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Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Spark supports multiple deployments types and each one supports different levels of security. Not all deployment types will be secure in all environments and none are secure by default. Be sure to evaluate your environment, what Spark supports, and take the appropriate measure to secure your Spark deployment.

There are many different types of security concerns. Spark does not necessarily protect against all things. Listed below are some of the things Spark supports. Also check the deployment documentation for the type of deployment you are using for deployment specific settings. Anything not documented, Spark does not support.

Spark currently supports authentication for RPC channels using a shared secret. Authentication can be turned on by setting the spark.authenticate configuration parameter.

The exact mechanism used to generate and distribute the shared secret is deployment-specific. Unless specified below, the secret must be defined by setting the spark.authenticate.secret config option. The same secret is shared by all Spark applications and daemons in that case, which limits the security of these deployments, especially on multi-tenant clusters.

The REST Submission Server and the MesosClusterDispatcher do not support authentication. You should ensure that all network access to the REST API & MesosClusterDispatcher (port 6066 and 7077 respectively by default) are restricted to hosts that are trusted to submit jobs.

For Spark on YARN, Spark will automatically handle generating and distributing the shared secret. Each application will use a unique shared secret. In the case of YARN, this feature relies on YARN RPC encryption being enabled for the distribution of secrets to be secure.

On Kubernetes, Spark will also automatically generate an authentication secret unique to each application. The secret is propagated to executor pods using environment variables. This means that any user that can list pods in the namespace where the Spark application is running can also see their authentication secret. Access control rules should be properly set up by the Kubernetes admin to ensure that Spark authentication is secure.

Alternatively, one can mount authentication secrets using files and Kubernetes secrets that the user mounts into their pods.

Note that when using files, Spark will not mount these files into the containers for you. It is up you to ensure that the secret files are deployed securely into your containers and that the driver's secret file agrees with the executors' secret file.

Spark supports AES-based encryption for RPC connections. For encryption to be enabled, RPC authentication must also be enabled and properly configured. AES encryption uses the Apache Commons Crypto library, and Spark's configuration system allows access to that library's configuration for advanced users.

There is also support for SASL-based encryption, although it should be considered deprecated. It is still required when talking to shuffle services from Spark versions older than 2.2.0.

The following table describes the different options available for configuring this feature.

Spark supports encrypting temporary data written to local disks. This covers shuffle files, shuffle spills and data blocks stored on disk (for both caching and broadcast variables). It does not cover encrypting output data generated by applications with APIs such as saveAsHadoopFile or saveAsTable. It also may not cover temporary files created explicitly by the user.

The following settings cover enabling encryption for data written to disk:

Enabling authentication for the Web UIs is done using javax servlet filters. You will need a filter that implements the authentication method you want to deploy. Spark does not provide any built-in authentication filters.

Spark also supports access control to the UI when an authentication filter is present. Each application can be configured with its own separate access control lists (ACLs). Spark differentiates between "view" permissions (who is allowed to see the application's UI), and "modify" permissions (who can do things like kill jobs in a running application).

ACLs can be configured for either users or groups. Configuration entries accept comma-separated lists as input, meaning multiple users or groups can be given the desired privileges. This can be used if you run on a shared cluster and have a set of administrators or developers who need to monitor applications they may not have started themselves. A wildcard (*) added to specific ACL means that all users will have the respective privilege. By default, only the user submitting the application is added to the ACLs.

Group membership is established by using a configurable group mapping provider. The mapper is configured using the spark.user.groups.mapping config option, described in the table below.

The following options control the authentication of Web UIs:

<br />By default, a Unix shell-based implementation is used, which collects this information
from the host OS.

<br /><em>Note:</em> This implementation supports only Unix/Linux-based environments.
Windows environment is currently <b>not</b> supported. However, a new platform/protocol can
be supported by implementing the trait mentioned above.

On YARN, the view and modify ACLs are provided to the YARN service when submitting applications, and control who has the respective privileges via YARN interfaces.

Authentication for the SHS Web UI is enabled the same way as for regular applications, using servlet filters.

To enable authorization in the SHS, a few extra options are used:

The SHS uses the same options to configure the group mapping provider as regular applications. In this case, the group mapping provider will apply to all UIs server by the SHS, and individual application configurations will be ignored.

Configuration for SSL is organized hierarchically. The user can configure the default SSL settings which will be used for all the supported communication protocols unless they are overwritten by protocol-specific settings. This way the user can easily provide the common settings for all the protocols without disabling the ability to configure each one individually. The following table describes the SSL configuration namespaces:

The full breakdown of available SSL options can be found below. The ${ns} placeholder should be replaced with one of the above namespaces.

  <br />The port must be defined within a specific namespace configuration. The default
  namespace is ignored when reading this configuration.

  <br />When not set, the SSL port will be derived from the non-SSL port for the
  same service. A value of "0" will make the service bind to an ephemeral port.
</td>

  <br />The reference list of protocols can be found in the "JSSE Cipher Suite Names" section
  of the Java security guide. The list for Java 8 can be found at
  <a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/StandardNames.html#ciphersuites">this</a>
  page.

