class KeyValueGroupedDataset[K, V] extends Serializable
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
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- KeyValueGroupedDataset
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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.
- Since
3.0.0
-
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.
- Since
3.0.0
-
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.
- Since
3.0.0
-
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.
- Since
3.0.0
-
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.
- Since
1.6.0
-
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.
- Since
1.6.0
-
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.
- Since
1.6.0
-
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.
- Since
1.6.0
-
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
andother
. 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
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
andother
. 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
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
andother
. 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.
- Since
3.4.0
- See also
-
def
cogroupSorted[U, R](other: KeyValueGroupedDataset[K, U])(thisSortExprs: Column*)(otherSortExprs: Column*)(f: (K, Iterator[V], Iterator[U]) ⇒ TraversableOnce[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
andother
. 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.
- Since
3.4.0
- See also
-
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
-
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.- Since
1.6.0
-
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](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 typeDataset[(K, S)]
to aKeyValueGroupedDataset[K, S]
, useds.groupByKey(x => x._1).mapValues(_._2)
See Encoder for more details on what types are encodable to Spark SQL.
- Since
3.2.0
-
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 Encoder for more details on what types are encodable to Spark SQL.
- Since
2.2.0
-
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 typeDataset[(K, S)]
to aKeyValueGroupedDataset[K, S]
, useds.groupByKey(x => x._1).mapValues(_._2)
See Encoder for more details on what types are encodable to Spark SQL.
- func
Function to be called on every group.
- Since
3.2.0
-
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]
(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 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
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.
- Since
3.4.0
- See also
-
def
flatMapSortedGroups[U](sortExprs: Column*)(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 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.
- Since
3.4.0
- See also
-
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: 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.- 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](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 Encoder for more details on what types are encodable to Spark SQL.
- Since
3.2.0
-
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 Encoder for more details on what types are encodable to Spark SQL.
- Since
2.2.0
-
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 Encoder for more details on what types are encodable to Spark SQL.
- Since
2.2.0
-
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 Encoder for more details on what types are encodable to Spark SQL.
- func
Function to be called on every group.
- Since
3.2.0
-
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 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]
(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 Encoder for more details on what types are encodable to Spark SQL.
- Since
2.2.0
-
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());
- Since
2.1.0
-
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
- 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.
- Since
1.6.0
-
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
-
def
toString(): String
- Definition Classes
- KeyValueGroupedDataset → AnyRef → Any