fix: support ARRAY data type when loading from DataFrame with Parquet

google/cloud/bigquery/_helpers.py

@@ -107,6 +107,9 @@ def verify_version(self):
 class PyarrowVersions:
     """Version comparisons for pyarrow package."""
 
+    # https://github.com/googleapis/python-bigquery/issues/781#issuecomment-883497414
+    _PYARROW_BAD_VERSIONS = frozenset([packaging.version.Version("2.0.0")])
+
     def __init__(self):
         self._installed_version = None
 
@@ -126,6 +129,14 @@ def installed_version(self) -> packaging.version.Version:
 
         return self._installed_version
 
+    @property
+    def is_bad_version(self) -> bool:
+        return self.installed_version in self._PYARROW_BAD_VERSIONS
+
+    @property
+    def use_compliant_nested_type(self) -> bool:
+        return self.installed_version.major >= 4
+
     def try_import(self, raise_if_error: bool = False) -> Any:
         """Verify that a recent enough version of pyarrow extra is
         installed.

google/cloud/bigquery/_pandas_helpers.py

@@ -79,8 +79,8 @@ def _to_wkb(v):
 _PANDAS_DTYPE_TO_BQ = {
     "bool": "BOOLEAN",
     "datetime64[ns, UTC]": "TIMESTAMP",
-    # BigQuery does not support uploading DATETIME values from Parquet files.
-    # See: https://github.com/googleapis/google-cloud-python/issues/9996
+    # TODO: Update to DATETIME in V3
+    # https://github.com/googleapis/python-bigquery/issues/985
     "datetime64[ns]": "TIMESTAMP",
     "float32": "FLOAT",
     "float64": "FLOAT",
@@ -396,7 +396,7 @@ def dataframe_to_bq_schema(dataframe, bq_schema):
     # column, but it was not found.
     if bq_schema_unused:
         raise ValueError(
-            u"bq_schema contains fields not present in dataframe: {}".format(
+            "bq_schema contains fields not present in dataframe: {}".format(
                 bq_schema_unused
             )
         )
@@ -405,7 +405,7 @@ def dataframe_to_bq_schema(dataframe, bq_schema):
     # pyarrow, if available.
     if unknown_type_fields:
         if not pyarrow:
-            msg = u"Could not determine the type of columns: {}".format(
+            msg = "Could not determine the type of columns: {}".format(
                 ", ".join(field.name for field in unknown_type_fields)
             )
             warnings.warn(msg)
@@ -444,7 +444,14 @@ def augment_schema(dataframe, current_bq_schema):
             continue
 
         arrow_table = pyarrow.array(dataframe[field.name])
-        detected_type = ARROW_SCALAR_IDS_TO_BQ.get(arrow_table.type.id)
+
+        if pyarrow.types.is_list(arrow_table.type):
+            # `pyarrow.ListType`
+            detected_mode = "REPEATED"
+            detected_type = ARROW_SCALAR_IDS_TO_BQ.get(arrow_table.values.type.id)
+        else:
+            detected_mode = field.mode
+            detected_type = ARROW_SCALAR_IDS_TO_BQ.get(arrow_table.type.id)
 
         if detected_type is None:
             unknown_type_fields.append(field)
@@ -453,15 +460,15 @@ def augment_schema(dataframe, current_bq_schema):
         new_field = schema.SchemaField(
             name=field.name,
             field_type=detected_type,
-            mode=field.mode,
+            mode=detected_mode,
             description=field.description,
             fields=field.fields,
         )
         augmented_schema.append(new_field)
 
     if unknown_type_fields:
         warnings.warn(
-            u"Pyarrow could not determine the type of columns: {}.".format(
+            "Pyarrow could not determine the type of columns: {}.".format(
                 ", ".join(field.name for field in unknown_type_fields)
             )
         )
@@ -500,7 +507,7 @@ def dataframe_to_arrow(dataframe, bq_schema):
     extra_fields = bq_field_names - column_and_index_names
     if extra_fields:
         raise ValueError(
-            u"bq_schema contains fields not present in dataframe: {}".format(
+            "bq_schema contains fields not present in dataframe: {}".format(
                 extra_fields
             )
         )
@@ -510,7 +517,7 @@ def dataframe_to_arrow(dataframe, bq_schema):
     missing_fields = column_names - bq_field_names
     if missing_fields:
         raise ValueError(
-            u"bq_schema is missing fields from dataframe: {}".format(missing_fields)
+            "bq_schema is missing fields from dataframe: {}".format(missing_fields)
         )
 
