Merge branch 'branch-24.06' into fea/move_to_latest_nanoarrow

cpp/src/io/json/json_column.cu

@@ -76,16 +76,16 @@ void print_tree(host_span<SymbolT const> input,
                 tree_meta_t const& d_gpu_tree,
                 rmm::cuda_stream_view stream)
 {
-  print_vec(cudf::detail::make_std_vector_async(d_gpu_tree.node_categories, stream),
+  print_vec(cudf::detail::make_std_vector_sync(d_gpu_tree.node_categories, stream),
             "node_categories",
             to_cat);
-  print_vec(cudf::detail::make_std_vector_async(d_gpu_tree.parent_node_ids, stream),
+  print_vec(cudf::detail::make_std_vector_sync(d_gpu_tree.parent_node_ids, stream),
             "parent_node_ids",
             to_int);
   print_vec(
-    cudf::detail::make_std_vector_async(d_gpu_tree.node_levels, stream), "node_levels", to_int);
-  auto node_range_begin = cudf::detail::make_std_vector_async(d_gpu_tree.node_range_begin, stream);
-  auto node_range_end   = cudf::detail::make_std_vector_async(d_gpu_tree.node_range_end, stream);
+    cudf::detail::make_std_vector_sync(d_gpu_tree.node_levels, stream), "node_levels", to_int);
+  auto node_range_begin = cudf::detail::make_std_vector_sync(d_gpu_tree.node_range_begin, stream);
+  auto node_range_end   = cudf::detail::make_std_vector_sync(d_gpu_tree.node_range_end, stream);
   print_vec(node_range_begin, "node_range_begin", to_int);
   print_vec(node_range_end, "node_range_end", to_int);
   for (int i = 0; i < int(node_range_begin.size()); i++) {
@@ -333,10 +333,11 @@ rmm::device_uvector<NodeIndexT> get_values_column_indices(TreeDepthT const row_a
  * @param stream CUDA stream
  * @return Vector of strings
  */
-std::vector<std::string> copy_strings_to_host(device_span<SymbolT const> input,
-                                              device_span<SymbolOffsetT const> node_range_begin,
-                                              device_span<SymbolOffsetT const> node_range_end,
-                                              rmm::cuda_stream_view stream)
+std::vector<std::string> copy_strings_to_host_sync(
+  device_span<SymbolT const> input,
+  device_span<SymbolOffsetT const> node_range_begin,
+  device_span<SymbolOffsetT const> node_range_end,
+  rmm::cuda_stream_view stream)
 {
   CUDF_FUNC_RANGE();
   auto const num_strings = node_range_begin.size();
@@ -371,12 +372,13 @@ std::vector<std::string> copy_strings_to_host(device_span<SymbolT const> input,
   auto to_host        = [stream](auto const& col) {
     if (col.is_empty()) return std::vector<std::string>{};
     auto const scv     = cudf::strings_column_view(col);
-    auto const h_chars = cudf::detail::make_std_vector_sync<char>(
+    auto const h_chars = cudf::detail::make_std_vector_async<char>(
       cudf::device_span<char const>(scv.chars_begin(stream), scv.chars_size(stream)), stream);
-    auto const h_offsets = cudf::detail::make_std_vector_sync(
+    auto const h_offsets = cudf::detail::make_std_vector_async(
       cudf::device_span<cudf::size_type const>(scv.offsets().data<cudf::size_type>() + scv.offset(),
                                                scv.size() + 1),
       stream);
+    stream.synchronize();
 
     // build std::string vector from chars and offsets
     std::vector<std::string> host_data;
@@ -528,15 +530,17 @@ void make_device_json_column(device_span<SymbolT const> input,
   auto column_range_beg =
     cudf::detail::make_std_vector_async(d_column_tree.node_range_begin, stream);
   auto max_row_offsets = cudf::detail::make_std_vector_async(d_max_row_offsets, stream);
-  std::vector<std::string> column_names = copy_strings_to_host(
+  std::vector<std::string> column_names = copy_strings_to_host_sync(
     input, d_column_tree.node_range_begin, d_column_tree.node_range_end, stream);
+  stream.synchronize();
   // array of arrays column names
   if (is_array_of_arrays) {
     TreeDepthT const row_array_children_level = is_enabled_lines ? 1 : 2;
     auto values_column_indices =
       get_values_column_indices(row_array_children_level, tree, col_ids, num_columns, stream);
     auto h_values_column_indices =
       cudf::detail::make_std_vector_async(values_column_indices, stream);
+    stream.synchronize();
     std::transform(unique_col_ids.begin(),
                    unique_col_ids.end(),
                    column_names.begin(),
@@ -609,7 +613,7 @@ void make_device_json_column(device_span<SymbolT const> input,
 
