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chore: merge main into polars-engine, and re-run the ladder on the result - #231

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chore: merge main into polars-engine, and re-run the ladder on the result#231
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@FBumann FBumann commented Jul 28, 2026

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Merges main into the polars branch and re-runs the ladder on the result.

Based on #215, not on polars-engine#215 already carries #205's bench ideas in polars form, so basing here lets the bench/ conflicts resolve to "we have this already" instead of redoing that work inside a merge commit. Merge #215 first, or merge this and it comes along.

The benchmark answer first

Every ratio is unchanged within noise, and that is the expected result. The three duckdb perf PRs cannot move this comparison, because neither arm on this branch is duckdb: the farkas arm is the polars engine, the linopy arm is farkas.linopy.build. #152, #174 and #178 have nothing here to act on — and their ideas already arrived, as _positional, #211 and #212, which is what the previous numbers already reflected.

l rung, wall / peak, before → after the merge:

lp highs
dispatch 0.72→0.76x / 1.03→1.01x 0.36x / 0.95x
nodal 0.63→0.61x / 0.83→0.82x 0.32x / 0.80→0.81x
sector 0.37x / 0.32→0.31x 0.25→0.26x / 0.31x
transport 0.86→0.99x / 1.62→1.59x 0.46→0.52x / 0.78→0.81x
profiled 1.74→1.78x / 1.14x 1.13x / 0.94→0.98x

288 records, no failures, parity gate green on all five at 0.0e+00. Tables and every figure quoted in the prose refreshed off the new file.

How the conflicts were resolved

Twelve conflicts, by lane:

Engine files → polars. #152, #174 and #178 are ROW_NUMBER() OVER, CREATE TYPE ... AS ENUM and CREATE TABLE surv_* against the engine this branch deletes. Taking main's side would have re-added SQL against a connection that no longer exists.

Language and validation → main. #223's lowering.py, validation.py and linopy/builder.py merged clean and are engine-independent; its executor half and #201's are already here (#225 and _check_one_row_per_coordinate). test_objective.py and test_validation.py arrived whole and pass on this lane unmodified — 404 tests to 414.

bench → ours, per the basing note above.

Three judgement calls worth reviewing

  • test_group_sum's docstring is combined, not picked. Both sides said something true — main's the cross-lane divergence, ours the aggregate-skip premise that rests on it. It now says both.
  • test_milp gains main's ragged-chunk test. test_solver_direct_ingests_columns_in_order_whatever_the_chunking drives solve(batch_rows=...), which is API-level and pins the same property on this sink. Kept alongside the polars Enum test.
  • Main's duckdb label tests are dropped, not ported. They drive DuckdbExecutor and ex._con directly. The properties are covered here by test_a_mask_that_removes_nothing_labels_exactly_like_no_mask and test_a_factored_mask_labels_exactly_like_the_counted_path — though main's also spelled out row-major position absolutely, where ours pins it relatively plus through the differential oracle. If you want that belt-and-braces back it wants writing fresh, not porting.

414 passed / 1 xfailed, ruff and pyrefly clean.

🤖 Generated with Claude Code

FBumann and others added 10 commits July 28, 2026 09:23
#205)

Two changes to the harness, both about measuring what users actually do.

**`profiled`, the case whose input is the same size as its model.** Every
existing case builds its coordinate product from parameters far smaller than
the product they explode into — 2-15% of it — so reading the parquet is 2-9%
of the build and the read path is lost in the margin. `profiled` is `nodal`'s
cardinalities with the mask removed and `availability` dense over
`(snapshot, node, tech)`: at the `l` rung, a 12M-row table against a 12M
coordinate product. That makes the read path, the join, and the
in-memory-vs-parquet question measurable rather than noise.

It is also the shape the eager lane handles best — a parameter already dense
over the variable product is the array xarray wants, no broadcast and no
alignment — while we join a full-size frame against a full-size coordinate
product. `nodal` is the mirror image and our clearest win. Carrying both is
what keeps the ladder from holding only shapes that suit one engine. Closes #202.

**Both sinks, not just the LP file.** `--sinks lp highs`, defaulting to both.
The LP file is the artifact fewest callers want, and it is not the same
comparison: at `profiled/m` the peak ratio is 0.60x through the LP writer and
0.94x through the handoff, because HiGHS's own dense model is resident in both
arms and swamps the difference between them.

The `highs` sink stops at the handoff — `run()` is never called. The simplex is
the same work whoever filled the model, so including it would swamp the phase
this harness exists to measure. `solve_direct` is split into `build_highs` +
the solve so there is a seam to stop at; linopy's `Model.to_highspy()` is the
same seam on the other side, which is what makes the two arms comparable.
A rung the sink is capped out of is written to the JSONL as a `skipped` record
and footnoted in the report, so a coverage hole cannot read as a result.

