Aggregation Reduction#

group aggregation_reduction

Enums

enum class scan_type : bool#

Enum to describe scan operation type.

Values:

enumerator INCLUSIVE#

enumerator EXCLUSIVE#

Functions

std::unique_ptr<scalar> reduce(column_view const &col, reduce_aggregation const &agg, data_type output_dtype, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Computes the reduction of the values in all rows of a column.

This function does not detect overflows in reductions. When output_dtype does not match the col.type(), their values may be promoted to int64_t or double for computing aggregations and then cast to output_dtype before returning.

Only min and max ops are supported for reduction of non-arithmetic types (e.g. timestamp or string).

Any null values are skipped for the operation.

If the column is empty or contains all null entries col.size()==col.null_count(), the output scalar value will be false for reduction type any and true for reduction type all. For all other reductions, the output scalar returns with is_valid()==false.

If the input column is an arithmetic type, the output_dtype can be any arithmetic type. If the input column is a non-arithmetic type (e.g. timestamp or string) the output_dtype must match the col.type(). If the reduction type is any or all, the output_dtype must be type BOOL8.

If the reduction fails, the output scalar returns with is_valid()==false.

Throws:

cudf::logic_error – if reduction is called for non-arithmetic output type and operator other than min and max.
cudf::logic_error – if input column data type is not convertible to output_dtype.
cudf::logic_error – if min or max reduction is called and the output type does not match the input column data type.
cudf::logic_error – if any or all reduction is called and the output type is not BOOL8.
cudf::logic_error – if mean, var, or std reduction is called and the output_dtype is not floating point.

Parameters:

col – Input column view
agg – Aggregation operator applied by the reduction
output_dtype – The output scalar type
stream – CUDA stream used for device memory operations and kernel launches
mr – Device memory resource used to allocate the returned scalar’s device memory

Returns:

Output scalar with reduce result

std::unique_ptr<scalar> reduce(column_view const &col, reduce_aggregation const &agg, data_type output_dtype, std::optional<std::reference_wrapper<scalar const>> init, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Computes the reduction of the values in all rows of a column with an initial value.

Only sum, product, min, max, any, and all reductions are supported.

Throws:

cudf::logic_error – if reduction is not sum, product, min, max, any, or all and init is specified.

Parameters:

col – Input column view
agg – Aggregation operator applied by the reduction
output_dtype – The output scalar type
init – The initial value of the reduction
stream – CUDA stream used for device memory operations and kernel launches
mr – Device memory resource used to allocate the returned scalar’s device memory

Returns:

Output scalar with reduce result

std::unique_ptr<column> segmented_reduce(column_view const &segmented_values, device_span<size_type const> offsets, segmented_reduce_aggregation const &agg, data_type output_dtype, null_policy null_handling, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Compute reduction of each segment in the input column.

This function does not detect overflows in reductions. When output_dtype does not match the segmented_values.type(), their values may be promoted to int64_t or double for computing aggregations and then cast to output_dtype before returning.

Null values are treated as identities during reduction.

If the segment is empty, the row corresponding to the result of the segment is null.

If any index in offsets is out of bound of segmented_values, the behavior is undefined.

If the input column has arithmetic type, output_dtype can be any arithmetic type. If the input column has non-arithmetic type, e.g. timestamp, the same output type must be specified.

If input is not empty, the result is always nullable.

Throws:

cudf::logic_error – if reduction is called for non-arithmetic output type and operator other than min and max.
cudf::logic_error – if input column data type is not convertible to output_dtype type.
cudf::logic_error – if min or max reduction is called and the output_dtype does not match the input column data type.
cudf::logic_error – if any or all reduction is called and the output_dtype is not BOOL8.

Parameters:

segmented_values – Column view of segmented inputs
offsets – Each segment’s offset of segmented_values. A list of offsets with size num_segments + 1. The size of ith segment is offsets[i+1] - offsets[i].
agg – Aggregation operator applied by the reduction
output_dtype – The output column type
null_handling – If INCLUDE, the reduction is valid if all elements in a segment are valid, otherwise null. If EXCLUDE, the reduction is valid if any element in the segment is valid, otherwise null.
stream – CUDA stream used for device memory operations and kernel launches
mr – Device memory resource used to allocate the returned scalar’s device memory

Returns:

Output column with results of segmented reduction

std::unique_ptr<column> segmented_reduce(column_view const &segmented_values, device_span<size_type const> offsets, segmented_reduce_aggregation const &agg, data_type output_dtype, null_policy null_handling, std::optional<std::reference_wrapper<scalar const>> init, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Compute reduction of each segment in the input column with an initial value. Only SUM, PRODUCT, MIN, MAX, ANY, and ALL aggregations are supported.

Parameters:

segmented_values – Column view of segmented inputs
offsets – Each segment’s offset of segmented_values. A list of offsets with size num_segments + 1. The size of ith segment is offsets[i+1] - offsets[i].
agg – Aggregation operator applied by the reduction
output_dtype – The output column type
null_handling – If INCLUDE, the reduction is valid if all elements in a segment are valid, otherwise null. If EXCLUDE, the reduction is valid if any element in the segment is valid, otherwise null.
init – The initial value of the reduction
stream – CUDA stream used for device memory operations and kernel launches
mr – Device memory resource used to allocate the returned scalar’s device memory

Returns:

Output column with results of segmented reduction.

std::unique_ptr<column> scan(column_view const &input, scan_aggregation const &agg, scan_type inclusive, null_policy null_handling = null_policy::EXCLUDE, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Computes the scan of a column.

The null values are skipped for the operation, and if an input element at i is null, then the output element at i will also be null.

Throws:

cudf::logic_error – if column datatype is not numeric type.

Parameters:

input – [in] The input column view for the scan
agg – [in] unique_ptr to aggregation operator applied by the scan
inclusive – [in] The flag for applying an inclusive scan if scan_type::INCLUSIVE, an exclusive scan if scan_type::EXCLUSIVE.
null_handling – [in] Exclude null values when computing the result if null_policy::EXCLUDE. Include nulls if null_policy::INCLUDE. Any operation with a null results in a null.
stream – [in] CUDA stream used for device memory operations and kernel launches
mr – [in] Device memory resource used to allocate the returned scalar’s device memory

Returns:

Scanned output column

std::pair<std::unique_ptr<scalar>, std::unique_ptr<scalar>> minmax(column_view const &col, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::device_async_resource_ref mr = rmm::mr::get_current_device_resource())#

Determines the minimum and maximum values of a column.

Parameters:

col – column to compute minmax
stream – CUDA stream used for device memory operations and kernel launches
mr – Device memory resource used to allocate the returned column’s device memory

Returns:

A std::pair of scalars with the first scalar being the minimum value and the second scalar being the maximum value of the input column.