Configuring Spark nodes

Modify the settings for Spark nodes security, performance, and logging.

To manage Spark performance and operations:

Set the replication factor for DSE Analytics keyspaces
Set environment variables
Protect Spark directories
Grant access to default Spark directories
Secure Spark nodes
Configure Spark memory and cores
Configure Spark logging options

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

The temporary directory for shuffle data, RDDs, and other ephemeral Spark data can be configured for both the locally running driver and for the Spark server processes managed by DSE (Spark Master, Workers, shuffle service, executor and driver running in cluster mode).

For the locally running Spark driver, the SPARK_LOCAL_DIRS environment variable can be customized in the user environment or in spark-env.sh. By default, it is set to the system temporary directory. For example, on Ubuntu it is /tmp/. If there’s no system temporary directory, then SPARK_LOCAL_DIRS is set to a .spark directory in the user’s home directory.

For all other Spark server processes, the SPARK_EXECUTOR_DIRS environment variable can be customized in the user environment or in spark-env.sh. By default it is set to /var/lib/spark/rdd.

The default SPARK_LOCAL_DIRS and SPARK_EXECUTOR_DIRS environment variable values differ from non-DSE Spark.

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_EXECUTOR_DIRS in the spark-env.sh 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 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. 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.

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:

Package installations: /etc/dse/spark/hive-site.xml
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.

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 memory_total option in resource_manager_options.worker_options section of dse.yaml configures the total system memory that you can assign to all executors that are run by the work pools on the particular node. The default work pool will use all of this memory if no other work pools are defined. If you define additional work pools, you can set the total amount of memory by setting the memory option in the work pool definition.
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 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 cores_total option in the resource_manager_options.worker_options section of dse.yaml configures the total number of system cores available to Spark Workers for executors. If no work pools are defined in the resource_manager_options.workpools section of dse.yaml the default work pool will use all the cores defined by cores_total. If additional work pools are defined, the default work pool will use the cores available after allocating the cores defined by the work pools.

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 resource_manager_options.worker_options section in the dse.yaml file has options to configure the proportion of system resources that are made available to Spark Workers and any defined work pools, or explicit resource settings. When specifying decimal values of system resources the available resources are calculated in the following way:

Spark Worker memory = memory_total * (total system memory - memory assigned to DSE)
Spark Worker cores = cores_total * total system cores

This calculation is used for any decimal values. If the setting is not specified, the default value 0.7 is used. If the value does not contain a decimal place, the setting is the explicit number of cores or amount of memory reserved by DSE for Spark.

Setting cores_total or a workpool’s cores to 1.0 is a decimal value, meaning 100% of the available cores will be reserved. Setting cores_total or cores to 1 (no decimal point) is an explicit value, and one core will be reserved.

The lowest values you can assign to a named work pool’s 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 following example shows a work pool named workpool1 with 1 core and 512 MB of RAM assigned to it. The remaining resources calculated from the values in worker_options are assigned to the default work pool.

resource_manager_options:
    worker_options:
        cores_total: 0.7
        memory_total: 0.7

        workpools:
              - name: workpool1
              cores: 1
              memory: 512M

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.

Disabling 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 enabled. To disable it, set the spark.dse.continuous_paging_enabled setting to false when starting the Spark SQL shell or in spark-defaults.conf. For example:

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

Using continuous paging can potentially improve performance up to 3 times, though the improvement will depend on the data and the queries. Some factors that impact the performance improvement are the number of executor JVMs per node and the number of columns included in the query. Greater performance gains were observed with fewer executor JVMs per node and more columns selected.

Configuring the Spark web interface ports

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

Open the spark-env.sh file in a text editor.
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
```
Repeat these steps for each Analytics node in your cluster.
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.

The default location of the Spark lib directory depends on the type of installation:

Package installations: /usr/share/dse/spark/lib
Tarball installations: /var/lib/spark

To add the Graphite 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/

Setting Spark properties for the driver and executor

Additional Spark properties for the Spark driver and executors are set in spark-defaults.conf. For example, to enable Spark’s commons-crypto encryption library:

spark.network.crypto.enabled true

Using authorization with Spark

See Analytic applications and Setting up DSE Spark application permissions.

Spark server configuration

The spark-daemon-defaults.conf file configures DSE Spark Masters and Workers.

Spark server configuration properties
Option	Default value	Description
`dse.spark.application.timeout`	30	The duration in seconds after which the application will be considered dead if no heartbeat is received.
`spark.dseShuffle.sasl.port`	7447	The port number on which a shuffle service for SASL secured applications is started. Bound to the `listen_address` in `cassandra.yaml`.
`spark.dseShuffle.noSasl.port`	7437	The port number on which a shuffle service for unsecured applications is started. Bound to the `listen_address` in `cassandra.yaml`.

By default Spark executor logs, which log the majority of your Spark Application output, are redirected to standard output. The output is managed by Spark Workers. Configure logging by adding spark.executor.logs.rolling.* properties to spark-daemon-defaults.conf file.

spark.executor.logs.rolling.maxRetainedFiles 3
spark.executor.logs.rolling.strategy size
spark.executor.logs.rolling.maxSize 50000

Additional Spark properties that affect the master and driver can be added to spark-daemon-defaults.conf. For example, to enable Spark’s commons-crypto encryption library:

spark.network.crypto.enabled true