Columnar execution core (opt-in) + the batch-size scaling report (TASK-61)

backlog/tasks/task-60 - Pyarrow-input-output-infer_arrowpa.Table-pa.Table-—-the-columnar-boundary.md

@@ -3,9 +3,10 @@ id: TASK-60
 title: >-
   Pyarrow input/output: infer_arrow(pa.Table) -> pa.Table — the columnar
   boundary
-status: In Progress
+status: Done
 assignee: []
 created_date: '2026-07-28 03:14'
+updated_date: '2026-07-28 03:22'
 labels:
   - specializer
   - columnar
@@ -25,9 +26,15 @@ Scope (v1, per the proposal's copy-first recommendation): fn.infer_arrow(batch)
 
 ## Acceptance Criteria
 <!-- AC:BEGIN -->
-- [ ] #1 infer_arrow serves arrow-in/arrow-out for all-scalar models with values byte-identical to infer() (differential test: infer_rows == infer_arrow converted, every serving scenario)
-- [ ] #2 Validity round-trips: NULLs in, NULLs out, incl. LEFT-join null-extensions under shape='many'
-- [ ] #3 Named rejections: multi-chunk, missing column, wrong dtype, struct/opaque model, constant path
-- [ ] #4 Measured: infer_arrow vs spec_dict vs python_dict on the serving scenarios (numbers in the PR)
-- [ ] #5 Full gates green on release build; PR opened
+- [x] #1 infer_arrow serves arrow-in/arrow-out for all-scalar models with values byte-identical to infer() (differential test: infer_rows == infer_arrow converted, every serving scenario)
+- [x] #2 Validity round-trips: NULLs in, NULLs out, incl. LEFT-join null-extensions under shape='many'
+- [x] #3 Named rejections: multi-chunk, missing column, wrong dtype, struct/opaque model, constant path
+- [x] #4 Measured: infer_arrow vs spec_dict vs python_dict on the serving scenarios (numbers in the PR)
+- [x] #5 Full gates green on release build; PR opened
 <!-- AC:END -->
+
+## Final Summary
+
+<!-- SECTION:FINAL_SUMMARY:BEGIN -->
+Shipped as PR #47 (merged, master 1d1bb73). infer_arrow(pa.Table|RecordBatch) -> pa.Table: ingest walks pyarrow buffers raw (address+size via the Python buffer API — no arrow-rs; validity/bool bitmaps LSB, utf8+large_utf8 offsets, non-zero slice offsets honored) into ColData; emit builds one pa.Array.from_buffers per output column from rust-built buffers (strings as large_string). Strict by-name matching; named rejections (multi-chunk, missing column, wrong dtype, struct models, constant path). Test suite = the differential contract: infer_rows == infer_arrow on every serving scenario + NULLs + shape='many' LEFT null-extensions — parity by construction. Measured: n>=1024 beats the row path everywhere (house_prices 5.31ms -> 2.80ms) and beats the handcrafted python twin on most scenarios (house 0.55x — first twin wins); n=64 pyarrow fixed API cost (~150us) favors the row path, documented. Full gate 621 + 13 xfail.
+<!-- SECTION:FINAL_SUMMARY:END -->

backlog/tasks/task-61 - Columnar-execution-core-the-batch-size-scaling-report-the-final-roadmap-item.md

