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Detect folds and fills in DataFusion's plans - #48
Fifth pull request of the restacked M1 (design v17, #42), stacked on #46. This is M1's detection. einfold finds the folds in the plans ddx, an XQL Systems project for automatic differentiation of SQL queries, already writes, with no change to ddx, as spike S28 found possible.
DetectFolds replaces two shapes, and leaves everything else alone. Its name is a verb phrase, as DataFusion's own rules' are (PushDownFilter), since EinFold names the operator, not the detector:
A fold: an Aggregate with one SUM(l · r) of DOUBLEs (no DISTINCT, FILTER or ordering), grouped by at least one column, over projections of an inner equi-join with no other condition, where each factor and group key reads one side. SUM is matched by type, not by name, as DataFusion asks of rules. The rule builds a FoldNode with one operand per side, naming its columns itself (a fold names none), plus a projection that restores the aggregate's exact schema: names, qualifiers, types and nullability.
A fold from a domain: a projection over d LEFT JOIN f, where f is a fold found by shape 1, possibly under aliases and renaming projections. The join matches every one of f's keys, and the projection reads only d's columns and f's sum. The fold gets d as its domain, and the projection, ddx's CASE, stays above it.
Declined on purpose: a join with a condition that isn't an equality (a mask, M2), no GROUP BY (SQL returns a row even when nothing joined), any other aggregate, a sum that isn't a product, and folds from a domain whose projection reads the fold's keys or use another kind of join.
The host's plan is the fallback. Each node keeps the plan it replaced. Where the dense algorithm doesn't apply, EinFoldExec runs that plan, counted in host_plans, instead of the hash algorithm, whose speed against DataFusion has never been shown. So a detected fold costs at most one extra read of its inputs over the host's own plan. DetectFolds::without_fallback() uses the hash path instead, for tests.
Why detection runs in the query planner. My first version put the rule among DataFusion's optimizer rules, and the M1 gate benchmark failed at once: ddx's ad::grad optimizes the loss query with the session's optimizer, then turns it into Substrait to differentiate it, and Substrait can't encode a Fold. EinFoldQueryPlanner::detecting(rule) now applies the rule to each optimized plan just before planning it, so optimized logical plans stay standard and only the plan that runs has Fold nodes. datafusion-python takes a query planner at the same point, through FFI, so the demos' host can use the same code (#39). install(builder) sets it up.
About 400 non-test lines.
Invariants tested (tests/detect_vs_host.rs). Random tables: BIGINT keys (dense, negative, or too large for positions) or VARCHAR keys, with NULL keys, duplicate tuples and missing rows, and values that are NULL, NaN, ±inf, -0.0 or small integers, so every sum is exact in any order. They're split across partitions and run on 1–6 threads. Random queries come in several spellings: factors that are expressions, group keys from either side or the join key, = or IS NOT DISTINCT FROM, one or two join keys, select lists in a different order from GROUP BY, ddx's left joins from a domain, and every look-alike above. Half the cases run without the fallback, so the folds the rule describes are checked through einfold's own kernels. Each case checks:
the same rows as DataFusion without einfold, as a multiset, bit for bit (every NaN alike, since even the host's NaN signs depend on partitioning);
no Fold in the optimized logical plan;
exactly one EinFoldExec for each fold, with a domain for each fold from a domain, and none for a look-alike;
that the dense path and the host's plan both ran in at least 2% of cases.
M1's gate, measured with the benchmark in the next pull request: ddx's matmul gradient program at n = 50,000, end to end, on a multi-threaded runtime, takes 368 ms with einfold off and 224 ms with it on, 1.6×, with the same gradients. That's short of the 4× gate. As S28 found, the rest of grad_0_a is ddx's join back and second GROUP BY around the fold (about 120 ms), which einfold can't remove without knowing the table's keys are unique. The change suggested on xqlsystems/ddx#208 (write the transpose as a plain left join) would let einfold take the table as the fold's domain too. S28 estimated that at about 4.8×.
