Change port from 3030 to 4040

4 files changed, 8 insertions, 8 deletions

diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 7025c23657..f679cad713 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 d4f85538b2..7940d41a27 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 e5f054cb14..790220500a 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 0e3606f71a..5f456b999b 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).