  <br />Note: If not set, the default cipher suite for the JRE will be used.
</td>

  <br />The reference list of protocols can be found in the "Additional JSSE Standard Names"
  section of the Java security guide. For Java 8, the list can be found at
  <a href="https://docs.oracle.com/javase/8/docs/technotes/guides/security/StandardNames.html#jssenames">this</a>
  page.
</td>

Spark also supports retrieving ${ns}.keyPassword, ${ns}.keyStorePassword and ${ns}.trustStorePassword from Hadoop Credential Providers. User could store password into credential file and make it accessible by different components, like:

hadoop credential create spark.ssl.keyPassword -value password \
    -provider jceks://hdfs@nn1.example.com:9001/user/backup/ssl.jceks

To configure the location of the credential provider, set the hadoop.security.credential.provider.path config option in the Hadoop configuration used by Spark, like:

  <property>
    <name>hadoop.security.credential.provider.path</name>
    <value>jceks://hdfs@nn1.example.com:9001/user/backup/ssl.jceks</value>
  </property>

Or via SparkConf "spark.hadoop.hadoop.security.credential.provider.path=jceks://hdfs@nn1.example.com:9001/user/backup/ssl.jceks".

Key stores can be generated by keytool program. The reference documentation for this tool for Java 8 is here. The most basic steps to configure the key stores and the trust store for a Spark Standalone deployment mode is as follows:

• Generate a key pair for each node
• Export the public key of the key pair to a file on each node
• Import all exported public keys into a single trust store
• Distribute the trust store to the cluster nodes

To provide a local trust store or key store file to drivers running in cluster mode, they can be distributed with the application using the --files command line argument (or the equivalent spark.files configuration). The files will be placed on the driver's working directory, so the TLS configuration should just reference the file name with no absolute path.

Distributing local key stores this way may require the files to be staged in HDFS (or other similar distributed file system used by the cluster), so it's recommended that the underlying file system be configured with security in mind (e.g. by enabling authentication and wire encryption).

The user needs to provide key stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in SPARK_MASTER_OPTS and in SPARK_WORKER_OPTS environment variables, or just in SPARK_DAEMON_JAVA_OPTS.

The user may allow the executors to use the SSL settings inherited from the worker process. That can be accomplished by setting spark.ssl.useNodeLocalConf to true. In that case, the settings provided by the user on the client side are not used.

Mesos 1.3.0 and newer supports Secrets primitives as both file-based and environment based secrets. Spark allows the specification of file-based and environment variable based secrets with spark.mesos.driver.secret.filenames and spark.mesos.driver.secret.envkeys, respectively.

Depending on the secret store backend secrets can be passed by reference or by value with the spark.mesos.driver.secret.names and spark.mesos.driver.secret.values configuration properties, respectively.

Reference type secrets are served by the secret store and referred to by name, for example /mysecret. Value type secrets are passed on the command line and translated into their appropriate files or environment variables.

Apache Spark can be configured to include HTTP headers to aid in preventing Cross Site Scripting (XSS), Cross-Frame Scripting (XFS), MIME-Sniffing, and also to enforce HTTP Strict Transport Security.

Generally speaking, a Spark cluster and its services are not deployed on the public internet. They are generally private services, and should only be accessible within the network of the organization that deploys Spark. Access to the hosts and ports used by Spark services should be limited to origin hosts that need to access the services.

Below are the primary ports that Spark uses for its communication and how to configure those ports.

Spark supports submitting applications in environments that use Kerberos for authentication. In most cases, Spark relies on the credentials of the current logged in user when authenticating to Kerberos-aware services. Such credentials can be obtained by logging in to the configured KDC with tools like kinit.

When talking to Hadoop-based services, Spark needs to obtain delegation tokens so that non-local processes can authenticate. Spark ships with support for HDFS and other Hadoop file systems, Hive and HBase.

When using a Hadoop filesystem (such HDFS or WebHDFS), Spark will acquire the relevant tokens for the service hosting the user's home directory.

An HBase token will be obtained if HBase is in the application's classpath, and the HBase configuration has Kerberos authentication turned (hbase.security.authentication=kerberos).

Similarly, a Hive token will be obtained if Hive is in the classpath, and the configuration includes URIs for remote metastore services (hive.metastore.uris is not empty).

Delegation token support is currently only supported in YARN and Mesos modes. Consult the deployment-specific page for more information.

The following options provides finer-grained control for this feature:

Long-running applications may run into issues if their run time exceeds the maximum delegation token lifetime configured in services it needs to access.

Spark supports automatically creating new tokens for these applications when running in YARN mode. Kerberos credentials need to be provided to the Spark application via the spark-submit command, using the --principal and --keytab parameters.

The provided keytab will be copied over to the machine running the Application Master via the Hadoop Distributed Cache. For this reason, it's strongly recommended that both YARN and HDFS be secured with encryption, at least.

The Kerberos login will be periodically renewed using the provided credentials, and new delegation tokens for supported will be created.

