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.
1.6.0
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.
1.6.0
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.
1.6.0
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.
1.6.0
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.
Applies the given function to each cogrouped data.
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.
1.6.0
Applies the given function to each cogrouped data.
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.
1.6.0
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.
1.6.0
Applies the given function to each group of data.
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 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.
1.6.0
Applies the given function to each group of data.
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 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.
1.6.0
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.
1.6.0
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.
1.6.0
Applies the given function to each group of data.
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 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.
1.6.0
Applies the given function to each group of data.
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 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.
1.6.0
Reduces the elements of each group of data using the specified binary function.
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.
1.6.0
Reduces the elements of each group of data using the specified binary function.
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.
1.6.0
:: 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.2.0.0