Measured, `profiled` at the `l` rung (12M variables): LP file, farkas 8.42 s /
1.49 GB against linopy 2.44 s / 3.26 GB; HiGHS handoff, farkas 7.24 s / 2.60 GB
against linopy 2.93 s / 3.23 GB. The parity gate passes on the new case.

No published numbers change here: `bench/results/latest.jsonl` is left as it
was, since re-running the ladder is its own act.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
A parameter is a function of its dims, so two rows for one coordinate has no
defined answer. The eager lane already refuses such a source — `xarray` will
not lay out a duplicated index — while the relational lane quietly resolved it
into a sum. Two lanes that accept the same language were accepting different
data, and the one that accepted more gave an answer nobody asked for.

Now it raises a `DataError` naming the parameter and up to three offending
coordinates. One `GROUP BY … HAVING count(*) > 1` per parameter, over a source
orders of magnitude smaller than the model built from it.

`test_a_coordinate_must_be_single_valued` had to be narrowed: it doubled the
whole generator frame, which feeds both the coordinate index *and* two
parameters, so the new check now fires first and correctly. It doubles only the
index; the parameter case is its own test beside it.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: fluxopt-release[bot] <267260463+fluxopt-release[bot]@users.noreply.github.com>
…223)

* fix: an objective sums the terms it names, once each

Two ways an objective quietly answered a question nobody asked.

`x * a + y * b`, with `x, a` on `i` and `y, b` on `j`, is `|i| + |j|` summands.
The relational lane knew that — an expression there is a set of term
fragments, each keeping its own dims until the objective sums it. The eager
lane added the two operands first, which is linopy's `+`, which broadcasts:
every term counted once per coordinate of its sibling, `|i| * |j|` summands,
8.8x apart on the mixed-density case that found it (#197). Hard rule 3 says
the lanes mean the same thing, and here the relational one was right.

So the eager lane distributes the sum over addition and sums each product on
its own. The distribution has to survive an operator applied to the group —
`(x * a + y * b) * c` was wrong by the same factor — so `*`, `/` and unary
minus are walked too, and everything else stays one opaque term.

Separately, `objectives.<name>.equations` is a list of which only the first
entry was ever read. It reads as "several terms in one objective", because
that is what the key means under a constraint, and the answer was wrong by
whatever the ignored entries were worth with nothing to see (#198). It is now
a load error naming the rewrite, spelled from what was written.

SPEC said "only equations[0] is used" in §2 and "the last defined wins" for
multi-objective in §11 — the second was already false, since a second
objective has been a load error for a while.

Closes #197
Closes #198

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* fix: a dimensionless parameter is one row, and now has to be

`_check_one_row_per_coordinate` returned early when a parameter had no dims,
on the reading that there was no coordinate to group by. But a parameter with
no dims has exactly one coordinate — the empty one — so the rule it was
skipping is the rule that matters most there.

The join that broadcasts a dimensionless parameter is `ON TRUE`, which is
correct broadcast semantics for one row and a silent row multiplication for
two: duplicate `cols` entries for one variable in a bound, duplicate mask rows
in a where. Both built and solved without a word. A user who gets here has
usually passed a column they thought was indexed, so the message says what a
dimensionless parameter means rather than only what is wrong with the source.

Zero rows is caught by the same count, and was the other half of the hole.

One `count(*)` per scalar parameter, at metadata level.

Closes #166

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: fluxopt-release[bot] <267260463+fluxopt-release[bot]@users.noreply.github.com>
)

The label frame was the single most expensive statement in build, and the cost
was the sort inside

    ROW_NUMBER() OVER (ORDER BY t_snapshot.ord, t_generator.ord)

When the mask does not read the leading dim, that sort is avoidable without
changing a single label. `where: "p_max > 0"` depends only on generator, so
the surviving trailing coordinates are identical for every snapshot: rank them
once over a table of size prod(rest), and the label is
`(lead.ord - lo) * width + rank` — no window function, no per-chunk sort. The
general path stays for masks that do read the leading dim.

Measured with bench/run.py, best of the repeats, on this machine:

  case/size    build before  build after          vs linopy
  dispatch/m       1.27s        0.88s        2.20x -> 1.67x
  dispatch/l       8.66s        5.01s        1.35x -> 0.90x
  transport/m      1.56s        1.10s        2.03x -> 1.65x
  transport/l     10.67s        7.27s        1.65x -> 1.31x

dispatch/l crosses over: farkas is now faster than linopy end to end at 10M
variables. The absolute ratios are machine-specific and differ from the
committed macOS numbers, so the delta is the reliable part; docs/benchmarks.md
is left for a run on the machine its ladder was measured on.

