java - 在 RDD 方法/闭包中使用 SparkContext hadoop 配置，例如 foreachPartition

Question

我正在使用 Spark 读取一堆文件，对它们进行详细说明，然后将它们全部保存为序列文件。我想要的是每个分区有 1 个序列文件，所以我这样做了:

SparkConf sparkConf = new SparkConf().setAppName("writingHDFS")
                .setMaster("local[2]")
                .set("spark.streaming.stopGracefullyOnShutdown", "true");
        final JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        jsc.hadoopConfiguration().addResource(hdfsConfPath + "hdfs-site.xml");
        jsc.hadoopConfiguration().addResource(hdfsConfPath + "core-site.xml");
        //JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(5*1000));

        JavaPairRDD<String, PortableDataStream> imageByteRDD = jsc.binaryFiles(sourcePath);
        if(!imageByteRDD.isEmpty())
            imageByteRDD.foreachPartition(new VoidFunction<Iterator<Tuple2<String,PortableDataStream>>>() {

                @Override
                public void call(Iterator<Tuple2<String, PortableDataStream>> arg0){
                        throws Exception {
                  [°°°SOME STUFF°°°]
                  SequenceFile.Writer writer = SequenceFile.createWriter(
                                     jsc.hadoopConfiguration(), 
//here lies the problem: how to pass the hadoopConfiguration I have put inside the Spark Context? 
Previously, I created a Configuration for each partition, and it works, but I'm sure there is a much more "sparky way"

有人知道如何在 RDD 闭包内部使用 Hadoop 配置对象吗？

Answer 1

这里的问题是 Hadoop 配置没有被标记为 Serializable，因此 Spark 不会将它们拉入 RDD。它们被标记为Writable，因此 Hadoop 的序列化机制可以对它们进行编码和解码，但 Spark 不直接使用它

两个长期修复选项是

1. 添加对在 Spark 中序列化可写对象的支持。也许SPARK-2421 ？
2. 使 Hadoop 配置可序列化。
3. 添加对序列化 Hadoop 配置的明确支持。

您不会对使 Hadoop conf 可序列化提出任何主要反对意见；如果您实现了委托(delegate)给可写 IO 调用的自定义 ser/deser 方法(并且只是遍历所有键/值对)。我是作为 Hadoop 提交者这么说的。

更新:下面是创建可序列化类的代码，该类确实编码 Hadoop 配置的内容。使用 val ser = new ConfSerDeser(hadoopConf) 创建它；在您的 RDD 中将其称为 ser.get()。

/*
 * 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.
 */

 import org.apache.hadoop.conf.Configuration

/**
 * Class to make Hadoop configurations serializable; uses the
 * `Writeable` operations to do this.
 * Note: this only serializes the explicitly set values, not any set
 * in site/default or other XML resources.
 * @param conf
 */
class ConfigSerDeser(var conf: Configuration) extends Serializable {

  def this() {
    this(new Configuration())
  }

  def get(): Configuration = conf

  private def writeObject (out: java.io.ObjectOutputStream): Unit = {
    conf.write(out)
  }

  private def readObject (in: java.io.ObjectInputStream): Unit = {
    conf = new Configuration()
    conf.readFields(in)
  }

  private def readObjectNoData(): Unit = {
    conf = new Configuration()
  }
}

请注意，对于某些人来说，为所有可写类创建泛型会相对简单；您只需要在构造函数中提供一个类名，并在反序列化期间使用它来实例化可写对象。