googleapis · TrevorBergeron · Apr 25, 2025 · Apr 15, 2025 · Apr 16, 2025 · Apr 18, 2025
@@ -97,27 +97,46 @@ def from_pyarrow(self, table: pa.Table) -> ManagedArrowTable:
         mat.validate()
         return mat
 
-    def to_pyarrow_table(
+    def to_arrow(
         self,
         *,
         offsets_col: Optional[str] = None,
         geo_format: Literal["wkb", "wkt"] = "wkt",
         duration_type: Literal["int", "duration"] = "duration",
         json_type: Literal["string"] = "string",
-    ) -> pa.Table:
-        pa_table = self.data
-        if offsets_col is not None:
-            pa_table = pa_table.append_column(
-                offsets_col, pa.array(range(pa_table.num_rows), type=pa.int64())
-            )
+    ) -> tuple[pa.Schema, Iterable[pa.RecordBatch]]:
         if geo_format != "wkt":
             raise NotImplementedError(f"geo format {geo_format} not yet implemented")
-        if duration_type != "duration":
-            raise NotImplementedError(
-                f"duration as {duration_type} not yet implemented"
-            )
         assert json_type == "string"
-        return pa_table
+
+        batches = self.data.to_batches()
+        schema = self.data.schema
+        if duration_type == "int":
+            schema = _schema_durations_to_ints(schema)
+            batches = map(functools.partial(_cast_pa_batch, schema=schema), batches)
+
+        if offsets_col is not None:
+            return schema.append(pa.field(offsets_col, pa.int64())), _append_offsets(
+                batches, offsets_col
+            )
+        else:
+            return schema, batches
+
+    def to_pyarrow_table(
+        self,
+        *,
+        offsets_col: Optional[str] = None,
+        geo_format: Literal["wkb", "wkt"] = "wkt",
+        duration_type: Literal["int", "duration"] = "duration",
+        json_type: Literal["string"] = "string",
+    ) -> pa.Table:
+        schema, batches = self.to_arrow(
+            offsets_col=offsets_col,
+            geo_format=geo_format,
+            duration_type=duration_type,
+            json_type=json_type,
+        )
+        return pa.Table.from_batches(batches, schema)
 
     def to_parquet(
         self,
@@ -391,6 +410,41 @@ def _physical_type_replacements(dtype: pa.DataType) -> pa.DataType:
     return dtype
 
 
+def _append_offsets(
+    batches: Iterable[pa.RecordBatch], offsets_col_name: str
+) -> Iterable[pa.RecordBatch]:
+    offset = 0
+    for batch in batches:
+        offsets = pa.array(range(offset, offset + batch.num_rows), type=pa.int64())
+        batch_w_offsets = pa.record_batch(
+            [*batch.columns, offsets],
+            schema=batch.schema.append(pa.field(offsets_col_name, pa.int64())),
+        )
+        offset += batch.num_rows
+        yield batch_w_offsets
+
+
+@_recursive_map_types
+def _durations_to_ints(type: pa.DataType) -> pa.DataType:
+    if pa.types.is_duration(type):
+        return pa.int64()
+    return type
+
+
+def _schema_durations_to_ints(schema: pa.Schema) -> pa.Schema:
+    return pa.schema(
+        pa.field(field.name, _durations_to_ints(field.type)) for field in schema
+    )
+
+
+# TODO: Use RecordBatch.cast once min pyarrow>=16.0
+def _cast_pa_batch(batch: pa.RecordBatch, schema: pa.Schema) -> pa.RecordBatch:
+    return pa.record_batch(
+        [arr.cast(type) for arr, type in zip(batch.columns, schema.types)],
+        schema=schema,
+    )
+
+
 def _pairwise(iterable):
     do_yield = False
     a = None

@@ -142,6 +142,9 @@ def label_to_identifier(label: typing.Hashable, strict: bool = False) -> str:
         identifier = re.sub(r"[^a-zA-Z0-9_]", "", identifier)
         if not identifier:
             identifier = "id"
+        elif identifier[0].isdigit():
+            # first character must be letter or underscore
+            identifier = "_" + identifier
     return identifier
 
 

@@ -255,6 +255,7 @@ def __init__(
             session=self,
             bqclient=self._clients_provider.bqclient,
             storage_manager=self._temp_storage_manager,
+            write_client=self._clients_provider.bqstoragewriteclient,
             default_index_type=self._default_index_type,
             scan_index_uniqueness=self._strictly_ordered,
             force_total_order=self._strictly_ordered,
@@ -731,7 +732,9 @@ def read_pandas(
                   workload is such that you exhaust the BigQuery load job
                   quota and your data cannot be embedded in SQL due to size or
                   data type limitations.
-
+                * "bigquery_write":
+                  [Preview] Use the BigQuery Storage Write API. This feature
+                  is in public preview.
         Returns:
             An equivalent bigframes.pandas.(DataFrame/Series/Index) object
 
@@ -805,6 +808,10 @@ def _read_pandas(
             return self._loader.read_pandas(
                 pandas_dataframe, method="stream", api_name=api_name
             )
+        elif write_engine == "bigquery_write":
             write_engine (str): 
             write_engine (str): 
+            return self._loader.read_pandas(
+                pandas_dataframe, method="write", api_name=api_name
+            )
         else:
             raise ValueError(f"Got unexpected write_engine '{write_engine}'")
 

@@ -134,6 +134,9 @@ def __init__(
         self._bqstoragereadclient: Optional[
             google.cloud.bigquery_storage_v1.BigQueryReadClient
         ] = None
+        self._bqstoragewriteclient: Optional[
+            google.cloud.bigquery_storage_v1.BigQueryWriteClient
+        ] = None
         self._cloudfunctionsclient: Optional[
             google.cloud.functions_v2.FunctionServiceClient
         ] = None
@@ -238,6 +241,34 @@ def bqstoragereadclient(self):
 
         return self._bqstoragereadclient
 
+    @property
+    def bqstoragewriteclient(self):
+        if not self._bqstoragewriteclient:
+            bqstorage_options = None
+            if "bqstoragewriteclient" in self._client_endpoints_override:
+                bqstorage_options = google.api_core.client_options.ClientOptions(
+                    api_endpoint=self._client_endpoints_override["bqstoragewriteclient"]
+                )
+            elif self._use_regional_endpoints:
+                bqstorage_options = google.api_core.client_options.ClientOptions(
+                    api_endpoint=_BIGQUERYSTORAGE_REGIONAL_ENDPOINT.format(
+                        location=self._location
+                    )
+                )
+
+            bqstorage_info = google.api_core.gapic_v1.client_info.ClientInfo(
+                user_agent=self._application_name
+            )
+            self._bqstoragewriteclient = (
+                google.cloud.bigquery_storage_v1.BigQueryWriteClient(
+                    client_info=bqstorage_info,
+                    client_options=bqstorage_options,
+                    credentials=self._credentials,
+                )
+            )
+
+        return self._bqstoragewriteclient
+
     @property
     def cloudfunctionsclient(self):
         if not self._cloudfunctionsclient:

@@ -23,6 +23,7 @@
 import typing
 from typing import (
     Dict,
+    Generator,
     Hashable,
     IO,
     Iterable,
@@ -36,12 +37,13 @@
 import bigframes_vendored.constants as constants
 import bigframes_vendored.pandas.io.gbq as third_party_pandas_gbq
 import google.api_core.exceptions
+from google.cloud import bigquery_storage_v1
 import google.cloud.bigquery as bigquery
-import google.cloud.bigquery.table
+from google.cloud.bigquery_storage_v1 import types as bq_storage_types
 import pandas
 import pyarrow as pa
 
-from bigframes.core import local_data, utils
+from bigframes.core import guid, local_data, utils
 import bigframes.core as core
 import bigframes.core.blocks as blocks
 import bigframes.core.schema as schemata
@@ -142,13 +144,15 @@ def __init__(
         self,
         session: bigframes.session.Session,
         bqclient: bigquery.Client,
+        write_client: bigquery_storage_v1.BigQueryWriteClient,
         storage_manager: bigframes.session.temporary_storage.TemporaryStorageManager,
         default_index_type: bigframes.enums.DefaultIndexKind,
         scan_index_uniqueness: bool,
         force_total_order: bool,
         metrics: Optional[bigframes.session.metrics.ExecutionMetrics] = None,
     ):
         self._bqclient = bqclient
+        self._write_client = write_client
         self._storage_manager = storage_manager
         self._default_index_type = default_index_type
         self._scan_index_uniqueness = scan_index_uniqueness
@@ -165,7 +169,7 @@ def __init__(
     def read_pandas(
         self,
         pandas_dataframe: pandas.DataFrame,
-        method: Literal["load", "stream"],
+        method: Literal["load", "stream", "write"],
         api_name: str,
     ) -> dataframe.DataFrame:
         # TODO: Push this into from_pandas, along with index flag
@@ -183,6 +187,8 @@ def read_pandas(
             array_value = self.load_data(managed_data, api_name=api_name)
         elif method == "stream":
             array_value = self.stream_data(managed_data)
+        elif method == "write":
+            array_value = self.write_data(managed_data)
         else:
             raise ValueError(f"Unsupported read method {method}")
 
@@ -198,7 +204,7 @@ def load_data(
         self, data: local_data.ManagedArrowTable, api_name: Optional[str] = None
     ) -> core.ArrayValue:
         """Load managed data into bigquery"""
-        ordering_col = "bf_load_job_offsets"
+        ordering_col = guid.generate_guid("load_offsets_")
 
         # JSON support incomplete
         for item in data.schema.items:
@@ -244,7 +250,7 @@ def load_data(
 
     def stream_data(self, data: local_data.ManagedArrowTable) -> core.ArrayValue:
         """Load managed data into bigquery"""
-        ordering_col = "bf_stream_job_offsets"
+        ordering_col = guid.generate_guid("stream_offsets_")
         schema_w_offsets = data.schema.append(
             schemata.SchemaItem(ordering_col, bigframes.dtypes.INT_DTYPE)
         )
@@ -277,6 +283,61 @@ def stream_data(self, data: local_data.ManagedArrowTable) -> core.ArrayValue:
             n_rows=data.data.num_rows,
         ).drop_columns([ordering_col])
 
+    def write_data(self, data: local_data.ManagedArrowTable) -> core.ArrayValue:
+        """Load managed data into bigquery"""
+        ordering_col = guid.generate_guid("stream_offsets_")
+        schema_w_offsets = data.schema.append(
+            schemata.SchemaItem(ordering_col, bigframes.dtypes.INT_DTYPE)
+        )
+        bq_schema = schema_w_offsets.to_bigquery(_STREAM_JOB_TYPE_OVERRIDES)
+        bq_table_ref = self._storage_manager.create_temp_table(
+            bq_schema, [ordering_col]
+        )
+
+        requested_stream = bq_storage_types.stream.WriteStream()
+        requested_stream.type_ = bq_storage_types.stream.WriteStream.Type.COMMITTED  # type: ignore
+
+        stream_request = bq_storage_types.CreateWriteStreamRequest(
+            parent=bq_table_ref.to_bqstorage(), write_stream=requested_stream
+        )
+        stream = self._write_client.create_write_stream(request=stream_request)
+
+        def request_gen() -> Generator[bq_storage_types.AppendRowsRequest, None, None]:
+            schema, batches = data.to_arrow(
+                offsets_col=ordering_col, duration_type="int"
+            )
+            offset = 0
+            for batch in batches:
+                request = bq_storage_types.AppendRowsRequest(
+                    write_stream=stream.name, offset=offset
+                )
+                request.arrow_rows.writer_schema.serialized_schema = (
+                    schema.serialize().to_pybytes()
+                )
+                request.arrow_rows.rows.serialized_record_batch = (
+                    batch.serialize().to_pybytes()
+                )
+                offset += batch.num_rows
+                yield request
+
+        for response in self._write_client.append_rows(requests=request_gen()):
+            if response.row_errors:
+                raise ValueError(
+                    f"Problem loading at least one row from DataFrame: {response.row_errors}. {constants.FEEDBACK_LINK}"
+                )
+        # This step isn't strictly necessary in COMMITTED mode, but avoids max active stream limits
+        response = self._write_client.finalize_write_stream(name=stream.name)
+        assert response.row_count == data.data.num_rows
+
+        destination_table = self._bqclient.get_table(bq_table_ref)
+        return core.ArrayValue.from_table(
+            table=destination_table,
+            schema=schema_w_offsets,
+            session=self._session,
+            offsets_col=ordering_col,
+            n_rows=data.data.num_rows,
+        ).drop_columns([ordering_col])
+
     def _start_generic_job(self, job: formatting_helpers.GenericJob):
         if bigframes.options.display.progress_bar is not None:
             formatting_helpers.wait_for_job(

@@ -42,6 +42,8 @@
     "google-cloud-bigtable >=2.24.0",
     "google-cloud-pubsub >=2.21.4",
     "google-cloud-bigquery[bqstorage,pandas] >=3.31.0",
+    # 2.30 needed for arrow support.
+    "google-cloud-bigquery-storage >= 2.30.0, < 3.0.0",
     "google-cloud-functions >=1.12.0",
     "google-cloud-bigquery-connection >=1.12.0",
     "google-cloud-iam >=2.12.1",

@@ -83,6 +83,7 @@ def test_df_construct_pandas_default(scalars_dfs):
         ("bigquery_inline"),
         ("bigquery_load"),
         ("bigquery_streaming"),
+        ("bigquery_write"),
     ],
 )
 def test_read_pandas_all_nice_types(
@@ -1772,7 +1773,7 @@ def test_len(scalars_dfs):
 )
 @pytest.mark.parametrize(
     "write_engine",
-    ["bigquery_load", "bigquery_streaming"],
+    ["bigquery_load", "bigquery_streaming", "bigquery_write"],
 )
 def test_df_len_local(session, n_rows, write_engine):
     assert (