def apply_join(col_lhs, col_rhs, how, method='hash'):
"""Returns a tuple of the left and right joined indices as gpu arrays.
"""
if (len(col_lhs) != len(col_rhs)):
msg = "Unequal #columns in list 'col_lhs' and list 'col_rhs'"
raise ValueError(msg)
joiner = _join_how_api[how]
method_api = _join_method_api[method]
gdf_context = ffi.new('gdf_context*')
if method == 'hash':
libgdf.gdf_context_view(gdf_context, 0, method_api, 0)
elif method == 'sort':
libgdf.gdf_context_view(gdf_context, 1, method_api, 0)
else:
msg = "method not supported"
raise ValueError(msg)
col_result_l = columnview(0, None, dtype=np.int32)
col_result_r = columnview(0, None, dtype=np.int32)
if (how in ['left', 'inner']):
list_lhs = []
list_rhs = []
for i in range(len(col_lhs)):
list_lhs.append(col_lhs[i].cffi_view)
list_rhs.append(col_rhs[i].cffi_view)
# Call libgdf
joiner(len(col_lhs), list_lhs, list_rhs, col_result_l, col_result_r,
gdf_context)
else:
joiner(col_lhs[0].cffi_view, col_rhs[0].cffi_view, col_result_l,
col_result_r)
# Extract result
# yield ((ary[0], ary[1]) if datasize > 0 else (ary, ary))
left = _as_numba_devarray(intaddr=int(
ffi.cast("uintptr_t", col_result_l.data)),
nelem=col_result_l.size,
dtype=np.int32)
right = _as_numba_devarray(intaddr=int(
ffi.cast("uintptr_t", col_result_r.data)),
nelem=col_result_r.size,
dtype=np.int32)
yield (left, right)
libgdf.gdf_column_free(col_result_l)
libgdf.gdf_column_free(col_result_r)
def _call_join_multi(api, ncols, col_left, col_right, ctxt):
l_res = new_column()
r_res = new_column()
api(ncols, col_left, col_right, l_res, r_res, ctxt)
l_idx = _copy_int_col_to_arr(l_res)
r_idx = _copy_int_col_to_arr(r_res)
joined_idx = np.array([l_idx, r_idx])
libgdf.gdf_column_free(l_res)
libgdf.gdf_column_free(r_res)
return joined_idx