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From r...@apache.org
Subject [31/50] git commit: Change port from 3030 to 4040
Date Fri, 20 Sep 2013 22:06:51 GMT
Change port from 3030 to 4040


Project: http://git-wip-us.apache.org/repos/asf/incubator-spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-spark/commit/bddf1356
Tree: http://git-wip-us.apache.org/repos/asf/incubator-spark/tree/bddf1356
Diff: http://git-wip-us.apache.org/repos/asf/incubator-spark/diff/bddf1356

Branch: refs/heads/master
Commit: bddf1356708f42352934313c82f48dbce0056a68
Parents: 2425eb8
Author: Patrick Wendell <pwendell@gmail.com>
Authored: Tue Sep 10 23:12:27 2013 -0700
Committer: Patrick Wendell <pwendell@gmail.com>
Committed: Wed Sep 11 10:01:38 2013 -0700

----------------------------------------------------------------------
 core/src/main/scala/org/apache/spark/ui/SparkUI.scala | 2 +-
 core/src/test/scala/org/apache/spark/ui/UISuite.scala | 2 +-
 docs/cluster-overview.md                              | 4 ++--
 docs/configuration.md                                 | 2 +-
 docs/hardware-provisioning.md                         | 4 ++--
 docs/monitoring.md                                    | 6 +++---
 ec2/spark_ec2.py                                      | 3 +--
 7 files changed, 11 insertions(+), 12 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
----------------------------------------------------------------------
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 48eb096..f1d86c0 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"
 }

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/core/src/test/scala/org/apache/spark/ui/UISuite.scala
----------------------------------------------------------------------
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 3321fb5..07c9f23 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())

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/docs/cluster-overview.md
----------------------------------------------------------------------
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 7025c23..f679cad 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

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/docs/configuration.md
----------------------------------------------------------------------
diff --git a/docs/configuration.md b/docs/configuration.md
index d4f8553..7940d41 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>

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/docs/hardware-provisioning.md
----------------------------------------------------------------------
diff --git a/docs/hardware-provisioning.md b/docs/hardware-provisioning.md
index e5f054c..7902205 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
 

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/docs/monitoring.md
----------------------------------------------------------------------
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 0e3606f..5f456b9 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). 

http://git-wip-us.apache.org/repos/asf/incubator-spark/blob/bddf1356/ec2/spark_ec2.py
----------------------------------------------------------------------
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 932e70d..419d0fe 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


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