diff options
Diffstat (limited to 'docs')
-rw-r--r-- | docs/configuration.md | 46 | ||||
-rw-r--r-- | docs/security.md | 131 | ||||
-rw-r--r-- | docs/spark-standalone.md | 92 |
3 files changed, 179 insertions, 90 deletions
diff --git a/docs/configuration.md b/docs/configuration.md index 7cd7f4124d..5e3eb0f087 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -566,6 +566,7 @@ Apart from these, the following properties are also available, and may be useful <td>(local hostname)</td> <td> Hostname or IP address for the driver to listen on. + This is used for communicating with the executors and the standalone Master. </td> </tr> <tr> @@ -573,6 +574,51 @@ Apart from these, the following properties are also available, and may be useful <td>(random)</td> <td> Port for the driver to listen on. + This is used for communicating with the executors and the standalone Master. + </td> +</tr> +<tr> + <td><code>spark.fileserver.port</code></td> + <td>(random)</td> + <td> + Port for the driver's HTTP file server to listen on. + </td> +</tr> +<tr> + <td><code>spark.broadcast.port</code></td> + <td>(random)</td> + <td> + Port for the driver's HTTP broadcast server to listen on. + This is not relevant for torrent broadcast. + </td> +</tr> +<tr> + <td><code>spark.replClassServer.port</code></td> + <td>(random)</td> + <td> + Port for the driver's HTTP class server to listen on. + This is only relevant for the Spark shell. + </td> +</tr> +<tr> + <td><code>spark.blockManager.port</code></td> + <td>(random)</td> + <td> + Port for all block managers to listen on. These exist on both the driver and the executors. + </td> +</tr> +<tr> + <td><code>spark.executor.port</code></td> + <td>(random)</td> + <td> + Port for the executor to listen on. This is used for communicating with the driver. + </td> +</tr> +<tr> + <td><code>spark.port.maxRetries</code></td> + <td>16</td> + <td> + Maximum number of retries when binding to a port before giving up. </td> </tr> <tr> diff --git a/docs/security.md b/docs/security.md index 8312f8d017..ec0523184d 100644 --- a/docs/security.md +++ b/docs/security.md @@ -7,6 +7,9 @@ Spark currently supports authentication via a shared secret. Authentication can * For Spark on [YARN](running-on-yarn.html) deployments, configuring `spark.authenticate` to `true` will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. * For other types of Spark deployments, the Spark parameter `spark.authenticate.secret` should be configured on each of the nodes. This secret will be used by all the Master/Workers and applications. +* **IMPORTANT NOTE:** *The experimental Netty shuffle path (`spark.shuffle.use.netty`) is not secured, so do not use Netty for shuffles if running with authentication.* + +## Web UI The Spark UI can also be secured by using [javax servlet filters](http://docs.oracle.com/javaee/6/api/javax/servlet/Filter.html) via the `spark.ui.filters` setting. A user may want to secure the UI if it has data that other users should not be allowed to see. The javax servlet filter specified by the user can authenticate the user and then once the user is logged in, Spark can compare that user versus the view ACLs to make sure they are authorized to view the UI. The configs `spark.acls.enable` and `spark.ui.view.acls` control the behavior of the ACLs. Note that the user who started the application always has view access to the UI. On YARN, the Spark UI uses the standard YARN web application proxy mechanism and will authenticate via any installed Hadoop filters. @@ -14,10 +17,132 @@ Spark also supports modify ACLs to control who has access to modify a running Sp Spark allows for a set of administrators to be specified in the acls who always have view and modify permissions to all the applications. is controlled by the config `spark.admin.acls`. This is useful on a shared cluster where you might have administrators or support staff who help users debug applications. +## Event Logging + If