Class

com.intersys.spark

SparkSessionEx

Related Doc: package spark

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implicit class SparkSessionEx extends AnyRef

Extends the given session with IRIS specific methods.

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Instance Constructors

  1. new SparkSessionEx(ss: SparkSession)

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    ss

    The active Spark session.

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. def dataframe(text: String, column: String, lo: Long, hi: Long, partitions: ): DataFrame

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    Executes a query on the default cluster to yield a suitably partitioned DataFrame.

    Executes a query on the default cluster to yield a suitably partitioned DataFrame.

    text

    The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.

    column

    The name of the integral valued column in the result set with which to further partition the query.

    lo

    The lower bound of the partitioning column.

    hi

    The upper bound of the partitioning column.

    partitions

    The number of partitions per instance to create.

    returns

    The results of the query in the form of a suitably partitioned RDD.

    Exceptions thrown

    SQLException if a database access error occurs.

    See also

    JDBC to Other Databases for more on the semantics of the column, lo, hi, and partitions parameters.

  7. def dataframe(text: String, mfpi: = 1): DataFrame

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    Executes a query on the default cluster to yield a suitably partitioned DataFrame.

    Executes a query on the default cluster to yield a suitably partitioned DataFrame.

    text

    The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.

    mfpi

    The maximum number of factors per distinct instance to include in the factorization implicitly performed by the server, or 0 if no limit is necessary.

    returns

    The results of the query in the form of a suitably partitioned RDD.

    Exceptions thrown

    SQLException if a database access error occurs.

  8. def dataset[α](text: String, column: String, lo: Long, hi: Long, partitions: )(implicit arg0: Encoder[α]): Dataset[α]

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    Executes a query on the default cluster to yield a suitably partitioned Dataset of elements of type α.

    Executes a query on the default cluster to yield a suitably partitioned Dataset of elements of type α.

    Typically α will be a user defined case class. For example:

    case class Person(name: String,age: Int)
    
    spark.dataset[Person]("SELECT name,age from Person","column",0,10000,2)

    returns a result set of type Dataset[Person] whose arguments name and age are matched against the meta data for the underlying DataFrame by their respective names.

    The only real requirement, however, is that the type be a member of the Encoder type class. This includes classes implementing the Product trait, and so, in particular, all small case classes.

    α

    Any type for which an Encoder is available; includes small case classes and extensions of trait Product.

    text

    The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.

    column

    The name of the integral valued column in the result set with which to further partition the query.

    lo

    The lower bound of the partitioning column.

    hi

    The upper bound of the partitioning column.

    partitions

    The number of partitions per instance to create.

    returns

    The results of the query in the form of a suitably partitioned Dataset[α].

    Exceptions thrown

    SQLException if a database access error occurs.

    See also

    JDBC to Other Databases for more on the semantics of the column, lo, hi, and partitions parameters.

  9. def dataset[α](text: String, mfpi: = 1)(implicit arg0: Encoder[α]): Dataset[α]

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    Executes a query on the default cluster to yield a suitably partitioned Dataset of elements of type α.

    Executes a query on the default cluster to yield a suitably partitioned Dataset of elements of type α.

    Typically α will be a user defined case class. For example:

    case class Person(name: String,age: Int)
    
    spark.dataset[Person]("SELECT name,age from Person")

    returns a result set of type Dataset[Person] whose arguments name and age are matched against the meta data for the underlying DataFrame by their respective names.

    The only real requirement, however, is that the type be a member of the Encoder type class. This includes classes implementing the Product trait, and so, in particular, all small case classes.

    α

    Any type for which an Encoder is available; includes small case classes and extensions of trait Product.

    text

    The text of a query to be executed on the cluster or the name of an existing table in the cluster to load.

    mfpi

    The maximum number of factors per distinct instance to include in the factorization implicitly performed by the server, or 0 if no limit is necessary.

    returns

    The results of the query in the form of a suitably partitioned Dataset[α].

    Exceptions thrown

    SQLException if a database access error occurs.

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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

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