Fixes from the fuzz soak (#65). Detection computes each operand's keys and factor on every row of the join's inputs, where the host's plan computes factors on joined rows only. So it now moves an expression below the join only if it can't fail and isn't volatile: columns, literals, + - *, comparisons, CASE, COALESCE, casts that widen, and abs, exp, ln, tanh and signum on DOUBLEs. sqrt isn't on that list: DataFusion 55 raises an error on a negative argument. EinFoldExec now resets its fallback in reset_state, so reset_plan_states, which a recursive query runs before each iteration, resets it too. The fallback stays out of the operator's children: as a child, the session's physical optimizer optimized it a second time whenever a query was planned, which cost ddx's gradient program about 2 ms. So EXPLAIN ANALYZE still shows only its host_plans count, not its operators. Tests: the issues' reproductions, plus a check that every allowed function evaluates without error on NaN, ±inf, ±0, negatives and NULL. Fixes#61. Fixes#62.
Eligible grouped joins and aggregations can now be automatically optimized for fold-based execution, including certain queries involving a domain table.
Queries retain their results when optimized execution does not apply, by running the original plan instead.
Fold detection can be configured in the query planner, with a setup helper that enables it by default.
The optimized path handles supported cases while falling back to standard execution for cases it cannot process.
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crates/einfold-datafusion/src/rule.rs
crates/einfold-datafusion/tests/detect_vs_host.rs
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crates/einfold-datafusion/src/rule.rs
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Walkthrough
The change adds logical fold detection for eligible grouped joins and domain joins. It integrates detection into DataFusion query planning and adds host-plan fallback execution when dense folding does not apply.
Changes: DataFusion fold integration
Layer / File(s)
Summary
Fold nodes and detection rewrites crates/einfold-datafusion/src/node.rs, crates/einfold-datafusion/src/rule.rs, crates/einfold-datafusion/tests/detect_vs_host.rs
FoldNode stores an optional original plan and whether detection created it. DetectFolds recognizes eligible grouped sums over inner joins and projections over domain left joins. Tests compare detected plans and results with host DataFusion, including expression-safety cases.
Planner detection and installation crates/einfold-datafusion/src/exec.rs, crates/einfold-datafusion/src/lib.rs, crates/einfold-datafusion/examples/matmul_gradient.rs, crates/einfold-datafusion/tests/operator.rs
EinFoldQueryPlanner can apply a detection rule before physical planning. The crate exports DetectFolds and provides install to configure a detecting planner. Example and test contexts use the planner’s default constructor.
Physical execution and fallback crates/einfold-datafusion/src/exec.rs, crates/einfold-kernels/src/lib.rs, crates/einfold-datafusion/tests/detect_vs_host.rs
EinFoldExec runs its host plan when dense folding returns no result and tracks host-plan executions. The kernel crate adds fold_dense, which does not invoke the hash algorithm. Tests cover fallback execution in recursive queries and after plan-state reset.
Change: Feature · Severity of issue fixed: Medium · Unblocks: 16 PRs
Sequence Diagram(s)
sequenceDiagram
participant QueryPlanner
participant DetectFolds
participant EinFoldPlanner
participant EinFoldExec
participant fold_dense
participant HostPlan
QueryPlanner->>DetectFolds: Rewrite eligible logical plans
QueryPlanner->>EinFoldPlanner: Plan the logical plan
EinFoldPlanner->>EinFoldExec: Attach physical fallback plan
EinFoldExec->>fold_dense: Attempt dense folding
fold_dense-->>EinFoldExec: Return batches or no result
EinFoldExec->>HostPlan: Collect partitions when dense folding returns no result
HostPlan-->>EinFoldExec: Return fallback batches
Loading
Possibly related issues
xqlsystems/einfold#39 — The install function and planner detection support hosts that cannot add logical optimizer rules.
xqlsystems/einfold#76 — The detection rule recognizes only supported product forms inside SUM; this issue describes another unsupported SUM expression form.
Possibly related PRs
xqlsystems/einfold#37 — This PR extends the FoldNode, EinFoldExec, and planner components introduced there.
xqlsystems/einfold#46 — This PR extends the existing fold execution path with host-plan fallback.
xqlsystems/einfold#45 — This PR adds the dense kernel path whose no-result outcome selects fallback execution.
Merge Risk: Minimal · up to 32d17
The previously reported nested fallback issue is addressed, with no remaining actionable merge risk identified. Merge after normal checks.