your applications are using event logging, the directory where the event logs go (`spark.eventLog.dir`) should be manually created and have the proper permissions set on it. If you want those log files secured, the permissions should be set to `drwxrwxrwxt` for that directory. The owner of the directory should be the super user who is running the history server and the group permissions should be restricted to super user group. This will allow all users to write to the directory but will prevent unprivileged users from removing or renaming a file unless they own the file or directory. The event log files will be created by Spark with permissions such that only the user and group have read and write access. -**IMPORTANT NOTE:** *The experimental Netty shuffle path (`spark.shuffle.use.netty`) is not secured, so do not use Netty for shuffles if running with authentication.* +## Configuring Ports for Network Security + +Spark makes heavy use of the network, and some environments have strict requirements for using tight +firewall settings. Below are the primary ports that Spark uses for its communication and how to +configure those ports. + +### Standalone mode only + +<table class="table"> + <tr> + <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration + Setting</th><th>Notes</th> + </tr> + <tr> + <td>Browser</td> + <td>Standalone Master</td> + <td>8080</td> + <td>Web UI</td> + <td><code>spark.master.ui.port /<br> SPARK_MASTER_WEBUI_PORT</code></td> + <td>Jetty-based. Standalone mode only.</td> + </tr> + <tr> + <td>Browser</td> + <td>Standalone Worker</td> + <td>8081</td> + <td>Web UI</td> + <td><code>spark.worker.ui.port /<br> SPARK_WORKER_WEBUI_PORT</code></td> + <td>Jetty-based. Standalone mode only.</td> + </tr> + <tr> + <td>Driver /<br> Standalone Worker</td> + <td>Standalone Master</td> + <td>7077</td> + <td>Submit job to cluster /<br> Join cluster</td> + <td><code>SPARK_MASTER_PORT</code></td> + <td>Akka-based. Set to "0" to choose a port randomly. Standalone mode only.</td> + </tr> + <tr> + <td>Standalone Master</td> + <td>Standalone Worker</td> + <td>(random)</td> + <td>Schedule executors</td> + <td><code>SPARK_WORKER_PORT</code></td> + <td>Akka-based. Set to "0" to choose a port randomly. Standalone mode only.</td> + </tr> +</table> + +### All cluster managers + +<table class="table"> + <tr> + <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration + Setting</th><th>Notes</th> + </tr> + <tr> + <td>Browser</td> + <td>Application</td> + <td>4040</td> + <td>Web UI</td> + <td><code>spark.ui.port</code></td> + <td>Jetty-based</td> + </tr> + <tr> + <td>Browser</td> + <td>History Server</td> + <td>18080</td> + <td>Web UI</td> + <td><code>spark.history.ui.port</code></td> + <td>Jetty-based</td> + </tr> + <tr> + <td>Executor /<br> Standalone Master</td> + <td>Driver</td> + <td>(random)</td> + <td>Connect to application /<br> Notify executor state changes</td> + <td><code>spark.driver.port</code></td> + <td>Akka-based. Set to "0" to choose a port randomly.</td> + </tr> + <tr> + <td>Driver</td> + <td>Executor</td> + <td>(random)</td> + <td>Schedule tasks</td> + <td><code>spark.executor.port</code></td> + <td>Akka-based. Set to "0" to choose a port randomly.</td> + </tr> + <tr> + <td>Executor</td> + <td>Driver</td> + <td>(random)</td> + <td>File server for files and jars</td> + <td><code>spark.fileserver.port</code></td> + <td>Jetty-based</td> + </tr> + <tr> + <td>Executor</td> + <td>Driver</td> + <td>(random)</td> + <td>HTTP Broadcast</td> + <td><code>spark.broadcast.port</code></td> + <td>Jetty-based. Not used by TorrentBroadcast, which sends data through the block manager + instead.</td> + </tr> + <tr> + <td>Executor</td> + <td>Driver</td> + <td>(random)</td> + <td>Class file server</td> + <td><code>spark.replClassServer.port</code></td> + <td>Jetty-based. Only used in Spark shells.