     arrow_arrays = []
@@ -530,7 +537,13 @@ def dataframe_to_arrow(dataframe, bq_schema):
     return pyarrow.Table.from_arrays(arrow_arrays, names=arrow_names)
 
 
-def dataframe_to_parquet(dataframe, bq_schema, filepath, parquet_compression="SNAPPY"):
+def dataframe_to_parquet(
+    dataframe,
+    bq_schema,
+    filepath,
+    parquet_compression="SNAPPY",
+    parquet_use_compliant_nested_type=True,
+):
     """Write dataframe as a Parquet file, according to the desired BQ schema.
 
     This function requires the :mod:`pyarrow` package. Arrow is used as an
@@ -551,14 +564,29 @@ def dataframe_to_parquet(dataframe, bq_schema, filepath, parquet_compression="SN
             The compression codec to use by the the ``pyarrow.parquet.write_table``
             serializing method. Defaults to "SNAPPY".
             https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
+        parquet_use_compliant_nested_type (bool):
+            Whether the ``pyarrow.parquet.write_table`` serializing method should write
+            compliant Parquet nested type (lists). Defaults to ``True``.
+            https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#nested-types
+            https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
+
+            This argument is ignored for ``pyarrow`` versions earlier than ``4.0.0``.
     """
     pyarrow = _helpers.PYARROW_VERSIONS.try_import(raise_if_error=True)
 
     import pyarrow.parquet
 
+    kwargs = (
+        {"use_compliant_nested_type": parquet_use_compliant_nested_type}
+        if _helpers.PYARROW_VERSIONS.use_compliant_nested_type
+        else {}
+    )
+
     bq_schema = schema._to_schema_fields(bq_schema)
     arrow_table = dataframe_to_arrow(dataframe, bq_schema)
-    pyarrow.parquet.write_table(arrow_table, filepath, compression=parquet_compression)
+    pyarrow.parquet.write_table(
+        arrow_table, filepath, compression=parquet_compression, **kwargs,
+    )
 
 
 def _row_iterator_page_to_arrow(page, column_names, arrow_types):

google/cloud/bigquery/client.py

@@ -27,19 +27,11 @@
 import json
 import math
 import os
-import packaging.version
 import tempfile
 from typing import Any, BinaryIO, Dict, Iterable, Optional, Sequence, Tuple, Union
 import uuid
 import warnings
 
-try:
-    import pyarrow
-
-    _PYARROW_VERSION = packaging.version.parse(pyarrow.__version__)
-except ImportError:  # pragma: NO COVER
-    pyarrow = None
-
 from google import resumable_media  # type: ignore
 from google.resumable_media.requests import MultipartUpload
 from google.resumable_media.requests import ResumableUpload
@@ -103,6 +95,10 @@
 from google.cloud.bigquery.table import TableListItem
 from google.cloud.bigquery.table import TableReference
 from google.cloud.bigquery.table import RowIterator
+from google.cloud.bigquery.format_options import ParquetOptions
+from google.cloud.bigquery import _helpers
+
+pyarrow = _helpers.PYARROW_VERSIONS.try_import()
 
 
 _DEFAULT_CHUNKSIZE = 100 * 1024 * 1024  # 100 MB
@@ -128,8 +124,6 @@
 # https://github.com/googleapis/python-bigquery/issues/438
 _MIN_GET_QUERY_RESULTS_TIMEOUT = 120
 
-# https://github.com/googleapis/python-bigquery/issues/781#issuecomment-883497414
-_PYARROW_BAD_VERSIONS = frozenset([packaging.version.Version("2.0.0")])
 
 TIMEOUT_HEADER = "X-Server-Timeout"
 
@@ -2469,10 +2463,10 @@ def load_table_from_dataframe(
             They are supported when using the PARQUET source format, but
             due to the way they are encoded in the ``parquet`` file,
             a mismatch with the existing table schema can occur, so
-            100% compatibility cannot be guaranteed for REPEATED fields when
+            REPEATED fields are not properly supported when using ``pyarrow<4.0.0``
             using the parquet format.
 
-            https://github.com/googleapis/python-bigquery/issues/17
+            https://github.com/googleapis/python-bigquery/issues/19
 
         Args:
             dataframe (pandas.DataFrame):
@@ -2519,18 +2513,18 @@ def load_table_from_dataframe(
                 :attr:`~google.cloud.bigquery.job.SourceFormat.PARQUET` are
                 supported.
             parquet_compression (Optional[str]):
-                 [Beta] The compression method to use if intermittently
-                 serializing ``dataframe`` to a parquet file.
-
-                 The argument is directly passed as the ``compression``
-                 argument to the underlying ``pyarrow.parquet.write_table()``
-                 method (the default value "snappy" gets converted to uppercase).
-                 https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
-
-                 If the job config schema is missing, the argument is directly
-                 passed as the ``compression`` argument to the underlying
-                 ``DataFrame.to_parquet()`` method.
-                 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet
+                [Beta] The compression method to use if intermittently
+                serializing ``dataframe`` to a parquet file.
+
+                The argument is directly passed as the ``compression``
+                argument to the underlying ``pyarrow.parquet.write_table()``
+                method (the default value "snappy" gets converted to uppercase).
+                https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
+
+                If the job config schema is missing, the argument is directly
+                passed as the ``compression`` argument to the underlying
+                ``DataFrame.to_parquet()`` method.
+                https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet
             timeout (Optional[float]):
                 The number of seconds to wait for the underlying HTTP transport
                 before using ``retry``.
@@ -2562,6 +2556,16 @@ def load_table_from_dataframe(
         if job_config.source_format is None:
             # default value
             job_config.source_format = job.SourceFormat.PARQUET
+
+        if (
+            job_config.source_format == job.SourceFormat.PARQUET
+            and job_config.parquet_options is None
+        ):
+            parquet_options = ParquetOptions()
+            # default value
+            parquet_options.enable_list_inference = True
+            job_config.parquet_options = parquet_options
+
         if job_config.source_format not in supported_formats:
             raise ValueError(
                 "Got unexpected source_format: '{}'. Currently, only PARQUET and CSV are supported".format(
@@ -2628,12 +2632,12 @@ def load_table_from_dataframe(
         try:
 
             if job_config.source_format == job.SourceFormat.PARQUET:
-                if _PYARROW_VERSION in _PYARROW_BAD_VERSIONS:
+                if _helpers.PYARROW_VERSIONS.is_bad_version:
                     msg = (
                         "Loading dataframe data in PARQUET format with pyarrow "
-                        f"{_PYARROW_VERSION} can result in data corruption. It is "
-                        "therefore *strongly* advised to use a different pyarrow "
-                        "version or a different source format. "
+                        f"{_helpers.PYARROW_VERSIONS.installed_version} can result in data "
+                        "corruption. It is therefore *strongly* advised to use a "
+                        "different pyarrow version or a different source format. "
                         "See: https://github.com/googleapis/python-bigquery/issues/781"
                     )
                     warnings.warn(msg, category=RuntimeWarning)
@@ -2647,9 +2651,19 @@ def load_table_from_dataframe(
                         job_config.schema,
                         tmppath,
                         parquet_compression=parquet_compression,
+                        parquet_use_compliant_nested_type=True,
                     )
                 else:
-                    dataframe.to_parquet(tmppath, compression=parquet_compression)
+                    dataframe.to_parquet(
+                        tmppath,
+                        engine="pyarrow",
+                        compression=parquet_compression,
+                        **(
+                            {"use_compliant_nested_type": True}
+                            if _helpers.PYARROW_VERSIONS.use_compliant_nested_type
+                            else {}
+                        ),
+                    )
 
             else: