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 的序列化机制可以编组和解组它们，但 Spark 不能直接使用它

两个长期修复选项是

1. 添加对 Spark 中可写序列化的支持。也许SPARK-2421？
2. 使 Hadoop 配置可序列化。
3. 添加对序列化 Hadoop 配置的显式支持。

对于使 Hadoop conf 可序列化，您不会遇到任何主要的反对意见；前提是您实现了委托给可写 IO 调用的自定义 ser/deser 方法（并且仅遍历所有键/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，该类对 Hadoop 配置的内容进行编组。使用val ser = new ConfSerDeser(hadoopConf) 创建它；在您的 RDD 中将其称为 ser.get()。

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 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，对于所有 Writeable 类来说，将其设为通用是相对简单的；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。

Answer 2

您可以使用org.apache.spark.SerializableWritable 序列化和反序列化org.apache.hadoop.conf.Configuration。

例如：

import org.apache.spark.SerializableWritable

...

val hadoopConf = spark.sparkContext.hadoopConfiguration
// serialize here
val serializedConf = new SerializableWritable(hadoopConf)


// then access the conf by calling .value on serializedConf
rdd.map(someFunction(serializedConf.value))

Answer 3

根据@Steve 的回答，这是一个 java 实现。

import java.io.Serializable;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;


public class SerializableHadoopConfiguration implements Serializable {
    Configuration conf;

    public SerializableHadoopConfiguration(Configuration hadoopConf) {
        this.conf = hadoopConf;

        if (this.conf == null) {
            this.conf = new Configuration();
        }
    }

    public SerializableHadoopConfiguration() {
        this.conf = new Configuration();
    }

    public Configuration get() {
        return this.conf;
    }

    private void writeObject(java.io.ObjectOutputStream out) throws IOException {
        this.conf.write(out);
    }

    private void readObject(java.io.ObjectInputStream in) throws IOException {
        this.conf = new Configuration();
        this.conf.readFields(in);
    }
}

Answer 4

好像做不到，所以这里是我用的代码：

final hdfsNameNodePath = "hdfs://quickstart.cloudera:8080";

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 {

                    Configuration conf = new Configuration();
                    conf.set("fs.defaultFS", hdfsNameNodePath);
                    //the string above should be passed as argument
SequenceFile.Writer writer = SequenceFile.createWriter(
                                     conf, 
                                     SequenceFile.Writer.file([***ETCETERA...

Answer 5

查看 Spark 内部代码库，应该广播 Hadoop 配置的序列化版本。

https://github.com/apache/spark/blob/5d45a415f3a29898d92380380cfd82bfc7f579ea/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/binaryfile/BinaryFileFormat.scala#L98-L99

val spark = SparkSession.builder.master("local").getOrCreate
val broadcastedHadoopConf = spark.sparkContext.broadcast(new org.apache.spark.util.SerializableConfiguration(spark.sparkContext.hadoopConfiguration))

val dfFiles = spark.read.format("binaryFile").load("/somepath").select("path")

val df = dfFiles.map {row => {
  val rawPath = row.getString(0)
  val path = new Path(new URI(rawPath.replace(" ", "%20")))

  // get hadoop configuration in RDD method
  val hadoopConf = broadcastedHadoopConf.value.value

  val fs = path.getFileSystem(hadoopConfiguration)
  val status = fs.getFileStatus(path)
  val inputStream = fs.open(status.getPath)
  // ... whatever you need to do to read data
}}