google
diff --git a/‎bigframes/core/compile/ibis_compiler/scalar_op_registry.py‎
Lines changed: 1 addition & 1 deletion b/‎bigframes/core/compile/ibis_compiler/scalar_op_registry.py‎
Lines changed: 1 addition & 1 deletion
diff --git a/‎bigframes/core/compile/sqlglot/expressions/comparison_ops.py‎
Lines changed: 1 addition & 6 deletions b/‎bigframes/core/compile/sqlglot/expressions/comparison_ops.py‎
Lines changed: 1 addition & 6 deletions
diff --git a/‎bigframes/dtypes.py‎
Lines changed: 5 additions & 2 deletions b/‎bigframes/dtypes.py‎
Lines changed: 5 additions & 2 deletions
diff --git a/‎scripts/tpch_result_verify.py‎
Lines changed: 0 additions & 128 deletions b/‎scripts/tpch_result_verify.py‎
Lines changed: 0 additions & 128 deletions
diff --git a/‎tests/system/large/test_tpch.py‎
Lines changed: 101 additions & 0 deletions b/‎tests/system/large/test_tpch.py‎
Lines changed: 101 additions & 0 deletions
@@ -962,7 +962,7 @@ def isin_op_impl(x: ibis_types.Value, op: ops.IsInOp):
                 # to actually cast it, as that could be lossy (eg float -> int)
                 item_inferred_type = ibis_types.literal(item).type()
                 if (
-                    x.type() == item_inferred_type
+                    x.type().name == item_inferred_type.name
                     or x.type().is_numeric()
                     and item_inferred_type.is_numeric()
                 ):
 
@@ -33,16 +33,11 @@
 @register_unary_op(ops.IsInOp, pass_op=True)
 def _(expr: TypedExpr, op: ops.IsInOp) -> sge.Expression:
     values = []
-    is_numeric_expr = dtypes.is_numeric(expr.dtype, include_bool=False)
     for value in op.values:
         if _is_null(value):
             continue
         dtype = dtypes.bigframes_type(type(value))
-        if (
-            expr.dtype == dtype
-            or is_numeric_expr
-            and dtypes.is_numeric(dtype, include_bool=False)
-        ):
+        if dtypes.can_compare(expr.dtype, dtype):
             values.append(sge.convert(value))
 
     if op.match_nulls:
 
@@ -370,8 +370,11 @@ def is_comparable(type_: ExpressionType) -> bool:
 
 
 def can_compare(type1: ExpressionType, type2: ExpressionType) -> bool:
-    coerced_type = coerce_to_common(type1, type2)
-    return is_comparable(coerced_type)
+    try:
+        coerced_type = coerce_to_common(type1, type2)
+        return is_comparable(coerced_type)
+    except TypeError:
+        return False
 
 
 def get_struct_fields(type_: ExpressionType) -> dict[str, Dtype]:
 
@@ -0,0 +1,101 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import re
+
+from google.cloud import bigquery
+import pandas as pd
+import pytest
+
+TPCH_PATH = "third_party/bigframes_vendored/tpch"
+PROJECT_ID = "bigframes-dev-perf"
+DATASET_ID = "tpch_0001g"
+DATASET = {
+    "line_item_ds": f"{PROJECT_ID}.{DATASET_ID}.LINEITEM",
+    "region_ds": f"{PROJECT_ID}.{DATASET_ID}.REGION",
+    "nation_ds": f"{PROJECT_ID}.{DATASET_ID}.NATION",
+    "supplier_ds": f"{PROJECT_ID}.{DATASET_ID}.SUPPLIER",
+    "part_ds": f"{PROJECT_ID}.{DATASET_ID}.PART",
+    "part_supp_ds": f"{PROJECT_ID}.{DATASET_ID}.PARTSUPP",
+    "customer_ds": f"{PROJECT_ID}.{DATASET_ID}.CUSTOMER",
+    "orders_ds": f"{PROJECT_ID}.{DATASET_ID}.ORDERS",
+}
+
+
+def _execute_sql_query(bigquery_client, sql_query):
+    sql_query = sql_query.format(**DATASET)
+
+    job_config = bigquery.QueryJobConfig(use_query_cache=False)
+    query_job = bigquery_client.query(sql_query, job_config=job_config)
+    query_job.result()
+    df = query_job.to_dataframe()
+    df.columns = df.columns.str.upper()
+    return df
+
+
+def _execute_bigframes_script(session, bigframes_script):
+    bigframes_script = re.sub(
+        r"next\((\w+)\.to_pandas_batches\((.*?)\)\)",
+        r"return \1.to_pandas()",
+        bigframes_script,
+    )
+    bigframes_script = re.sub(r"_\s*=\s*(\w+)", r"return \1", bigframes_script)
+
+    bigframes_script = (
+        bigframes_script
+        + f"\nresult = q('{PROJECT_ID}', '{DATASET_ID}', _initialize_session)"
+    )
+    exec_globals = {"_initialize_session": session}
+    exec(bigframes_script, exec_globals)
+    bigframes_result = exec_globals.get("result")
+    return bigframes_result
+
+
+def _verify_result(bigframes_result, sql_result):
+    if isinstance(bigframes_result, pd.DataFrame):
+        pd.testing.assert_frame_equal(
+            sql_result.reset_index(drop=True),
+            bigframes_result.reset_index(drop=True),
+            check_dtype=False,
+        )
+    else:
+        assert sql_result.shape == (1, 1)
+        sql_scalar = sql_result.iloc[0, 0]
+        assert sql_scalar == bigframes_result
+
+
+@pytest.mark.parametrize("query_num", range(1, 23))
+@pytest.mark.parametrize("ordered", [True, False])
+def test_tpch_correctness(session, unordered_session, query_num, ordered):
+    """Runs verification of TPCH benchmark script outputs to ensure correctness."""
+    # Execute SQL:
+    sql_file_path = f"{TPCH_PATH}/sql_queries/q{query_num}.sql"
+    assert os.path.exists(sql_file_path)
+    with open(sql_file_path, "r") as f:
+        sql_query = f.read()
+
+    sql_result = _execute_sql_query(session.bqclient, sql_query)
+
+    # Execute BigFrames:
+    file_path = f"{TPCH_PATH}/queries/q{query_num}.py"
+    assert os.path.exists(file_path)
+    with open(file_path, "r") as file:
+        bigframes_script = file.read()
+
+    bigframes_result = _execute_bigframes_script(
+        session if ordered else unordered_session, bigframes_script
+    )
+
+    _verify_result(bigframes_result, sql_result)