⚡ Bolt: DBML import O(N^2) 병목 현상 제거 - #635
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Pull request overview
This PR improves performance of the backend DBML import path by removing an O(N²) column-position calculation inside parse_dbml, replacing it with an O(1) per-table counter keyed by relation_oid. This fits the codebase’s goal of efficiently importing large schemas into the snapshot JSON format.
Changes:
- Replaced
sum(1 for ...)column counting with acol_countsdictionary counter to computecolumn_positionin O(1). - Added a short internal “Bolt” learning note documenting the anti-pattern and the preferred fix.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
| backend/app/spec/dbml_import.py | Replaces per-column O(N) scan with an O(1) dictionary counter for column_position, removing an O(N²) hotspot. |
| .Jules/bolt.md | Documents the performance lesson and recommended pattern for avoiding inline generator-based counting in loops. |
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Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
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Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
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Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
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Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head500f9b328e8e2a1e3f267b83a4d19259734ecf7b. -
Head SHA:
500f9b328e8e2a1e3f267b83a4d19259734ecf7b -
Workflow run: 30497677154
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Workflow attempt: 1
Coverage evidence
Coverage evidence job did not run or did not publish coverage evidence.
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file: bolt.md"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file: bolt.md"]
R1 --> V1["required checks"]
Evidence --> S2["Backend: dbml_import.py"]
S2 --> I2["API and service runtime"]
I2 --> R2["Review risk: Backend: dbml_import.py"]
R2 --> V2["backend tests"]
OpenCode Review Overview
Pull request overviewOpenCode cannot approve yet because required coverage evidence did not pass. Review outcome1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
Coverage evidenceCoverage evidence job did not run or did not publish coverage evidence. Changed-File Evidence Mapflowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file: bolt.md"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file: bolt.md"]
R1 --> V1["required checks"]
Evidence --> S2["Backend: dbml_import.py"]
S2 --> I2["API and service runtime"]
I2 --> R2["Review risk: Backend: dbml_import.py"]
R2 --> V2["backend tests"]
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💡 What:
backend/app/spec/dbml_import.py의parse_dbml함수 내에서 컬럼의 위치를 계산할 때 사용하던 O(N^2) 복잡도의 inline generator expression(sum(1 for ...))을 O(1) 복잡도의 dictionary counter(col_counts)로 대체했습니다.🎯 Why: 기존 코드는 컬럼을 추가할 때마다 이전에 추가된 모든 컬럼 배열을 순회하여 위치를 계산했습니다. 이로 인해 컬럼 개수가 많은 대규모 DBML 스키마를 파싱할 때 심각한 성능 저하와 CPU 사이클 낭비가 발생했습니다.
📊 Impact: 루프 내 순회 연산을 O(N)에서 O(1) 딕셔너리 참조로 변경하여 파싱 로직의 시간 복잡도를 획기적으로 개선했습니다.
🔬 Measurement: 수천 개의 컬럼을 가진 대형 DBML 파일 파싱 시 측정 가능한 파싱 속도 향상이 기대됩니다. 기존 단위 테스트(
test_dbml_import.py)를 통해 논리적 동일성을 확인했습니다.PR created automatically by Jules for task 12755090022107559055 started by @seonghobae