omnigraph/.context/experiments/factorized-batches.md

Experiment 1.1 — Factorized batches through DataFusion ops

Ticket: MR-925 §1.1 (validates MR-737 §5.2 / Open Q2). Prototype: validation-prototypes/factorized-batches/ (branch devin/mr-925-pre-phase-0-validation-experiment-code-dive-agenda-to-de). Substrate pin: DataFusion 52.5 + Arrow 57.3 (matches engine workspace). Date: 2026-05-12.

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Hypothesis

DataFusion's HashJoinExec, AggregateExec, FilterExec, SortExec, and ProjectionExec either (a) handle a List<UInt64> neighbor-set column correctly with acceptable performance, or (b) require explicit Flatten before them. MR-737 §5.2 currently assumes mostly (b); this experiment maps the actual frontier so the §5.2 rule list lands on validated ground.

Method

factorized-batches/ builds an in-memory RecordBatch with schema (src_id: UInt64, payload: Utf8, weight: Float64, _neighbors: List<UInt64>) plus a flat-row baseline of (src_id, payload, weight, dst: UInt64) produced by exploding _neighbors to one row per (src, dst) pair.

For each cell {n_src = 10_000} × {fanout ∈ uniform{1, 10, 100, 1000}, skewed(target=10, heavy=2%)} we run six pipelines on each input shape via SessionContext::sql:

Op probe	SQL
filter	SELECT * FROM t WHERE src_id < 5000
project	SELECT src_id, _neighbors FROM t
sort	SELECT src_id, _neighbors FROM t ORDER BY src_id DESC LIMIT 1000
aggregate_scalar	SELECT substr(payload,1,4) AS b, count(*) FROM t GROUP BY 1
aggregate_on_list	SELECT _neighbors, count(*) FROM t GROUP BY _neighbors
join_scalar	SELECT a.src_id, a._neighbors FROM t a JOIN t b ON a.src_id = b.src_id LIMIT 100
join_on_list	SELECT count(*) FROM t a JOIN t b ON a._neighbors = b._neighbors
unnest_flatten	SELECT src_id, n.* FROM t CROSS JOIN UNNEST(_neighbors) AS n(dst)

Measurements: accepts_list_input (planning + execution complete), wall-clock ms, output row count, output bytes (sum of get_array_memory_size over all emitted batches). Memory is exercised but not directly capped — the goal is go/no-go and order-of-magnitude calibration, not a tight benchmark.

Run with cargo run --release -p factorized-batches (release profile — LTO-thin, opt-level 3). Sample output captured at validation-prototypes/factorized-batches/sample-output.txt.

Results (n_src = 10 000, runs single-threaded on the bench VM)

Acceptance + speedup matrix (factorized vs flat baseline)

op	fanout=1	fanout=10	fanout=100	fanout=1000	skew=10/0.02
filter	OK (0.32×)	OK (0.72×)	OK (1.95×)	OK (0.48×)	OK (1.11×)
project	OK (0.81×)	OK (1.03×)	OK (1.26×)	OK (1.43×)	OK (0.88×)
sort (TopK 1000)	OK (0.94×)	OK (7.18×)	OK (70.18×)	OK (336.28×)	OK (10.05×)
aggregate_scalar	OK (0.71×)	OK (2.77×)	OK (16.47×)	OK (140.36×)	OK (2.32×)
aggregate_on_list	OK (—)	OK (—)	OK (—)	OK (—) — 1.6 s @ 10M edges	OK (—)
join_scalar (LIMIT 100)	OK (0.83×)	OK (3.57×)	OK (4.15×)	OK (33.88×)	OK (2.65×)
join_on_list	OK (—)	OK (—)	OK (—) — 26 ms	OK (—) — 659 ms	OK (—)
unnest_flatten	FAILS	FAILS	FAILS	FAILS	FAILS

OK means the physical plan compiled and the stream drained without error. Speedup = time_flat / time_factorized; > 1 means factorized is faster. (—) means no flat-row analogue: GROUP BY / JOIN on a List value is semantically different from the flat-row equivalent (it groups / joins on full neighbor-set equality).