Security architecture risk: Moderate · up to b4544
When enabled, ordinary qualifying queries reach a buffering path whose memory checks occur after collection. This can weaken resource containment despite a configured query memory limit. The fallback can also reread inputs. Effective exposure depends on who can submit queries and whether execution resources are shared.
Retained concerns
Medium · security · inferred: Automatic detection makes whole-input buffering before memory admission reachable through qualifying SQL. The new host fallback also buffers its complete output before requesting the corresponding reservation. A configured pool can therefore reject the operation only after these collections have materialized, creating a potential worker-memory exhaustion path. Input accounting already behaved this way for explicit FoldNodes; this PR broadens that exposure and adds fallback buffering. Process-wide impact depends on the embedding host and underlying operators.
Security review details
Security Blast Radius
inferred — The relevant attack source is a query author in a session with detection enabled, operating on data already available to that session. The sensitive outcome is execution-resource exhaustion, not a demonstrated increase in data-access authority. Other queries sharing the worker could be affected; tenant and deployment isolation are unknown.
Security Findings and Attack Paths
inferred — ARCH-RESOURCE-001 follows qualifying SQL through automatic detection into complete input collection and, on dense decline, complete fallback-result collection. The operator requests the corresponding memory only afterward. Memory-limit tests check eventual failure and retained-output accounting, but do not establish bounded peak allocation during these collections.
Trust Boundaries and Controls
observed — The rewrite constrains moved expressions by syntax, types, and a scalar-function name allowlist. Common fallible operations and random factors are declined in the inspected tests. Fallback planning and execution retain the session and TaskContext rather than introducing a separate credential or authority context.
Resilience and Maintainability Implications
observed — The fallback is outside the ordinary child list, so reset_state explicitly resets it. Tests cover recursive fallback and reset-and-rerun on static tables. The blocking kernel holds a shared reservation while it owns inputs, addressing premature accounting release if the surrounding future is dropped; complete cancellation cleanup was not verified.
Hardening Proposals
proposed — Admit buffered batches against the memory pool as they arrive, including fallback output, or stream the fallback result instead of collecting it whole. Define the repeatability requirement for fallback sources and validate peak-memory containment and nested execution counts separately from final-row equivalence.
Pre-merge checks: 3 · 2
Failed checks: 2 warnings
Check name
Status
Explanation
Resolution
Linked Issues check
[#61] The rule checks factors and join-key expressions with movable before it projects them below the join. The check rejects unsupported, fallible, and volatile expressions. This prevents the repor…
For [#62], make the fallback a child of EinFoldExec, or implement the issue's lazy-planning alternative. Ensure plan traversal and physical-plan visitors reach the fallback while preserving correct execution and avoiding repeated physical…
Docstring Coverage
Docstring coverage is 70.59% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 3 passed
Check name
Status
Explanation
Title check
The title clearly identifies the main change: detecting folds and applying them in DataFusion plans.
Description check
The description explains the change, motivation, implementation, testing strategy, invariants, benchmark results, and fixes. It is mostly complete, although the required issue reference is blank and t…
Out of Scope Changes check
The changes implement fold detection, safe expression movement, fallback execution and reset behavior, query-planner integration, kernel support, and related tests. These changes support the PR object…
Full details: Description check
Explanation
The description explains the change, motivation, implementation, testing strategy, invariants, benchmark results, and fixes. It is mostly complete, although the required issue reference is blank and the testing content is not under the exact “How it is tested” heading.
Full details: Out of Scope Changes check
Explanation
The changes implement fold detection, safe expression movement, fallback execution and reset behavior, query-planner integration, kernel support, and related tests. These changes support the PR objectives and linked issues. No demonstrated unrelated change is present.
Full details: Linked Issues check
Explanation
[#61] The rule checks factors and join-key expressions with movable before it projects them below the join. The check rejects unsupported, fallible, and volatile expressions. This prevents the reported cast and division errors. [#62] EinFoldExec::reset_state resets the fallback plan, so reruns and recursive iterations can reset its state. However, children() still returns only operands and the domain. The fallback remains invisible to reset_plan_states callers other than this custom override, physical-plan visitors, and fallback metrics. The issue's required plan visibility is therefore not met.
