public class KeyValueGroupedDataset<K,V>
extends Object
implements scala.Serializable
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
.
| 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> |
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. |
<U1,U2,U3,U4,U5> |
agg(TypedColumn<V,U1> col1,
TypedColumn<V,U2> col2,
TypedColumn<V,U3> col3,
TypedColumn<V,U4> col4,
TypedColumn<V,U5> col5)
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,U5,U6> |
agg(TypedColumn<V,U1> col1,
TypedColumn<V,U2> col2,
TypedColumn<V,U3> col3,
TypedColumn<V,U4> col4,
TypedColumn<V,U5> col5,
TypedColumn<V,U6> col6)
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,U5,U6,U7> |
agg(TypedColumn<V,U1> col1,
TypedColumn<V,U2> col2,
TypedColumn<V,U3> col3,
TypedColumn<V,U4> col4,
TypedColumn<V,U5> col5,
TypedColumn<V,U6> col6,
TypedColumn<V,U7> col7)
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,U5,U6,U7,U8> |
agg(TypedColumn<V,U1> col1,
TypedColumn<V,U2> col2,
TypedColumn<V,U3> col3,
TypedColumn<V,U4> col4,
TypedColumn<V,U5> col5,
TypedColumn<V,U6> col6,
TypedColumn<V,U7> col7,
TypedColumn<V,U8> col8)
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$15)
(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)
(Java-specific)
Applies the given function to each group of data, while maintaining a user-defined per-group
state.
|
<S,U> Dataset<U> |
flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K,V,S,U> func,
OutputMode outputMode,
Encoder<S> stateEncoder,
Encoder<U> outputEncoder,
GroupStateTimeout timeoutConf,
KeyValueGroupedDataset<K,S> initialState)
(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$11,
Encoder<U> evidence$12)
(Scala-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,
KeyValueGroupedDataset<K,S> initialState,
scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,scala.collection.Iterator<U>> func,
Encoder<S> evidence$13,
Encoder<U> evidence$14)
(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)
(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)
(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,
KeyValueGroupedDataset<K,S> initialState,
scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func,
Encoder<S> evidence$9,
Encoder<U> evidence$10)
(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)
(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)
(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,
KeyValueGroupedDataset<K,S> initialState)
(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() |
public <U1> Dataset<scala.Tuple2<K,U1>> agg(TypedColumn<V,U1> col1)
Dataset
of tuples for each unique key
and the result of computing this aggregation over all elements in the group.
col1
- (undocumented)public <U1,U2> Dataset<scala.Tuple3<K,U1,U2>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)public <U1,U2,U3> Dataset<scala.Tuple4<K,U1,U2,U3>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)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)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)col4
- (undocumented)public <U1,U2,U3,U4,U5> Dataset<scala.Tuple6<K,U1,U2,U3,U4,U5>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3, TypedColumn<V,U4> col4, TypedColumn<V,U5> col5)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)col4
- (undocumented)col5
- (undocumented)public <U1,U2,U3,U4,U5,U6> Dataset<scala.Tuple7<K,U1,U2,U3,U4,U5,U6>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3, TypedColumn<V,U4> col4, TypedColumn<V,U5> col5, TypedColumn<V,U6> col6)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)col4
- (undocumented)col5
- (undocumented)col6
- (undocumented)public <U1,U2,U3,U4,U5,U6,U7> Dataset<scala.Tuple8<K,U1,U2,U3,U4,U5,U6,U7>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3, TypedColumn<V,U4> col4, TypedColumn<V,U5> col5, TypedColumn<V,U6> col6, TypedColumn<V,U7> col7)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)col4
- (undocumented)col5
- (undocumented)col6
- (undocumented)col7
- (undocumented)public <U1,U2,U3,U4,U5,U6,U7,U8> Dataset<scala.Tuple9<K,U1,U2,U3,U4,U5,U6,U7,U8>> agg(TypedColumn<V,U1> col1, TypedColumn<V,U2> col2, TypedColumn<V,U3> col3, TypedColumn<V,U4> col4, TypedColumn<V,U5> col5, TypedColumn<V,U6> col6, TypedColumn<V,U7> col7, TypedColumn<V,U8> col8)
Dataset
of tuples for each unique key
and the result of computing these aggregations over all elements in the group.
