[Data] Add partitioning parameter to read_parquet (#47553)

To extract path partition information with `read_parquet`, you pass a
PyArrow `partitioning` object to `dataset_kwargs`. For example:
```
schema = pa.schema([("one", pa.int32()), ("two", pa.string())])
partitioning = pa.dataset.partitioning(schema, flavor="hive")
ds = ray.data.read_parquet(... dataset_kwargs=dict(partitioning=partitioning))
```

This is problematic for two reasons:
1. It tightly couples the interface with the implementation;
partitioning only works if we use `pyarrow.Dataset` in a specific way in
the implementation.
2. It's inconsistent with all of the other file-based API. All other
APIs use expose a top-level `partitioning` parameter (rather than
`dataset_kwargs`) where you pass a Ray Data `Partitioning` object
(rather than a PyArrow partitioning object).

---------

Signed-off-by: Balaji Veeramani <[email protected]>

python/ray/data/_internal/datasource/parquet_datasource.py

@@ -35,7 +35,12 @@
     _handle_read_os_error,
 )
 from ray.data.datasource.parquet_meta_provider import ParquetMetadataProvider
-from ray.data.datasource.partitioning import PathPartitionFilter
+from ray.data.datasource.partitioning import (
+    PartitionDataType,
+    Partitioning,
+    PathPartitionFilter,
+    PathPartitionParser,
+)
 from ray.data.datasource.path_util import (
     _has_file_extension,
     _resolve_paths_and_filesystem,
@@ -164,6 +169,7 @@ def __init__(
         schema: Optional[Union[type, "pyarrow.lib.Schema"]] = None,
         meta_provider: ParquetMetadataProvider = ParquetMetadataProvider(),
         partition_filter: PathPartitionFilter = None,
+        partitioning: Optional[Partitioning] = Partitioning("hive"),
         shuffle: Union[Literal["files"], None] = None,
         include_paths: bool = False,
         file_extensions: Optional[List[str]] = None,
@@ -214,10 +220,22 @@ def __init__(
         if dataset_kwargs is None:
             dataset_kwargs = {}
 
+        if "partitioning" in dataset_kwargs:
+            raise ValueError(
+                "The 'partitioning' parameter isn't supported in 'dataset_kwargs'. "
+                "Use the top-level 'partitioning' parameter instead."
+            )
+
+        # This datasource manually adds partition data at the Ray Data-level. To avoid
+        # duplicating the partition data, we disable PyArrow's partitioning.
+        dataset_kwargs["partitioning"] = None
+
         pq_ds = get_parquet_dataset(paths, filesystem, dataset_kwargs)
 
         if schema is None:
             schema = pq_ds.schema
+            schema = _add_partition_fields_to_schema(partitioning, schema, pq_ds)
+
         if columns:
             schema = pa.schema(
                 [schema.field(column) for column in columns], schema.metadata
@@ -280,6 +298,7 @@ def __init__(
         self._schema = schema
         self._file_metadata_shuffler = None
         self._include_paths = include_paths
+        self._partitioning = partitioning
         if shuffle == "files":
             self._file_metadata_shuffler = np.random.default_rng()
 
@@ -358,13 +377,15 @@ def get_read_tasks(self, parallelism: int) -> List[ReadTask]:
                 columns,
                 schema,
                 include_paths,
+                partitioning,
             ) = (
                 self._block_udf,
                 self._to_batches_kwargs,
                 self._default_read_batch_size_rows,
                 self._columns,
                 self._schema,
                 self._include_paths,
+                self._partitioning,
             )
             read_tasks.append(
                 ReadTask(
@@ -376,6 +397,7 @@ def get_read_tasks(self, parallelism: int) -> List[ReadTask]:
                         schema,
                         f,
                         include_paths,
+                        partitioning,
                     ),
                     meta,
                 )
@@ -403,6 +425,7 @@ def read_fragments(
     schema,
     serialized_fragments: List[SerializedFragment],
     include_paths: bool,
+    partitioning: Partitioning,
 ) -> Iterator["pyarrow.Table"]:
     # This import is necessary to load the tensor extension type.
     from ray.data.extensions.tensor_extension import ArrowTensorType  # noqa
@@ -421,6 +444,18 @@ def read_fragments(
     use_threads = to_batches_kwargs.pop("use_threads", False)
     batch_size = to_batches_kwargs.pop("batch_size", default_read_batch_size_rows)
     for fragment in fragments:
+        partitions = {}
+        if partitioning is not None:
+            parse = PathPartitionParser(partitioning)
+            partitions = parse(fragment.path)
+
+        # Filter out partitions that aren't in the user-specified columns list.
+        if columns is not None:
+            partitions = {
+                field_name: value
+                for field_name, value in partitions.items()
+                if field_name in columns
+            }
 
