Add configuration option to control cloning of Hadoop JobConf.

core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala

@@ -122,20 +122,54 @@ class HadoopRDD[K, V](
       minPartitions)
   }
 
+  protected val jobConfCacheKey = "rdd_%d_job_conf".format(id)
+
   protected val inputFormatCacheKey = "rdd_%d_input_format".format(id)
 
   // used to build JobTracker ID
   private val createTime = new Date()
 
+  private val shouldCloneJobConf = sc.conf.get("spark.hadoop.cloneConf", "false").toBoolean
+
   // Returns a JobConf that will be used on slaves to obtain input splits for Hadoop reads.
   protected def getJobConf(): JobConf = {
     val conf: Configuration = broadcastedConf.value.value
-    HadoopRDD.CONFIGURATION_INSTANTIATION_LOCK.synchronized {
-      val newJobConf = new JobConf(conf)
-      if (!conf.isInstanceOf[JobConf]) {
-        initLocalJobConfFuncOpt.map(f => f(newJobConf))
+    if (shouldCloneJobConf) {
+      // Hadoop Configuration objects are not thread-safe, which may lead to various problems if
+      // one job modifies a configuration while another reads it (SPARK-2546).  This problem occurs
+      // somewhat rarely because most jobs treat the configuration as though it's immutable.  One
+      // solution, implemented here, is to clone the Configuration object.  Unfortunately, this
+      // clone can be very expensive.  To avoid unexpected performance regressions for workloads and
+      // Hadoop versions that do not suffer from these thread-safety issues, this cloning is
+      // disabled by default.
+      HadoopRDD.CONFIGURATION_INSTANTIATION_LOCK.synchronized {
+        logDebug("Cloning Hadoop Configuration")
+        val newJobConf = new JobConf(conf)
+        if (!conf.isInstanceOf[JobConf]) {
+          initLocalJobConfFuncOpt.map(f => f(newJobConf))
+        }
+        newJobConf
+      }
+    } else {
+      if (conf.isInstanceOf[JobConf]) {
+        logDebug("Re-using user-broadcasted JobConf")
+        conf.asInstanceOf[JobConf]
+      } else if (HadoopRDD.containsCachedMetadata(jobConfCacheKey)) {
+        logDebug("Re-using cached JobConf")
+        HadoopRDD.getCachedMetadata(jobConfCacheKey).asInstanceOf[JobConf]
+      } else {
+        // Create a JobConf that will be cached and used across this RDD's getJobConf() calls in the
+        // local process. The local cache is accessed through HadoopRDD.putCachedMetadata().
+        // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects.
+        // Synchronize to prevent ConcurrentModificationException (SPARK-1097, HADOOP-10456).
+        HadoopRDD.CONFIGURATION_INSTANTIATION_LOCK.synchronized {
+          logDebug("Creating new JobConf and caching it for later re-use")
+          val newJobConf = new JobConf(conf)
+          initLocalJobConfFuncOpt.map(f => f(newJobConf))
+          HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
+          newJobConf
+        }
       }
-      newJobConf
     }
   }
 

docs/configuration.md

@@ -590,6 +590,15 @@ Apart from these, the following properties are also available, and may be useful
     output directories. We recommend that users do not disable this except if trying to achieve compatibility with
     previous versions of Spark. Simply use Hadoop's FileSystem API to delete output directories by hand.</td>
 </tr>
+<tr>
+    <td><code>spark.hadoop.cloneConf</code></td>
+    <td>false</td>
+    <td>If set to true, clones a new Hadoop <code>Configuration</code> object for each task.  This
+    option should be enabled to work around <code>Configuration</code> thread-safety issues (see
+    <a href="https://issues.apache.org/jira/browse/SPARK-2546">SPARK-2546</a> for more details).
+    This is disabled by default in order to avoid unexpected performance regressions for jobs that
+    are not affected by these issues.</td>
+</tr>
 <tr>
     <td><code>spark.executor.heartbeatInterval</code></td>
     <td>10000</td>