The latest version for DAS is WSO2 Data Analytics Server 3.1.0. View documentation for the latest release.
WSO2 Data Analytics Server is succeeded by WSO2 Stream Processor. To view the latest documentation for WSO2 SP, see WSO2 Stream Processor Documentation.

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The embedded Spark server in WSO2 Data Analytics Server can be used in several deployment modes, depending on your requirement.

ModeDescriptionWhen to use
Local (default)

In this mode, all of the Spark related work is done within a single node/JVM.

This is ideally suited for evaluation purposes and testing Spark queries in DAS.

Cluster (recommended)

DAS creates its own Spark cluster in the Carbon environment (using Hazelcast). This mode can be used with several high availability (HA) clustering patterns to handle failover scenarios.

Additionally, in the Cluster mode, DAS can be setup without a Spark application. This allows other components to use the DAS cluster as an external Spark cluster. For example, WSO2 Machine Learner can use the Spark cluster embedded in WSO2 DAS.

For clustered production setups.
Client

In this mode, DAS acts only as a Spark client pointing to a separate Spark master. 

This is suited to scenarios where you want to submit DAS analytics jobs to an external Spark cluster.

The following topics list out the configuration instructions for the different deployment modes and also provide instructions on disabling Spark applications.

Before setting up your production environment:

It is recommended to secure your Spark environment by restricting access to the Spark UIs. This can be achieved by addiong the 4040 and 8081 ports to the block list when configuring your firewalls. This blocks the external users from accessing the Spark UI and viewing information related to your production environment.

Local mode

This is the default mode for a typical DAS instance. This mode enables users to evaluate Spark analytics in the Data Analytics Server. In this mode, a separate master or worker is not spawned. Instead, everything would run on a single JVM. Therefore, certain options like Spark Master UI and Spark Worker UI are not active.

Do the following to configure local mode.

  1. Ensure that Carbon clustering is disabled. To do this, open the <DAS_HOME>/repository/conf/axis2/axis2.xml file and set enable=”false” as shown below.
    <clustering class="org.wso2.carbon.core.clustering.hazelcast.HazelcastClusteringAgent" enable="false"> 
  2. Set the Spark master to local. To do this, open the <DAS_HOME>/repository/conf/analytics/spark/spark-defaults.conf file and add the following entry (unless it already exists).
    carbon.spark.master local[<number of cores>]

Cluster mode

Cluster mode is the recommended deployment pattern for DAS in the production environment. Here, DAS would create its own Spark cluster using the Carbon environment and Hazelcast. In this clustering approach, the Spark Standalone mode is used along with a custom implementation of the Standalone Recovery Mode API in Spark.

Since this mode uses a custom standalone recovery mode, the following configurations are passed into the server by default.

# Standalone Cluster Configs
spark.deploy.recoveryMode CUSTOM
spark.deploy.recoveryMode.factory org.wso2.carbon.analytics.spark.core.deploy.AnalyticsRecoveryModeFactory

Do the following to configure cluster mode.

  1. Enable Carbon clustering. To do this, in the <DAS_HOME>/repository/conf/axis2/axis2.xml set enable=”true” for clustering as shown below.
    <clustering class="org.wso2.carbon.core.clustering.hazelcast.HazelcastClusteringAgent" enable="true">  
  2. In the <DAS_HOME>/repository/conf/analytics/spark/spark-defaults.conf file, set the number of masters in the cluster by adding the following entry.
    carbon.spark.master.count <number of masters in the cluster>

While configuring this, make a note of the following.

  • Ensure that the “carbon.spark.master local” configuration remains unchanged. This acts as a flag to use Carbon clustering.
  • Each node can start as both a master and worker. So, in a two node cluster there would be two masters and two workers, one of the master nodes is active and the other is passive. You must specify the total number of masters in the cluster based on your requirement.

Client mode

Client mode is where DAS submits all the Spark related jobs to an external Spark cluster. Since this uses an external Spark cluster, you must ensure that all the .jar files required by the Carbon Spark App are included in the Spark master's and worker's SPARK_CLASSPATH.

Do the following to configure client mode.

  • In the <DAS_HOME>/repository/conf/analytics/spark/spark-defaults.conf file, add the following entry.
    carbon.spark.master spark://<host1:port1host2:port2, ...>

You can include a single master or a list of masters to ensure high-availability.

Disabling a Spark application

In addition to the above modes, you can also configure DAS to startup without a Spark application. Up until the current Spark version (version 1.4.0), there can only be one active Sparkcontext inside a single JVM. Therefore, it is not possible to allow multiple Spark applications to be created in a single JVM. Furthermore, by default, applications submitted to the standalone mode cluster run in FIFO (first-in-first-out) order, and each application attempts to use all available nodes. The Carbon Spark application used for DAS analytics runs throughout the lifetime of the DAS cluster. Therefore, even if you create a separate Spark application in a different JVM, it can only use the resources in the cluster when the Carbon Spark application is terminated.

In order to allow other clients to use the DAS Spark cluster, there is an option provided to disable this Carbon Spark application. This can be done by setting a system variable in the server startup. See Disabling DAS components in the DAS documentation for more information.

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