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Commit b9128d34 authored by Patrick Wendell's avatar Patrick Wendell
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Merge pull request #922 from pwendell/port-change

Change default port number from 3030 to 4030.
parents e07eef83 bddf1356
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......@@ -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"
}
......@@ -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())
......
......@@ -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
......
......@@ -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>
......
......@@ -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
......
......@@ -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).
......
......@@ -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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