Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 2 Next »

This is a sub-task (single user story) of Cask Hydrator++.

JIRA: 

Error rendering macro 'jira' : Unable to locate Jira server for this macro. It may be due to Application Link configuration.

 

User Story:

User should be able to run a spark job that operates as a transform in ETL Batch scenario.


API Design:

Below is the SparkTransform class that users can implement to define their Spark transform plugin.
Because it extends BatchConfigurable, users will be able to override:

  • configurePipeline - in order to add datasets and streams needed
  • prepareRun - configure the job before starting the run
  • onRunFinish - perform any end of run logic

The SparkMLLib also exposes a transform method, which gives access to an RDD representing data from previous stages of the pipeline.

  • transform - perform any transformations and return an RDD representing the transformed RDD.

SparkTransform
public abstract class SparkTransform<IN, OUT> extends BatchConfigurable<SparkPluginContext> implements Serializable {

  public static final String PLUGIN_TYPE = "sparktransform";

  private static final long serialVersionUID = -8600555200583639593L;

  /**
   * User Spark code which will be executed to compute the transformed RDD
   *
   * @param context {@link SparkPluginContext} for this job
   * @param input the input from previous stages of the Batch run.
   */
  public abstract JavaRDD<OUT> transform(SparkPluginContext context, JavaRDD<IN> input) throws Exception;

}


Users will have access to a

SparkPluginContext

 which exposes functionality that would be available to a regular Spark program via SparkContext, except it excludes the following methods.:

  • getMetrics
  • getWorkflowToken
  • getTaskLocalizationContext
  • getSpecification
  • getServiceDiscoverer
  • setExecutorResources


Implementation Summary:

  1. SmartWorkflow will break up any stages of SparkTransform type into its own phase to be run in ETLSpark, instead of ETLMapReduce. It will be broken up into its own phase because ETLSpark doesn't support certain sources and transforms.
  2. Because SparkTransform extends BatchConfigurable, its prepareRun and onFinish will automatically be handled by ETLSpark, though we will need to special case for the type of context passed in.
  • No labels