TY - GEN
T1 - Jellyfish
T2 - 21st International Middleware Conference, Middleware 2020
AU - Yeon, Jeseong
AU - Lee, Hyeon Gyu
AU - Kim, Leeju
AU - Lee, Eunji
AU - Han, Youil
AU - Kim, Bryan S.
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Multi-version concurrency control is a widely employed concurrency control mechanism, as it allows non-blocking accesses while providing isolation among transactions. However, maintaining multiple versions increases the latency for both point lookups and ranged retrievals because of the overhead in finding the right version. In particular, the append-only skip list-widely used in the state-of-the-art key-value stores (KVS)-shows a significant performance degradation due to its append-only nature. This paper presents a novel skip list implementation called JellyFish. JellyFish reduces the overhead of multi-version concurrency control by separating the per-key updates from the key indexing. We implement our design on top of RocksDB and compare it against a wide variety of data structures. Our evaluation with micro-benchmarks and real-world workloads show that we not only improve the throughput by up to 93%, but also reduce the latency of update operations by up to 42%.
AB - Multi-version concurrency control is a widely employed concurrency control mechanism, as it allows non-blocking accesses while providing isolation among transactions. However, maintaining multiple versions increases the latency for both point lookups and ranged retrievals because of the overhead in finding the right version. In particular, the append-only skip list-widely used in the state-of-the-art key-value stores (KVS)-shows a significant performance degradation due to its append-only nature. This paper presents a novel skip list implementation called JellyFish. JellyFish reduces the overhead of multi-version concurrency control by separating the per-key updates from the key indexing. We implement our design on top of RocksDB and compare it against a wide variety of data structures. Our evaluation with micro-benchmarks and real-world workloads show that we not only improve the throughput by up to 93%, but also reduce the latency of update operations by up to 42%.
UR - http://www.scopus.com/inward/record.url?scp=85098509744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098509744&partnerID=8YFLogxK
U2 - 10.1145/3423211.3425672
DO - 10.1145/3423211.3425672
M3 - Conference contribution
AN - SCOPUS:85098509744
T3 - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
SP - 134
EP - 148
BT - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
PB - Association for Computing Machinery, Inc
Y2 - 7 December 2020 through 11 December 2020
ER -