SparkSessionEx

Instance Constructors

new SparkSessionEx(ss: SparkSession)

ss
The active Spark session.

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[java.lang]
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@throws( ... )
def dataframe(text: String, column: String, lo: Long, hi: Long, partitions: ℕ): DataFrame

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

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

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

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.
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def toString(): String

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final def wait(): Unit

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

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final def wait(arg0: Long): Unit

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Related Doc: package spark

implicit class SparkSessionEx extends AnyRef

Instance Constructors

new SparkSessionEx(ss: SparkSession)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

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

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

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

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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