Resolution
For [#62], make the fallback a child of EinFoldExec, or implement the issue's lazy-planning alternative. Ensure plan traversal and physical-plan visitors reach the fallback while preserving correct execution and avoiding repeated physical optimization.
Full details: Docstring Coverage
Explanation
Docstring coverage is 70.59% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 34 functions across 8 files.
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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Fallible factors move below the join (Detection evaluates fallible factors below the join, so einfold fails queries DataFusion answers #61). Each operand is Projection(keys…, factor) over the join's input, so CAST(l.t AS DOUBLE), CAST(n / m AS DOUBLE) and the like run on rows the join drops (NULL keys, unmatched keys), and einfold fails queries DataFusion answers. The fallback doesn't help, since EinFoldExec reads its inputs before choosing a path. lessons.md lists movable() among the things worth keeping from the first round, and its rule ("move only expressions that cannot fail") isn't in this stack. Volatile factors (random()) also change meaning: one draw per operand row, not per joined pair.
The fallback isn't a child of EinFoldExec (EinFoldExec's fallback isn't a child: a fold in WITH RECURSIVE panics, and a reset plan reruns wrongly #62). reset_plan_states, which DataFusion runs on a recursive query's term each iteration, never reaches it. A fold inside WITH RECURSIVE whose fallback runs panics with partition not used yet, and a reset plan rerun returns fewer rows. Its work is also invisible to EXPLAIN ANALYZE and to physical optimizer rules, and with_new_children shares the one Arc between copies.
What held up, in #65's soak (generated spellings, over 120,000 cases, with those two avoided):
the schema: names, qualifiers, types, nullability and field metadata, planned and in batches, matched DataFusion's in every case;
comma joins, swapped sides, CTE and subquery operands, filters pushed below the join, HAVING (declined, rightly), UNION ALL of two folds, and a fold whose operand is another fold's result;
IS NOT DISTINCT FROM with NULL keys, and mixed = and IS NOT DISTINCT FROM (declined as a join filter).
A coverage note: in the soak's plain-fold cases, the rule fired in about half, and the dense path ran in only ~2% of those, because most generated group keys include a join key or a key from both sides. That's the right decline, but the dense path's coverage comes from the domain shapes, not from plain folds.
EinFoldRule runs after DataFusion's own rules and replaces two shapes with
FoldNodes: a SUM of a product over an inner equi-join grouped by columns,
and a projection over a left join from a relation onto such a fold, which
becomes a fold with a domain. Each node keeps the plan it replaced, which
EinFoldExec runs where the dense algorithm doesn't apply, so a detected
fold is never slower than the host's own plan. install() adds the rule and
the planner to a session.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
ddx turns optimized plans into Substrait to differentiate them, and
Substrait can't encode a Fold, so detection among the optimizer's rules
broke ddx's gradients. EinFoldQueryPlanner::detecting now applies the rule
to each optimized plan just before planning it, which install() sets up;
it is also where datafusion-python takes a query planner. The fill search
follows aliases and renaming projections down to the fold, since the rule
now sees them unmerged. The property test checks that optimized plans have
no Fold, counts folds in the physical plan, runs half its cases through
einfold's hash path rather than the host's plan, and adds fills'
look-alikes.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
…main
Follow the name-free IR in the rule, too: it names the node's columns.
EinFold names the operator, and the rule detects folds rather than being
one, so it takes a verb phrase, as DataFusion's own rules do. The IR calls
the relation a fold's output is left-joined from its domain, so the rule's
second shape is a fold from a domain rather than ddx's fill.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
A fold's operands compute their keys and factors on every row of the
join's inputs, where the host's plan computes factors on joined rows
only. So a factor that can fail (CAST('oops' AS DOUBLE), integer / or %)
failed queries DataFusion answers, and a volatile one was drawn once per
input row. Detection now declines unless every node of a factor and key
is infallible and deterministic. sqrt is not: it raises an error on a
negative number in DataFusion 55. Fixes#61.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
The host's plan an EinFoldExec falls back to is held in a field, not as
a child, so reset_plan_states, which a recursive query runs on its
recursive term each iteration, never reached it: its repartitions kept
their first run's state, so a fold in WITH RECURSIVE panicked, and a
reset plan reran with fewer rows. The operator now resets its fallback
in reset_state.
