Upgrade duckdb to 0.10

lilac/data/dataset_duckdb.py

@@ -1787,7 +1787,7 @@ def stats(self, leaf_path: Path, include_deleted: bool = False) -> StatsResult:
     avg_text_length: Optional[int] = None
     if leaf.dtype in (STRING, STRING_SPAN):
       avg_length_query = f"""
-        SELECT avg(length(val))
+        SELECT avg(length(CAST(val AS VARCHAR)))
         FROM (SELECT {inner_select} AS val FROM t {where_clause})
         USING SAMPLE {SAMPLE_AVG_TEXT_LENGTH};
       """
@@ -1835,10 +1835,16 @@ def stats(self, leaf_path: Path, include_deleted: bool = False) -> StatsResult:
       """
       row = self._query(min_max_query)[0]
       result.min_val, result.max_val = row
+      if is_temporal(leaf.dtype):
+        sample_where_clause = ''
+      else:
+        sample_where_clause = (
+          f'WHERE val != 0 {'AND NOT isnan(val)' if is_float(leaf.dtype) else ''}'
+        )
       sample_query = f"""
         SELECT COALESCE(ARRAY_AGG(val), [])
         FROM (SELECT {inner_select} as val FROM t {where_clause})
-        WHERE val != 0 {'AND NOT isnan(val)' if is_float(leaf.dtype) else ''}
+        {sample_where_clause}
         USING SAMPLE 100;
       """
       result.value_samples = list(self._query(sample_query)[0][0])
@@ -1894,9 +1900,9 @@ def select_groups(
       sql_bounds = []
       for label, start, end in named_bins:
         if start is None:
-          start = cast(float, "'-Infinity'")
+          start = cast(float, "'-Infinity'::FLOAT")
         if end is None:
-          end = cast(float, "'Infinity'")
+          end = cast(float, "'Infinity'::FLOAT")
         sql_bounds.append(f"('{label}', {start}, {end})")
 
       bin_index_col = 'col0'

lilac/data/dataset_test.py

@@ -177,8 +177,8 @@ def test_select_star(make_test_data: TestDataMaker) -> None:
   # Select * plus an inner `info.age` column.
   result = dataset.select_rows(['*', ('info', 'age')])
   assert list(result) == [
-    {'info.age': 40, 'info.age_2': 40, 'name': 'A'},
-    {'info.age': 42, 'info.age_2': 42, 'name': 'B'},
+    {'info.age': 40, 'info.age_1': 40, 'name': 'A'},
+    {'info.age': 42, 'info.age_1': 42, 'name': 'B'},
   ]
 
 

lilac/sources/csv_source.py

@@ -60,28 +60,31 @@ def setup(self) -> None:
 
     # NOTE: We use duckdb here to increase parallelism for multiple files.
     # NOTE: We turn off the parallel reader because of https://github.com/lilacai/lilac/issues/373.
-    self._con.execute(
-      f"""
-      CREATE SEQUENCE serial START 1;
-      CREATE VIEW t as (SELECT nextval('serial') as "{LINE_NUMBER_COLUMN}", * FROM read_csv_auto(
+    csv_source_sql = f"""read_csv_auto(
         {duckdb_paths},
         SAMPLE_SIZE=500000,
         HEADER={self.header},
         {f'NAMES={self.names},' if self.names else ''}
         DELIM='{self.delim or ','}',
         IGNORE_ERRORS=true,
         PARALLEL=false
-    ));
-    """
-    )
-
-    res = self._con.execute('SELECT COUNT(*) FROM t').fetchone()
+    )"""
+    res = self._con.execute(f'SELECT COUNT(*) FROM {csv_source_sql}').fetchone()
     num_items = cast(tuple[int], res)[0]
 
-    self._reader = self._con.execute('SELECT * from t').fetch_record_batch(rows_per_batch=10_000)
+    self._reader = self._con.execute(
+      f'SELECT 1::BIGINT as "{LINE_NUMBER_COLUMN}", * from {csv_source_sql}'
+    ).fetch_record_batch(rows_per_batch=10_000)
     # Create the source schema in prepare to share it between process and source_schema.
     schema = arrow_schema_to_schema(self._reader.schema)
     self._source_schema = SourceSchema(fields=schema.fields, num_items=num_items)
+    self._con.execute(
+      f"""
+      CREATE SEQUENCE serial START 1;
+      CREATE VIEW t as (
+        SELECT nextval('serial') as "{LINE_NUMBER_COLUMN}", * FROM {csv_source_sql});
+    """
+    )
 
   @override
   def source_schema(self) -> SourceSchema: