class KafkaJob extends SparkJob
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Instance Constructors
- new KafkaJob(kafkaJobConfig: KafkaJobConfig)(implicit settings: Settings)
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final
def
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final
def
##(): Int
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==(arg0: Any): Boolean
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def
analyze(fullTableName: String): Any
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clone(): AnyRef
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def
createSparkViews(views: Views, sqlParameters: Map[String, String]): Unit
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hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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- val kafkaJobConfig: KafkaJobConfig
- def load(): Try[SparkJobResult]
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val
logger: Logger
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def
notifyAll(): Unit
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- def offload(): Try[SparkJobResult]
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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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- 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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- protected
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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 job to implement its entry point using within the "run" method
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lazy val
session: SparkSession
- Definition Classes
- SparkJob
- implicit val settings: Settings
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lazy val
sparkEnv: SparkEnv
- Definition Classes
- SparkJob
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final
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synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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wait(arg0: Long): Unit
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