@@ -0,0 +1,49 @@
+---
+id: TASK-61
+title: >-
+  Columnar execution core + the batch-size scaling report (the final roadmap
+  item)
+status: Done
+assignee: []
+created_date: '2026-07-28 03:22'
+updated_date: '2026-07-28 03:56'
+labels:
+  - specializer
+  - columnar
+  - performance
+dependencies: []
+type: feature
+ordinal: 55000
+---
+
+## Description
+
+<!-- SECTION:DESCRIPTION:BEGIN -->
+User-approved finale: a vectorized execution core, then a REPORT with the batch-size-vs-performance scaling plot comparing columnar-ours, row-path-ours, duckdb-on-arrow, and handcrafted python.
+
+Design (mask-based vectorization of the existing IR — no new IR): exec/columnar.rs compiles a verified Program into column-at-a-time kernels. The acyclic non-'many' CFG flattens to straight-line masked execution: blocks process in topo order, each with an active-row MASK; Brif splits the mask by the cond lane; block params merge predecessor arg lanes under edge masks; kernels compute whole lanes (branch-free, wasted lanes are masked); trap-capable kernels check only ACTIVE rows and report the first failing row (row-order parity with the row loop); Emit/Skip blocks mark per-row emission — gathering emitted rows in ROW ORDER reproduces the row loop's output exactly (max one emit per row without 'many').
+
+Coverage grows kernel by kernel behind a clean compile-time rejection: anything unimplemented (incl. all stage-B multiplicity) -> the caller's existing row-path fallback, so every commit ships. infer_arrow prefers the columnar fn when it compiles; row APIs keep the row backends; fn.backend reports 'columnar'. Differential from day one: columnar vs interp over the gen.rs random-program fuzz (skip-and-count rejects) AND the serving scenarios via the existing infer_arrow==infer_rows suite.
+
+Report at the end: bench all four engines across n = 1..262144, matplotlib plot (log-log), delivered as a doc + updated artifact edition.
+<!-- SECTION:DESCRIPTION:END -->
+
+## Acceptance Criteria
+<!-- AC:BEGIN -->
+- [x] #1 exec/columnar.rs executes the four serving scenarios bit-identically to the interpreter (differential over gen.rs programs + the scenario suite)
+- [x] #2 Traps fire for the first ACTIVE failing row only (masked rows never trap); WHERE/CASE/joins (1:1) covered
+- [ ] #3 infer_arrow uses the columnar core when it compiles, falls back cleanly otherwise; fn.backend reports it
+- [x] #4 Bench: columnar vs row vs duckdb-on-arrow vs python twin across batch sizes with the scaling PLOT; report delivered
+- [x] #5 Full gates green; PR opened
+- [x] #6 (amended #3) infer_arrow uses the columnar core under SPECIALIZER_COLUMNAR=1 — opt-in, not default: the measured v1 core is at row-core compute parity with a small-batch allocation cost, so default-on would regress (honest deviation, recorded in the PR + report)
+<!-- AC:END -->
+
+## Final Summary
+
+<!-- SECTION:FINAL_SUMMARY:BEGIN -->
+Shipped as PR #48. The columnar core (exec/columnar.rs, +1942 lines, built by a worktree agent under TDD): mask-based vectorization of the existing IR, all 37 kernels, exact trap row-order parity, stage-B rejected to the row path; verified by a 500-seed interp differential + fixtures + the python infer_arrow==infer_rows suite through the core (cargo 177, pytest 622+13).
+
+Measured verdict (benchmarks/scaling_results.json, 4 engines x 4 scenarios x n=1..262144): the v1 core computes at ROW-CORE PARITY (kernels call the same scalar helpers per row) — house ~1.35x better, titanic/fraud ~tie, store behind, plus a per-call lane-allocation cost at tiny n. Wired OPT-IN (SPECIALIZER_COLUMNAR=1) to avoid regressing the default; arrow_backend reports the engine. Next lever identified: true vectorized kernels + lane reuse.
+
+The scaling report (the user's headline ask) is published as a mobile-friendly artifact with the dependency-free log-log per-row plot: https://claude.ai/code/artifact/40dbeb1c-f102-489e-9e65-d57d48385846 — regime map: serving calls (1-1k rows) ours by 2-3 orders (DuckDB pays ~7-12ms per query); DuckDB takes over at ~4-16k rows/call; the middle band within ~1.5x. This closes the entire user-approved roadmap: shape contract -> stage B (corpus 550/678) -> arrow boundary -> columnar foundation + report.
+<!-- SECTION:FINAL_SUMMARY:END -->

benchmarks/scaling_results.json

@@ -0,0 +1,278 @@
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