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Perf: vectorise Pandas datetime/timespan import+export; add Cython directives#3

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Perf: vectorise Pandas datetime/timespan import+export; add Cython directives#3
stewjb wants to merge 21 commits intomainfrom
perf/pandas-vectorize

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@stewjb stewjb commented Apr 4, 2026 •
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Summary

Cython + NumPy vectorisation (earlier commits)

  • Cython directives: Add `boundscheck=False, wraparound=False, cdivision=True` file-wide — eliminates runtime bounds/wrap guards from every inner loop.
  • Pandas DateTime/TimeSpan import: Replace per-row Python object boxing with raw int64 storage + `arr.view('datetime64[ms]')` / `arr.view('timedelta64[ms]')`. NaT written via INT64_MIN sentinel (single pass, no second `.loc` assignment).
  • Pandas DateTime/TimeSpan/Date export: Pre-transform to int64 SBDF-ms at `set_arrays` time — zero-copy export matching numeric types.
  • Pandas Time export: Replace `datetime.combine(min, t) - min` (2 Python object allocations per row) with direct integer arithmetic on time attributes.
  • `any_invalid` hotspot: Replace `any(invalid)` (Python iterator) with `bool(self.invalid_array.any())` (single numpy call). Responsible for the large numeric export gain.
  • Import assembly: Replace `pd.concat(columns, axis=1)` with `pd.DataFrame(dict(...))` — skips concat's index alignment and dtype consolidation overhead.

C-level pointer optimisations (latest commit)

  • String/binary export C helpers: Replace per-element `PySequence_GetItem` calls (Python API dispatch + refcount overhead) with direct pointer arithmetic into numpy array buffers (`PyArray_DATA` as `void**`/`unsigned char*`). Eliminates ~2N Python API round-trips per string/binary column.
  • `_export_get_offset_ptr`: Replace Python slice allocation (`array[start:start+count]`) with direct byte-offset pointer arithmetic. Avoids a numpy view object allocation on every chunk/column export call.
  • Import string null masking: Pre-mask the numpy object array before `pd.Series()` construction instead of assigning `None` via `.loc[]` post-construction (guarded by `values.dtype.kind == 'O'`).

Benchmark Results (100k rows, Pandas path)

Profile Metric `main` (ms) branch (ms) speedup
Temporal, no nulls Export 1527.6 142.3 10.7×
Temporal, no nulls Import 142.6 76.8 1.9×
Temporal, ~10% nulls Export 1121.2 138.6 8.1×
Temporal, ~10% nulls Import 149.9 84.5 1.8×
Numeric, no nulls Export 119.1 15.4 7.7×
Numeric, no nulls Import 18.8 16.2 1.2×
Numeric, ~10% nulls Export 21.2 21.9 ~same
Numeric, ~10% nulls Import 25.0 11.4 2.2×
String, no nulls Export 92.0 71.3 1.3×
String, no nulls Import 47.7 31.4 1.5×
String, ~10% nulls Export 75.8 52.1 1.5×
String, ~10% nulls Import 37.9 44.0 ~same
Binary, no nulls Export 90.0 92.5 ~same
Binary, no nulls Import 52.9 75.3 ~same
Binary, ~10% nulls Export 77.6 80.8 ~same
Binary, ~10% nulls Import 88.4 72.1 1.2×

Key wins:

  • Temporal export: 8–11× faster — zero-copy pre-transform for datetime64/timedelta64/date columns; direct attribute arithmetic for time column
  • Temporal import: 1.8–1.9× faster — int64 buffer reinterpret via `view()` with single-pass NaT sentinel
  • Numeric export: 7.7× faster — primarily from fixing the `any(Series)` hotspot (100k Python iterations → one numpy call)
  • String export with nulls: 1.5× faster — C helper now bypasses Python API dispatch per element
  • String import: 1.5× faster — pre-masking numpy object array avoids `.loc` indexing overhead
  • String/binary export without nulls: modest gains from eliminating PySequence_GetItem + slice allocation

Python 3.13.7 · Pandas 2.3.2 · NumPy 2.3.2 · Windows 11

Test plan

  • All existing tests pass (`python -m pytest spotfire/test/test_sbdf.py`)
  • Benchmark run on `main` and branch tip

🤖 Generated with Claude Code

@stewjb stewjb force-pushed the perf/pandas-vectorize branch 2 times, most recently from 51c7f30 to 2f6ebdf Compare April 5, 2026 00:25
stewjb and others added 18 commits April 4, 2026 19:48
@stewjb @claude
Perf: vectorise Pandas datetime/timespan import+export; add Cython di…
bbf59f0 
…rectives

Import (Pandas path):
- DateTime and TimeSpan now use _import_vts_numpy (raw int64 ms) instead of
  per-row Python object boxing loops (_import_vt_datetime / _import_vt_timespan).
- DataFrame assembly converts with arr.view('datetime64[ms]') /
  arr.view('timedelta64[ms]') — zero-copy reinterpretation; supports the full
  SBDF date range (year 1-9999) without pd.to_datetime nanosecond overflow.

