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LSM Tree Compaction Offloading Using NVMe-oF
Sungho Moon, Hera Koo, Hyeongjun Jeon, Beomseok Nam
http://doi.org/10.5626/JOK.2022.49.7.569
NVMe-over-Fabrics (NVMe-oF) is drawing attraction in the industry as an alternative to disaggregated storage by providing fast access to remote NVMe SSDs through NVMe commands. In this paper, we propose RocksDB-oF, an LSM-Tree-based key-value store optimized for disaggregated storage using NVMe-oF. RocksDB-oF alleviated the Write Stall problem by offloading compaction from the computing node onto the storage node in consideration of the characteristics of NVMe-oF. In addition, a file system that uses Storage Performance Development Kit (SPDK) effectively solves the file system consistency problem of two nodes accessing the same NVMe SSD at the same time. Experimentally, in a disaggregated storage environment with NVMe-oF, RocksDB-oF showed higher write throughput than legacy RocksDB.
Recursive Compaction Method of LSM-tree based Key-value Store
Jongbin Kim, Seohui Son, Hyunsoo Cho, Hyungsoo Jung
http://doi.org/10.5626/JOK.2019.46.9.946
LSM-tree-based key-value stores exhibit an optimized structure for data writing operations and typically maintain the form of LSM tree by executing a compaction operation. The compaction operation which reads data from the storage device into memory for sorting it and writes back the result data in to the storage device several times causes some problems. In this paper, we analyzed the performance degradation and the write amplification caused by the compaction, and proposed a new compaction method known as recursive compaction. Recursive compaction alleviates the problems involving the compaction operation by utilizing multiple threads to perform multiple compactions at a time, handling read operation and garbage collection properly. We implemented this technique for Google LevelDB and analyzed the results.
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