EXPLAIN plans

aggregate_scalar (factorized input):

SortPreservingMergeExec: [bucket@0 ASC NULLS LAST]
  SortExec: expr=[bucket@0 ASC NULLS LAST], preserve_partitioning=[true]
    ProjectionExec: ...
      AggregateExec: mode=FinalPartitioned, gby=[substr(...)@0], aggr=[count(...)]
        RepartitionExec: partitioning=Hash([substr(...)@0], 2)
          AggregateExec: mode=Partial, gby=[substr(payload@0,1,4)], aggr=[count(...)]
            DataSourceExec: partitions=1

The _neighbors column is correctly pruned from the scan projection (projection=[payload]). When the group key is scalar, the List column never hits the aggregator at all — it's column-pruned away.

join_scalar (factorized input):

ProjectionExec: expr=[src_id@1 as src_id, _neighbors@2 as _neighbors]
  GlobalLimitExec: skip=0, fetch=100
    HashJoinExec: mode=CollectLeft, join_type=Inner, on=[(src_id@0, src_id@0)]
      DataSourceExec: partitions=1
      DataSourceExec: partitions=1

The List column rides through as a passthrough projection — it never enters the hash table. HashJoinExec hashes only the join key (src_id).

aggregate_on_list (factorized input):

ProjectionExec: expr=[_neighbors@0, count(Int64(1))@1 as n]
  AggregateExec: mode=FinalPartitioned, gby=[_neighbors@0 as _neighbors], aggr=[count(...)]
    RepartitionExec: partitioning=Hash([_neighbors@0], 2)
      AggregateExec: mode=Partial, gby=[_neighbors@0 as _neighbors], aggr=[count(...)]
        DataSourceExec: partitions=1

This is the headline surprise: DataFusion's AggregateExec is happy to use a List<UInt64> column as a hash-grouping key, and the partitioner is happy to hash-repartition by it. Cost scales with total edge count, not distinct-list-count: 12 ms @ 100K edges, 113 ms @ 1M edges, 1.6 s @ 10M edges (roughly linear in edge volume). Semantically this groups by full neighbor-set equality — useful for "find all sources with the same neighbor set" but not the same as "GROUP BY exploded neighbor".

sort (factorized input):

SortExec: TopK(fetch=1000), expr=[src_id@0 DESC]
  DataSourceExec: partitions=1

The List column rides through the TopK fetch with no penalty.

unnest_flatten (SELECT src_id, n.* FROM t CROSS JOIN UNNEST(_neighbors) AS n(dst)):

execute: This feature is not implemented:
  Physical plan does not support logical expression
  OuterReferenceColumn(Field { name: "_neighbors", data_type: List(UInt64) },
                       Column { table: "t", name: "_neighbors" })

CROSS JOIN UNNEST(<correlated column>) is the cleanest SQL syntax for exploding a List, but DataFusion 52.5 hits the unimplemented-physical-lowering branch for the correlated reference. The failure surface is physical — the logical plan compiles, the physical plan refuses to construct.

Per-op recommendation

Op	DataFusion 52.5 behavior	Recommendation
FilterExec (scalar pred)	Passthrough for List columns, no perf cost.	KEEP_FACTORIZED — no Flatten needed.
ProjectionExec	Passthrough; identical perf to flat.	KEEP_FACTORIZED.
SortExec (scalar key)	List passes through; at fanout ≥ 10, factorized is 7–336× faster.	KEEP_FACTORIZED. Stronger than §5.2 expected.
AggregateExec (scalar key)	List column-pruned at the scan; 2.7–140× faster at fanout ≥ 10.	KEEP_FACTORIZED. §5.2 should call this out.
AggregateExec (list key)	Works; groups by full-list equality.	MULTIPLICITY_AWARE_FUTURE. Semantically distinct from GROUP BY exploded.
HashJoinExec (scalar key)	List rides through; 2.6–34× faster than the flat baseline.	KEEP_FACTORIZED. §5.2 should call this out.
HashJoinExec (list key)	Works; semantics = match on full-list equality.	MULTIPLICITY_AWARE_FUTURE. Rare workload, but available.
UNNEST flatten	Fails at physical lowering for correlated CROSS JOIN UNNEST(col).	FLATTEN_BEFORE must use the SELECT-clause UNNEST(col) form, the DataFrame unnest_columns API, or a custom FlattenExec. Do not rely on CROSS JOIN UNNEST in IR.