The fallback stays out of the children: as a child, the session's
physical optimizer optimized it a second time whenever a query was
planned, which cost ddx's gradient program about 2 ms. Fixes#62.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @crates/einfold-datafusion/src/rule.rs:
- Around line 545-553: Update construction of original so its right-side plan
replaces the inner FoldNode with that node’s fallback, aliasing fallback columns
to the FoldNode’s output names so the restore projection still resolves. Rebuild
the Join with this unfolded right side while preserving its existing schema and
qualifiers, then build the Projection from that host plan instead of directly
from p.input.
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…#52)
🪭 Claude (einfold)
Stacked on #48, and under #49 (M1's gate benchmark). The maintainer
chose to have einfold close the gap to M1's gate itself, rather than
wait on xqlsystems/ddx#208, so ddx and einfold stay siblings. ddx is an
XQL Systems project for automatic differentiation of SQL queries.
**The shape.** Each of ddx's gradient steps is a fold joined back to the
table being differentiated, grouped again by that table's keys, then
left-joined from it:
```sql
SELECT d.<columns>, s.v
FROM d LEFT JOIN (
SELECT d2.<keys>, SUM(CASE WHEN d2.v IS NULL THEN NULL ELSE f.v END) AS v
FROM d d2 JOIN (<a fold>) f ON d2.<key> = f.<output> AND …
GROUP BY d2.<keys>
) s ON d.<key> = s.<key> AND …
```
When no two rows of `d` reach one of the fold's groups, the second
`GROUP BY` holds one row per group, and this is the fold with `d` as its
domain. Each row of `d` then gets `0.0 + e` on itself and its group's
value, which is what `SUM` of one value computes. `DetectFolds` now
finds the shape, in `join_back.rs`. Its conditions:
- `d2` is `d` under other names, compared through aliases, renames and
which columns each scan reads;
- the inner join matches every one of the fold's outputs;
- the second `GROUP BY` and the left join use exactly those columns;
- the sum's argument is NULL wherever the fold's value is;
- the projection above reads none of the second `GROUP BY`'s keys;
- `=` in the left join doesn't sit outside `IS NOT DISTINCT FROM` in the
inner one.
**Uniqueness is checked at run time.** Whether `d`'s keys are unique is
known only when the query runs. So the node requires a unique domain
(`FoldNode::requiring_unique_domain`, shown as `+ unique domain` in
`EXPLAIN`), and `EinFoldExec` passes `Options::unique_domain` to the
dense algorithm. The kernel marks each output cell as the domain's rows
are placed, and declines on the second row to reach one. That's a pass
over the domain the dense path makes anyway. When it declines, the
host's own plan runs. So the shape is found only when a fallback exists
(`DetectFolds::default()`, which `install` uses).
**M1's gate, with this (#49):** ddx's `matmul` gradient at n = 50,000
takes 365 ms with einfold off and 117 ms on, **3.1–3.2×**, with the same
gradients. Ā goes from 247 ms to 22. That's still short of 4×. The rest
is:
- ddx's checks, 32 ms;
- an elementwise step, 18 ms;
- einfold's single-threaded passes around the arithmetic in its three
folds, 20 ms each, which the next pull request takes on.
About 450 non-test lines: `join_back.rs` is 340, half of it
documentation and conditions.
**Invariants tested.**
- `einfold-kernels/tests/kernels_vs_sql.rs`, new invariant 4: where the
dense path runs with a domain, the checked path declines whenever two
domain rows have one group's keys. Where it accepts, it gives the same
rows, bit for bit.
- `einfold-datafusion/tests/detect_vs_host.rs`: the same rows as
DataFusion without einfold, bit for bit. The fold joined back is found
exactly when expected, with a domain only when there is a fallback. Its
look-alikes keep the fold alone:
- a sum that isn't NULL with the value (`f.v * 2.0`, `COALESCE`);
- a filtered `d`;
- a projection reading the second `GROUP BY`'s keys;
- a second `GROUP BY` by other columns;
- `=` outside `IS NOT DISTINCT FROM`.
