Configuring Spark nodes

Set environment variables

DataStax recommends using the default values of Spark environment variables unless you need to increase the memory settings due to an OutOfMemoryError condition or garbage collection taking too long. Use the Spark memory configuration options in the dse.yaml and spark-env.sh files.

You can set a user-specific SPARK_HOME directory if you also set ALLOW_SPARK_HOME=true in your environment before starting DSE.

For example, on Debian or Ubuntu using a package installation:

export SPARK_HOME=$HOME/spark && 
export ALLOW_SPARK_HOME=true 
&& sudo service dse start

To configure worker cleanup, modify the SPARK_WORKER_OPTS environment variable and add the cleanup properties. The SPARK_WORKER_OPTS environment variable can be set in the user environment or in spark-env.sh. For example, the following enables worker cleanup,.sets the cleanup interval to 30 minutes (i.e. 1800 seconds), and sets the length of time application worker directories will be retained to 7 days (i.e. 604800 seconds).

export SPARK_WORKER_OPTS="$SPARK_WORKER_OPTS \ 
  -Dspark.worker.cleanup.enabled=true \ 
  -Dspark.worker.cleanup.interval=1800 \ 
  -Dspark.worker.cleanup.appDataTtl=604800"

Protect Spark directories

After you start up a Spark cluster, DataStax Enterprise creates a Spark work directory for each Spark Worker on worker nodes. A worker node can have more than one worker, configured by the SPARK_WORKER_INSTANCES option in spark-env.sh. If SPARK_WORKER_INSTANCES is undefined, a single worker is started. The work directory contains the standard output and standard error of executors and other application specific data stored by Spark Worker and executors; the directory is writable only by the DSE user.

By default, the Spark parent work directory is located in /var/lib/spark/work, with each worker in a subdirectory named worker-number, where the number starts at 0. To change the parent worker directory, configure SPARK_WORKER_DIR in the spark-env.sh file.

The Spark RDD directory is the directory where RDDs are placed when executors decide to spill them to disk. This directory might contain the data from the database or the results of running Spark applications. If the data in the directory is confidential, prevent access by unauthorized users. The RDD directory might contain a significant amount of data, so configure its location on a fast disk. The directory is writable only by the cassandra user. The default location of the Spark RDD directory is /var/lib/spark/rdd. The directory should be located on a fast disk. To change the RDD directory, configure SPARK_LOCAL_DIRS in the spark-env.sh file.

Grant access to default Spark directories

sudo mkdir -p /var/lib/spark/rdd; sudo chmod a+w /var/lib/spark/rdd; sudo chown -R  $USER:$GROUP /var/lib/spark/rdd &&
sudo mkdir -p /var/log/spark; sudo chown -R  $USER:$GROUP /var/log/spark

In multiple datacenter clusters, use a virtual datacenter to isolate Spark jobs. Running Spark jobs consume resources that can affect latency and throughput.

DataStax Enterprise supports the use of virtual nodes (vnodes) with Spark.

Secure Spark nodes

Client-to-node SSL

Ensure that the truststore entries in cassandra.yaml are present as described in Client-to-node encryption, even when client authentication is not enabled.

Enabling security and authentication

Security is enabled using the spark_security_enabled option in dse.yaml. Setting it to enabled turns on authentication between the Spark Master and Worker nodes, and allows you to enable encryption. To encrypt Spark connections for all components except the web UI, enable spark_security_encryption_enabled. The length of the shared secret used to secure Spark components is set using the spark_shared_secret_bit_length option, with a default value of 256 bits. These options are described in DSE Analytics options. For production clusters, enable these authentication and encryption. Doing so does not significantly affect performance.

Authentication and Spark applications

If authentication is enabled, users need to be authenticated in order to submit an application.

Note: DSE 5.1.4, DSE 5.1.5, and 5.1.6 users should refer to the release notes for information on using Spark SQL applications and DSE authentication.

Authorization and Spark applications

If DSE authorization is enabled, users needs permission to submit an application. Additionally, the user submitting the application automatically receives permission to manage the application, which can optionally be extended to other users.

Database credentials for the Spark SQL Thrift server

In the hive-site.xml file, configure authentication credentials for the Spark SQL Thrift server. Ensure that you use the hive-site.xml file in the Spark directory:

Installer-Services and Package installations	/etc/dse/spark/hive-site.xml
Installer-No Services and Tarball installations	installation_location/resources/spark/conf/hive-site.xml

Kerberos with Spark

With Kerberos authentication, the Spark launcher connects to DSE with Kerberos credentials and requests DSE to generate a delegation token. The Spark driver and executors use the delegation token to connect to the cluster. For valid authentication, the delegation token must be renewed periodically. For security reasons, the user who is authenticated with the token should not be able to renew it. Therefore, delegation tokens have two associated users: token owner and token renewer.

The token renewer is none so that only a DSE internal process can renew it. When the application is submitted, DSE automatically renews delegation tokens that are associated with Spark application. When the application is unregistered (finished), the delegation token renewal is stopped and the token is cancelled.

Set Kerberos options, see Defining a Kerberos scheme.

