Class/Object

com.ebiznext.comet.job.convert

Parquet2CSV

Related Docs: object Parquet2CSV | package convert

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class Parquet2CSV extends SparkJob

Convert parquet files to CSV. The folder hierarchy should be in the form /input_folder/domain/schema/part*.parquet Once converted the csv files is put in the folder /output_folder/domain/schema.csv file When the specified number of parittions is 1 then /output_folder/domain/schema.csv is the file containing the data otherwise, it is a folder containing the part*.csv files. When output_folder is not specified, then the input_folder is used a the base output folder.

Linear Supertypes
SparkJob, JobBase, StrictLogging, AnyRef, Any
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Inherited
  1. Parquet2CSV
  2. SparkJob
  3. JobBase
  4. StrictLogging
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new Parquet2CSV(config: Parquet2CSVConfig, storageHandler: StorageHandler)(implicit settings: Settings)

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Type Members

  1. type JdbcConfigName = String

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    Definition Classes
    JobBase

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  4. def analyze(fullTableName: String): Any

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    Attributes
    protected
    Definition Classes
    SparkJob

  5. def appendToFile(storageHandler: StorageHandler, dataToSave: DataFrame, path: Path, datasetName: String, tableName: String): Unit

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    Saves a dataset.

    Saves a dataset. If the path is empty (the first time we call metrics on the schema) then we can write.

    If there's already parquet files stored in it, then create a temporary directory to compute on, and flush the path to move updated metrics in it

    dataToSave

    : dataset to be saved

    path

    : Path to save the file at

    Attributes
    protected
    Definition Classes
    SparkJob

  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  8. def createSparkViews(views: Views, sqlParameters: Map[String, String]): Unit

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    Attributes
    protected
    Definition Classes
    SparkJob

  9. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  12. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  15. val logger: Logger

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    Attributes
    protected
    Definition Classes
    StrictLogging

  16. def name: String

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    Definition Classes
    Parquet2CSV → JobBase

  17. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  18. final def notify(): Unit

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    Definition Classes
    AnyRef

  19. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  20. def parseViewDefinition(valueWithEnv: String): (SinkType, Option[JdbcConfigName], String)

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    Attributes
    protected
    Definition Classes
    JobBase

  21. def partitionDataset(dataset: DataFrame, partition: List[String]): DataFrame

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    Attributes
    protected
    Definition Classes
    SparkJob

  22. def partitionedDatasetWriter(dataset: DataFrame, partition: List[String]): DataFrameWriter[Row]

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    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

    Attributes
    protected
    Definition Classes
    SparkJob

  23. def registerUdf(udf: String): Unit

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    Attributes
    protected
    Definition Classes
    SparkJob

  24. def run(): Try[JobResult]

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    Just to force any job to implement its entry point using within the "run" method

    Just to force any job to implement its entry point using within the "run" method

    returns

    : Spark Dataframe for Spark Jobs None otherwise

    Definition Classes
    Parquet2CSV → JobBase

  25. lazy val session: SparkSession

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    Definition Classes
    SparkJob

  26. implicit val settings: Settings

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    Definition Classes
    Parquet2CSV → JobBase

  27. lazy val sparkEnv: SparkEnv

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    Definition Classes
    SparkJob

  28. val storageHandler: StorageHandler

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  29. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  30. def toString(): String

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    Definition Classes
    AnyRef → Any

  31. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  32. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  33. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from SparkJob

Inherited from JobBase

Inherited from StrictLogging

Inherited from AnyRef

Inherited from Any

Ungrouped