Merge pull request #922 from pwendell/port-change

Change default port number from 3030 to 4030.

core/src/main/scala/org/apache/spark/ui/SparkUI.scala

@@ -84,6 +84,6 @@ private[spark] class SparkUI(sc: SparkContext) extends Logging {
 }
 
 private[spark] object SparkUI {
-  val DEFAULT_PORT = "3030"
+  val DEFAULT_PORT = "4040"
   val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static"
 }

core/src/test/scala/org/apache/spark/ui/UISuite.scala

@@ -24,7 +24,7 @@ import org.eclipse.jetty.server.Server
 
 class UISuite extends FunSuite {
   test("jetty port increases under contention") {
-    val startPort = 3030
+    val startPort = 4040
     val server = new Server(startPort)
     server.start()
     val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq())

docs/cluster-overview.md

@@ -59,8 +59,8 @@ and `addFile`.
 
 # Monitoring
 
-Each driver program has a web UI, typically on port 3030, that displays information about running
-tasks, executors, and storage usage. Simply go to `http://<driver-node>:3030` in a web browser to
+Each driver program has a web UI, typically on port 4040, that displays information about running
+tasks, executors, and storage usage. Simply go to `http://<driver-node>:4040` in a web browser to
 access this UI. The [monitoring guide](monitoring.html) also describes other monitoring options.
 
 # Job Scheduling

docs/configuration.md

@@ -111,7 +111,7 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td>spark.ui.port</td>
-  <td>3030</td>
+  <td>4040</td>
   <td>
     Port for your application's dashboard, which shows memory and workload data
   </td>

docs/hardware-provisioning.md

@@ -43,7 +43,7 @@ rest for the operating system and buffer cache.
 
 How much memory you will need will depend on your application. To determine how much your
 application uses for a certain dataset size, load part of your dataset in a Spark RDD and use the
-Storage tab of Spark's monitoring UI (`http://<driver-node>:3030`) to see its size in memory.
+Storage tab of Spark's monitoring UI (`http://<driver-node>:4040`) to see its size in memory.
 Note that memory usage is greatly affected by storage level and serialization format -- see
 the [tuning guide](tuning.html) for tips on how to reduce it.
 
@@ -59,7 +59,7 @@ In our experience, when the data is in memory, a lot of Spark applications are n
 Using a **10 Gigabit** or higher network is the best way to make these applications faster.
 This is especially true for "distributed reduce" applications such as group-bys, reduce-bys, and
 SQL joins. In any given application, you can see how much data Spark shuffles across the network
-from the application's monitoring UI (`http://<driver-node>:3030`).
+from the application's monitoring UI (`http://<driver-node>:4040`).
 
 # CPU Cores
 

docs/monitoring.md

@@ -7,17 +7,17 @@ There are several ways to monitor Spark applications.
 
 # Web Interfaces
 
-Every SparkContext launches a web UI, by default on port 3030, that 
+Every SparkContext launches a web UI, by default on port 4040, that 
 displays useful information about the application. This includes:
 
 * A list of scheduler stages and tasks
 * A summary of RDD sizes and memory usage
 * Information about the running executors
 * Environmental information.
 
-You can access this interface by simply opening `http://<driver-node>:3030` in a web browser.
+You can access this interface by simply opening `http://<driver-node>:4040` in a web browser.
 If multiple SparkContexts are running on the same host, they will bind to succesive ports
-beginning with 3030 (3031, 3032, etc).
+beginning with 4040 (4041, 4042, etc).
 
 Spark's Standlone Mode cluster manager also has its own 
 [web UI](spark-standalone.html#monitoring-and-logging). 

ec2/spark_ec2.py

@@ -215,11 +215,10 @@ def launch_cluster(conn, opts, cluster_name):
     master_group.authorize(src_group=slave_group)
     master_group.authorize('tcp', 22, 22, '0.0.0.0/0')
     master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
-    master_group.authorize('tcp', 33000, 33000, '0.0.0.0/0')
     master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
     master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')
     master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0')
-    master_group.authorize('tcp', 3030, 3035, '0.0.0.0/0')
+    master_group.authorize('tcp', 4040, 4045, '0.0.0.0/0')
     if opts.ganglia:
       master_group.authorize('tcp', 5080, 5080, '0.0.0.0/0')
   if slave_group.rules == []: # Group was just now created