Column Search

group column_search

Functions

std::unique_ptr<column> lower_bound(table_view const &haystack, table_view const &needles, std::vector<order> const &column_order, std::vector<null_order> const &null_precedence, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource())

Find smallest indices in a sorted table where values should be inserted to maintain order.

For each row in needles, find the first index in haystack where inserting the row still maintains its sort order.

Example:

 Single column:
     idx        0   1   2   3   4
  haystack = { 10, 20, 20, 30, 50 }
  needles  = { 20 }
  result   = {  1 }

 Multi Column:
     idx          0    1    2    3    4
  haystack = {{  10,  20,  20,  20,  20 },
              { 5.0,  .5,  .5,  .7,  .7 },
              {  90,  77,  78,  61,  61 }}
  needles  = {{ 20 },
              { .7 },
              { 61 }}
  result   = {   3 }

Parameters:

• haystack – The table containing search space

• needles – Values for which to find the insert locations in the search space

• column_order – Vector of column sort order

• null_precedence – Vector of null_precedence enums needles

• 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 non-nullable column of elements containing the insertion points

std::unique_ptr<column> upper_bound(table_view const &haystack, table_view const &needles, std::vector<order> const &column_order, std::vector<null_order> const &null_precedence, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource())

Find largest indices in a sorted table where values should be inserted to maintain order.

For each row in needles, find the last index in haystack where inserting the row still maintains its sort order.

Example:

 Single Column:
     idx        0   1   2   3   4
  haystack = { 10, 20, 20, 30, 50 }
  needles  = { 20 }
  result   = {  3 }

 Multi Column:
     idx          0    1    2    3    4
  haystack = {{  10,  20,  20,  20,  20 },
              { 5.0,  .5,  .5,  .7,  .7 },
              {  90,  77,  78,  61,  61 }}
  needles  = {{ 20 },
              { .7 },
              { 61 }}
  result =     { 5 }

Parameters:

• haystack – The table containing search space

• needles – Values for which to find the insert locations in the search space

• column_order – Vector of column sort order

• null_precedence – Vector of null_precedence enums needles

• 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 non-nullable column of elements containing the insertion points

bool contains(column_view const &haystack, scalar const &needle, rmm::cuda_stream_view stream = cudf::get_default_stream())

Check if the given needle value exists in the haystack column.

Single Column:
 idx           0   1   2   3   4
 haystack = { 10, 20, 20, 30, 50 }
 needle   = { 20 }
 result   = true

Throws:

cudf::logic_error – If haystack.type() != needle.type().

Parameters:

• haystack – The column containing search space

• needle – A scalar value to check for existence in the search space

• stream – CUDA stream used for device memory operations and kernel launches

Returns:

true if the given needle value exists in the haystack column

std::unique_ptr<column> contains(column_view const &haystack, column_view const &needles, rmm::cuda_stream_view stream = cudf::get_default_stream(), rmm::mr::device_memory_resource *mr = rmm::mr::get_current_device_resource())

Check if the given needles values exists in the haystack column.

The new column will have type BOOL and have the same size and null mask as the input needles column. That is, any null row in the needles column will result in a nul row in the output column.

haystack = { 10, 20, 30, 40, 50 }
needles  = { 20, 40, 60, 80 }
result   = { true, true, false, false }

Throws:

cudf::logic_error – If haystack.type() != needles.type()

Parameters:

• haystack – The column containing search space

• needles – A column of values to check for existence in the search space

• 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 BOOL column indicating if each element in needles exists in the search space