SplitGrid

Asymmetric split-grid serialization for rectangular numeric and mixed-type grids.

This package was pulled out of WriterAgent (LibreOffice Writer/Calc/Draw AI extension). It is the host flatten + child unpack codec that lived in plugin.scripting.payload_codec, plus the optional Cython flatten accelerator from native/writeragent_vec.

Why this exists

A spreadsheet host often cannot import NumPy: LibreOffice’s embedded Python is a different interpreter from the user’s venv, and loading the user’s C extensions into the host is an ABI footgun. Heavy compute therefore runs in a child process that does have NumPy. The range still has to cross that boundary.

On the wire, a Calc-style range is a nested list: list[list[float | int | str | None]]. Standard pickle walks one heap object per cell. On a 20,000 × 5 grid that is ~12 ms for dump + load. Pack the same numbers into a contiguous float64 buffer and Pickle Protocol 5 moves the bytes in ~0.015 ms; the child can np.frombuffer in ~0.002 ms.

Almost all of the remaining time is host flatten — turning nested Python objects into that buffer without NumPy (~8.3 ms pure Python on that shape; ~3 ms with the Cython helper). SplitGrid is that flatten/unpack. Length-prefixed Pickle 5 framing stays in the application; this package is the codec only.

Column-wise blobs and JSON + Base64 were tried first. Transposing columns on the host builds extra object graphs; Base64 and per-column reconstructs lose the frombuffer win. One row-major float64 buffer plus a sparse string map is faster and simpler.

How it works

The path is asymmetric:

Host pack (stdlib only) flattens a 1D or 2D rectangular grid into a contiguous float64 buffer plus a sparse integer-keyed strings map. Empty cells become NaN. Zip codes like "02138" stay strings — they are never parsed as floats.
Child unpack (NumPy) materializes a pure-numeric grid (strings == {}) with np.frombuffer (ndarray). Mixed grids use a vectorized object-masking path and return nested lists, restoring None for NaN holes.
Host unpack preserves float('nan') from the buffer (does not coerce holes to None). That is the locked egress policy: NaN becomes a spreadsheet error, not a silent blank.

Grids with fewer than 100 cells (BINARY_MIN_CELLS) stay nested Python lists. Force "always" / "never" overrides that threshold (used by A/B tests).

Wire envelope (Pickle5-friendly dict):

{
    "__wa_payload__": "split_grid",
    "dtype": "float64",
    "column_kinds": ["int", "float"],  # per column: int / float / bool
    "shape": [rows, cols],             # or [n] for 1D
    "buffer": b"...",                  # row-major float64 bytes
    "strings": {7: "banana"},          # integer keys, not str(idx)
}

Cell value	`buffer` (float64)	`strings`
`None` (empty cell)	`NaN`	—
`int` / `float`	numeric value	—
`bool`	`0.0` / `1.0`	—
`str` (including `"02138"`)	`NaN`	text by flat index

There is no datetime lane on the float64 buffer. Python datetime objects stringify into strings. Do not add a 'date' column kind.

Jagged 2D grids raise ValueError (spreadsheet ranges are rectangular).

Numbers

Median timings from an asymmetric bench (host = stdlib pack; child = deserialize + materialize).

Ingress, 20,000 × 5 (100k cells):

Format	Pack	Dump	Load	Materialize	Total
JSON nested lists	6.2 ms	22.8 ms	14.6 ms	2.1 ms	45.7 ms
Pickle 5 nested lists	6.1 ms	1.4 ms	2.9 ms	2.0 ms	12.4 ms
Pickle 5 + split-grid	8.3 ms	0.013 ms	0.015 ms	0.002 ms	8.3 ms

Child materialize of a 100 × 100 numeric grid: nested-list pickle then np.array ~0.6 ms; split-grid frombuffer ~0.016 ms. Below 100 cells the envelope is not worth it — that is why BINARY_MIN_CELLS exists. Host pack still dominates large ingress; Cython only speeds that loop.

Python vs Cython

Host flatten is an optimized pure-Python loop:

identity type checks (type(val) is float, val is None)
bound-method capture (buf_append = buf.append)
None as NaN on the fast path
lazy column-state upgrades
rectangular validation before the hot loop

The optional Cython module splitgrid.pack exposes fast_flatten_grid_1d / fast_flatten_grid_2d. It is loaded dynamically and canary-tested at import. If the extension is missing or fails the canary, the codec uses pure Python. Importing splitgrid never requires a compiler. Extension builds use release flags (-O3 -DNDEBUG -g0 on Unix, /O2 /DNDEBUG on Windows).

Install

pip install splitgrid

PyPI wheels include the compiled Cython flatten accelerator. From a source checkout (Cython is built if a compiler is available):

pip install .
pip install -e ".[test]"      # editable + pytest, hypothesis, deal, numpy
pip install -e ".[numpy]"     # child unpack/pack
pip install -e ".[verify]"    # crosshair-tool

Host pack stays NumPy-free. Child child_unpack_* / child_pack_* import NumPy locally.

Test

pytest                  # default: -m "not slow"
pytest -m "not slow"    # same
pytest -m slow          # CrossHair subprocess checks (optional)
make verify             # deal contracts + hypothesis round-trips, no CrossHair

Default pytest does not require the compiled extension. When splitgrid.pack is built, tests/test_cython_parity.py compares Cython and Python flatten components on the same grids.

Public API

from splitgrid import (
    BINARY_MIN_CELLS,
    host_pack_split_grid,
    host_unpack_split_grid,
    child_unpack_split_grid,
    child_pack_split_grid,
    host_pack_data,
    host_unpack_data,
    child_unpack_data,
    child_pack_result,
    is_split_grid,
    load_cython_accelerator,
    get_cython_status_info,
)

host_pack_data(..., force="auto"|"always"|"never") chooses split-grid vs nested list. host_pack_multi_data is a thin multi_data wrapper over the same per-grid packing.

License

GPL-3.0-or-later.

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splitgrid 0.1.1

SplitGrid

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