Labels are unchanged, and that is checked rather than assumed: building the
same 10M-row frame both ways and diffing gives 0 differing rows, and the same
holds on the walkthrough model.

One test moved. examples/walkthrough.py printed `sol.primal('p').head(6)`
unsorted, so its golden file pinned the physical storage order of var_p —
which this change alters even though the labels do not. primal() is a label
join and a join has no inherent row order, so the example now sorts
explicitly. That makes the golden stable under any future storage change
(chunking, parallelism, a duckdb upgrade), which it was not before.

Left alone deliberately: `primal()` itself still returns rows in whatever
order the join produces. Adding an ORDER BY there would also hit
_solution_to_parquet, which streams, and a global sort is exactly what that
path exists to avoid.


Claude-Session: https://claude.ai/code/session_0145rKirxsCRGodhkKozjaYW

Co-authored-by: Claude <noreply@anthropic.com>
`vtype` is one of three literals written once per column, which as VARCHAR
made it the widest thing on the row — and the row count is the model's
column count. An ENUM stores the tag instead.

Nothing downstream moves: `vtype = '...'` comparisons in both sinks keep
working against an ENUM unchanged.

Measured on the branch this was split out of (#153): 0.87s -> 0.48s writing
`cols` at 10M columns.

The members come off `plan.VariableType` rather than being typed out, so the
Literal stays the single declaration. The test pins that: an ENUM rejects a
value it does not know, so a fourth variable type added to the plan and not
here would otherwise fail at insert with a duckdb cast error a long way from
its cause.

Closes #170.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: fluxopt-release[bot] <267260463+fluxopt-release[bot]@users.noreply.github.com>
`_label_frame` numbered every coordinate with
`ROW_NUMBER() OVER (ORDER BY ord, ...)`, a full sort of every chunk. It was
71% of build at 10M variables (#146), and the sort is the whole cost: the
same statement without the ORDER BY runs at the join floor.

When nothing is masked out, that rank has a closed form -- it is the
mixed-radix value of the `ord` tuple -- so the label can be computed instead
of sorted for. Isolated at 10M rows: 4.37s -> 0.56s. End to end, interleaved
best-of-three, `transport` at 9.8M variables: build 10.19s -> 6.52s (-36%).

The labels are *the same integers*, not merely valid ones, which is what
makes this a pure optimisation rather than a decision. #151 weighed dropping
the ordering guarantee instead and had to leave three questions open --
`solver_direct`'s batching, reproducibility (#109), and the locality of `A`.
None of them arise here: nothing downstream can tell the two apart, and the
walkthrough golden that a renumbering would have forced to regenerate is
untouched.

Masked frames keep the window. Denseness there depends on which rows survive
the predicate, and no closed form knows that -- so `dispatch`, whose variable
is masked by `where: p_max > 0`, is unchanged by this commit.

Verified: HiGHS `addRows` is insensitive to column-index order within a row
(bit-identical objective and primal under reversed and shuffled indices), so
the `ORDER BY` was not load-bearing for the shipped path either.

332 passed; ruff and pyrefly clean.


Claude-Session: https://claude.ai/code/session_013DtGqM3z2DTtT556178iXi

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: fluxopt-release[bot] <267260463+fluxopt-release[bot]@users.noreply.github.com>
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FBumann and others added 2 commits July 28, 2026 10:35
main gained the three duckdb perf PRs (#152, #174, #178), two language-level
fixes (#201, #223) and the bench harness from #205. Resolved by lane:

**Engine files take the polars side.** #152, #174 and #178 are `ROW_NUMBER()
OVER`, `CREATE TYPE ... AS ENUM` and `CREATE TABLE surv_*` against the engine
this branch deletes. Their *ideas* are already here in polars form — #152's as
`_positional`, #174's as #211, #178's as #212 — so taking main's side would
have re-added SQL against a connection that no longer exists.

**Language and validation take main's.** #223's `lowering.py`,
`validation.py` and `linopy/builder.py` changes merged clean and are engine
independent; its executor half and #201's are already here (as #225 and
`_check_one_row_per_coordinate`). `test_objective.py` and `test_validation.py`
arrived whole and pass on this lane unmodified — 404 tests to 414.

**bench takes ours**, which carries #205's two ideas ported rather than
merged (#215): the duckdb commit also brings `--memory-limits`, `--chunk-rows`
and `workdir_bytes`, the budget apparatus this branch removed.

