org.apache.spark.sql

Class KeyValueGroupedDataset<K,V>

Object

org.apache.spark.sql.KeyValueGroupedDataset<K,V>

All Implemented Interfaces:

java.io.Serializable

public class KeyValueGroupedDataset<K,V>
extends Object
implements scala.Serializable

:: Experimental :: 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.

Since:

2.0.0

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
<U1> Dataset<scala.Tuple2<K,U1>>	agg(TypedColumn<V,U1> col1) 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.
<U1,U2> Dataset<scala.Tuple3<K,U1,U2>>	agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2) 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.
<U1,U2,U3> Dataset<scala.Tuple4<K,U1,U2,U3>>	agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3) 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.
<U1,U2,U3,U4> Dataset<scala.Tuple5<K,U1,U2,U3,U4>>	agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3, TypedColumn<V,U4> col4) 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.
<U,R> Dataset<R>	cogroup(KeyValueGroupedDataset<K,U> other, CoGroupFunction<K,V,U,R> f, Encoder<R> encoder) (Java-specific) Applies the given function to each cogrouped data.
<U,R> Dataset<R>	cogroup(KeyValueGroupedDataset<K,U> other, scala.Function3<K,scala.collection.Iterator<V>,scala.collection.Iterator<U>,scala.collection.TraversableOnce<R>> f, Encoder<R> evidence$11) (Scala-specific) Applies the given function to each cogrouped data.
Dataset<scala.Tuple2<K,Object>>	count() Returns a Dataset that contains a tuple with each key and the number of items present for that key.
<U> Dataset<U>	flatMapGroups(FlatMapGroupsFunction<K,V,U> f, Encoder<U> encoder) (Java-specific) Applies the given function to each group of data.
<U> Dataset<U>	flatMapGroups(scala.Function2<K,scala.collection.Iterator<V>,scala.collection.TraversableOnce<U>> f, Encoder<U> evidence$3) (Scala-specific) Applies the given function to each group of data.
<S,U> Dataset<U>	flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K,V,S,U> func, OutputMode outputMode, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf) ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<S,U> Dataset<U>	flatMapGroupsWithState(OutputMode outputMode, GroupStateTimeout timeoutConf, scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,scala.collection.Iterator<U>> func, Encoder<S> evidence$9, Encoder<U> evidence$10) ::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<L> KeyValueGroupedDataset<L,V>	keyAs(Encoder<L> evidence$1) Returns a new KeyValueGroupedDataset where the type of the key has been mapped to the specified type.
Dataset<K>	keys() Returns a Dataset that contains each unique key.
<U> Dataset<U>	mapGroups(scala.Function2<K,scala.collection.Iterator<V>,U> f, Encoder<U> evidence$4) (Scala-specific) Applies the given function to each group of data.
<U> Dataset<U>	mapGroups(MapGroupsFunction<K,V,U> f, Encoder<U> encoder) (Java-specific) Applies the given function to each group of data.
<S,U> Dataset<U>	mapGroupsWithState(scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func, Encoder<S> evidence$5, Encoder<U> evidence$6) ::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<S,U> Dataset<U>	mapGroupsWithState(GroupStateTimeout timeoutConf, scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func, Encoder<S> evidence$7, Encoder<U> evidence$8) ::Experimental:: (Scala-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<S,U> Dataset<U>	mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func, Encoder<S> stateEncoder, Encoder<U> outputEncoder) ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<S,U> Dataset<U>	mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf) ::Experimental:: (Java-specific) Applies the given function to each group of data, while maintaining a user-defined per-group state.
<W> KeyValueGroupedDataset<K,W>	mapValues(scala.Function1<V,W> func, Encoder<W> evidence$2) Returns a new KeyValueGroupedDataset where the given function func has been applied to the data.
<W> KeyValueGroupedDataset<K,W>	mapValues(MapFunction<V,W> func, Encoder<W> encoder) Returns a new KeyValueGroupedDataset where the given function func has been applied to the data.
org.apache.spark.sql.execution.QueryExecution	queryExecution()
Dataset<scala.Tuple2<K,V>>	reduceGroups(scala.Function2<V,V,V> f) (Scala-specific) Reduces the elements of each group of data using the specified binary function.
Dataset<scala.Tuple2<K,V>>	reduceGroups(ReduceFunction<V> f) (Java-specific) Reduces the elements of each group of data using the specified binary function.
String	toString()

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Method Detail

agg

public <U1> Dataset<scala.Tuple2<K,U1>> agg(TypedColumn<V,U1> col1)

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.

Parameters:

col1 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

agg

public <U1,U2> Dataset<scala.Tuple3<K,U1,U2>> agg(TypedColumn<V,U1> col1,
                                                  TypedColumn<V,U2> col2)

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.

Parameters:

col1 - (undocumented)

col2 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

agg

public <U1,U2,U3> Dataset<scala.Tuple4<K,U1,U2,U3>> agg(TypedColumn<V,U1> col1,
                                                        TypedColumn<V,U2> col2,
                                                        TypedColumn<V,U3> col3)

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.

Parameters:

col1 - (undocumented)

col2 - (undocumented)

col3 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

agg

public <U1,U2,U3,U4> Dataset<scala.Tuple5<K,U1,U2,U3,U4>> agg(TypedColumn<V,U1> col1,
                                                              TypedColumn<V,U2> col2,
                                                              TypedColumn<V,U3> col3,
                                                              TypedColumn<V,U4> col4)

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.

