Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  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

5.  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

6.  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

7.  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

8.  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

9.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

10.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )

11.  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.

    Since

    1.6.0

12.  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

13.  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

14.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

15.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

16.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

17.  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

18.  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

19.  def flatMapGroupsWithState[S, U](func: FlatMapGroupsWithStateFunction[K, V, S, U], outputMode: OutputMode, stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout): Dataset[U]

    ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.

    ::Experimental:: (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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

20.  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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

21.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

22.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

23.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

24.  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

25.  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

26.  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

27.  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

28.  def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, U], stateEncoder: Encoder[S], outputEncoder: Encoder[U], timeoutConf: GroupStateTimeout): Dataset[U]

    ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.

    ::Experimental:: (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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

29.  def mapGroupsWithState[S, U](func: MapGroupsWithStateFunction[K, V, S, U], stateEncoder: Encoder[S], outputEncoder: Encoder[U]): Dataset[U]

    ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.

    ::Experimental:: (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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

30.  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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

31.  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.

    Annotations

    @Experimental() @Evolving()

    Since

    2.2.0

32.  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>
    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

33.  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

34.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

35.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

36.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

37.  val queryExecution: QueryExecution
38.  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

39.  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

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

    Definition Classes

    AnyRef

41.  def toString(): String

    Definition Classes

    KeyValueGroupedDataset → AnyRef → Any

42.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

44.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )