googleapis · chelsea-lin · May 14, 2025
@@ -17,6 +17,7 @@
 import dataclasses
 import typing
 
+from google.cloud import bigquery
 import pyarrow as pa
 import sqlglot as sg
 import sqlglot.dialects.bigquery
@@ -104,6 +105,24 @@ def from_pyarrow(
         )
         return cls(expr=sg.select(sge.Star()).from_(expr), uid_gen=uid_gen)
 
+    @classmethod
+    def from_query_string(
+        cls,
+        query_string: str,
+    ) -> SQLGlotIR:
+        """Builds SQLGlot expression from a query string"""
+        uid_gen: guid.SequentialUIDGenerator = guid.SequentialUIDGenerator()
+        cte_name = sge.to_identifier(
+            next(uid_gen.get_uid_stream("bfcte_")), quoted=cls.quoted
+        )
+        cte = sge.CTE(
+            this=query_string,
+            alias=cte_name,
+        )
+        select_expr = sge.Select().select(sge.Star()).from_(sge.Table(this=cte_name))
+        select_expr.set("with", sge.With(expressions=[cte]))
+        return cls(expr=select_expr, uid_gen=uid_gen)
+
     def select(
         self,
         selected_cols: tuple[tuple[str, sge.Expression], ...],
@@ -133,6 +152,36 @@ def project(
         select_expr = self.expr.select(*projected_cols_expr, append=True)
         return SQLGlotIR(expr=select_expr)
 
+    def insert(
+        self,
+        destination: bigquery.TableReference,
+    ) -> str:
+        return sge.insert(self.expr.subquery(), _table(destination)).sql(
+            dialect=self.dialect, pretty=self.pretty
+        )
+
+    def replace(
+        self,
+        destination: bigquery.TableReference,
+    ) -> str:
+        # Workaround for SQLGlot breaking change:
+        # https://github.com/tobymao/sqlglot/pull/4495
+        whens_expr = [
+            sge.When(matched=False, source=True, then=sge.Delete()),
+            sge.When(matched=False, then=sge.Insert(this=sge.Var(this="ROW"))),
+        ]
+        whens_str = "\n".join(
+            when_expr.sql(dialect=self.dialect, pretty=self.pretty)
+            for when_expr in whens_expr
+        )
+
+        merge_str = sge.Merge(
+            this=_table(destination),
+            using=self.expr.subquery(),
+            on=_literal(False, dtypes.BOOL_DTYPE),
+        ).sql(dialect=self.dialect, pretty=self.pretty)
+        return f"{merge_str}\n{whens_str}"
+
     def _encapsulate_as_cte(
         self,
     ) -> sge.Select:
@@ -190,3 +239,11 @@ def _literal(value: typing.Any, dtype: dtypes.Dtype) -> sge.Expression:
 
 def _cast(arg: typing.Any, to: str) -> sge.Cast:
     return sge.Cast(this=arg, to=to)
+
+
+def _table(table: bigquery.TableReference) -> sge.Table:
+    return sge.Table(
+        this=sg.to_identifier(table.table_id, quoted=True),
+        db=sg.to_identifier(table.dataset_id, quoted=True),
+        catalog=sg.to_identifier(table.project, quoted=True),
+    )
@@ -29,9 +29,11 @@
 
 import bigframes.core
 from bigframes.core import compile, rewrite
+import bigframes.core.compile.sqlglot.sqlglot_ir as sqlglot_ir
 import bigframes.core.guid
 import bigframes.core.nodes as nodes
 import bigframes.core.ordering as order
+import bigframes.core.schema as schemata
 import bigframes.core.tree_properties as tree_properties
 import bigframes.dtypes
 import bigframes.exceptions as bfe
@@ -206,17 +208,45 @@ def export_gbq(
         if bigframes.options.compute.enable_multi_query_execution:
             self._simplify_with_caching(array_value)
 
-        dispositions = {
-            "fail": bigquery.WriteDisposition.WRITE_EMPTY,
-            "replace": bigquery.WriteDisposition.WRITE_TRUNCATE,
-            "append": bigquery.WriteDisposition.WRITE_APPEND,
-        }
+        table_exists = True
+        try:
+            table = self.bqclient.get_table(destination)
+            if if_exists == "fail":
+                raise ValueError(f"Table already exists: {destination.__str__()}")
+        except google.api_core.exceptions.NotFound:
+            table_exists = False
+
+        if len(cluster_cols) != 0:
+            if table_exists and table.clustering_fields != cluster_cols:
+                raise ValueError(
+                    "Table clustering fields cannot be changed after the table has "
+                    f"been created. Existing clustering fields: {table.clustering_fields}"
+                )
+
         sql = self.to_sql(array_value, ordered=False)
-        job_config = bigquery.QueryJobConfig(
-            write_disposition=dispositions[if_exists],
-            destination=destination,
-            clustering_fields=cluster_cols if cluster_cols else None,
-        )
+        if table_exists and _if_schama_match(table.schema, array_value.schema):
+            # b/409086472: Uses DML for table appends and replacements to avoid
+            # BigQuery `RATE_LIMIT_EXCEEDED` errors, as per quota limits:
+            # https://cloud.google.com/bigquery/quotas#standard_tables
+            job_config = bigquery.QueryJobConfig()
+            ir = sqlglot_ir.SQLGlotIR.from_query_string(sql)
+            if if_exists == "append":
+                sql = ir.insert(destination)
+            else:  # for "replace"
+                assert if_exists == "replace"
+                sql = ir.replace(destination)
+        else:
+            dispositions = {
+                "fail": bigquery.WriteDisposition.WRITE_EMPTY,
+                "replace": bigquery.WriteDisposition.WRITE_TRUNCATE,
+                "append": bigquery.WriteDisposition.WRITE_APPEND,
+            }
+            job_config = bigquery.QueryJobConfig(
+                write_disposition=dispositions[if_exists],
+                destination=destination,
+                clustering_fields=cluster_cols if cluster_cols else None,
+            )
+
         # TODO(swast): plumb through the api_name of the user-facing api that
         # caused this query.
         _, query_job = self._run_execute_query(
@@ -572,6 +602,21 @@ def _execute_plan(
         )
 
 
+def _if_schama_match(
+    table_schema: Tuple[bigquery.SchemaField, ...], schema: schemata.ArraySchema
+) -> bool:
+    if len(table_schema) != len(schema.items):
+        return False
+    for field in table_schema:
+        if field.name not in schema.names:
+            return False
+        if bigframes.dtypes.convert_schema_field(field)[1] != schema.get_type(
+            field.name
+        ):
+            return False
+    return True
+
+
 def _sanitize(
     schema: Tuple[bigquery.SchemaField, ...]
 ) -> Tuple[bigquery.SchemaField, ...]:

@@ -458,7 +458,7 @@ def test_to_csv_tabs(
     [True, False],
 )
 @pytest.mark.skipif(pandas_gbq is None, reason="required by pd.read_gbq")
-def test_to_gbq_index(scalars_dfs, dataset_id, index):
+def test_to_gbq_w_index(scalars_dfs, dataset_id, index):
     """Test the `to_gbq` API with the `index` parameter."""
     scalars_df, scalars_pandas_df = scalars_dfs
     destination_table = f"{dataset_id}.test_index_df_to_gbq_{index}"
@@ -485,48 +485,67 @@ def test_to_gbq_index(scalars_dfs, dataset_id, index):
     pd.testing.assert_frame_equal(df_out, expected, check_index_type=False)
 
 
-@pytest.mark.parametrize(
-    ("if_exists", "expected_index"),
-    [
-        pytest.param("replace", 1),
-        pytest.param("append", 2),
-        pytest.param(
-            "fail",
-            0,
-            marks=pytest.mark.xfail(
-                raises=google.api_core.exceptions.Conflict,
-            ),
-        ),
-        pytest.param(
-            "unknown",
-            0,
-            marks=pytest.mark.xfail(
-                raises=ValueError,
-            ),
-        ),
-    ],
-)
-@pytest.mark.skipif(pandas_gbq is None, reason="required by pd.read_gbq")
-def test_to_gbq_if_exists(
-    scalars_df_default_index,
-    scalars_pandas_df_default_index,
-    dataset_id,
-    if_exists,
-    expected_index,
-):
-    """Test the `to_gbq` API with the `if_exists` parameter."""
-    destination_table = f"{dataset_id}.test_to_gbq_if_exists_{if_exists}"
+def test_to_gbq_if_exists_is_fail(scalars_dfs, dataset_id):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_fails"
+    scalars_df.to_gbq(destination_table)
 