</td> + </tr> + <tr> + <td>Executor / Driver</td> + <td>Executor / Driver</td> + <td>(random)</td> + <td>Block Manager port</td> + <td><code>spark.blockManager.port</code></td> + <td>Raw socket via ServerSocketChannel</td> + </tr> +</table> -See the [configuration page](configuration.html) for more details on the security configuration parameters. -See <a href="{{site.SPARK_GITHUB_URL}}/tree/master/core/src/main/scala/org/apache/spark/SecurityManager.scala"><code>org.apache.spark.SecurityManager</code></a> for implementation details about security. +See the [configuration page](configuration.html) for more details on the security configuration +parameters, and <a href="{{site.SPARK_GITHUB_URL}}/tree/master/core/src/main/scala/org/apache/spark/SecurityManager.scala"> +<code>org.apache.spark.SecurityManager</code></a> for implementation details about security. diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index 293a7ac9bc..c791c81f8b 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -299,97 +299,15 @@ You can run Spark alongside your existing Hadoop cluster by just launching it as # Configuring Ports for Network Security -Spark makes heavy use of the network, and some environments have strict requirements for using tight -firewall settings. Below are the primary ports that Spark uses for its communication and how to -configure those ports. - -<table class="table"> - <tr> - <th>From</th><th>To</th><th>Default Port</th><th>Purpose</th><th>Configuration - Setting</th><th>Notes</th> - </tr> - <!-- Web UIs --> - <tr> - <td>Browser</td> - <td>Standalone Cluster Master</td> - <td>8080</td> - <td>Web UI</td> - <td><code>spark.master.ui.port</code></td> - <td>Jetty-based</td> - </tr> - <tr> - <td>Browser</td> - <td>Driver</td> - <td>4040</td> - <td>Web UI</td> - <td><code>spark.ui.port</code></td> - <td>Jetty-based</td> - </tr> - <tr> - <td>Browser</td> - <td>History Server</td> - <td>18080</td> - <td>Web UI</td> - <td><code>spark.history.ui.port</code></td> - <td>Jetty-based</td> - </tr> - <tr> - <td>Browser</td> - <td>Worker</td> - <td>8081</td> - <td>Web UI</td> - <td><code>spark.worker.ui.port</code></td> - <td>Jetty-based</td> - </tr> - <!-- Cluster interactions --> - <tr> - <td>Application</td> - <td>Standalone Cluster Master</td> - <td>7077</td> - <td>Submit job to cluster</td> - <td><code>spark.driver.port</code></td> - <td>Akka-based. Set to "0" to choose a port randomly</td> - </tr> - <tr> - <td>Worker</td> - <td>Standalone Cluster Master</td> - <td>7077</td> - <td>Join cluster</td> - <td><code>spark.driver.port</code></td> - <td>Akka-based. Set to "0" to choose a port randomly</td> - </tr> - <tr> - <td>Application</td> - <td>Worker</td> - <td>(random)</td> - <td>Join cluster</td> - <td><code>SPARK_WORKER_PORT</code> (standalone cluster)</td> - <td>Akka-based</td> - </tr> - - <!-- Other misc stuff --> - <tr> - <td>Driver and other Workers</td> - <td>Worker</td> - <td>(random)</td> - <td> - <ul> - <li>File server for file and jars</li> - <li>Http Broadcast</li> - <li>Class file server (Spark Shell only)</li> - </ul> - </td> - <td>None</td> - <td>Jetty-based. Each of these services starts on a random port that cannot be configured</td> - </tr> - -</table> +Spark makes heavy use of the network, and some environments have strict requirements for using +tight firewall settings. For a complete list of ports to configure, see the +[security page](security.html#configuring-ports-for-network-security). # High Availability By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below. -## Standby Masters with ZooKeeper +# Standby Masters with ZooKeeper **Overview** @@ -429,7 +347,7 @@ There's an important distinction to be made between "registering with a Master" Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that _new_ applications and Workers can find it to register with in case it becomes the leader. Once registered, you're taken care of. -## Single-Node Recovery with Local File System +# Single-Node Recovery with Local File System **Overview** |