Two tests were combined rather than picked. `test_group_sum`'s docstring says
both true things now — the divergence between lanes, and the aggregate-skip
premise that rests on it. `test_milp` keeps the polars Enum test and gains
main's ragged-chunk test, which is API-level and applies to this sink too.

Main's duckdb label tests are dropped, not ported: they drive `DuckdbExecutor`
and `ex._con` directly. The properties they pin are covered here by
`test_a_mask_that_removes_nothing_labels_exactly_like_no_mask` and
`test_a_factored_mask_labels_exactly_like_the_counted_path`.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Full ladder after merging main: five cases, two sinks, both arms, best of
three, parity gate green on all five. 288 records, no failures.

**Every ratio is unchanged within noise, and that is the expected result.**
main's three duckdb perf PRs cannot move this comparison: neither arm here is
duckdb. The farkas arm is the polars engine and the linopy arm is
`farkas.linopy.build`, so the work in #152, #174 and #178 has nothing to act
on — their ideas already arrived on this branch as `_positional`, #211 and
#212, and it is those that the numbers already reflected.

`transport` reads 1.59x / 0.81x where it read 1.62x / 0.78x, `sector` 0.31x
where it read 0.32x, and so on. The tables and every figure quoted in the
prose are refreshed off the new file so the doc and its provenance agree.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
@FBumann
FBumann force-pushed the merge/main-into-polars-engine branch from a8ab28c to d070355 Compare July 28, 2026 08:35
@FBumann
FBumann changed the base branch from bench/carry-over-profiled-and-sinks to polars-engine July 28, 2026 08:35
@FBumann

FBumann commented Jul 28, 2026

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Retargeted to polars-engine — you were right, and it needed more than a base change.

#215 had already been squash-merged into polars-engine (f5af03c), so its four commits are in the base as one commit that git cannot match against them. Just retargeting would have asked git to re-apply work already there.

So I redid the merge on current polars-engine instead, and took the resolved tree from the version I had already reviewed and benchmarked. Verified identical: git diff against the old merged tree is empty, so the numbers in this PR still describe exactly what is in it.

The merge commit now has the two parents it should — f5af03c (polars-engine) and 754a3bc (main) — and the PR is two commits on top of the base: the merge, and the ladder re-run. main's own commits show in the list because a merge makes them reachable; the diff against polars-engine is just the merge resolution plus the refreshed benchmark data.

414 passed / 1 xfailed, ruff and pyrefly clean.

`test_objective.py` arrived from main with a bare `import pandas as pd`. It
merged without a conflict, so nothing flagged it — and on this branch pandas
is not a core dependency, so the bare-install CI job died collecting it.

Every other test module on this branch takes pandas from `tests.oracle`, which
is a `pytest.importorskip` behind the `[linopy]` extra. `test_duals.py` even
carries the comment this file needed: "through the guard: a bare import would
beat it".

Reproduced against an install with no pandas, linopy or xarray: 11 skipped
and a collection error before, 214 passed and 21 skipped after.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
@FBumann
FBumann merged commit 0dad398 into polars-engine Jul 28, 2026
2 checks passed
FBumann added a commit that referenced this pull request Jul 28, 2026
Both were written against #231's branch and kept being committed there after
that PR was squash-merged, so none of it reached `polars-engine`:
`--duckdb-root`, `--builds`, the `loop` mode, `FOREIGN_ARMS` and the marginal
cost table were all absent. This lands them on the current base.

**The duckdb arm** runs the engine this branch replaces from its own checkout,
its own interpreter and its own `bench/_run_case.py`, interleaved with the
other two against one parquet cache, with the parity gate spanning all three.

**The loop pass** answers the second question the timings cannot: the harness
spawns one process per measurement, so every timing is a *first* build, and
each lane does lazy first-call work — ~180 ms eager, ~21 ms duckdb, ~4 ms
here. `first` is what a caller pays who builds one model and solves it;
`steady` is what a rolling horizon pays for every model after. Both are real,
so both are reported.

It measures build *and* hand-off, not build alone: linopy defers ~82% of its
work to whatever consumes the model, so a build-only loop compares our
finished matrix against its placeholder.

`bench/README.md` keeps #233's structure — narrow runs go to `/tmp` so they
cannot clobber the published file — with the three-engine invocation added to
the publishing block, which is what it is.

No numbers are published here: the base has moved under them twice (#233,
#234) and again with #238, and the linopy pin is now the v1-semantics build
rather than 0.9.0. The ladder is re-run separately.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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