-    scalars_df_default_index.to_gbq(destination_table)
-    scalars_df_default_index.to_gbq(destination_table, if_exists=if_exists)
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == len(scalars_pandas_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns)
 
-    gcs_df = pd.read_gbq(destination_table)
-    assert len(gcs_df.index) == expected_index * len(
-        scalars_pandas_df_default_index.index
-    )
-    pd.testing.assert_index_equal(
-        gcs_df.columns, scalars_pandas_df_default_index.columns
-    )
+    # Test default value is "fails"
+    with pytest.raises(ValueError, match="Table already exists"):
+        scalars_df.to_gbq(destination_table)
+
+    with pytest.raises(ValueError, match="Table already exists"):
+        scalars_df.to_gbq(destination_table, if_exists="fail")
+
+
+def test_to_gbq_if_exists_is_replace(scalars_dfs, dataset_id):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_replace"
+    scalars_df.to_gbq(destination_table)
+
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == len(scalars_pandas_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns)
+
+    # When replacing a table with same schema
+    scalars_df.to_gbq(destination_table, if_exists="replace")
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == len(scalars_pandas_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns)
+
+    # When replacing a table with different schema
+    partitial_scalars_df = scalars_df.drop(columns=["string_col"])
+    partitial_scalars_df.to_gbq(destination_table, if_exists="replace")
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == len(partitial_scalars_df)
+    pd.testing.assert_index_equal(gcs_df.columns, partitial_scalars_df.columns)
+
+
+def test_to_gbq_if_exists_is_append(scalars_dfs, dataset_id):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    destination_table = f"{dataset_id}.test_to_gbq_if_exists_is_append"
+    scalars_df.to_gbq(destination_table)
+
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == len(scalars_pandas_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns)
+
+    # When appending to a table with same schema
+    scalars_df.to_gbq(destination_table, if_exists="append")
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == 2 * len(scalars_pandas_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_pandas_df.columns)
+
+    # When appending to a table with different schema
+    partitial_scalars_df = scalars_df.drop(columns=["string_col"])
+    partitial_scalars_df.to_gbq(destination_table, if_exists="append")
+    gcs_df = pd.read_gbq(destination_table, index_col="rowindex")
+    assert len(gcs_df) == 3 * len(partitial_scalars_df)
+    pd.testing.assert_index_equal(gcs_df.columns, scalars_df.columns)
 
 
 def test_to_gbq_w_duplicate_column_names(
@@ -773,6 +792,27 @@ def test_to_gbq_w_clustering_no_destination(
     assert table.expires is not None
 
 
+def test_to_gbq_w_clustering_existing_table(
+    scalars_df_default_index,
+    dataset_id,
+    bigquery_client,
+):
+    destination_table = f"{dataset_id}.test_to_gbq_w_clustering_existing_table"
+    scalars_df_default_index.to_gbq(destination_table)
+
+    table = bigquery_client.get_table(destination_table)
+    assert table.clustering_fields is None
+    assert table.expires is None
+
+    with pytest.raises(ValueError, match="Table clustering fields cannot be changed"):
+        clustering_columns = ["int64_col"]
+        scalars_df_default_index.to_gbq(
+            destination_table,
+            if_exists="replace",
+            clustering_columns=clustering_columns,
+        )
+
+
 def test_to_gbq_w_invalid_destination_table(scalars_df_index):
     with pytest.raises(ValueError):
         scalars_df_index.to_gbq("table_id")

@@ -49,3 +49,8 @@ def test_dataframe_to_pandas(mock_df, api_name, kwargs):
     mock_df.to_pandas.assert_called_once_with(
         allow_large_results=kwargs["allow_large_results"]
     )
+
+
+def test_to_gbq_if_exists_invalid(mock_df):
+    with pytest.raises(ValueError, match="Got invalid value 'invalid' for if_exists."):
+        mock_df.to_gbq("a.b.c", if_exists="invalid")