Parameters:

col1 - (undocumented)

col2 - (undocumented)

col3 - (undocumented)

col4 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

cogroup

public <U,R> Dataset<R> cogroup(KeyValueGroupedDataset<K,U> other,
                                scala.Function3<K,scala.collection.Iterator<V>,scala.collection.Iterator<U>,scala.collection.TraversableOnce<R>> f,
                                Encoder<R> evidence$11)

(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.

Parameters:

other - (undocumented)

f - (undocumented)

evidence$11 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

cogroup

public <U,R> Dataset<R> cogroup(KeyValueGroupedDataset<K,U> other,
                                CoGroupFunction<K,V,U,R> f,
                                Encoder<R> encoder)

(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.

Parameters:

other - (undocumented)

f - (undocumented)

encoder - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

count

public Dataset<scala.Tuple2<K,Object>> count()

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

Returns:

(undocumented)

Since:

1.6.0

flatMapGroups

public <U> Dataset<U> flatMapGroups(scala.Function2<K,scala.collection.Iterator<V>,scala.collection.TraversableOnce<U>> f,
                                    Encoder<U> evidence$3)

(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.

Parameters:

f - (undocumented)

evidence$3 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

flatMapGroups

public <U> Dataset<U> flatMapGroups(FlatMapGroupsFunction<K,V,U> f,
                                    Encoder<U> encoder)

(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.

Parameters:

f - (undocumented)

encoder - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

flatMapGroupsWithState

public <S,U> Dataset<U> flatMapGroupsWithState(OutputMode outputMode,
                                               GroupStateTimeout timeoutConf,
                                               scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,scala.collection.Iterator<U>> func,
                                               Encoder<S> evidence$9,
                                               Encoder<U> evidence$10)

::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.

Parameters:

func - Function to be called on every group.

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.

evidence$9 - (undocumented)

evidence$10 - (undocumented)

Returns:

(undocumented)

Since:

2.2.0

flatMapGroupsWithState

public <S,U> Dataset<U> flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K,V,S,U> func,
                                               OutputMode outputMode,
                                               Encoder<S> stateEncoder,
                                               Encoder<U> outputEncoder,
                                               GroupStateTimeout timeoutConf)

::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.

Parameters:

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.

Returns:

(undocumented)

Since:

2.2.0

keyAs

public <L> KeyValueGroupedDataset<L,V> keyAs(Encoder<L> evidence$1)

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.

Parameters:

evidence$1 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

keys

public Dataset<K> keys()

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.

Returns:

(undocumented)

Since:

1.6.0

mapGroups

public <U> Dataset<U> mapGroups(scala.Function2<K,scala.collection.Iterator<V>,U> f,
                                Encoder<U> evidence$4)

(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.

Parameters:

f - (undocumented)

evidence$4 - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

mapGroups

public <U> Dataset<U> mapGroups(MapGroupsFunction<K,V,U> f,
                                Encoder<U> encoder)

(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.

Parameters:

f - (undocumented)

encoder - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

mapGroupsWithState

public <S,U> Dataset<U> mapGroupsWithState(scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func,
                                           Encoder<S> evidence$5,
                                           Encoder<U> evidence$6)

::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.

Parameters:

func - Function to be called on every group.

See Encoder for more details on what types are encodable to Spark SQL.

evidence$5 - (undocumented)

evidence$6 - (undocumented)

Returns:

(undocumented)

Since:

2.2.0

mapGroupsWithState

public <S,U> Dataset<U> mapGroupsWithState(GroupStateTimeout timeoutConf,
                                           scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func,
                                           Encoder<S> evidence$7,
                                           Encoder<U> evidence$8)

::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.

Parameters:

func - Function to be called on every group.

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.

evidence$7 - (undocumented)

evidence$8 - (undocumented)

Returns:

(undocumented)

Since:

2.2.0

mapGroupsWithState

public <S,U> Dataset<U> mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func,
                                           Encoder<S> stateEncoder,
                                           Encoder<U> outputEncoder)

::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.

Parameters:

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.

Returns:

(undocumented)

Since:

2.2.0

mapGroupsWithState

public <S,U> Dataset<U> mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func,
                                           Encoder<S> stateEncoder,
                                           Encoder<U> outputEncoder,
                                           GroupStateTimeout timeoutConf)

::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.

Parameters:

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.

Returns:

(undocumented)

Since:

2.2.0

mapValues

public <W> KeyValueGroupedDataset<K,W> mapValues(scala.Function1<V,W> func,
                                                 Encoder<W> evidence$2)

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
 

Parameters:

func - (undocumented)

evidence$2 - (undocumented)

Returns:

(undocumented)

Since:

2.1.0

mapValues

public <W> KeyValueGroupedDataset<K,W> mapValues(MapFunction<V,W> func,
                                                 Encoder<W> encoder)

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());
 

Parameters:

func - (undocumented)

encoder - (undocumented)

Returns:

(undocumented)

Since:

2.1.0

queryExecution

public org.apache.spark.sql.execution.QueryExecution queryExecution()

reduceGroups

public Dataset<scala.Tuple2<K,V>> reduceGroups(scala.Function2<V,V,V> f)

(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.

Parameters:

f - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

reduceGroups

public Dataset<scala.Tuple2<K,V>> reduceGroups(ReduceFunction<V> f)

(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.

Parameters:

f - (undocumented)

Returns:

(undocumented)

Since:

1.6.0

toString

public String toString()

Overrides:

toString in class Object