When talking to Hadoop-based services behind Kerberos, it was noted that Spark needs to obtain delegation tokens so that non-local processes can authenticate. These delegation tokens in Kubernetes are stored in Secrets that are shared by the Driver and its Executors. As such, there are three ways of submitting a Kerberos job:

In all cases you must define the environment variable: HADOOP_CONF_DIR or spark.kubernetes.hadoop.configMapName.

It also important to note that the KDC needs to be visible from inside the containers.

If a user wishes to use a remote HADOOP_CONF directory, that contains the Hadoop configuration files, this could be achieved by setting spark.kubernetes.hadoop.configMapName to a pre-existing ConfigMap.

1. Submitting with a $kinit that stores a TGT in the Local Ticket Cache:

/usr/bin/kinit -kt <keytab_file> <username>/<krb5 realm>
/opt/spark/bin/spark-submit \
    --deploy-mode cluster \
    --class org.apache.spark.examples.HdfsTest \
    --master k8s://<KUBERNETES_MASTER_ENDPOINT> \
    --conf spark.executor.instances=1 \
    --conf spark.app.name=spark-hdfs \
    --conf spark.kubernetes.container.image=spark:latest \
    --conf spark.kubernetes.kerberos.krb5.path=/etc/krb5.conf \
    local:///opt/spark/examples/jars/spark-examples_<VERSION>.jar \
    <HDFS_FILE_LOCATION>

2. Submitting with a local Keytab and Principal

/opt/spark/bin/spark-submit \
    --deploy-mode cluster \
    --class org.apache.spark.examples.HdfsTest \
    --master k8s://<KUBERNETES_MASTER_ENDPOINT> \
    --conf spark.executor.instances=1 \
    --conf spark.app.name=spark-hdfs \
    --conf spark.kubernetes.container.image=spark:latest \
    --conf spark.kerberos.keytab=<KEYTAB_FILE> \
    --conf spark.kerberos.principal=<PRINCIPAL> \
    --conf spark.kubernetes.kerberos.krb5.path=/etc/krb5.conf \
    local:///opt/spark/examples/jars/spark-examples_<VERSION>.jar \
    <HDFS_FILE_LOCATION>

3. Submitting with pre-populated secrets, that contain the Delegation Token, already existing within the namespace

/opt/spark/bin/spark-submit \
    --deploy-mode cluster \
    --class org.apache.spark.examples.HdfsTest \
    --master k8s://<KUBERNETES_MASTER_ENDPOINT> \
    --conf spark.executor.instances=1 \
    --conf spark.app.name=spark-hdfs \
    --conf spark.kubernetes.container.image=spark:latest \
    --conf spark.kubernetes.kerberos.tokenSecret.name=<SECRET_TOKEN_NAME> \
    --conf spark.kubernetes.kerberos.tokenSecret.itemKey=<SECRET_ITEM_KEY> \
    --conf spark.kubernetes.kerberos.krb5.path=/etc/krb5.conf \
    local:///opt/spark/examples/jars/spark-examples_<VERSION>.jar \
    <HDFS_FILE_LOCATION>

3b. Submitting like in (3) however specifying a pre-created krb5 ConfigMap and pre-created HADOOP_CONF_DIR ConfigMap

/opt/spark/bin/spark-submit \
    --deploy-mode cluster \
    --class org.apache.spark.examples.HdfsTest \
    --master k8s://<KUBERNETES_MASTER_ENDPOINT> \
    --conf spark.executor.instances=1 \
    --conf spark.app.name=spark-hdfs \
    --conf spark.kubernetes.container.image=spark:latest \
    --conf spark.kubernetes.kerberos.tokenSecret.name=<SECRET_TOKEN_NAME> \
    --conf spark.kubernetes.kerberos.tokenSecret.itemKey=<SECRET_ITEM_KEY> \
    --conf spark.kubernetes.hadoop.configMapName=<HCONF_CONFIG_MAP_NAME> \
    --conf spark.kubernetes.kerberos.krb5.configMapName=<KRB_CONFIG_MAP_NAME> \
    local:///opt/spark/examples/jars/spark-examples_<VERSION>.jar \
    <HDFS_FILE_LOCATION>

If your applications are using event logging, the directory where the event logs go (spark.eventLog.dir) should be manually created with proper permissions. To secure the log files, the directory permissions should be set to drwxrwxrwxt. The owner and group of the directory should correspond to the super user who is running the Spark History Server.

This will allow all users to write to the directory but will prevent unprivileged users from reading, removing or renaming a file unless they own it. The event log files will be created by Spark with permissions such that only the user and group have read and write access.

If your applications persist driver logs in client mode by enabling spark.driver.log.persistToDfs.enabled, the directory where the driver logs go (spark.driver.log.dfsDir) should be manually created with proper permissions. To secure the log files, the directory permissions should be set to drwxrwxrwxt. The owner and group of the directory should correspond to the super user who is running the Spark History Server.

This will allow all users to write to the directory but will prevent unprivileged users from reading, removing or renaming a file unless they own it. The driver log files will be created by Spark with permissions such that only the user and group have read and write access.