col1
- (undocumented)col2
- (undocumented)col3
- (undocumented)col4
- (undocumented)col5
- (undocumented)col6
- (undocumented)col7
- (undocumented)col8
- (undocumented)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$15)
Dataset
this
and other
. The function can return an iterator containing elements of an
arbitrary type which will be returned as a new Dataset
.
other
- (undocumented)f
- (undocumented)evidence$15
- (undocumented)public <U,R> Dataset<R> cogroup(KeyValueGroupedDataset<K,U> other, CoGroupFunction<K,V,U,R> f, Encoder<R> encoder)
Dataset
this
and other
. The function can return an iterator containing elements of an
arbitrary type which will be returned as a new Dataset
.
other
- (undocumented)f
- (undocumented)encoder
- (undocumented)public Dataset<scala.Tuple2<K,Object>> count()
Dataset
that contains a tuple with each key and the number of items present
for that key.
public <U> Dataset<U> flatMapGroups(scala.Function2<K,scala.collection.Iterator<V>,scala.collection.TraversableOnce<U>> f, Encoder<U> evidence$3)
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.
f
- (undocumented)evidence$3
- (undocumented)public <U> Dataset<U> flatMapGroups(FlatMapGroupsFunction<K,V,U> f, Encoder<U> encoder)
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.
f
- (undocumented)encoder
- (undocumented)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$11, Encoder<U> evidence$12)
GroupState
for more details.
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$11
- (undocumented)evidence$12
- (undocumented)public <S,U> Dataset<U> flatMapGroupsWithState(OutputMode outputMode, GroupStateTimeout timeoutConf, KeyValueGroupedDataset<K,S> initialState, scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,scala.collection.Iterator<U>> func, Encoder<S> evidence$13, Encoder<U> evidence$14)
GroupState
for more details.
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.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 {@link Encoder} for more details on what types are encodable to Spark SQL.
@since 3.2.0evidence$13
- (undocumented)evidence$14
- (undocumented)public <S,U> Dataset<U> flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K,V,S,U> func, OutputMode outputMode, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf)
GroupState
for more details.
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.
public <S,U> Dataset<U> flatMapGroupsWithState(FlatMapGroupsWithStateFunction<K,V,S,U> func, OutputMode outputMode, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf, KeyValueGroupedDataset<K,S> initialState)
GroupState
for more details.
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 {@link Encoder} for more details on what types are encodable to Spark SQL.
@since 3.2.0public <L> KeyValueGroupedDataset<L,V> keyAs(Encoder<L> evidence$1)
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
.
evidence$1
- (undocumented)public Dataset<K> keys()
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.
public <U> Dataset<U> mapGroups(scala.Function2<K,scala.collection.Iterator<V>,U> f, Encoder<U> evidence$4)
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.
f
- (undocumented)evidence$4
- (undocumented)public <U> Dataset<U> mapGroups(MapGroupsFunction<K,V,U> f, Encoder<U> encoder)
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.
f
- (undocumented)encoder
- (undocumented)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)
GroupState
for more details.
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)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)
GroupState
for more details.
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)public <S,U> Dataset<U> mapGroupsWithState(GroupStateTimeout timeoutConf, KeyValueGroupedDataset<K,S> initialState, scala.Function3<K,scala.collection.Iterator<V>,GroupState<S>,U> func, Encoder<S> evidence$9, Encoder<U> evidence$10)
GroupState
for more details.
func
- Function to be called on every group.timeoutConf
- Timeout Conf, see GroupStateTimeout for more detailsinitialState
- 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 {@link Encoder} for more details on what types are encodable to Spark SQL.
