KeyValueGroupedDataset

class KeyValueGroupedDataset[K, V] extends sql.api.KeyValueGroupedDataset[K, V, Dataset]

A Dataset has been logically grouped by a user specified grouping key. Users should not construct a KeyValueGroupedDataset directly, but should instead call groupByKey on an existing Dataset.

Source: KeyValueGroupedDataset.scala
Since: 2.0.0

Linear Supertypes

api.KeyValueGroupedDataset[K, V, Dataset], Serializable, AnyRef, Any

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KeyValueGroupedDataset
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Type Members

type KVDS[KY, VL] = KeyValueGroupedDataset[KY, VL]
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset

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, U5, U6, U7, U8](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4], col5: TypedColumn[V, U5], col6: TypedColumn[V, U6], col7: TypedColumn[V, U7], col8: TypedColumn[V, U8]): Dataset[(K, U1, U2, U3, U4, U5, U6, U7, U8)]
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2, U3, U4, U5, U6, U7](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4], col5: TypedColumn[V, U5], col6: TypedColumn[V, U6], col7: TypedColumn[V, U7]): Dataset[(K, U1, U2, U3, U4, U5, U6, U7)]
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2, U3, U4, U5, U6](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4], col5: TypedColumn[V, U5], col6: TypedColumn[V, U6]): Dataset[(K, U1, U2, U3, U4, U5, U6)]
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2, U3, U4, U5](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4], col5: TypedColumn[V, U5]): Dataset[(K, U1, U2, U3, U4, U5)]
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2, U3, U4](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3], col4: TypedColumn[V, U4]): 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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2, U3](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2], col3: TypedColumn[V, U3]): 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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1, U2](col1: TypedColumn[V, U1], col2: TypedColumn[V, U2]): 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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def agg[U1](col1: TypedColumn[V, U1]): 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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def aggUntyped(columns: TypedColumn[_, _]*): Dataset[_]
Internal helper function for building typed aggregations that return tuples.
Internal helper function for building typed aggregations that return tuples. For simplicity and code reuse, we do this without the help of the type system and then use helper functions that cast appropriately for the user facing interface.
Attributes
protected
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
final def asInstanceOf[T0]: T0
Definition Classes
Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def cogroup[U, R](other: KeyValueGroupedDataset[K, U], f: CoGroupFunction[K, V, U, R], encoder: Encoder[R]): Dataset[R]
(Java-specific) Applies the given function to each cogrouped data.
(Java-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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def cogroup[U, R](other: KeyValueGroupedDataset[K, U])(f: (K, Iterator[V], Iterator[U]) => IterableOnce[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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def cogroupSorted[U, R](other: KeyValueGroupedDataset[K, U], thisSortExprs: Array[Column], otherSortExprs: Array[Column], f: CoGroupFunction[K, V, U, R], encoder: Encoder[R]): Dataset[R]
(Java-specific) Applies the given function to each sorted cogrouped data.
(Java-specific) Applies the given function to each sorted cogrouped data. For each unique group, the function will be passed the grouping key and 2 sorted 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.
This is equivalent to KeyValueGroupedDataset#cogroup, except for the iterators to be sorted according to the given sort expressions. That sorting does not add computational complexity.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def cogroupSorted[U, R](other: KeyValueGroupedDataset[K, U])(thisSortExprs: Column*)(otherSortExprs: Column*)(f: (K, Iterator[V], Iterator[U]) => IterableOnce[R])(implicit arg0: Encoder[R]): Dataset[R]
(Scala-specific) Applies the given function to each sorted cogrouped data.
(Scala-specific) Applies the given function to each sorted cogrouped data. For each unique group, the function will be passed the grouping key and 2 sorted 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.
This is equivalent to KeyValueGroupedDataset#cogroup, except for the iterators to be sorted according to the given sort expressions. That sorting does not add computational complexity.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
def flatMapGroups[U](f: FlatMapGroupsFunction[K, V, U], encoder: Encoder[U]): Dataset[U]
(Java-specific) Applies the given function to each group of data.
(Java-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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapGroups[U](f: (K, Iterator[V]) => IterableOnce[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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, U], outputMode: OutputMode, stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout, initialState: KeyValueGroupedDataset[K, S]): Dataset[U]
(Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(Java-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.
func
Function to be called on every group.
outputMode
The output mode of the function.
stateEncoder
Encoder for the state type.
outputEncoder
Encoder for the output type.
timeoutConf
Timeout configuration for groups that do not receive data for a while.
initialState
The user provided state that will be initialized when the first batch of data is processed in the streaming query. The user defined function will be called on the state data even if there are no other values in the group. To covert a Dataset ds of type of type Dataset[(K, S)] to a KeyValueGroupedDataset[K, S], use
```
ds.groupByKey(x => x._1).mapValues(_._2)
```
See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, U], outputMode: OutputMode, stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout): Dataset[U]
(Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(Java-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.
func
Function to be called on every group.
outputMode
The output mode of the function.
stateEncoder
Encoder for the state type.
outputEncoder
Encoder for the output type.
timeoutConf
Timeout configuration for groups that do not receive data for a while. See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapGroupsWithState[S, U](outputMode: OutputMode, timeoutConf: GroupStateTimeout, initialState: KeyValueGroupedDataset[K, S])(func: (K, Iterator[V], GroupState[S]) => Iterator[U])(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]
(Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(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.
initialState
The user provided state that will be initialized when the first batch of data is processed in the streaming query. The user defined function will be called on the state data even if there are no other values in the group. To covert a Dataset ds of type of type Dataset[(K, S)] to a KeyValueGroupedDataset[K, S], use
```
ds.groupByKey(x => x._1).mapValues(_._2)
```
See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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]
(Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(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 org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapSortedGroups[U](SortExprs: Array[Column], f: FlatMapGroupsFunction[K, V, U], encoder: Encoder[U]): Dataset[U]
(Java-specific) Applies the given function to each group of data.
(Java-specific) Applies the given function to each group of data. For each unique group, the function will be passed the group key and a sorted 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.
This is equivalent to KeyValueGroupedDataset#flatMapGroups, except for the iterator to be sorted according to the given sort expressions. That sorting does not add computational complexity.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def flatMapSortedGroups[U](sortExprs: Column*)(f: (K, Iterator[V]) => IterableOnce[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 a sorted 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.
This is equivalent to KeyValueGroupedDataset#flatMapGroups, except for the iterator to be sorted according to the given sort expressions. That sorting does not add computational complexity.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroups[U](f: MapGroupsFunction[K, V, U], encoder: Encoder[U]): Dataset[U]
(Java-specific) Applies the given function to each group of data.
(Java-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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, U], stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout, initialState: KeyValueGroupedDataset[K, S]): Dataset[U]
(Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(Java-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.
func
Function to be called on every group.
stateEncoder
Encoder for the state type.
outputEncoder
Encoder for the output type.
timeoutConf
Timeout configuration for groups that do not receive data for a while.
initialState
The user provided state that will be initialized when the first batch of data is processed in the streaming query. The user defined function will be called on the state data even if there are no other values in the group. See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, U], stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout): Dataset[U]
(Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(Java-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.
func
Function to be called on every group.
stateEncoder
Encoder for the state type.
outputEncoder
Encoder for the output type.
timeoutConf
Timeout configuration for groups that do not receive data for a while. See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, U], stateEncoder: Encoder[S], outputEncoder: Encoder[U]): Dataset[U]
(Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(Java-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.
func
Function to be called on every group.
stateEncoder
Encoder for the state type.
outputEncoder
Encoder for the output type. See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](timeoutConf: GroupStateTimeout, initialState: KeyValueGroupedDataset[K, S])(func: (K, Iterator[V], GroupState[S]) => U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]
(Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(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 Conf, see GroupStateTimeout for more details
initialState
The user provided state that will be initialized when the first batch of data is processed in the streaming query. The user defined function will be called on the state data even if there are no other values in the group. To convert a Dataset ds of type Dataset[(K, S)] to a KeyValueGroupedDataset[K, S] do
```
ds.groupByKey(x => x._1).mapValues(_._2)
```
See org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](timeoutConf: GroupStateTimeout)(func: (K, Iterator[V], GroupState[S]) => U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]
(Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(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 org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
func
Function to be called on every group.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapGroupsWithState[S, U](func: (K, Iterator[V], GroupState[S]) => U)(implicit arg0: Encoder[S], arg1: Encoder[U]): Dataset[U]
(Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
(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 org.apache.spark.sql.Encoder for more details on what types are encodable to Spark SQL.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
def mapValues[W](func: MapFunction[V, W], encoder: 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 Integer values grouped by String key from a Dataset<Tuple2<String, Integer>>
Dataset<Tuple2<String, Integer>> ds = ...;
KeyValueGroupedDataset<String, Integer> grouped =
  ds.groupByKey(t -> t._1, Encoders.STRING()).mapValues(t -> t._2, Encoders.INT());
```
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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
```
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
val queryExecution: QueryExecution
def reduceGroups(f: ReduceFunction[V]): Dataset[(K, V)]
(Java-specific) Reduces the elements of each group of data using the specified binary function.
(Java-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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
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.
Definition Classes
KeyValueGroupedDataset → KeyValueGroupedDataset
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
KeyValueGroupedDataset → AnyRef → Any
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Packages

KeyValueGroupedDataset

class KeyValueGroupedDataset[K, V] extends sql.api.KeyValueGroupedDataset[K, V, Dataset]

Type Members

Value Members

Deprecated Value Members

Inherited from api.KeyValueGroupedDataset[K, V, Dataset]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

KeyValueGroupedDataset

class KeyValueGroupedDataset[K, V] extends sql.api.KeyValueGroupedDataset[K, V, Dataset]

Type Members

Value Members

Deprecated Value Members

Inherited from api.KeyValueGroupedDataset[K, V, Dataset]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped

KeyValueGroupedDataset