KeyValueGroupedDataset

Instance Constructors

new KeyValueGroupedDataset(underlying: org.apache.spark.sql.KeyValueGroupedDataset[K, V])

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def agg[U1, U2, U3, U4](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4]): TryAnalysis[Dataset[(K, U1, U2, U3, U4)]]

Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.

Since
1.6.0
def agg[U1, U2, U3](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3]): TryAnalysis[Dataset[(K, U1, U2, U3)]]

Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.

Since
1.6.0
def agg[U1, U2](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2]): TryAnalysis[Dataset[(K, U1, U2)]]

Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.
Computes the given aggregations, returning a Dataset of tuples for each unique key and the result of computing these aggregations over all elements in the group.

Since
1.6.0
def agg[U1](col1: TypedColumn[V, U1]): TryAnalysis[Dataset[(K, U1)]]

Computes the given aggregation, returning a Dataset of tuples for each unique key and the result of computing this aggregation over all elements in the group.
Computes the given aggregation, returning a Dataset of tuples for each unique key and the result of computing this aggregation over all elements in the group.

Since
1.6.0
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def cogroup[U, R](other: KeyValueGroupedDataset[K, U])(f: (K, Iterator[V], Iterator[U]) ⇒ TraversableOnce[R])(implicit arg0: Encoder[R]): Dataset[R]

(Scala-specific) Applies the given function to each cogrouped data.
(Scala-specific) Applies the given function to each cogrouped data. For each unique group, the function will be passed the grouping key and 2 iterators containing all elements in the group from Dataset this and other. The function can return an iterator containing elements of an arbitrary type which will be returned as a new Dataset.

Since
1.6.0
def count: Dataset[(K, Long)]

Returns a Dataset that contains a tuple with each key and the number of items present for that key.
Returns a Dataset that contains a tuple with each key and the number of items present for that key.

Since
1.6.0
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def flatMapGroups[U](f: (K, Iterator[V]) ⇒ TraversableOnce[U])(implicit arg0: Encoder[U]): Dataset[U]

(Scala-specific) Applies the given function to each group of data.
(Scala-specific) Applies the given function to each group of data. For each unique group, the function will be passed the group key and an iterator that contains all of the elements in the group. The function can return an iterator containing elements of an arbitrary type which will be returned as a new Dataset.
This function does not support partial aggregation, and as a result requires shuffling all the data in the Dataset. If an application intends to perform an aggregation over each key, it is best to use the reduce function or an org.apache.spark.sql.expressions#Aggregator.
Internally, the implementation will spill to disk if any given group is too large to fit into memory. However, users must take care to avoid materializing the whole iterator for a group (for example, by calling toList) unless they are sure that this is possible given the memory constraints of their cluster.

Since
1.6.0
def flatMapGroupsWithState[S, U](outputMode: OutputMode, timeoutConf: GroupStateTimeout)(func: (K, Iterator[V], GroupState[S]) ⇒ Iterator[U])(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state. The result Dataset will represent the objects returned by the function. For a static batch Dataset, the function will be invoked once per group. For a streaming Dataset, the function will be invoked for each group repeatedly in every trigger, and updates to each group's state will be saved across invocations. See GroupState for more details.
S
The type of the user-defined state. Must be encodable to Spark SQL types.
U
The type of the output objects. Must be encodable to Spark SQL types.
outputMode
The output mode of the function.
timeoutConf
Timeout configuration for groups that do not receive data for a while. See Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.

Since
2.2.0
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def keyAs[L](implicit arg0: Encoder[L]): KeyValueGroupedDataset[L, V]

Returns a new KeyValueGroupedDataset where the type of the key has been mapped to the specified type.
Returns a new KeyValueGroupedDataset where the type of the key has been mapped to the specified type. The mapping of key columns to the type follows the same rules as as on Dataset.

Since
1.6.0
def keys: Dataset[K]

Returns a Dataset that contains each unique key.
Returns a Dataset that contains each unique key. This is equivalent to doing mapping over the Dataset to extract the keys and then running a distinct operation on those.

Since
1.6.0
def mapGroups[U](f: (K, Iterator[V]) ⇒ U)(implicit arg0: Encoder[U]): Dataset[U]

(Scala-specific) Applies the given function to each group of data.
(Scala-specific) Applies the given function to each group of data. For each unique group, the function will be passed the group key and an iterator that contains all of the elements in the group. The function can return an element of arbitrary type which will be returned as a new Dataset.
This function does not support partial aggregation, and as a result requires shuffling all the data in the Dataset. If an application intends to perform an aggregation over each key, it is best to use the reduce function or an org.apache.spark.sql.expressions#Aggregator.
Internally, the implementation will spill to disk if any given group is too large to fit into memory. However, users must take care to avoid materializing the whole iterator for a group (for example, by calling toList) unless they are sure that this is possible given the memory constraints of their cluster.

Since
1.6.0
def mapGroupsWithState[S, U](timeoutConf: GroupStateTimeout)(func: (K, Iterator[V], GroupState[S]) ⇒ U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state. The result Dataset will represent the objects returned by the function. For a static batch Dataset, the function will be invoked once per group. For a streaming Dataset, the function will be invoked for each group repeatedly in every trigger, and updates to each group's state will be saved across invocations. See org.apache.spark.sql.streaming.GroupState for more details.
S
The type of the user-defined state. Must be encodable to Spark SQL types.
U
The type of the output objects. Must be encodable to Spark SQL types.
timeoutConf
Timeout configuration for groups that do not receive data for a while. See Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.

Since
2.2.0
def mapGroupsWithState[S, U](func: (K, Iterator[V], GroupState[S]) ⇒ U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state. The result Dataset will represent the objects returned by the function. For a static batch Dataset, the function will be invoked once per group. For a streaming Dataset, the function will be invoked for each group repeatedly in every trigger, and updates to each group's state will be saved across invocations. See org.apache.spark.sql.streaming.GroupState for more details.
S
The type of the user-defined state. Must be encodable to Spark SQL types.
U
The type of the output objects. Must be encodable to Spark SQL types.
func
Function to be called on every group. See Encoder for more details on what types are encodable to Spark SQL.

Since
2.2.0
def mapValues[W](func: (V) ⇒ W)(implicit arg0: Encoder[W]): KeyValueGroupedDataset[K, W]

Returns a new KeyValueGroupedDataset where the given function func has been applied to the data.
Returns a new KeyValueGroupedDataset where the given function func has been applied to the data. The grouping key is unchanged by this.
```
// Create values grouped by key from a Dataset[(K, V)]
ds.groupByKey(_._1).mapValues(_._2) // Scala
```
Since
2.1.0
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def reduceGroups(f: (V, V) ⇒ V): Dataset[(K, V)]

(Scala-specific) Reduces the elements of each group of data using the specified binary function.
(Scala-specific) Reduces the elements of each group of data using the specified binary function. The given function must be commutative and associative or the result may be non-deterministic.

Since
1.6.0
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def transformation[KNew, VNew](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.KeyValueGroupedDataset[KNew, VNew]): KeyValueGroupedDataset[KNew, VNew]

Applies a transformation to the underlying KeyValueGroupedDataset.
val underlying: org.apache.spark.sql.KeyValueGroupedDataset[K, V]
def unpack[U](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.Dataset[U]): Dataset[U]

Unpack the underlying KeyValueGroupedDataset into a DataFrame.
def unpackWithAnalysis[U](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.Dataset[U]): TryAnalysis[Dataset[U]]

Unpack the underlying KeyValueGroupedDataset into a DataFrame, it is used for transformations that can fail due to an AnalysisException.
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package sql

final case class KeyValueGroupedDataset[K, V](underlying: org.apache.spark.sql.KeyValueGroupedDataset[K, V]) extends Product with Serializable

Instance Constructors

new KeyValueGroupedDataset(underlying: org.apache.spark.sql.KeyValueGroupedDataset[K, V])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def agg[U1, U2, U3, U4](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4]): TryAnalysis[Dataset[(K, U1, U2, U3, U4)]]

def agg[U1, U2, U3](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3]): TryAnalysis[Dataset[(K, U1, U2, U3)]]

def agg[U1, U2](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2]): TryAnalysis[Dataset[(K, U1, U2)]]

def agg[U1](col1: TypedColumn[V, U1]): TryAnalysis[Dataset[(K, U1)]]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def cogroup[U, R](other: KeyValueGroupedDataset[K, U])(f: (K, Iterator[V], Iterator[U]) ⇒ TraversableOnce[R])(implicit arg0: Encoder[R]): Dataset[R]

def count: Dataset[(K, Long)]

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

def flatMapGroups[U](f: (K, Iterator[V]) ⇒ TraversableOnce[U])(implicit arg0: Encoder[U]): Dataset[U]

def flatMapGroupsWithState[S, U](outputMode: OutputMode, timeoutConf: GroupStateTimeout)(func: (K, Iterator[V], GroupState[S]) ⇒ Iterator[U])(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

def keyAs[L](implicit arg0: Encoder[L]): KeyValueGroupedDataset[L, V]

def keys: Dataset[K]

def mapGroups[U](f: (K, Iterator[V]) ⇒ U)(implicit arg0: Encoder[U]): Dataset[U]

def mapGroupsWithState[S, U](timeoutConf: GroupStateTimeout)(func: (K, Iterator[V], GroupState[S]) ⇒ U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

def mapGroupsWithState[S, U](func: (K, Iterator[V], GroupState[S]) ⇒ U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]

def mapValues[W](func: (V) ⇒ W)(implicit arg0: Encoder[W]): KeyValueGroupedDataset[K, W]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def reduceGroups(f: (V, V) ⇒ V): Dataset[(K, V)]

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

def transformation[KNew, VNew](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.KeyValueGroupedDataset[KNew, VNew]): KeyValueGroupedDataset[KNew, VNew]

val underlying: org.apache.spark.sql.KeyValueGroupedDataset[K, V]

def unpack[U](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.Dataset[U]): Dataset[U]

def unpackWithAnalysis[U](f: (org.apache.spark.sql.KeyValueGroupedDataset[K, V]) ⇒ org.apache.spark.sql.Dataset[U]): TryAnalysis[Dataset[U]]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped