Packages

abstract class ETL[T, C <: Configuration] extends AnyRef

Defines a common workflow for ETL jobs. By definition an ETL can take 1..N sources as input and can produce 1..N output.

T

Type used to capture data changes in the ETL

C

Configuration type

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Known Subclasses
ETLP, SingleETL, TransformationsETL, Consequences, UpdateClinvarVariants, Variants, BaseConsequences, GeneCentric, GenesSuggestions, VariantCentric, VariantsSuggestions, DBNSFP, Genes, RareVariant, SpliceAi, AnnovarScores, Clinvar, DBNSFPRaw, DBSNP, DDDGeneSet, EnsemblMapping, HPOGeneSet, OneThousandGenomes, SpliceAi, TopMed, CosmicGeneSet, CosmicMutationSet, GnomadConstraint, GnomadV3, OmimGeneSet, OrphanetGeneSet, RefSeqAnnotation, RefSeqHumanGenes
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Instance Constructors

  1. new ETL(context: ETLContext[T, C])

    context

    runtime configuration

Abstract Value Members

  1. abstract def extract(lastRunValue: T = minValue, currentRunValue: T = defaultCurrentValue): Map[String, DataFrame]

    Reads data from a file system and produces a Map[DatasetConf, DataFrame].

    Reads data from a file system and produces a Map[DatasetConf, DataFrame]. This method should avoid transformation and joins but can implement filters in order to make the ETL more efficient.

    returns

    all the data needed to pass to the transform method and produce the desired output.

  2. abstract def mainDestination: DatasetConf
  3. abstract def transform(data: Map[String, DataFrame], lastRunValue: T = minValue, currentRunValue: T = defaultCurrentValue): Map[String, DataFrame]

    Takes a Map[DatasetConf, DataFrame] as input and applies a set of transformations to it to produce the ETL output.

    Takes a Map[DatasetConf, DataFrame] as input and applies a set of transformations to it to produce the ETL output. It is recommended to not read any additional data but to use the extract() method instead to inject input data.

    data

    input data

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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  6. implicit val conf: Configuration
  7. val defaultCurrentValue: T
  8. def defaultRepartition: (DataFrame) ⇒ DataFrame
  9. def defaultSampling: PartialFunction[String, (DataFrame) ⇒ DataFrame]
  10. final def eq(arg0: AnyRef): Boolean
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  11. def equals(arg0: Any): Boolean
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  12. final def getClass(): Class[_]
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  13. def getLastRunValue(ds: DatasetConf): T

    If possible, fetch the last run value from the dataset passed in argument.

    If possible, fetch the last run value from the dataset passed in argument. Usually a date or an id.

    ds

    dataset

    returns

    the last run value or the minValue

  14. def hashCode(): Int
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  15. final def isInstanceOf[T0]: Boolean
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  16. def load(data: Map[String, DataFrame], lastRunValue: T = minValue, currentRunValue: T = defaultCurrentValue): Map[String, DataFrame]

    Loads the output data into a persistent storage.

    Loads the output data into a persistent storage. The output destination can be any of: object store, database or flat files...

    data

    output data produced by the transform method.

  17. def loadDataset(df: DataFrame, ds: DatasetConf): DataFrame
  18. val log: Logger
  19. val minValue: T
  20. final def ne(arg0: AnyRef): Boolean
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  21. final def notify(): Unit
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  22. final def notifyAll(): Unit
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  23. def publish(): Unit

    OPTIONAL - Contains all actions needed to be done in order to make the data available to users like creating a view with the data.

  24. def replaceWhere: Option[String]

    replaceWhere is used in for OverWriteStaticPartition load.

    replaceWhere is used in for OverWriteStaticPartition load. It avoids to compute dataframe to infer which partitions to replace. Most of the time, these partitions can be inferred statically. Always prefer that to dynamically overwrite partitions.

  25. def reset(): Unit

    Reset the ETL by removing the destination dataset.

  26. def run(lastRunValue: Option[T] = None, currentRunValue: Option[T] = None): Map[String, DataFrame]

    Entry point of the etl - execute this method in order to run the whole ETL

  27. def sampling: PartialFunction[String, (DataFrame) ⇒ DataFrame]

    Logic used when the ETL is run as a RunStep.sample step.

  28. implicit val spark: SparkSession
  29. final def synchronized[T0](arg0: ⇒ T0): T0
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  30. def toMain(df: ⇒ DataFrame): Map[String, DataFrame]
  31. def toString(): String
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  32. final def wait(arg0: Long, arg1: Int): Unit
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  33. final def wait(arg0: Long): Unit
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  34. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    Deprecated

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