class ConnectionLoadJob extends SparkJob
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Instance Constructors
- new ConnectionLoadJob(cliConfig: ConnectionLoadConfig)(implicit settings: Settings)
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def
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def
analyze(fullTableName: String): Any
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- SparkJob
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- val conf: Configuration
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def
createSparkViews(views: Views, sqlParameters: Map[String, String]): Unit
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val
logger: Logger
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def
name: String
- Definition Classes
- ConnectionLoadJob → JobBase
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notify(): Unit
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notifyAll(): Unit
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def
parseViewDefinition(valueWithEnv: String): (SinkType, Option[JdbcConfigName], String)
- valueWithEnv
in the form [SinkType:[configName:]]viewName
- returns
(SinkType, configName, viewName)
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- Definition Classes
- JobBase
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def
partitionDataset(dataset: DataFrame, partition: List[String]): DataFrame
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- Definition Classes
- SparkJob
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def
partitionedDatasetWriter(dataset: DataFrame, partition: List[String]): DataFrameWriter[Row]
Partition a dataset using dataset columns.
Partition a dataset using dataset columns. To partition the dataset using the ingestion time, use the reserved column names :
- comet_date
- comet_year
- comet_month
- comet_day
- comet_hour
- comet_minute These columns are renamed to "date", "year", "month", "day", "hour", "minute" in the dataset and their values is set to the current date/time.
- dataset
: Input dataset
- partition
: list of columns to use for partitioning.
- returns
The Spark session used to run this job
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- SparkJob
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def
registerUdf(udf: String): Unit
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- Definition Classes
- SparkJob
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def
run(): Try[JobResult]
Just to force any spark job to implement its entry point using within the "run" method
Just to force any spark job to implement its entry point using within the "run" method
- returns
: Spark Session used for the job
- Definition Classes
- ConnectionLoadJob → JobBase
- def runJDBC(): Try[SparkJobResult]
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lazy val
session: SparkSession
- Definition Classes
- SparkJob
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implicit
val
settings: Settings
- Definition Classes
- ConnectionLoadJob → JobBase
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lazy val
sparkEnv: SparkEnv
- Definition Classes
- SparkJob
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