Packages

class OrphanetGeneSet extends ETLP

Linear Supertypes
ETLP, ETL, AnyRef, Any
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  1. OrphanetGeneSet
  2. ETLP
  3. ETL
  4. AnyRef
  5. Any
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Visibility
  1. Public
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Instance Constructors

  1. new OrphanetGeneSet()(implicit conf: Configuration)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val HGNC_gene_id: (Node) ⇒ Option[String]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val association_status: (Node) ⇒ Option[String]
  7. val association_type: (Node) ⇒ Option[String]
  8. val association_type_id: (Node) ⇒ Option[Long]
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  10. implicit val conf: Configuration
    Definition Classes
    ETL
  11. val destination: DatasetConf
    Definition Classes
    OrphanetGeneSet → ETL
  12. val ensembl_gene_id: (Node) ⇒ Option[String]
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def extract(lastRunDateTime: LocalDateTime, currentRunDateTime: LocalDateTime)(implicit spark: SparkSession): Map[String, DataFrame]

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

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

    spark

    an instance of SparkSession

    returns

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

    Definition Classes
    OrphanetGeneSet → ETL
  16. val fs: FileSystem

    Default file system

    Default file system

    Definition Classes
    ETL
  17. val genatlas_gene_id: (Node) ⇒ Option[String]
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  19. def getLastRunDateFor(ds: DatasetConf)(implicit spark: SparkSession): LocalDateTime

    If possible, fetch the last run date time from the dataset passed in argument

    If possible, fetch the last run date time from the dataset passed in argument

    ds

    dataset

    spark

    a spark session

    returns

    the last run date or the minDateTime

    Definition Classes
    ETL
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. def lastReleaseId(implicit spark: SparkSession): String
    Definition Classes
    ETLP
  23. def load(data: DataFrame, lastRunDateTime: LocalDateTime, currentRunDateTime: LocalDateTime)(implicit spark: SparkSession): 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.

    spark

    an instance of SparkSession

    Definition Classes
    OrphanetGeneSet → ETL
  24. val maxDateTime: LocalDateTime
    Definition Classes
    ETL
  25. val minDateTime: LocalDateTime
    Definition Classes
    ETL
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  29. val omim_gene_id: (Node) ⇒ Option[String]
  30. val orphanet_disease_history: DatasetConf
  31. val orphanet_gene_association: DatasetConf
  32. def parseProduct6XML(doc: Elem): Seq[OrphanetGeneAssociation]
  33. def parseProduct9XML(doc: Elem): Seq[OrphanetDiseaseHistory]
  34. def publish()(implicit spark: SparkSession): 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.

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

    spark

    an instance of SparkSession

    Definition Classes
    ETLP → ETL
  35. val reactome_gene_id: (Node) ⇒ Option[String]
  36. def reset(): Unit

    Reset the ETL by removing the destination dataset.

    Reset the ETL by removing the destination dataset.

    Definition Classes
    ETL
  37. def run()(implicit spark: SparkSession): DataFrame

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

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

    spark

    an instance of SparkSession

    Definition Classes
    ETL
  38. def run(lastRunDateTime: LocalDateTime = minDateTime, currentRunDateTime: LocalDateTime = LocalDateTime.now())(implicit spark: SparkSession): DataFrame

    Entry point of the etl - execute this method in order to run the whole ETL for a specific date

    Entry point of the etl - execute this method in order to run the whole ETL for a specific date

    lastRunDateTime

    the last time this etl was run. default is minDateTime

    currentRunDateTime

    the time at which the etl needs to be ran, usually now().

    spark

    an instance of SparkSession

    Definition Classes
    ETL
  39. val swiss_prot_gene_id: (Node) ⇒ Option[String]
  40. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  41. def toString(): String
    Definition Classes
    AnyRef → Any
  42. def transform(data: Map[String, DataFrame], lastRunDateTime: LocalDateTime, currentRunDateTime: LocalDateTime)(implicit spark: SparkSession): DataFrame

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

    Takes a Map[DatasetConf, DataFrame] as input and apply a set of transformation 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

    spark

    an instance of SparkSession

    Definition Classes
    OrphanetGeneSet → ETL
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  45. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from ETLP

Inherited from ETL

Inherited from AnyRef

Inherited from Any

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