Configuring Spark nodes

Configure Spark nodes in datacenters that are separate from nodes running other types of workloads, such as Cassandra real-time and DSE Search.

Spark nodes must be configured in their own datacenters. Do not run Spark node types in the same datacenter with nodes running Cassandra node types. DSE SearchAnalytics clusters can use DSE Search queries within DSE Analytics jobs. You can run Spark alongside integrated Hadoop or BYOH, but not on the same node. Be sure to follow the workload isolation guidelines.

There are two instances of the hive-site.xml file.

For use with Spark, the default location of the hive-site.xml file is:

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

For use with Hive, the default location of the hive-site.xml file is:

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


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 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
The location of the dse.yaml file depends on the type of installation:
Installer-Services /etc/dse/dse.yaml
Package installations /etc/dse/dse.yaml
Installer-No Services install_location/resources/dse/conf/dse.yaml
Tarball installations install_location/resources/dse/conf/dse.yaml
The default location of the file depends on the type of installation:
Installer-Services and Package installations /etc/dse/spark/
Installer-No Services and Tarball installations install_location/resources/spark/conf/

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 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 Cassandra 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 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 file.

Grant access to default Spark directories

Before starting up nodes on a tarball installation, you need permission to access the default Spark directory locations: /var/lib/spark and /var/log/spark. Change ownership of these directories as follows:
$ 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 Cassandra 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
Shared secret authentication is enabled using the spark_security_enabled option. To encrypt Spark connections for all components except the web UI, enable spark_security_encryption_enabled. Both options are described in DSE Analytics options.
JAR files on CFS
When JAR files are on the Cassandra file system (CFS) and authentication is enabled, enable Spark applications in cluster mode.
Cassandra credentials for the Spark SQL Thrift server
In the hive-site.xml file, configure Cassandra 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 install_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.

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-thriftserver and history-server. Add or change this setting in the 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 1.6.2 Available Properties. For example: spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"

Core management

You can manage the number of cores by configuring these options.
  • Spark Worker cores

    The SPARK_WORKER_CORES option configures the number of cores offered by Spark Worker for use by executors. A single executor can borrow more than one core from the worker. The number of cores used by the executor relates to the number of parallel tasks the executor might perform. The number of cores offered by the cluster is the sum of cores offered by all the workers in the cluster.

  • Application cores

    In the Spark configuration object of your application, you configure the number of application cores that the application requests from the cluster using either the spark.cores.max configuration property or the --total-executor-cores cores argument to the dse spark command.

See the Spark documentation for details about memory and core allocation.
DataStax Enterprise can control the memory and cores offered by particular Spark Workers in semi-automatic fashion. The initial_spark_worker_resources parameter in the dse.yaml file specifies the fraction of system resources that are made available to the Spark Worker. The available resources are calculated in the following way:
  • Spark Worker memory = initial_spark_worker_resources * (total system memory - memory assigned to Cassandra)
  • Spark Worker cores = initial_spark_worker_resources * total system cores
The lowest values you can assign to Spark Worker memory and cores are 64 MB and 1 core, respectively. If the results are lower, no exception is thrown and the values are automatically limited. The range of the initial_spark_worker_resources value is 0.01 to 1. If the range is not specified, the default value 0.7 is used.

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 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 snitch setup file to true:

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

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

The default location of the file depends on the type of installation:
Installer-Services and Package installations /etc/dse/cassandra/
Installer-No Services and Tarball installations install_location/resources/cassandra/conf/

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.

The default location of the spark-defaults.conf file depends on the type of installation:

Package installations
Installer-Services installations


Tarball installations
Installer-No Services installations


Adding additional JARs to Spark Nodes

Users can add third party JARs to Spark nodes by adding them to the Spark lib directory on each node and restart the cluster.

The default location of the Spark lib directory depends on the type of installation:
Installer-Services and Package installations /usr/share/dse/spark/lib
Installer-No Services and Tarball installations install_location/resources/spark/lib/
For example, to add metrics reporter JARs to Spark in a package installation, copy them to the Spark lib directory:
cp metrics-graphite-3.1.2.jar /usr/share/dse/spark/lib/
$ cp metrics-json-3.1.2.jar /usr/share/dse/spark/lib/