diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 48eb096063510007bc04284d6deb8c3808a65660..f1d86c0221fea897133068e17e8bf37e1048e5c2 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/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"
}
diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
index 3321fb5eb71b5315d207c6013324fc67d6f504ac..07c9f2382b4353a175bef9d3a72a7cd18c379ea6 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala
+++ b/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())
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 7025c236574b63d3c6179472c720463a8b4b609f..f679cad713769d45abfc9f10936fa39c3d724d8b 100644
--- a/docs/cluster-overview.md
+++ b/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
diff --git a/docs/configuration.md b/docs/configuration.md
index d4f85538b2a2d41c9178f6ff50898f07a2b7f8c2..7940d41a27ab5929c533ee716a1a014a65ecd2d8 100644
--- a/docs/configuration.md
+++ b/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>
diff --git a/docs/hardware-provisioning.md b/docs/hardware-provisioning.md
index e5f054cb14bf5d6d5f9bc43dfd12db0919b6ebe5..790220500a1b3f00b8dcb66f0c03844a84d9abca 100644
--- a/docs/hardware-provisioning.md
+++ b/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
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 0e3606f71ae54603f4f356a83dd70c6d55a31487..5f456b999b0e187434bfcee09b572a2cde508657 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -7,7 +7,7 @@ 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
@@ -15,9 +15,9 @@ displays useful information about the application. This includes:
* 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).
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 932e70db96e1eb5402592f8a585f3e578ac5b4d5..419d0fe13f944621dd06df9620f16c3f28bbf605 100755
--- a/ec2/spark_ec2.py
+++ b/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