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d1dbb40362Tools for profiling Datasette's request/response cycle in-process with Python 3.15's profiling.sampling, plus the patches proposed by the study (one per optimisation, and ALL-combined.diff). The patches are not applied to the code. A report with measurements follows. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01F8mEgRS8swZfYqwqrVMeo5
21 lines
1,011 B
Diff
21 lines
1,011 B
Diff
diff --git a/datasette/views/table.py b/datasette/views/table.py
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index 92473bc..1aa5d10 100644
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--- a/datasette/views/table.py
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+++ b/datasette/views/table.py
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@@ -2420,8 +2420,14 @@ async def table_view_data(
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# Apply transform_value for columns with assigned types
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ct_map = await datasette.get_column_types(database_name, table_name)
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transformed_rows = []
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- for r in raw_sqlite_rows:
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- row_dict = dict(r)
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+ row_keys = raw_sqlite_rows[0].keys() if raw_sqlite_rows else []
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+ # dict(sqlite3.Row) looks every key up by name (a linear,
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+ # case-insensitive scan), so zip with the keys when they are unique
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+ if len({k.lower() for k in row_keys}) == len(row_keys):
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+ dict_rows = (dict(zip(row_keys, r)) for r in raw_sqlite_rows)
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+ else:
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+ dict_rows = (dict(r) for r in raw_sqlite_rows)
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+ for row_dict in dict_rows:
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for col_name, ct in ct_map.items():
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if col_name in row_dict:
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row_dict[col_name] = await ct.transform_value(
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