perf(unique): make verifyUniqueWithinMutation linear - #9822
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🟡 Changes recommended
The new linear algorithm should be backed by targeted unit tests asserting the core within-mutation @unique semantics to guard against regressions.
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Pull request overview
This PR optimizes the in-request duplicate check for @unique predicates by replacing the previous quadratic pairwise scan with a linear, map-based pass keyed by (predicate, value), reducing CPU and allocations for large batched mutations.
Changes:
- Introduces a
uniqueValueKeyto represent(predicate, value)identity for within-mutation duplicate detection. - Rewrites
verifyUniqueWithinMutationto track first-seen subjects in aseenmap, making the check O(N) in the number of unique edges. - Preserves prior semantics around same-subject duplicates, nil
ObjectValueskipping, and pruned-mutation handling.
File summaries
| File | Description |
|---|---|
edgraph/server.go |
Replaces O(N²) within-mutation @unique duplicate detection with a single-pass seen-map keyed by (predicate, value). |
Review details
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Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughChangesUnique mutation validation
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Refactor · Severity of issue fixed: Medium Suggested reviewers: Merge Risk: ⚪ Minimal · up to The linear uniqueness check retains the covered validation behavior without an identified correctness regression, so no merge-blocking risk remains. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Inline comments:
In `@edgraph/server.go`:
- Around line 2451-2516: Update uniqueValueKeyFrom so its key preserves the
former dynamic Go value-type equality rather than distinguishing
Value_DefaultVal and Value_StrVal via tv.Tid. Keep the existing slice content
normalization for []byte and []float32, while ensuring equivalent string values
from mixed JSON and RDF mutations produce the same duplicate-check key.
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edgraph/server.goedgraph/server_test.go
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Fixes #9814. The in-request duplicate check for @unique predicates compared every unique-predicate edge against every other, calling dql.TypeValFrom once per pair: O(N^2) time and allocations in the number of edges per mutation. At 8k edges one check took 1.67s and 64M allocations, dominating batched writes on @unique predicates. Replace the nested scan with a single pass over a seen-map keyed on (predicate, value), remembering the first subject that set each value. Semantics are unchanged: duplicate values from the same subject remain allowed, nil ObjectValues are skipped, entries pruned by updateMutations are still ignored, and the error message is identical. Value identity still uses the interface{} produced by TypeValFrom, so type identity participates in the comparison exactly as it did with ==. Measured (M4 Pro, benchstat over 6 runs, all p=0.002): 5.20ms -> 32.6us at 500 edges, 1.67s -> 585us at 8000 edges (-99.96%); allocs/op drops from N^2 (64M at 8k) to ~N (8k). The after curve doubles per doubling of N, i.e. linear.
…d tests
Review catch: uniqueValueKey held whatever dql.TypeValFrom returned,
and five of its branches return slice types ([]byte for
bytes/geo/datetime/bigfloat, []float32 for vfloat) - hashing one
panics, there is no recover on the mutation path, and the chunker makes
it reachable from a plain JSON mutation ("[1.0, 2.0]" on a string
@unique predicate parses as Vfloat32Val before the schema is
consulted). Worse than the old code, whose == comparison only ran once
two edges shared a predicate.
Slice values are now keyed by exact byte content
(string(v) / FloatArrayAsBytes), and types.TypeID joins the key so
equal bytes of different types never collide. The previous code
panicked on any two same-predicate slice values, so content equality
replaces a crash rather than changing working behavior.
Tests added as requested, next to the existing bounds checks:
- TestVerifyUniqueWithinMutationSemantics: different-subject duplicate
rejected with the exact established error message; same-subject
repeats, distinct values/predicates/types, nil ObjectValues and
cross-mutation duplicates in one request.
- TestVerifyUniqueWithinMutationNonScalarValues: panic regression
driving the reviewer's JSON repro through the real chunker (guarded
against going vacuous), plus []byte content equality and
string-vs-equal-bytes non-collision. Verified to panic with "hash of
unhashable type: []float32" on the previous commit.
Perf holds: 38us @500 edges to 651us @8k, growth 2.0x per doubling
(linear); still -99.96% vs the O(N^2) code at 8k edges.
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Resolved in e8a6f1d (hashable key + tests). Superseded by the follow-up review.
matthewmcneely
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matthewmcneely
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Both findings are resolved at aa7f597. LGTM.
byteContent puts the key back on Go dynamic-type identity for everything comparable, so map equality is the old == equality again, and only the five slice branches get content-normalized. Re-ran the repros in a clean worktree:
- single vfloat edge: no panic
DefaultValvsStrValin one RDF mutation: duplicate rejected- RDF mutation + JSON mutation in one request: duplicate rejected
I also swept all five slice-returning branches of dql.TypeValFrom (bytes, geo, datetime, bigfloat, vfloat), single edge and duplicate pair each — no panic, duplicates caught by content. Everything reaching the default arm is int64, string, bool, or float64, so the hashability hole is closed.
The parser-driven tests are the right shape. Those anti-vacuity guards mean the suite fails loudly if rdf_parser.go or the chunker ever stops producing the value kinds these findings rested on, rather than passing for the wrong reason.
gofmt, go vet, and the full edgraph package are clean locally, and CI is green. Thanks for the thorough turnaround on both rounds.
Two bits of bot noise worth ignoring rather than chasing: CodeRabbit's ast-grep uint32(len(...)) warnings land on the bounds-check line this PR only moved, and the api.NQuad lock-copy vet hit at edgraph/server.go:1114 predates this change.
Fixes #9814.
The in-request duplicate check for @unique predicates compared every unique-predicate edge against every other, calling dql.TypeValFrom once per pair: O(N^2) time and allocations in the number of edges per mutation. At 8k edges one check took 1.67s and 64M allocations, dominating batched writes on @unique predicates.
Replace the nested scan with a single pass over a seen-map keyed on (predicate, value), remembering the first subject that set each value. Semantics are unchanged: duplicate values from the same subject remain allowed, nil ObjectValues are skipped, entries pruned by updateMutations are still ignored, and the error message is identical. Value identity still uses the interface{} produced by TypeValFrom, so type identity participates in the comparison exactly as it did with ==.
Measured (M4 Pro, benchstat over 6 runs, all p=0.002): 5.20ms -> 32.6us at 500 edges, 1.67s -> 585us at 8000 edges (-99.96%); allocs/op drops from N^2 (64M at 8k) to ~N (8k).
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