@since 3.2.0evidence$9
- (undocumented)evidence$10
- (undocumented)public <S,U> Dataset<U> mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func, Encoder<S> stateEncoder, Encoder<U> outputEncoder)
GroupState
for more details.
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.
public <S,U> Dataset<U> mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf)
GroupState
for more details.
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.
public <S,U> Dataset<U> mapGroupsWithState(MapGroupsWithStateFunction<K,V,S,U> func, Encoder<S> stateEncoder, Encoder<U> outputEncoder, GroupStateTimeout timeoutConf, KeyValueGroupedDataset<K,S> initialState)
GroupState
for more details.
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.
public <W> KeyValueGroupedDataset<K,W> mapValues(scala.Function1<V,W> func, Encoder<W> evidence$2)
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
func
- (undocumented)evidence$2
- (undocumented)public <W> KeyValueGroupedDataset<K,W> mapValues(MapFunction<V,W> func, Encoder<W> encoder)
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());
func
- (undocumented)encoder
- (undocumented)public org.apache.spark.sql.execution.QueryExecution queryExecution()
public Dataset<scala.Tuple2<K,V>> reduceGroups(scala.Function2<V,V,V> f)
f
- (undocumented)public Dataset<scala.Tuple2<K,V>> reduceGroups(ReduceFunction<V> f)
f
- (undocumented)public String toString()
toString
in class Object


Scope is the set of variables you have access to.
In JavaScript, objects and functions are also variables.
In JavaScript, scope is the set of variables, objects, and functions you have access to.
JavaScript has function scope: The scope changes inside functions.
Variables declared within a JavaScript function, become LOCAL to the function.
Local variables have local scope: They can only be accessed within the function.
Since local variables are only recognized inside their functions, variables with the same name can be used in different functions.
Local variables are created when a function starts, and deleted when the function is completed.
A variable declared outside a function, becomes GLOBAL.
A global variable has global scope: All scripts and functions on a web page can access it.
If you assign a value to a variable that has not been declared, it will automatically become a GLOBAL variable.
This code example will declare carName as a global variable, even if it is executed inside a function.
The lifetime of a JavaScript variable starts when it is declared.
Local variables are deleted when the function is completed.
Global variables are deleted when you close the page.
Function arguments (parameters) work as local variables inside functions.
With JavaScript, the global scope is the complete JavaScript environment.
In HTML, the global scope is the window object: All global variables belong to the window object.
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Your global variables (or functions) can overwrite window variables (or functions). Any function, including the window object, can overwrite your global variables and functions. |


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DeviceNameHelperRK
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Data typically stored in retained memory or EEPROM to avoid having to fetch the name so often. More...
#include <DeviceNameHelperRK.h>
Data Fields | |
| uint32_t | magic |
| Magic bytes, DeviceNameHelper::DATA_MAGIC. More... | |
| uint8_t | size |
| Size of this structure, currently 44 bytes. Used to detect when it changes to invalidate the old version. | |
| uint8_t | flags |
| Flag bits, not currently used. Currently 0. | |
| uint16_t | reserved |
| Reserved for future use, currently 0. | |
| long | lastCheck |
| Last time the name was checked from Time.now() (seconds past January 1, 1970, UTC). | |
| char | name [DEVICENAMEHELPER_MAX_NAME_LEN+1] |
| The device name. | |
Data typically stored in retained memory or EEPROM to avoid having to fetch the name so often.
This structure is currently 44 bytes. It cannot be larger than 255 bytes because the length is stored in a uint8_t. If the structure size is changed, any previously saved data will be discarded and the name fetched again.
Also note that DEVICENAMEHELPER_MAX_NAME_LEN affects the size of this structure.
| uint32_t DeviceNameHelperData::magic |
Magic bytes, DeviceNameHelper::DATA_MAGIC.
This value (0x7787a2f2) is used to see if the structure has been initialized