A second test, `folds_joined_back_check_their_domain`, draws only this
shape, over operands the dense path takes and a domain whose keys are
duplicated half the time. Both outcomes of the check run: dense in 80 of
300 cases and the host's plan in 94.
<!--
- [x] 3,000-case sweeps from seeds 900000, 1200000 and 1500000
- [x] mutations, each caught: no uniqueness requirement on the node; the
kernel's check off; any sum argument; any NULL equalities; any second
GROUP BY; any relation as d2; a projection reading the keys
- [x] survived, as equivalent: dropping `0.0 +`, since the fold's value
is itself a SUM that starts at 0.0 and is never -0.0
- [x] matching scans by handle (`Arc::ptr_eq`) passed the tests but
declined every ddx plan, whose scans each hold their own handle; the
gate benchmark caught it. Scans now match by table name and schema
- [x] fmt, clippy -D warnings, doc -D warnings, reuse lint
-->
**Fix from the fuzz soak (#65).** The regroup's `CASE` tests were
applied in a projection above the node, so when the uniqueness check
declined and the host's plan ran, they were applied a second time, and a
row of a duplicated key could get NULL where SQL gives its group's sum.
The node now computes the tests as a Boolean column of its domain and
makes its own values NULL where one holds, so the host's plan, when it
runs, is left alone. The tests now run on every row of the domain, so
they must be unable to fail. `detect_vs_host.rs` now draws the test from
three spellings, and the outer projection as ddx's, plain `s.v`, or
`COALESCE(s.v, 0.0)`. Fixes#60.
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
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<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* Improved query optimization for grouped sums joined back to their
domain, with results preserved when domain keys are duplicated by
falling back to the original query plan.
* Fold results can now be returned as NULL for rows selected by a domain
condition.
* **Bug Fixes**
* Added checks to prevent incorrect results when multiple domain rows
map to the same group.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
🪤 Claude (fuzz adversary)
This adds a simulation test of einfold in DataFusion, a soak mode that
runs it on fresh seeds for a time budget, and a nightly workflow that
runs the soak and files an issue when a property fails. It's modeled on
the soaks of ddx, the XQL Systems project for automatic differentiation
of SQL queries, and shares their log format and their triage script, so
a failure in either project is reported the same way.
`detect_vs_host.rs` checks that detection fires on the shapes it should,
over a handful of spellings each. `simulation.rs` generates the
spellings instead: joins written with `ON` or as comma joins, sides
swapped, operands behind subqueries and CTEs, filters and `HAVING`,
factors that are expressions, keys of eight types (integers of several
widths, unsigned keys past `BIGINT`, text, dictionary text, and floats
with `-0.0`, NaNs of both signs and two payloads), NULL slots holding
garbage values, empty batches, domains with duplicate keys, the
regroup's `CASE` testing columns other than the keys, two folds under
`UNION ALL`, a fold of a fold's result, and `ORDER BY … LIMIT` above.
Each case runs on plain DataFusion and on DataFusion with einfold, with
different partition counts and batch sizes. It also varies the session's
settings, the same for both: sort-merge rather than hash joins, no
repartitioning, no dynamic filters, and small memory pools (8 KB, 32 KB,
256 KB) that DataFusion's operators can spill from. A sixth shape is a
masked fold: a `WHERE` comparing two of a fold's outputs, or a
look-alike comparing an output with a summed column, half of them under
a domain.
The invariants it checks, each tagged in failure reports:
- `[error]`: einfold answers wherever DataFusion does. `[masks-error]`:
and fails wherever DataFusion fails.
- `[schema]`: the same columns, names, types, nullability and metadata,
both planned and in the batches.
- `[rows]`: the same rows as a multiset, bit for bit with exact values.
With ill-conditioned values, within the summation bound `2·(n −
1)·u·Σ|terms|`, where DataFusion computes `n` and `Σ|terms|` by running
the same query over twin tables holding 1.0 and `|v|`.
- `[host-settings]`: DataFusion gives the same exact answer under the
case's settings as under the defaults. Where it doesn't, the bug is
DataFusion's, and einfold can't match both.
- `[rerun]`: the physical plan, reset with `reset_plan_states` and run
again, gives the same rows. DataFusion's own plan, rerun, is the
control. Reruns use sessions of their own without dynamic filters, which
`reset_plan_states` doesn't support.
