Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/16053#discussion_r94463665
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskDescription.scala ---
@@ -17,27 +17,123 @@
package org.apache.spark.scheduler
+import java.io.{DataInputStream, DataOutputStream}
import java.nio.ByteBuffer
+import java.util.Properties
-import org.apache.spark.util.SerializableBuffer
+import scala.collection.JavaConverters._
+import scala.collection.mutable.{HashMap, Map}
+
+import org.apache.spark.util.{ByteBufferInputStream,
ByteBufferOutputStream, Utils}
/**
* Description of a task that gets passed onto executors to be executed,
usually created by
- * `TaskSetManager.resourceOffer`.
+ * `TaskSetManager.resourceOffer`.
+ *
+ * TaskDescriptions and the associated Task need to be serialized
carefully for two reasons:
+ *
+ * (1) When a TaskDescription is received by an Executor, the Executor
needs to first get the
+ * list of JARs and files and add these to the classpath, and set
the properties, before
+ * deserializing the Task object (serializedTask). This is why the
Properties are included
+ * in the TaskDescription, even though they're also in the
serialized task.
+ * (2) Because a TaskDescription is serialized and sent to an executor
for each task, efficient
+ * serialization (both in terms of serialization time and
serialized buffer size) is
+ * important. For this reason, we serialize TaskDescriptions
ourselves with the
+ * TaskDescription.encode and TaskDescription.decode methods.
This results in a smaller
+ * serialized size because it avoids serializing unnecessary
fields in the Map objects
+ * (which can introduce significant overhead when the maps are
small).
*/
private[spark] class TaskDescription(
val taskId: Long,
val attemptNumber: Int,
val executorId: String,
val name: String,
val index: Int, // Index within this task's TaskSet
- _serializedTask: ByteBuffer)
- extends Serializable {
+ val addedFiles: Map[String, Long],
+ val addedJars: Map[String, Long],
+ val properties: Properties,
+ val serializedTask: ByteBuffer) {
- // Because ByteBuffers are not serializable, wrap the task in a
SerializableBuffer
- private val buffer = new SerializableBuffer(_serializedTask)
+ override def toString: String = "TaskDescription(TID=%d,
index=%d)".format(taskId, index)
+}
- def serializedTask: ByteBuffer = buffer.value
+private[spark] object TaskDescription {
+ def encode(taskDescription: TaskDescription): ByteBuffer = {
+ val bytesOut = new ByteBufferOutputStream(4096)
+ val dataOut = new DataOutputStream(bytesOut)
- override def toString: String = "TaskDescription(TID=%d,
index=%d)".format(taskId, index)
+ dataOut.writeLong(taskDescription.taskId)
+ dataOut.writeInt(taskDescription.attemptNumber)
+ dataOut.writeUTF(taskDescription.executorId)
+ dataOut.writeUTF(taskDescription.name)
+ dataOut.writeInt(taskDescription.index)
+
+ // Write files.
+ dataOut.writeInt(taskDescription.addedFiles.size)
+ for ((name, timestamp) <- taskDescription.addedFiles) {
+ dataOut.writeUTF(name)
+ dataOut.writeLong(timestamp)
+ }
+
+ // Write jars.
+ dataOut.writeInt(taskDescription.addedJars.size)
+ for ((name, timestamp) <- taskDescription.addedJars) {
+ dataOut.writeUTF(name)
+ dataOut.writeLong(timestamp)
+ }
+
+ // Write properties.
+ dataOut.writeInt(taskDescription.properties.size())
+ taskDescription.properties.stringPropertyNames.asScala.foreach { name
=>
+ dataOut.writeUTF(name)
+ dataOut.writeUTF(taskDescription.properties.getProperty(name))
+ }
+
+ // Write the task. The task is already serialized, so write it
directly to the byte buffer
+ // (this requires first flushing the data output stream, so that all
of the data has been
+ // written from the data output stream so the underlying
ByteBufferOutputStream before
+ // we write the task).
+ dataOut.flush()
--- End diff --
Cool thanks for noticing this -- fixed.
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