Using authorization with Spark

There are two kinds of authorization permissions which apply to Spark. Work pool permissions control the ability to submit a Spark application to DSE. Submission permissions control the ability to manage a particular application. All the following instructions assume you are issuing the CQL commands as a database superuser.

Use GRANT CREATE ON ANY WORKPOOL TO role to grant permission to submit a Spark application to any Analytics datacenter.

Use GRANT CREATE ON WORKPOOL datacenter_name TO role to grant permission to submit a Spark application to a particular Analytics datacenter.

There are similar revoke commands:

REVOKE CREATE ON ANY WORKPOOL FROM role

REVOKE CREATE ON WORKPOOL datacenter_name FROM role

When an application is submitted, the user who submits that application is automatically granted permission to manage and remove the application. You may also grant the ability to manage the application to another user or role.

Use GRANT MODIFY ON ANY SUBMISSION TO role to grant permission to manage any submission in any work pool to the specified role.

Use GRANT MODIFY ON ANY SUBMISSION IN WORKPOOL datacenter_name TO role to grant permission to manage any submission in a specified datacenter.

Use GRANT MODIFY ON SUBMISSION id IN WORKPOOL datacenter_name TO role to grant permission to manage a submission identified by the provided id in a given datacenter.

There are similar revoke commands:

REVOKE MODIFY ON ANY SUBMISSION FROM role

REVOKE MODIFY ON ANY SUBMISSION IN WORKPOOL datacenter_name FROM role

REVOKE MODIFY ON SUBMISSION id IN WORKPOOL datacenter_name FROM role

In order to issue these commands as a regular database user, the user needs to have permission to use the DSE resource manager RPC:

GRANT ALL ON REMOTE OBJECT DseResourceManager TO role

Each DSE Analytics user needs to have permission to use the client tools RPC:

GRANT ALL ON REMOTE OBJECT DseClientTool TO role

Configure Spark memory and cores

Spark memory options affect different components of the Spark ecosystem:

Spark History server and the Spark Thrift server memory

The SPARK_DAEMON_MEMORY option configures the memory that is used by the Spark SQL Thrift server and history-server. Add or change this setting in the spark-env.sh file on nodes that run these server applications.

Spark Worker memory

The SPARK_WORKER_MEMORY option configures the total amount of memory that you can assign to all executors that are run by a single Spark Worker on the particular node.

Application executor memory

You can configure the amount of memory that each executor can consume for the application. Spark uses a 512MB default. Use either the spark.executor.memory option, described in "Spark 1.6.2 Available Properties", or the --executor-memory mem argument to the dse spark command.

Application memory

You can configure additional Java options that are applied by the worker when spawning an executor for the application. Use the spark.executor.extraJavaOptions property, described in Spark 2.0.2 Available Properties. For example: spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"

Core management

This mechanism is used by default to set the Spark Worker memory and cores. To override the default, uncomment and edit one or both SPARK_WORKER_MEMORY and SPARK_WORKER_CORES options in the spark-env.sh file.

Running Spark clusters in cloud environments

If you are using a cloud infrastructure provider like Amazon EC2, you must explicitly open the ports for publicly routable IP addresses in your cluster. If you do not, the Spark workers will not be able to find the Spark Master.

One work-around is to set the prefer_local setting in your cassandra-rackdc.properties snitch setup file to true:

# Uncomment the following line to make this snitch prefer the internal ip when possible, as the Ec2MultiRegionSnitch does.
prefer_local=true

This tells the cluster to communicate only on private IP addresses within the datacenter rather than the public routable IP addresses.

Configuring the number of retries to retrieve Spark configuration

When Spark fetches configuration settings from DSE, it will not fail immediately if it cannot retrieve the configuration data, but will retry 5 times by default, with increasing delay between retries. The number of retries can be set in the Spark configuration, by modifying the spark.dse.configuration.fetch.retries configuration property when calling the dse spark command, or in spark-defaults.conf.

Enabling continuous paging

Continuous paging streams bulk amounts of records from DSE to the DataStax Java Driver used by DSE Spark. By default, continuous paging in queries is disabled. To enable it, set the spark.dse.continuous_paging_enabled setting to true when starting the Spark SQL shell or in spark-defaults.conf. For example:

dse spark-sql --conf spark.dse.continuous_paging_enabled=true

Configuring the Spark web interface ports

By default the Spark web UI runs on port 7080. To change the port number, do the following:

1. Open the spark-env.sh file in a text editor.
2. Set the SPARK_MASTER_WEBUI_PORT variable to the new port number. For example, to set it to port 7082:

   export SPARK_MASTER_WEBUI_PORT=7082

3. Repeat these steps for each Analytics node in your cluster.
4. Restart the nodes in the cluster.

Enabling Graphite Metrics in DSE Spark

Users can add third party JARs to Spark nodes by adding them to the Spark lib directory on each node and restart the cluster. Add the Graphite Metrics JARs to this directory to enable metrics in DSE Spark.

cp metrics-graphite-3.1.2.jar /usr/share/dse/spark/lib/ && 
cp metrics-json-3.1.2.jar /usr/share/dse/spark/lib/