- `[threads]`: where einfold computed every sum itself, the same bits on
another partition count and batch size.
- `[partition]`: a mask comparing two of a fold's outputs decides each
group whole. So the groups of the mask, of its negation and of its `IS
NULL` case are, between them, exactly the unmasked fold's, with the same
values. An exact check that needs no oracle.
- `[hash]`: every fold detection finds, run through einfold's own
kernels (`DetectFolds::without_fallback`), also matches.
- `[stall]` (soak only): no case runs longer than two minutes. A
watchdog reports a hang as a failure of its seed.
Under a small pool, either side may run out of memory where the other
doesn't. Those cases are tallied rather than failed. Coverage is
asserted on long runs: each shape fires, and the dense algorithm, the
hash algorithm and the host's plan each run.
Findings, each filed with a minimal reproduction:
- #60 (#52): a fold joined back to a domain with duplicate keys gave
wrong answers on the host-plan fallback. Fixed on main.
- #61 (#48): factors were evaluated below the join, so einfold failed
queries DataFusion answers. Fixed on main.
- #62 (#48): the fallback wasn't a child of `EinFoldExec`, so a fold in
`WITH RECURSIVE` panicked, and a reset plan reran wrongly. Fixed on
main.
- #66 (#48, from #62's fix): with the fallback now a child, DataFusion's
physical optimizer runs over it twice, and some folds fail to plan.
- #63 (upstream, already apache/datafusion#26618): DataFusion's TopK
gives timing-dependent answers over `-0.0` and `0.0`.
- #67 (upstream): DataFusion's sort-merge join on a float key and
another key drops a match when a side holds both `-0.0` and `0.0`.
- apache/datafusion#24414, fixed after 55.2: a sort-merge join's filter
reads stale columns once a projection is pushed into it.
- #71 (upstream): DataFusion's plans can hang under a small memory pool
when they repartition.
- #21, a new spelling: an `=` in `WHERE` over an `IS NOT DISTINCT FROM`
join matches NULL to NULL. The shrinker in #104 cut it down to one row
per table.
- #88 (#78, on M2's stack): a masked fold left-joined from a domain
ignores its mask.
The upstream ones are pinned in `datafusion_behaviors.rs`, and each pin
fails once DataFusion fixes its bug. The simulation steps around each
open bug (`Known` in `simulation.rs`), so the soak keeps finding new
ones. `EINFOLD_SIM_KNOWN=1` brings them back, to check a fix. It passes
150 cases in a release build (60 in CI's debug build).
An ignored `export` test writes generated cases to files, so another
host can run them. The Python package's differential runs (#75) used it.
#104 adds shrinking on top, through #103's test kit. The nightly
workflow runs three ten-minute regions on disjoint seeds, and keeps at
most one open `fuzz-finding` issue: a recurrence comments on it rather
than opening another. Reproduce any reported failure with
`EINFOLD_SEED=<seed> cargo test -p einfold-datafusion --test simulation
-- --nocapture simulation`, which prints the case's SQL and tables.
<!--
- [x] cargo fmt / clippy / test / doc / reuse lint
-->
🤖 Generated with [Claude Code](https://claude.com/claude-code)
https://claude.ai/code/session_01SCMAk87qC9VKCk22P7HDH1
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Tests**
* Expanded coverage for query behavior involving joins, filters,
sorting, signed zeros, and varying partition and batch sizes.
* Added randomized comparisons of query results and schemas,
repeatability checks, and longer-running simulations.
* Nightly and manually started soak runs now test multiple seed regions
and summarize failures with reproduction details when available.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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🪭 Claude (einfold)
Fifth pull request of the restacked M1 (design v17, #42), stacked on #46. This is M1's detection. einfold finds the folds in the plans ddx, an XQL Systems project for automatic differentiation of SQL queries, already writes, with no change to ddx, as spike S28 found possible.