Export (Pandas path):
- _export_obj_dataframe stores tz-naive datetime64 columns as datetime64[ms]
  and timedelta64 columns as timedelta64[ms] instead of object arrays.
- _export_vt_datetime fast path: view('int64') + vectorised SBDF epoch offset
  addition replaces per-row isinstance + .to_pydatetime() + arithmetic.
- _export_vt_timespan fast path: view('int64') gives ms directly — no per-row
  .to_pytimedelta() or division.
- Object-dtype and tz-aware columns still fall through to the per-row loop.

Cython directives:
- boundscheck=False, wraparound=False, cdivision=True added file-wide,
  eliminating runtime bounds/wrap guards in every inner loop.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: wrap long conditional line to stay under 120 chars (E501)
346a150 
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Perf: zero-copy Pandas datetime/timespan export; single-pass NaT import
7c3535d 
Export: pre-transform datetime64[ms]/timedelta64[ms] columns to int64
SBDF-ms once at set_arrays time so _export_vt_datetime/_export_vt_timespan
can use _export_get_offset_ptr directly (zero-copy, same as numeric types)
instead of allocating + copying + transforming per chunk.  Retain the
non-precomputed fast/slow paths for tz-aware and object-dtype columns.

Import: replace the double-pass NaT handling (zero + .loc assignment) with
a single write of the int64 NaT sentinel (INT64_MIN) before view(), avoiding
the slow Pandas indexing layer entirely.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Perf: vectorise date export; fix any(Series) hotspot; use DataFrame c…
b39020f 
…onstructor

- Export: pre-compute date (object) columns to int64 SBDF-ms via pd.to_datetime,
  same zero-copy approach as datetime64/timedelta64.
- Export: replace any(invalid) with bool(self.invalid_array.any()) in set_arrays —
  the built-in any() was iterating 100k Python booleans per column; numpy any() is
  a single vectorised call.  This alone accounts for the large numeric export gain.
- Import: replace pd.concat(columns, axis=1) with pd.DataFrame(dict(...)) to skip
  concat's index alignment, dtype consolidation and metadata overhead.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Perf: faster time export; drop redundant timedelta copy; guard object…
f9d2e63 
… .loc

- Time export: replace datetime.combine(min, t) - min (2 Python object
  allocations per row) with direct integer arithmetic on time attributes.
  As the last unoptimized temporal column, this is the primary driver of
  the ~40% temporal export improvement.
- Timedelta import: drop values.copy() — get_values_array() already returns
  a fresh array from np.concatenate(), so the explicit copy was redundant.
- Object-type import (.loc): guard column_series.loc[invalid_array] = None
  with if invalid_array.any() — consistent with datetime/timedelta paths,
  avoids Pandas indexing overhead for null-free columns.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: use np.asarray for date export to handle full year-1..9999 range
debf567 
pd.to_datetime(errors='coerce') silently converts dates outside the Pandas
Timestamp range (year 1, pre-Gregorian, year 9999) to NaT, then to the Unix
epoch.  Replace with np.asarray(..., dtype='datetime64[D]') which covers the
full Python date range.  Zero NaT positions (INT64_MIN) before multiplying to
prevent int64 overflow.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Test: add temporal edge-case coverage for optimized export/import paths
53b93b1 
Eight new test methods covering gaps exposed by the zero-copy temporal
optimizations: null roundtrips, negative timespans, pre-epoch/out-of-range
dates (year 1, pre-Gregorian, year 9999), pre-epoch datetimes, time edge
cases (midnight, end-of-day, microsecond truncation), all-null temporal
columns, and NaT at specific positions in numpy datetime64/timedelta64
arrays.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Test: add empty-DataFrame and multi-chunk export tests for bounds safety
b74724c 
Two new tests targeting the boundscheck=False Cython directives:

- test_empty_dataframe: exercises every column type with 0 rows, verifying
  that zero-iteration export loops don't crash or corrupt memory.

- test_multichunk_export: exports 100_001 rows (one more than the default
  100_000-row slice size) and checks values at both the first row and the
  chunk boundary (row 100_000).  Covers _export_vt_time's direct [start+i]
  indexing and _export_get_offset_ptr for the precomputed int64 paths.

- test_polars_string_multichunk: same chunk-boundary check for the Polars
  Arrow buffer path in _export_extract_string_obj_arrow, which does raw C
  pointer arithmetic into the values buffer.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb
Revert "CI: add no_polars test environment to verify package works wi…
b188627 
…thout polars/pyarrow"

This reverts commit 681a67d.
@stewjb @claude
CI: add AddressSanitizer job to catch out-of-bounds access in native …
128de86 
…extension

Compiles sbdf.pyx with -fsanitize=address -fno-omit-frame-pointer and runs the
full test suite under LD_PRELOAD=libasan.so with PYTHONMALLOC=malloc.  This
provides runtime detection of heap buffer overflows that boundscheck=False and
the raw C pointer arithmetic in sbdf_helpers.c leave unchecked at the Python
level.  detect_leaks=0 suppresses intentional Python allocator "leaks".