         def get_batch_iterable():
             return fragment.to_batches(
@@ -440,6 +475,9 @@ def get_batch_iterable():
             table = pa.Table.from_batches([batch], schema=schema)
             if include_paths:
                 table = table.append_column("path", [[fragment.path]] * len(table))
+            if partitions:
+                table = _add_partitions_to_table(partitions, table)
+
             # If the table is empty, drop it.
             if table.num_rows > 0:
                 if block_udf is not None:
@@ -633,3 +671,51 @@ def sample_fragments(
     sample_bar.close()
 
     return sample_infos
+
+
+def _add_partitions_to_table(
+    partitions: Dict[str, PartitionDataType], table: "pyarrow.Table"
+) -> "pyarrow.Table":
+    import pyarrow as pa
+
+    for field_name, value in partitions.items():
+        column = pa.array([value] * len(table))
+        field_index = table.schema.get_field_index(field_name)
+        if field_index != -1:
+            table = table.set_column(field_index, field_name, column)
+        else:
+            table = table.append_column(field_name, column)
+
+    return table
+
+
+def _add_partition_fields_to_schema(
+    partitioning: Partitioning,
+    schema: "pyarrow.Schema",
+    parquet_dataset: "pyarrow.dataset.Dataset",
+) -> "pyarrow.Schema":
+    """Return a new schema with partition fields added.
+
+    This function infers the partition fields from the first file path in the dataset.
+    """
+    import pyarrow as pa
+
+    # If the dataset is empty, we can't infer the partitioning.
+    if len(parquet_dataset.fragments) == 0:
+        return schema
+
+    # If the dataset isn't partitioned, we don't need to add any fields.
+    if partitioning is None:
+        return schema
+
+    first_path = parquet_dataset.fragments[0].path
+    parse = PathPartitionParser(partitioning)
+    partitions = parse(first_path)
+    for field_name in partitions:
+        if field_name in partitioning.field_types:
+            field_type = pa.from_numpy_dtype(partitioning.field_types[field_name])
+        else:
+            field_type = pa.string()
+        schema = schema.append(pa.field(field_name, field_type))
+
+    return schema

python/ray/data/datasource/partitioning.py

@@ -1,14 +1,17 @@
 import posixpath
 from dataclasses import dataclass
 from enum import Enum
-from typing import TYPE_CHECKING, Callable, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union
 
 from ray.util.annotations import DeveloperAPI, PublicAPI
 
 if TYPE_CHECKING:
     import pyarrow
 
 
+PartitionDataType = Type[Union[int, float, str, bool]]
+
+
 @DeveloperAPI
 class PartitionStyle(str, Enum):
     """Supported dataset partition styles.
@@ -82,13 +85,19 @@ class Partitioning:
     #: Required when parsing DIRECTORY partitioned paths or generating
     #: HIVE partitioned paths.
     field_names: Optional[List[str]] = None
+    #: A dictionary that maps partition key names to their desired data type. If not
+    #: provided, the data type defaults to string.
+    field_types: Optional[Dict[str, PartitionDataType]] = None
     #: Filesystem that will be used for partition path file I/O.
     filesystem: Optional["pyarrow.fs.FileSystem"] = None
 
     def __post_init__(self):
         if self.base_dir is None:
             self.base_dir = ""
 
+        if self.field_types is None:
+            self.field_types = {}
+
         self._normalized_base_dir = None
         self._resolved_filesystem = None
 
@@ -165,6 +174,7 @@ def of(
         style: PartitionStyle = PartitionStyle.HIVE,
         base_dir: Optional[str] = None,
         field_names: Optional[List[str]] = None,
+        field_types: Optional[Dict[str, PartitionDataType]] = None,
         filesystem: Optional["pyarrow.fs.FileSystem"] = None,
     ) -> "PathPartitionParser":
         """Creates a path-based partition parser using a flattened argument list.
@@ -180,12 +190,14 @@ def of(
                 partition key field names must match the order and length of partition
                 directories discovered. Partition key field names are not required to
                 exist in the dataset schema.
+            field_types: A dictionary that maps partition key names to their desired
+                data type. If not provided, the data type default to string.
             filesystem: Filesystem that will be used for partition path file I/O.
 
