Add one-level list encoding support in parquet reader

cpp/include/cudf/io/detail/parquet.hpp

@@ -54,12 +54,10 @@ class reader {
    *
    * @param sources Input `datasource` objects to read the dataset from
    * @param options Settings for controlling reading behavior
-   * @param stream CUDA stream used for device memory operations and kernel launches
    * @param mr Device memory resource to use for device memory allocation
    */
   explicit reader(std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
                   parquet_reader_options const& options,
-                  rmm::cuda_stream_view stream,
                   rmm::mr::device_memory_resource* mr);
 
   /**

cpp/include/cudf/io/parquet.hpp

@@ -46,7 +46,7 @@ constexpr size_type default_row_group_size_rows = 1000000;
 class parquet_reader_options_builder;
 
 /**
- * @brief Settings or `read_parquet()`.
+ * @brief Settings for `read_parquet()`.
  */
 class parquet_reader_options {
   source_info _source;

cpp/src/io/functions.cpp

@@ -402,8 +402,7 @@ table_with_metadata read_parquet(parquet_reader_options const& options,
   CUDF_FUNC_RANGE();
 
   auto datasources = make_datasources(options.get_source());
-  auto reader      = std::make_unique<detail_parquet::reader>(
-    std::move(datasources), options, rmm::cuda_stream_default, mr);
+  auto reader      = std::make_unique<detail_parquet::reader>(std::move(datasources), options, mr);
 
   return reader->read(options);
 }

cpp/src/io/parquet/page_data.cu

@@ -1643,10 +1643,10 @@ extern "C" __global__ void __launch_bounds__(block_size)
 
   bool has_repetition = s->col.max_level[level_type::REPETITION] > 0;
 
-  // optimization : it might be useful to have a version of gpuDecodeStream that could go
-  // wider than 1 warp.  Currently it only only uses 1 warp so that it can overlap work
-  // with the value decoding step when in the actual value decoding kernel.  however during
-  // this preprocess step we have no such limits -  we could go as wide as block_size
+  // optimization : it might be useful to have a version of gpuDecodeStream that could go wider than
+  // 1 warp.  Currently it only uses 1 warp so that it can overlap work with the value decoding step
+  // when in the actual value decoding kernel. However, during this preprocess step we have no such
+  // limits -  we could go as wide as block_size
   if (t < 32) {
     constexpr int batch_size = 32;
     int target_input_count   = batch_size;

cpp/src/io/parquet/parquet.hpp

@@ -198,6 +198,12 @@ struct SchemaElement {
   //  };
   // }
   bool is_stub() const { return repetition_type == REPEATED && num_children == 1; }
+
+  // https://github.com/apache/parquet-cpp/blob/642da05/src/parquet/schema.h#L49-L50
+  // One-level LIST encoding: Only allows required lists with required cells:
+  //   repeated value_type name
+  bool is_one_level_list() const { return repetition_type == REPEATED and num_children == 0; }
+
   // in parquet terms, a group is a level of nesting in the schema. a group
   // can be a struct or a list
   bool is_struct() const

cpp/src/io/parquet/reader_impl.cu

@@ -411,6 +411,9 @@ class aggregate_metadata {
     // walk upwards, skipping repeated fields
     while (schema_index > 0) {
       if (!pfm.schema[schema_index].is_stub()) { depth++; }
+      // schema of one-level encoding list doesn't contain nesting information, so we need to
+      // manually add an extra nesting level
+      if (pfm.schema[schema_index].is_one_level_list()) { depth++; }
       schema_index = pfm.schema[schema_index].parent_idx;
     }
     return depth;
@@ -596,7 +599,10 @@ class aggregate_metadata {
 
         // if we're at the root, this is a new output column
         auto const col_type =
-          to_type_id(schema_elem, strings_to_categorical, timestamp_type_id, strict_decimal_types);
+          schema_elem.is_one_level_list()
+            ? type_id::LIST
+            : to_type_id(
+                schema_elem, strings_to_categorical, timestamp_type_id, strict_decimal_types);
         auto const dtype = col_type == type_id::DECIMAL32 || col_type == type_id::DECIMAL64
                              ? data_type{col_type, numeric::scale_type{-schema_elem.decimal_scale}}
                              : data_type{col_type};
@@ -629,6 +635,26 @@ class aggregate_metadata {
         if (schema_elem.num_children == 0) {
           input_column_info& input_col =
             input_columns.emplace_back(input_column_info{schema_idx, schema_elem.name});
+
+          // set up child output column for one-level encoding list
+          if (schema_elem.is_one_level_list()) {
+            // determine the element data type
+            auto const element_type = to_type_id(
+              schema_elem, strings_to_categorical, timestamp_type_id, strict_decimal_types);
+            auto const element_dtype =
+              element_type == type_id::DECIMAL32 || element_type == type_id::DECIMAL64
+                ? data_type{element_type, numeric::scale_type{-schema_elem.decimal_scale}}
+                : data_type{element_type};
+
+            column_buffer element_col(element_dtype, schema_elem.repetition_type == OPTIONAL);
+            // store the index of this element
+            nesting.push_back(static_cast<int>(output_col.children.size()));
+            // TODO: not sure if we should assign a name or leave it blank
+            element_col.name = "element";
+
+            output_col.children.push_back(std::move(element_col));
+          }
+
           std::copy(nesting.cbegin(), nesting.cend(), std::back_inserter(input_col.nesting));
           path_is_valid = true;  // If we're able to reach leaf then path is valid
         }
@@ -849,6 +875,10 @@ void generate_depth_remappings(std::map<int, std::pair<std::vector<int>, std::ve
           // if this is a repeated field, map it one level deeper
           shallowest = cur_schema.is_stub() ? cur_depth + 1 : cur_depth;
         }
+        // if it's one-level encoding list
+        else if (cur_schema.is_one_level_list()) {
+          shallowest = cur_depth - 1;
+        }
         if (!cur_schema.is_stub()) { cur_depth--; }
         schema_idx = cur_schema.parent_idx;
       }
@@ -1772,7 +1802,6 @@ table_with_metadata reader::impl::read(size_type skip_rows,
 // Forward to implementation
 reader::reader(std::vector<std::unique_ptr<cudf::io::datasource>>&& sources,
                parquet_reader_options const& options,
-               rmm::cuda_stream_view stream,
                rmm::mr::device_memory_resource* mr)
   : _impl(std::make_unique<impl>(std::move(sources), options, mr))
 {

python/cudf/cudf/tests/test_parquet.py

@@ -2255,6 +2255,15 @@ def test_parquet_reader_brotli(datadir):
     assert_eq(expect, got)
 
 
+def test_parquet_reader_one_level_list(datadir):
+    fname = datadir / "one_level_list.parquet"
+
+    expect = pd.read_parquet(fname)
+    got = cudf.read_parquet(fname).to_pandas(nullable=True)
+
+    assert_eq(expect, got)
+
+
 @pytest.mark.parametrize("size_bytes", [4_000_000, 1_000_000, 600_000])
 @pytest.mark.parametrize("size_rows", [1_000_000, 100_000, 10_000])
 def test_parquet_writer_row_group_size(