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.

(Scala-specific) Applies the given function to each sorted cogrouped data. For each unique group, the function will be passed the grouping key and 2 sorted iterators containing all elements in the group from Dataset this and other. The function can return an iterator containing elements of an arbitrary type which will be returned as a new Dataset.

This is equivalent to KeyValueGroupedDataset#cogroup, except for the iterators to be sorted according to the given sort expressions. That sorting does not add computational complexity.

(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.

(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.

(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.

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.

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.

(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.

(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.

(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.

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

(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.

(Scala-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. Functions as the function above, but with additional eventTimeColumnName for output.

(Scala-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. Functions as the function above, but with additional initial state.

(Scala-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. We allow the user to act on per-group set of input rows along with keyed state and the user can choose to output/return 0 or more rows. For a streaming dataframe, we will repeatedly invoke the interface methods for new rows in each trigger and the user's state/state variables will be stored persistently across invocations.

Downstream operators would use specified eventTimeColumnName to calculate watermark. Note that TimeMode is set to EventTime to ensure correct flow of watermark.

(Scala-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. We allow the user to act on per-group set of input rows along with keyed state and the user can choose to output/return 0 or more rows. For a streaming dataframe, we will repeatedly invoke the interface methods for new rows in each trigger and the user's state/state variables will be stored persistently across invocations.

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.

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.

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.

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 aggregation, returning a Dataset of tuples for each unique key and the result of computing this aggregation over all elements in the group.

(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.

(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.

(Java-specific) Applies the given function to each sorted cogrouped data. For each unique group, the function will be passed the grouping key and 2 sorted iterators containing all elements in the group from Dataset this and other. The function can return an iterator containing elements of an arbitrary type which will be returned as a new Dataset.

This is equivalent to KeyValueGroupedDataset#cogroup, except for the iterators to be sorted according to the given sort expressions. That sorting does not add computational complexity.

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

(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.

(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.

(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.

(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.

(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.

(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.

(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.

(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.

(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.

(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.

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());

(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.

(Java-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. Functions as the function above, but with additional eventTimeColumnName for output.

Downstream operators would use specified eventTimeColumnName to calculate watermark. Note that TimeMode is set to EventTime to ensure correct flow of watermark.

(Java-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. Functions as the function above, but with additional initialStateEncoder for state encoding.

(Java-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. We allow the user to act on per-group set of input rows along with keyed state and the user can choose to output/return 0 or more rows.

For a streaming dataframe, we will repeatedly invoke the interface methods for new rows in each trigger and the user's state/state variables will be stored persistently across invocations.

Downstream operators would use specified eventTimeColumnName to calculate watermark. Note that TimeMode is set to EventTime to ensure correct flow of watermark.

(Java-specific) Invokes methods defined in the stateful processor used in arbitrary state API v2. We allow the user to act on per-group set of input rows along with keyed state and the user can choose to output/return 0 or more rows. For a streaming dataframe, we will repeatedly invoke the interface methods for new rows in each trigger and the user's state/state variables will be stored persistently across invocations.