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그래프 데이터베이스 성능 벤치마크
그래프 DB와 관계형 DB 간의 탐색 지연 시간, 메모리 사용량 및 쿼리 효율을 비교 분석한 성능 평가 도구.
문제 및 해결책
다단계 소셜 그래프 및 추천 시스템에서 최적의 데이터 저장소를 선정하기 위한 데이터 기반 분석 필요.
기능
- 탐색 깊이별 자동화된 순회 벤치마크 테스트
- 지연 시간 백분위수(p50, p95, p99) 정밀 비교
- 메모리 사용량 및 CPU 점유율 실시간 추적
- Docker 기반 완전 재현 가능한 테스트 환경
기술 스택
| 기술 | 버전 | 목적 | 선택 이유 |
|---|---|---|---|
| Go | 1.25.1 | Primary language | Strong concurrency primitives, fast compilation, good driver ecosystem |
| neo4j-go-driver/v6 | v6.2.0 | Bolt protocol client for CognoDB, Neo4j, Memgraph | Official driver, supports Bolt 5.x, parameterized queries |
| go-echarts/v2 | v2.7.2 | HTML chart generation | Go-native, generates standalone HTML |
| godotenv | v1.5.1 | Environment configuration | Standard .env loading |
| net/http | stdlib | REST client for TigerGraph | No external dependency needed |
아키텍처
Benchmark Engine
- Adapter pattern with common GraphDatabase interface (16 methods)
- 5 database adapters (4 active, FalkorDB disabled due to OOM)
- Sequential workload executor with warm-up and measured iterations
- Concurrent workload executor with N goroutines and sync.WaitGroup
- Factory function for adapter creation from configuration
CLI Pipeline
- 10 CLI entry points: benchmark, captest, charts, cleanup, dataset, load, runall, summary, verify, verify-load
- JSONL append-only result output for crash resilience
- CSV summary aggregation with percentile calculation
- HTML chart generation via go-echarts
- Pipeline automation via runall (verify → load → benchmark → report)
프로젝트 구조
cmd
benchmark
captest
charts
cleanup
dataset
load
runall
summary
verify
verify-load
internal
benchmark
concurrent.go
measurement.go
runner.go
safe.go
database
cognodb
memgraph
neo4j
tigergraph
types
factory.go
dataset
parser.go
API 엔드포인트
Query Workloads (포트 0)
| 메서드 | 경로 | 목적 |
|---|---|---|
| Cypher | MATCH (n:Person {id: $id}) RETURN n.id AS id | Point lookup — single node by ID |
| Cypher | MATCH (n:Person {region: $val}) RETURN n.id AS id LIMIT 10 | Indexed lookup — nodes by region |
| Cypher | MATCH (n:Person {id: $id})-[:FRIEND]->(m:Person) RETURN collect(m.id) | 1-hop traversal — direct friends |
| Cypher | MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id) | 2-hop traversal — friends of friends |
| Cypher | MATCH (n:Person {id: $id})-[:FRIEND]->()-[:FRIEND]->()-[:FRIEND]->(m:Person) RETURN collect(DISTINCT m.id) | 3-hop traversal — extended network |
| Cypher | MATCH (n:Person) RETURN n.region AS region, count(n) AS cnt ORDER BY cnt DESC LIMIT 10 | Aggregation — region distribution |
| Cypher | CREATE (n:Person {id: $id, region: $region, ...}) | Write — create new node |
데이터베이스 설계
Person (node label)
| 컬럼 | 유형 | 비고 |
|---|---|---|
| idPK | STRING | Unique constraint, indexed |
| region | STRING | Indexed |
| gender | STRING | Not indexed |
| age | STRING | Indexed (CognoDB, Neo4j, Memgraph only) |
| completion_percentage | STRING | Not indexed |
49,683 nodes extracted from SNAP soc-Pokec dataset. All properties are strings.
FRIEND (relationship type)
| 컬럼 | 유형 | 비고 |
|---|---|---|
| source_id | STRING | Person.id (from) |
| target_id | STRING | Person.id (to) |
100,000 directed relationships. No properties on edges. Many-to-many cardinality.
Directed graph: Person --[FRIEND]--> Person. Many-to-many via graph relationships (no junction table). Relationships are first-class citizens in the graph model.
주요 설계 결정
Single Bolt Driver for 3 Databases
결정: Use neo4j-go-driver/v6 for CognoDB, Neo4j, and Memgraph
이유: All three support Bolt protocol; Cypher is logically equivalent
이점: Code reuse, consistent behavior across databases, adapter pattern isolates DB-specific behavior
트레이드오프: Cannot test database-specific optimizations; Memgraph has its own Go driver
MaxPoolSize = 1
결정: Single connection per benchmark run despite constant defining 10
이유: Measure single-connection latency; avoid connection pool effects
이점: Clean latency measurement without pool contention
트레이드오프: Doesn't test connection pooling behavior
JSONL for Raw Results
결정: One JSON object per line, append-only output
이유: Stream results without loading all into memory; easy to append across runs
이점: Resilient to crashes (partial results preserved)
트레이드오프: Requires full file scan for aggregation
No Retries on Measured Queries
결정: Zero retries for benchmark queries to capture true latency
이유: Network blips and failures are part of real-world performance
이점: Honest performance measurement including failure modes
트레이드오프: Network blips cause false negatives
Goroutine-Per-Client Concurrency
결정: Each concurrent client creates its own database connection
이유: Simulates real-world multi-client scenarios; avoids shared connection contention
이점: Realistic concurrency testing with independent connections
트레이드오프: Higher resource usage; connection limits become the bottleneck
MERGE for Idempotent Node Loading
결정: Use MERGE (upsert) instead of CREATE for node loading
이유: Makes loading idempotent — running multiple times doesn't create duplicates
이점: Safe re-runs, consistent dataset state
트레이드오프: MERGE is slightly slower than CREATE due to existence checks
테스트
None (no _test.go files)•단위 테스트 0개
테스트 항목
- Capability test matrix (cmd/captest/) — tests all 8 workload types against all databases with small 10-node graph
- Connection verification (cmd/verify/) — tests basic connectivity and parameterized queries
- Data integrity verification (cmd/verify-load/) — verifies node/relationship counts (49,683 nodes, 100,000 relationships)
부족한 커버리지
- No unit tests for individual functions (_test.go files do not exist)
- No tests for statistical functions (percentile, mean, stddev calculation)
- No tests for CSV parsing edge cases (empty datasets, malformed CSVs)
- No tests for concurrent behavior
- No tests for chart generation correctness
- No test framework installed (no testify, gomock)
주요 과제
- 서로 다른 데이터 모델에 공정하고 동일한 부하 조건 재현
- 깊은 순회 시 메모리 오버헤드 통제
주요 학습
- 네이티브 그래프 엔진과 RDBMS 간의 실제 트레이드오프 분석
- 고부하 환경에서의 정밀한 성능 벤치마킹 방법론