   std::vector<uint8_t> is_str_column_all_nulls{};
   if (is_enabled_mixed_types_as_string) {
-    is_str_column_all_nulls = cudf::detail::make_std_vector_async(
+    is_str_column_all_nulls = cudf::detail::make_std_vector_sync(
       is_all_nulls_each_column(input, d_column_tree, tree, col_ids, options, stream), stream);
   }
 

docs/cudf/source/user_guide/pandas-comparison.md

@@ -87,9 +87,17 @@ using `.from_arrow()` or `.from_pandas()`.
 
 ## Result ordering
 
-By default, `join` (or `merge`), `value_counts` and `groupby` operations in cuDF
-do *not* guarantee output ordering.
-Compare the results obtained from Pandas and cuDF below:
+In Pandas, `join` (or `merge`), `value_counts` and `groupby` operations provide
+certain guarantees about the order of rows in the result returned.  In a Pandas
+`join`, the order of join keys is (depending on the particular style of join
+being performed) either preserved or sorted lexicographically by default.
+`groupby` sorts the group keys, and preserves the order of rows within each
+group. In some cases, disabling this option in Pandas can yield better
+performance.
+
+By contrast, cuDF's default behavior is to return rows in a
+non-deterministic order to maximize performance.  Compare the results
+obtained from Pandas and cuDF below:
 
 ```{code} python
 >>> import cupy as cp
@@ -114,13 +122,16 @@ a
 4  342.000000
 ```
 
-To match Pandas behavior, you must explicitly pass `sort=True`
-or enable the `mode.pandas_compatible` option when trying to
-match Pandas behavior with `sort=False`:
+In most cases, the rows of a DataFrame are accessed by index labels
+rather than by position, so the order in which rows are returned
+doesn't matter. However, if you require that results be returned in a
+predictable (sorted) order, you can pass the `sort=True` option
+explicitly or enable the `mode.pandas_compatible` option when trying
+to match Pandas behavior with `sort=False`:
 
 ```{code} python
->>> df.to_pandas().groupby("a", sort=True).mean().head()
-            b
+>>> df.groupby("a", sort=True).mean().head()
+         b
 a
 0   70.000000
 1  356.333333

python/cudf/cudf/_lib/pylibcudf/CMakeLists.txt

@@ -44,3 +44,5 @@ rapids_cython_create_modules(
   LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX pylibcudf_ ASSOCIATED_TARGETS cudf
 )
 link_to_pyarrow_headers(pylibcudf_interop)
+
+add_subdirectory(strings)

python/cudf/cudf/_lib/pylibcudf/__init__.pxd

@@ -17,6 +17,7 @@ from . cimport (
     search,
     sorting,
     stream_compaction,
+    strings,
     types,
     unary,
 )
@@ -48,6 +49,7 @@ __all__ = [
     "rolling",
     "search",
     "stream_compaction",
+    "strings",
     "sorting",
     "types",
     "unary",

python/cudf/cudf/_lib/pylibcudf/__init__.py

@@ -17,6 +17,7 @@
     search,
     sorting,
     stream_compaction,
+    strings,
     types,
     unary,
 )
@@ -48,6 +49,7 @@
     "rolling",
     "search",
     "stream_compaction",
+    "strings",
     "sorting",
     "types",
     "unary",

python/cudf/cudf/_lib/pylibcudf/strings/CMakeLists.txt

@@ -0,0 +1,21 @@
+# =============================================================================
+# Copyright (c) 2024, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
+# in compliance with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software distributed under the License
+# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
+# or implied. See the License for the specific language governing permissions and limitations under
+# the License.
+# =============================================================================
+
+set(cython_sources case.pyx)
+set(linked_libraries cudf::cudf)
+rapids_cython_create_modules(
+  CXX
+  SOURCE_FILES "${cython_sources}"
+  LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX pylibcudf_ ASSOCIATED_TARGETS cudf
+)

python/cudf/cudf/_lib/pylibcudf/strings/__init__.pxd

@@ -0,0 +1,3 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+from . import case

python/cudf/cudf/_lib/pylibcudf/strings/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+from . import case