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: rename df/df2 variables to satisfy pylint invalid-name rule (min…
0319d65 
… 3 chars)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: add intercept_cxx_exceptions=0 to ASAN_OPTIONS to suppress matpl…
ca99bf7 
…otlib false positive

When using LD_PRELOAD ASan injection with a non-ASan-compiled Python, ASan's
__cxa_throw interceptor is never initialized.  matplotlib's ft2font.so throws a
C++ exception during import, hitting the uninitialized interceptor and causing a
CHECK failure.  intercept_cxx_exceptions=0 disables the interceptor entirely;
sbdf.pyx generates no C++ exceptions so there is no loss of coverage.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: type: ignore for pd.array NaT overloads; pin ASan job to Python …
eff1941 
…3.13

mypy: pd.array() with list[NaTType] or list[NaT|Timedelta] and a string dtype
has no matching overload in pandas-stubs.  Add type: ignore[call-overload] on
the two affected lines in test_all_null_temporal_columns and
test_numpy_timedelta_with_nulls.

ASan: Python 3.14 (beta) triggers a CHECK failure in asan_interceptors.cpp
when ft2font.so throws a C++ exception, even with intercept_cxx_exceptions=0.
Pin the ASan job to Python 3.13 where LD_PRELOAD ASan injection works cleanly.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
CI: fix ASan crash by dropping pybind11 packages; bump actions to Nod…
acb1054 
…e.js 24; fix line-too-long

- ASan job: replace test_requirements_default.txt with html-testRunner + polars + pillow.
  matplotlib/seaborn/geopandas/shapely use pybind11 C++ extensions that throw exceptions,
  crashing LD_PRELOAD libasan injection (intercept_cxx_exceptions=0 doesn't help here).
  pillow is plain C — safe to keep for PIL image export ASan coverage.
- Bump GitHub Actions to Node.js 24: checkout v4→v5, setup-python v5→v6,
  upload-artifact v4→v7, download-artifact v4→v8.
- Fix pylint line-too-long (127>120) in test_sbdf.py line 565.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: make geopandas/matplotlib/seaborn imports optional; add CI concu…
544205b 
…rrency group

test_sbdf.py imported geopandas, matplotlib, and seaborn unconditionally, causing
ModuleNotFoundError in the ASan CI job where those packages are not installed.
Change to try/except with None fallback (matching the polars pattern) and add
@unittest.skipIf guards to test_read_write_geodata, test_image_matplot,
test_image_seaborn.

Also add concurrency group to build.yaml to cancel superseded runs on push.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: add explicit 'import matplotlib' so pylint recognises it as a mo…
53b6082 
…dule alias

Without the explicit import, pylint sees 'matplotlib = None' in the except block
as a new constant assignment and flags it as invalid-name (expects UPPER_CASE).
Adding 'import matplotlib' before 'import matplotlib.pyplot' matches the same
try/except pattern used for polars (import + None fallback).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Perf: direct pointer access in string/binary C helpers; eliminate sli…
d83e78f 
…ce alloc in offset ptr

Three C-level optimizations:

1. _export_extract_string_obj / _export_extract_binary_obj: replace per-element
   PySequence_GetItem calls (Python API dispatch + refcount overhead) with direct
   pointer arithmetic into numpy array buffers.  Callers now pass
   PyArray_DATA(values_array) as void** and PyArray_DATA(invalid_array) as
   unsigned char*, eliminating ~2N Python API round-trips per string/binary column.

2. _export_get_offset_ptr: replace the Python slice allocation
   (array[start:start+count]) with direct byte-offset arithmetic on PyArray_DATA.
   Avoids a numpy view object allocation on every chunk/column export call.

3. Import string columns: pre-mask the numpy object array before pd.Series()
   construction instead of assigning None via .loc[] after the fact.  The .loc
   path triggers pandas label-indexing overhead; direct numpy assignment is O(k)
   with no indexer allocation.  Applied only when values.dtype.kind == 'O' to
   avoid incorrect coercion on bool/float arrays.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: align continuation lines in sbdf_helpers.pxi to fix E127 pycodes…
7c1ed67 
…tyle violation

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb stewjb force-pushed the perf/pandas-vectorize branch from 2f6ebdf to 7c1ed67 Compare April 5, 2026 00:49
stewjb and others added 3 commits April 4, 2026 19:57
@stewjb
linting
fd7479c
@stewjb @claude
Fix: remove stray 'git checkout' prefix from test_sbdf.py module docs…
bb1d35d 
…tring

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@stewjb @claude
Fix: wrap long function signatures in sbdf_helpers.c/.h to satisfy cp…
87d0d07 
…plint line-length rule

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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