fix: Prefer remote schema instead of throwing on materialize conflicts (#1644)

Author: TrevorBergeron

bigframes/session/executor.py

@@ -16,6 +16,7 @@
 
 import abc
 import dataclasses
+import itertools
 from typing import Callable, Iterator, Literal, Mapping, Optional, Sequence, Union
 
 from google.cloud import bigquery
@@ -37,10 +38,16 @@ def to_arrow_table(self) -> pyarrow.Table:
         # Need to provide schema if no result rows, as arrow can't infer
         # If ther are rows, it is safest to infer schema from batches.
         # Any discrepencies between predicted schema and actual schema will produce errors.
-        return pyarrow.Table.from_batches(
-            self.arrow_batches(),
-            self.schema.to_pyarrow() if not self.total_rows else None,
-        )
+        batches = iter(self.arrow_batches())
+        peek_it = itertools.islice(batches, 0, 1)
+        peek_value = list(peek_it)
+        # TODO: Enforce our internal schema on the table for consistency
+        if len(peek_value) > 0:
+            return pyarrow.Table.from_batches(
+                itertools.chain(peek_value, batches),  # reconstruct
+            )
+        else:
+            return self.schema.to_pyarrow().empty_table()
 
 
 class Executor(abc.ABC):

tests/system/small/test_dataframe.py

@@ -5283,6 +5283,16 @@ def test_to_gbq_and_create_dataset(session, scalars_df_index, dataset_id_not_cre
     assert not loaded_scalars_df_index.empty
 
 
+def test_read_gbq_to_pandas_no_exec(unordered_session: bigframes.Session):
+    metrics = unordered_session._metrics
+    execs_pre = metrics.execution_count
+    df = unordered_session.read_gbq("bigquery-public-data.ml_datasets.penguins")
+    df.to_pandas()
+    execs_post = metrics.execution_count
+    assert df.shape == (344, 7)
+    assert execs_pre == execs_post
+
+
 def test_to_gbq_table_labels(scalars_df_index):
     destination_table = "bigframes-dev.bigframes_tests_sys.table_labels"
     result_table = scalars_df_index.to_gbq(