Decision impact on MR-737 §5.2 / Open Q2

§5.2 currently reads as "factorize-local, flatten before DataFusion ops" with the expectation that most ops need flattening. The data flips this for scalar-keyed ops:

1. Sort, Aggregate (scalar key), HashJoin (scalar key), Filter, Project all KEEP factorized at every cell tested. Speedup over the flat baseline is monotonically increasing with fanout for the memory-shape-sensitive ops (Sort up to 336×, AggregateExec up to 140×, HashJoinExec up to 34×). The List column is either column-pruned (when not referenced) or passthrough-projected (when referenced).

2. Aggregate / Join on a list-typed key works, but the semantics are "match on full-list equality", not "match on any exploded element". This is genuinely useful (neighbor-set deduplication, signature joins) but needs its own §5.2 sub-section so callers don't reach for it expecting element-wise semantics. Recommendation: MULTIPLICITY_AWARE_FUTURE.

3. Flatten via CROSS JOIN UNNEST(col) is broken in DF 52.5. This is the syntax §5.2 most naturally reaches for ("emit a Flatten by wrapping in CROSS JOIN UNNEST"). The fix has three live paths:

   • SELECT-clause UNNEST(_neighbors) (not yet exercised here — TODO extend the probe — but the prior art in datafusion/src/sql/expr.rs suggests this form is implemented).
   • DataFrame API unnest_columns(&["_neighbors"]).
   • A custom FlattenExec physical operator (which we'll already need for the custom-operator experiment 1.3).

   The §5.2 rule should be reworded to "insert Flatten via the DataFrame unnest_columns API or our own FlattenExec; do NOT lower to CROSS JOIN UNNEST in IR".

4. Expand-shaped workloads (the dominant case for graph traversal) benefit dramatically from factorization on scalar-keyed pipelines, which matches the §0 hop-1 spike result (MR-376 measured 72× on local FS for a related shape; here we see >70× on sort + >140× on aggregate at fanout=100). §5.2 should harden its claim from "factorized helps" to "factorized is the default; flatten is the exception".

5. Open Q2 ("does the factorized-IR pay off for DataFusion ops?") is resolved YES. §10's open-question bullet for Q2 can flip to RESOLVED with this writeup as evidence.

No fundamental seam mismatch was uncovered, so §5.11 (substrate decision) does NOT need to be re-opened.

Caveats / what this experiment did NOT measure

• Memory pool ceiling: probes ran with the default unbounded pool. The table reports out_bytes per emitted batch but not peak in-aggregator state. Re-running with TrackConsumersPool is a follow-up if §5.7 cost model needs tighter calibration numbers.
• Parallelism: cells ran with the default DF partition count (2 in this environment). Cliff behavior at higher partition counts isn't probed.
• Spill behavior: dataset sizes top out at ~10M edges (1 GB-ish in flat shape). No on-disk spill triggered.
• Vector / FTS columns: only List<UInt64> exercised. Other list payloads (e.g. List<Float32> vectors) may have different hash / compare costs.
• SELECT-clause UNNEST: only the CROSS JOIN UNNEST form was probed. Need a follow-up cell to confirm SELECT UNNEST(_neighbors) FROM t and df.unnest_columns(&["_neighbors"]) both work.

Follow-ups

• Add a SELECT UNNEST(...) and a DataFrame unnest_columns(...) cell so the writeup pins down at least one working Flatten path. (Cheap; ~30 min.)
• File a DataFusion issue for CROSS JOIN UNNEST(<correlated column>) hitting "Physical plan does not support logical expression OuterReferenceColumn". Probably already tracked — search first.
• Extend probe to List<Float32> (vector-shape) and List<List<UInt64>> (nested neighbor sets, e.g. multi-hop staging) before Phase 0 lowers Vector ANN results into the factorized IR.