Spark Security

Spark currently supports authentication via a shared secret. Authentication can be configured to be on via the spark.authenticate configuration parameter. This parameter controls whether the Spark communication protocols do authentication using the shared secret. This authentication is a basic handshake to make sure both sides have the same shared secret and are allowed to communicate. If the shared secret is not identical they will not be allowed to communicate. The shared secret is created as follows:

Web UI

The Spark UI can also be secured by using javax servlet filters 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.

Spark also supports modify ACLs to control who has access to modify a running Spark application. This includes things like killing the application or a task. This is controlled by the configs spark.acls.enable and spark.modify.acls. Note that if you are authenticating the web UI, in order to use the kill button on the web UI it might be necessary to add the users in the modify acls to the view acls also. On YARN, the modify acls are passed in and control who has modify access via YARN interfaces.

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.

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

FromToDefault PortPurposeConfiguration SettingNotes
Browser Standalone Master 8080 Web UI spark.master.ui.port /
Jetty-based. Standalone mode only.
Browser Standalone Worker 8081 Web UI spark.worker.ui.port /
Jetty-based. Standalone mode only.
Driver /
Standalone Worker
Standalone Master 7077 Submit job to cluster /
Join cluster
SPARK_MASTER_PORT Akka-based. Set to "0" to choose a port randomly. Standalone mode only.
Standalone Master Standalone Worker (random) Schedule executors SPARK_WORKER_PORT Akka-based. Set to "0" to choose a port randomly. Standalone mode only.

All cluster managers

FromToDefault PortPurposeConfiguration SettingNotes
Browser Application 4040 Web UI spark.ui.port Jetty-based
Browser History Server 18080 Web UI spark.history.ui.port Jetty-based
Executor /
Standalone Master
Driver (random) Connect to application /
Notify executor state changes
spark.driver.port Akka-based. Set to "0" to choose a port randomly.
Driver Executor (random) Schedule tasks spark.executor.port Akka-based. Set to "0" to choose a port randomly.
Executor Driver (random) File server for files and jars spark.fileserver.port Jetty-based
Executor Driver (random) HTTP Broadcast spark.broadcast.port Jetty-based. Not used by TorrentBroadcast, which sends data through the block manager instead.
Executor Driver (random) Class file server spark.replClassServer.port Jetty-based. Only used in Spark shells.
Executor / Driver Executor / Driver (random) Block Manager port spark.blockManager.port Raw socket via ServerSocketChannel

See the configuration page for more details on the security configuration parameters, and org.apache.spark.SecurityManager for implementation details about security.