| Autor: |
Kim, Donguk, Lee, Jongsung, Lim, Keun Soo, Heo, Jun, Ham, Tae Jun, Lee, Jae W. |
| Předmět: |
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| Zdroj: |
ACM Transactions on Storage; Feb2024, Vol. 20 Issue 1, p1-24, 24p |
| Abstrakt: |
Modern log-structured merge (LSM) tree-based key-value stores are widely used to process update-heavy workloads effectively as the LSM tree sequentializes write requests to a storage device to maximize storage performance. However, this append-only approach leaves many outdated copies of frequently updated key-value pairs, which need to be routinely cleaned up through the operation called compaction. When the system load is modest, compaction happens in background. However, at a high system load, it can quickly become the major performance bottleneck. To address this compaction bottleneck and further improve the write throughput of LSM tree-based key-value stores, we propose LAB-DB, which augments the existing LSM tree with a pair of B+ trees on byte-addressable nonvolatile memory (NVM). The auxiliary B+ trees on NVM reduce both compaction frequency and compaction time, hence leading to lower compaction overhead for writes and fewer storage accesses for reads. According to our evaluation of LAB-DB on RocksDB with YCSB benchmarks, LAB-DB achieves 94% and 67% speedups on two write-intensive workloads (Workload A and F), and also a 43% geomean speedup on read-intensive YCSB Workload B, C, D, and E. This performance gain comes with a low cost of NVM whose size is just 0.6% of the entire dataset to demonstrate the scalability of LAB-DB with an ever increasing volume of future datasets. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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