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bewithgaurav
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- Replace placeholder allocation (rows.append(py::none())) with direct PyList_New - Use PyList_Append + Py_DECREF for cleaner refcount management - Eliminates double allocation: no more placeholder → real row replacement - Uses rowWrapper for pybind11 compatibility while row is raw PyObject* - Removed final rows[initialSize + i] = row assignment (already appended) - Expected improvement: 15-20ms for 10K rows, reduces memory allocator pressure - Added PERF_TIMER for rows_append to measure append overhead
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- Replaced placeholder allocation (py::none()) with direct PyList_New + PyList_Append - Pre-allocate all row lists before populating them - Uses PyList_GET_ITEM to retrieve pre-allocated rows (no bounds checking) - Wrap raw PyObject* in py::list for pybind11 compatibility - Eliminates redundant row assignment at end of loop (row already in list) - Renamed PERF_TIMER from allocate_placeholder_rows to rows_append - Reduces memory allocator calls from O(2×rows) to O(rows)
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Problem: -------- Row creation and assignment had multiple layers of overhead: 1. Per-row allocation: py::list(numCols) creates pybind11 wrapper for each row 2. Cell assignment: row[col-1] = value uses pybind11 operator[] with bounds checking 3. Final assignment: rows[i] = row uses pybind11 list assignment with refcount overhead 4. Fragmented allocation: 1,000 separate py::list() calls instead of batch allocation For 1,000 rows: ~30-50 CPU cycles × 1,000 = 30K-50K wasted cycles Solution: --------- Replace pybind11 wrappers with direct Python C API throughout: 1. Row creation: PyList_New(numCols) instead of py::list(numCols) 2. Cell assignment: PyList_SET_ITEM(row, col-1, value) instead of row[col-1] = value 3. Final assignment: PyList_SET_ITEM(rows.ptr(), i, row) instead of rows[i] = row This completes the transition to direct Python C API started in OPT #2. Changes: -------- - Replaced py::list row(numCols) → PyObject* row = PyList_New(numCols) - Updated all NULL/SQL_NO_TOTAL handlers to use PyList_SET_ITEM - Updated all zero-length data handlers to use direct Python C API - Updated string handlers (SQL_CHAR, SQL_WCHAR) to use PyList_SET_ITEM - Updated complex type handlers (DECIMAL, DATETIME, DATE, TIME, TIMESTAMPOFFSET, GUID, BINARY) - Updated final row assignment to use PyList_SET_ITEM(rows.ptr(), i, row) All cell assignments now use direct Python C API: - Numeric types: Already done in OPT #2 (PyLong_FromLong, PyFloat_FromDouble, etc.) - Strings: PyUnicode_FromStringAndSize, PyUnicode_FromString - Binary: PyBytes_FromStringAndSize - Complex types: .release().ptr() to transfer ownership Impact: ------- - ✅ Eliminates pybind11 wrapper overhead for row creation - ✅ No bounds checking in hot loop (PyList_SET_ITEM is a macro) - ✅ Clean reference counting (objects created with refcount=1, transferred to list) - ✅ Consistent with OPT #2 (entire row/cell management via Python C API) - ✅ Expected 5-10% improvement (smaller than OPT #3, but completes the stack) All type handlers now bypass pybind11 for maximum performance.
bewithgaurav
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bewithgaurav
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…ild fix) Same issue as OPT #3 - Windows compiler treats warnings as errors (/WX). The columnSize variable was extracted but unused in SQL_CHAR and SQL_WCHAR cases after OPTIMIZATION #4. Changes: -------- - Removed unused 'SQLULEN columnSize' from SQL_CHAR/VARCHAR/LONGVARCHAR - Removed unused 'SQLULEN columnSize' from SQL_WCHAR/WVARCHAR/WLONGVARCHAR - Retained fetchBufferSize and isLob which are actively used Fixes Windows build error C4189 treated as error C2220.
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Added technical details about the description of the driver.