googleapis · TrevorBergeron · Feb 3, 2025 · Feb 1, 2025 · Feb 3, 2025 · Feb 3, 2025
@@ -152,24 +152,28 @@ def validate_table(
     return False
 
 
-def are_index_cols_unique(
+def infer_unique_columns(
     bqclient: bigquery.Client,
     table: bigquery.table.Table,
     index_cols: List[str],
     api_name: str,
     metadata_only: bool = False,
-) -> bool:
-    if len(index_cols) == 0:
-        return False
+) -> Tuple[str, ...]:
+    """Return a set of columns that can provide a unique row key or empty if none can be inferred.
+
+    Note: primary keys are not enforced, but these are assumed to be unique
+    by the query engine, so we make the same assumption here.
+    """
     # If index_cols contain the primary_keys, the query engine assumes they are
     # provide a unique index.
-    primary_keys = frozenset(_get_primary_keys(table))
-    if (len(primary_keys) > 0) and primary_keys <= frozenset(index_cols):
-        return True
+    primary_keys = tuple(_get_primary_keys(table))
+    if (len(primary_keys) > 0) and frozenset(primary_keys) <= frozenset(index_cols):
+        # Essentially, just reordering the primary key to match the index col order
+        return tuple(index_col for index_col in index_cols if index_col in primary_keys)
 
-    if metadata_only:
+    if primary_keys or metadata_only or (not index_cols):
         # Sometimes not worth scanning data to check uniqueness
-        return False
+        return primary_keys
     # TODO(b/337925142): Avoid a "SELECT *" subquery here by ensuring
     # table_expression only selects just index_cols.
     is_unique_sql = bigframes.core.sql.is_distinct_sql(index_cols, table.reference)
@@ -178,7 +182,9 @@ def are_index_cols_unique(
     results = bqclient.query_and_wait(is_unique_sql, job_config=job_config)
     row = next(iter(results))
 
-    return row["total_count"] == row["distinct_count"]
+    if row["total_count"] == row["distinct_count"]:
+        return tuple(index_cols)
+    return ()
 
 
 def _get_primary_keys(
@@ -279,54 +285,3 @@ def get_index_cols(
         index_cols = primary_keys
 
     return index_cols
-
-
-def get_time_travel_datetime_and_table_metadata(
-    bqclient: bigquery.Client,
-    table_ref: bigquery.TableReference,
-    *,
-    api_name: str,
-    cache: Dict[bigquery.TableReference, Tuple[datetime.datetime, bigquery.Table]],
-    use_cache: bool = True,
-) -> Tuple[datetime.datetime, bigquery.Table]:
-    cached_table = cache.get(table_ref)
-    if use_cache and cached_table is not None:
-        snapshot_timestamp, _ = cached_table
-
-        # Cache hit could be unexpected. See internal issue 329545805.
-        # Raise a warning with more information about how to avoid the
-        # problems with the cache.
-        msg = (
-            f"Reading cached table from {snapshot_timestamp} to avoid "
-            "incompatibilies with previous reads of this table. To read "
-            "the latest version, set `use_cache=False` or close the "
-            "current session with Session.close() or "
-            "bigframes.pandas.close_session()."
-        )
-        # There are many layers before we get to (possibly) the user's code:
-        # pandas.read_gbq_table
-        # -> with_default_session
-        # -> Session.read_gbq_table
-        # -> _read_gbq_table
-        # -> _get_snapshot_sql_and_primary_key
-        # -> get_snapshot_datetime_and_table_metadata
-        warnings.warn(msg, stacklevel=7)
-        return cached_table
-
-    # TODO(swast): It's possible that the table metadata is changed between now
-    # and when we run the CURRENT_TIMESTAMP() query to see when we can time
-    # travel to. Find a way to fetch the table metadata and BQ's current time
-    # atomically.
-    table = bqclient.get_table(table_ref)
-
-    job_config = bigquery.QueryJobConfig()
-    job_config.labels["bigframes-api"] = api_name
-    snapshot_timestamp = list(
-        bqclient.query(
-            "SELECT CURRENT_TIMESTAMP() AS `current_timestamp`",
-            job_config=job_config,
-        ).result()
-    )[0][0]
-    cached_table = (snapshot_timestamp, table)
-    cache[table_ref] = cached_table
-    return cached_table
@@ -424,7 +424,7 @@ def read_gbq_table(
         # in the query that checks for index uniqueness.
         # TODO(b/338065601): Provide a way to assume uniqueness and avoid this
         # check.
-        is_index_unique = bf_read_gbq_table.are_index_cols_unique(
+        primary_key = bf_read_gbq_table.infer_unique_columns(
             bqclient=self._bqclient,
             table=table,
             index_cols=index_cols,
@@ -440,12 +440,12 @@ def read_gbq_table(
             schema=schema,
             predicate=filter_str,
             at_time=time_travel_timestamp if enable_snapshot else None,
-            primary_key=index_cols if is_index_unique else (),
+            primary_key=primary_key,
             session=self._session,
         )
         # if we don't have a unique index, we order by row hash if we are in strict mode
         if self._force_total_order:
-            if not is_index_unique:
+            if not primary_key:
                 array_value = array_value.order_by(
                     [
                         bigframes.core.ordering.OrderingExpression(

@@ -27,23 +27,28 @@
 @pytest.mark.parametrize(
     ("index_cols", "primary_keys", "values_distinct", "expected"),
     (
-        (["col1", "col2"], ["col1", "col2", "col3"], False, False),
-        (["col1", "col2", "col3"], ["col1", "col2", "col3"], True, True),
+        (["col1", "col2"], ["col1", "col2", "col3"], False, ("col1", "col2", "col3")),
+        (
+            ["col1", "col2", "col3"],
+            ["col1", "col2", "col3"],
+            True,
+            ("col1", "col2", "col3"),
+        ),
         (
             ["col2", "col3", "col1"],
             [
                 "col3",
                 "col2",
             ],
             True,
-            True,
+            ("col2", "col3"),
         ),
-        (["col1", "col2"], [], False, False),
-        ([], ["col1", "col2", "col3"], False, False),
-        ([], [], False, False),
+        (["col1", "col2"], [], False, ()),
+        ([], ["col1", "col2", "col3"], False, ("col1", "col2", "col3")),
+        ([], [], False, ()),
     ),
 )
-def test_are_index_cols_unique(index_cols, primary_keys, values_distinct, expected):
+def test_infer_unique_columns(index_cols, primary_keys, values_distinct, expected):
     """If a primary key is set on the table, we use that as the index column
     by default, no error should be raised in this case.
 
@@ -87,6 +92,6 @@ def test_are_index_cols_unique(index_cols, primary_keys, values_distinct, expect
     )
     table._properties["location"] = session._location
 
-    result = bf_read_gbq_table.are_index_cols_unique(bqclient, table, index_cols, "")
+    result = bf_read_gbq_table.infer_unique_columns(bqclient, table, index_cols, "")
 
     assert result == expected
@@ -217,10 +217,10 @@ def test_read_gbq_cached_table():
         table,
     )
 
-    session.bqclient.get_table.return_value = table
-    session.bqclient.query_and_wait.return_value = (
-        {"total_count": 3, "distinct_count": 2},
+    session.bqclient.query_and_wait = mock.MagicMock(
+        return_value=({"total_count": 3, "distinct_count": 2},)
     )
+    session.bqclient.get_table.return_value = table
 
     with pytest.warns(UserWarning, match=re.escape("use_cache=False")):
         df = session.read_gbq("my-project.my_dataset.my_table")