abstract class SmartDataLakeBuilder extends SmartDataLakeLogger
Abstract Smart Data Lake Command Line Application.
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- new SmartDataLakeBuilder()
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- val appType: String
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def
execActionDAG(actionDAGRun: ActionDAGRun, actionsSelected: Seq[Action], context: ActionPipelineContext, lastStartTime: Option[LocalDateTime] = None)(implicit session: SparkSession): Seq[SubFeed]
Execute one action DAG iteration and call recursion if streaming mode Must be implemented with tail recursion to avoid stack overflow error for long running streaming jobs.
Execute one action DAG iteration and call recursion if streaming mode Must be implemented with tail recursion to avoid stack overflow error for long running streaming jobs.
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def
initConfigFromEnvironment: SmartDataLakeBuilderConfig
Create a new SDL configuration.
Create a new SDL configuration.
Could be used in the future to set default values.
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a new, initialized SmartDataLakeBuilderConfig.
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instanceRegistry: InstanceRegistry
InstanceRegistry instance
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logger: Logger
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def
parseCommandLineArguments(args: Array[String], config: SmartDataLakeBuilderConfig): Option[SmartDataLakeBuilderConfig]
Parses the supplied (command line) arguments.
Parses the supplied (command line) arguments.
This method parses command line arguments and creates the corresponding SmartDataLakeBuilderConfig
- args
an Array of command line arguments.
- config
a configuration initialized with default values.
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a new configuration with default values overwritten from the supplied command line arguments.
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val
parser: OptionParser[SmartDataLakeBuilderConfig]
The Parser defines how to extract the options from the command line args.
The Parser defines how to extract the options from the command line args. Subclasses SmartDataLakeBuilder can define additional options to be extracted.
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def
run(appConfig: SmartDataLakeBuilderConfig): Map[RuntimeEventState, Int]
Run the application with the provided configuration.
Run the application with the provided configuration.
- appConfig
Application configuration (parsed from command line).
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def
startSimulation(appConfig: SmartDataLakeBuilderConfig, initialSubFeeds: Seq[SparkSubFeed], dataObjectsState: Seq[DataObjectState] = Seq())(implicit instanceRegistry: InstanceRegistry, session: SparkSession): (Seq[SparkSubFeed], Map[RuntimeEventState, Int])
Start a simulation run.
Start a simulation run. This executes the DAG and returns all subfeeds including the transformed DataFrames. Only prepare and init are executed. All initial subfeeds must be provided as input.
Note: this only works with SparkActions for now
- appConfig
application configuration
- initialSubFeeds
initial subfeeds for DataObjects at the beginning of the DAG
- dataObjectsState
state for incremental DataObjects
- returns
tuple of list of final subfeeds and statistics (action count per RuntimeEventState)
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