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Limits: 효과가 spatial locality에 의존하고 일반 YCSB에서는 우위가 작다.</property:Applicability>
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LSM compaction은 대규모 순차·공간적 데이터 수집에서도 반복 재쓰기를 만든다.

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Canonical title: EvenDB: Optimizing Key-Value Storage for Spatial Locality
Question: 공간 지역성이 있는 분석 KV 작업에서 LSM write amplification을 줄일 수 있는가?
Context: LSM compaction은 대규모 순차·공간적 데이터 수집에서도 반복 재쓰기를 만든다.
Method: EvenDB는 공간 파티셔닝과 LSM batch I/O를 결합한다.
Evaluation: workloads=256 GB production analytics dataset; YCSB without locality; baselines=RocksDB; metrics=ingestion throughput; write amplification; results=4.4× ingest and nearly 4× lower WAF; parity on no-locality YCSB.
Interpretation: 키 공간의 의미적 지역성을 compaction 단위로 노출하면 재쓰기를 줄일 수 있다.
Reusable lesson: 데이터의 공간 구조를 key-value 저장 레이아웃에 보존하라.
Applicability: 공간·분석 데이터 ingest KV.
Limits: 효과가 spatial locality에 의존하고 일반 YCSB에서는 우위가 작다.</property:Evidence_note>
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Canonical title: EvenDB: Optimizing Key-Value Storage for Spatial Locality
Question: 공간 지역성이 있는 분석 KV 작업에서 LSM write amplification을 줄일 수 있는가?
Context: LSM compaction은 대규모 순차·공간적 데이터 수집에서도 반복 재쓰기를 만든다.
Method: EvenDB는 공간 파티셔닝과 LSM batch I/O를 결합한다.
Evaluation: workloads=256 GB production analytics dataset; YCSB without locality; baselines=RocksDB; metrics=ingestion throughput; write amplification; results=4.4× ingest and nearly 4× lower WAF; parity on no-locality YCSB.
Interpretation: 키 공간의 의미적 지역성을 compaction 단위로 노출하면 재쓰기를 줄일 수 있다.
Reusable lesson: 데이터의 공간 구조를 key-value 저장 레이아웃에 보존하라.
Applicability: 공간·분석 데이터 ingest KV.
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