConverterRegistry
+ keyConverter.foreach(registry.registerKeyConverter(_))
+ valueConverter.foreach(registry.registerValueConverter(_))
--- End diff --
Yes, the registry idea originally came from the fact that a new
```WritableConverter``` was being instantiated on each call to ```convert```
(see L48). I was trying to make it so that one converter was instantiated and
cached in the registry for reuse.
I haven't gotten to that part yet but I was thinking of getting this in and
improving that efficiency later on.
I'll take a look at using ```RDD.first``` rather to inspect the types and
instantiate the correct ```WritableConverter``` in the case of the
```DefaultConverter```
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