DetectFoldsreplaces two shapes, and leaves everything else alone. Its name is a verb phrase, as DataFusion's own rules' are (PushDownFilter), since EinFold names the operator, not the detector:Aggregatewith oneSUM(l · r)ofDOUBLEs (noDISTINCT,FILTERor ordering), grouped by at least one column, over projections of an inner equi-join with no other condition, where each factor and group key reads one side.SUMis matched by type, not by name, as DataFusion asks of rules. The rule builds aFoldNodewith one operand per side, naming its columns itself (a fold names none), plus a projection that restores the aggregate's exact schema: names, qualifiers, types and nullability.d LEFT JOIN f, wherefis a fold found by shape 1, possibly under aliases and renaming projections. The join matches every one off's keys, and the projection reads onlyd's columns andf's sum. The fold getsdas its domain, and the projection, ddx'sCASE, stays above it.Declined on purpose: a join with a condition that isn't an equality (a mask, M2), no
GROUP BY(SQL returns a row even when nothing joined), any other aggregate, a sum that isn't a product, and folds from a domain whose projection reads the fold's keys or use another kind of join.The host's plan is the fallback. Each node keeps the plan it replaced. Where the dense algorithm doesn't apply,
EinFoldExecruns that plan, counted inhost_plans, instead of the hash algorithm, whose speed against DataFusion has never been shown. So a detected fold costs at most one extra read of its inputs over the host's own plan.DetectFolds::without_fallback()uses the hash path instead, for tests.Why detection runs in the query planner. My first version put the rule among DataFusion's optimizer rules, and the M1 gate benchmark failed at once: ddx's
ad::gradoptimizes the loss query with the session's optimizer, then turns it into Substrait to differentiate it, and Substrait can't encode aFold.EinFoldQueryPlanner::detecting(rule)now applies the rule to each optimized plan just before planning it, so optimized logical plans stay standard and only the plan that runs hasFoldnodes. datafusion-python takes a query planner at the same point, through FFI, so the demos' host can use the same code (#39).install(builder)sets it up.About 400 non-test lines.
Invariants tested (
tests/detect_vs_host.rs). Random tables:BIGINTkeys (dense, negative, or too large for positions) orVARCHARkeys, with NULL keys, duplicate tuples and missing rows, and values that are NULL, NaN, ±inf,-0.0or small integers, so every sum is exact in any order. They're split across partitions and run on 1–6 threads. Random queries come in several spellings: factors that are expressions, group keys from either side or the join key,=orIS NOT DISTINCT FROM, one or two join keys, select lists in a different order fromGROUP BY, ddx's left joins from a domain, and every look-alike above. Half the cases run without the fallback, so the folds the rule describes are checked through einfold's own kernels. Each case checks:Foldin the optimized logical plan;EinFoldExecfor each fold, with a domain for each fold from a domain, and none for a look-alike;M1's gate, measured with the benchmark in the next pull request: ddx's
matmulgradient program at n = 50,000, end to end, on a multi-threaded runtime, takes 368 ms with einfold off and 224 ms with it on, 1.6×, with the same gradients. That's short of the 4× gate. As S28 found, the rest ofgrad_0_ais ddx's join back and secondGROUP BYaround the fold (about 120 ms), which einfold can't remove without knowing the table's keys are unique. The change suggested on xqlsystems/ddx#208 (write the transpose as a plain left join) would let einfold take the table as the fold's domain too. S28 estimated that at about 4.8×.Fixes from the fuzz soak (#65). Detection computes each operand's keys and factor on every row of the join's inputs, where the host's plan computes factors on joined rows only. So it now moves an expression below the join only if it can't fail and isn't volatile: columns, literals,
+ - *, comparisons,CASE,COALESCE, casts that widen, andabs,exp,ln,tanhandsignumonDOUBLEs.sqrtisn't on that list: DataFusion 55 raises an error on a negative argument.EinFoldExecnow resets its fallback inreset_state, soreset_plan_states, which a recursive query runs before each iteration, resets it too. The fallback stays out of the operator's children: as a child, the session's physical optimizer optimized it a second time whenever a query was planned, which cost ddx's gradient program about 2 ms. SoEXPLAIN ANALYZEstill shows only itshost_planscount, not its operators. Tests: the issues' reproductions, plus a check that every allowed function evaluates without error on NaN, ±inf, ±0, negatives and NULL. Fixes #61. Fixes #62.🤖 Generated with Claude Code
https://claude.ai/code/session_01M96xSez6PZwTmfaGh2eJgS
Summary by CodeRabbit