Use SparkContext hadoop configuration within RDD methods/closures, like foreachPartition

The problem here is that Hadoop Configuration's aren't tagged as Serializable, so Spark wont pull them into RDDs. They are marked as Writable, so Hadoop's serialization mechanism can marshall and unmarshall them, but Spark doesn't directly work with that

The two long term fix options would be

1. Add Support for serializing writables in Spark. Maybe SPARK-2421?
2. Make Hadoop Configuration Serializable.
3. Add explicit support for serializing Hadoop Configs.

You aren't going to hit any major objections to making Hadoop conf serializable; provided you implement custom ser/deser methods which delegate to the writable IO calls (and which just iterate through all key/value pairs). I say that as a Hadoop committer.

Update: Here's the code to create a serlializable class which does marshall the contents of a Hadoop config. Create it with val ser = new ConfSerDeser(hadoopConf); refer to it in your RDD as ser.get().

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 * this work for additional information regarding copyright ownership.
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 * (the "License"); you may not use this file except in compliance with
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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()
  }
}

Note that it would be relatively straightforward for someone to make this generic for all Writeable classes; you'd just need to provide a classname in the constructor and use that to instantiate the writeable during deserialization.

This is a java implementation, according to @Steve's Answer.

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);
    }
}

You can serialize and deserialize the org.apache.hadoop.conf.Configuration using org.apache.spark.SerializableWritable.

For example:

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))