CALL: Context-Aware Low-Latency Retrieval in Disk-Based Vector Databases
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911171515252736 |
|---|---|
| author | Jeong, Yeonwoo Cho, Hyunji Park, Kyuri Kim, Youngjae Park, Sungyong |
| author_facet | Jeong, Yeonwoo Cho, Hyunji Park, Kyuri Kim, Youngjae Park, Sungyong |
| contents | Embedding models capture both semantic and syntactic structures of queries, often mapping different queries to similar regions in vector space. This results in non-uniform cluster access patterns in modern disk-based vector databases. While existing approaches optimize individual queries, they overlook the impact of cluster access patterns, failing to account for the locality effects of queries that access similar clusters. This oversight increases cache miss penalty. To minimize the cache miss penalty, we propose CALL, a context-aware query grouping mechanism that organizes queries based on shared cluster access patterns. Additionally, CALL incorporates a group-aware prefetching method to minimize cache misses during transitions between query groups and latency-aware cluster loading. Experimental results show that CALL reduces the 99th percentile tail latency by up to 33% while consistently maintaining a higher cache hit ratio, substantially reducing search latency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18670 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CALL: Context-Aware Low-Latency Retrieval in Disk-Based Vector Databases Jeong, Yeonwoo Cho, Hyunji Park, Kyuri Kim, Youngjae Park, Sungyong Databases Embedding models capture both semantic and syntactic structures of queries, often mapping different queries to similar regions in vector space. This results in non-uniform cluster access patterns in modern disk-based vector databases. While existing approaches optimize individual queries, they overlook the impact of cluster access patterns, failing to account for the locality effects of queries that access similar clusters. This oversight increases cache miss penalty. To minimize the cache miss penalty, we propose CALL, a context-aware query grouping mechanism that organizes queries based on shared cluster access patterns. Additionally, CALL incorporates a group-aware prefetching method to minimize cache misses during transitions between query groups and latency-aware cluster loading. Experimental results show that CALL reduces the 99th percentile tail latency by up to 33% while consistently maintaining a higher cache hit ratio, substantially reducing search latency. |
| title | CALL: Context-Aware Low-Latency Retrieval in Disk-Based Vector Databases |
| topic | Databases |
| url | https://arxiv.org/abs/2509.18670 |