         Returns:
             The new path-based partition parser.
         """
-        scheme = Partitioning(style, base_dir, field_names, filesystem)
+        scheme = Partitioning(style, base_dir, field_names, field_types, filesystem)
         return PathPartitionParser(scheme)
 
     def __init__(self, partitioning: Partitioning):
@@ -226,14 +238,20 @@ def __call__(self, path: str) -> Dict[str, str]:
 
         Args:
             path: Input file path to parse.
+
         Returns:
             Dictionary mapping directory partition keys to values from the input file
             path. Returns an empty dictionary for unpartitioned files.
         """
         dir_path = self._dir_path_trim_base(path)
         if dir_path is None:
             return {}
-        return self._parser_fn(dir_path)
+        partitions: Dict[str, str] = self._parser_fn(dir_path)
+
+        for field, data_type in self._scheme.field_types.items():
+            partitions[field] = _cast_value(partitions[field], data_type)
+
+        return partitions
 
     @property
     def scheme(self) -> Partitioning:
@@ -317,6 +335,7 @@ def of(
         style: PartitionStyle = PartitionStyle.HIVE,
         base_dir: Optional[str] = None,
         field_names: Optional[List[str]] = None,
+        field_types: Optional[Dict[str, PartitionDataType]] = None,
         filesystem: Optional["pyarrow.fs.FileSystem"] = None,
     ) -> "PathPartitionFilter":
         """Creates a path-based partition filter using a flattened argument list.
@@ -358,12 +377,14 @@ def do_assert(val, msg):
                 partition key field names must match the order and length of partition
                 directories discovered. Partition key field names are not required to
                 exist in the dataset schema.
+            field_types: A dictionary that maps partition key names to their desired
+                data type. If not provided, the data type defaults to string.
             filesystem: Filesystem that will be used for partition path file I/O.
 
         Returns:
             The new path-based partition filter.
         """
-        scheme = Partitioning(style, base_dir, field_names, filesystem)
+        scheme = Partitioning(style, base_dir, field_names, field_types, filesystem)
         path_partition_parser = PathPartitionParser(scheme)
         return PathPartitionFilter(path_partition_parser, filter_fn)
 
@@ -422,3 +443,14 @@ def __call__(self, paths: List[str]) -> List[str]:
     def parser(self) -> PathPartitionParser:
         """Returns the path partition parser for this filter."""
         return self._parser
+
+
+def _cast_value(value: str, data_type: PartitionDataType) -> Any:
+    if data_type is int:
+        return int(value)
+    elif data_type is float:
+        return float(value)
+    elif data_type is bool:
+        return value.lower() == "true"
+    else:
+        return value

python/ray/data/read_api.py

@@ -598,6 +598,7 @@ def read_parquet(
     tensor_column_schema: Optional[Dict[str, Tuple[np.dtype, Tuple[int, ...]]]] = None,
     meta_provider: Optional[ParquetMetadataProvider] = None,
     partition_filter: Optional[PathPartitionFilter] = None,
+    partitioning: Optional[Partitioning] = Partitioning("hive"),
     shuffle: Union[Literal["files"], None] = None,
     include_paths: bool = False,
     file_extensions: Optional[List[str]] = None,
@@ -703,6 +704,8 @@ def read_parquet(
         partition_filter: A
             :class:`~ray.data.datasource.partitioning.PathPartitionFilter`. Use
             with a custom callback to read only selected partitions of a dataset.
+        partitioning: A :class:`~ray.data.datasource.partitioning.Partitioning` object
+            that describes how paths are organized. Defaults to HIVE partitioning.
         shuffle: If setting to "files", randomly shuffle input files order before read.
             Defaults to not shuffle with ``None``.
         arrow_parquet_args: Other parquet read options to pass to PyArrow. For the full
@@ -747,6 +750,7 @@ def read_parquet(
         schema=schema,
         meta_provider=meta_provider,
         partition_filter=partition_filter,
+        partitioning=partitioning,
         shuffle=shuffle,
         include_paths=include_paths,
         file_extensions=file_extensions,