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PSL-DB: Non-Volatile Memory-optimized LSM-Tree with Skip List
Chanyeol Park, Dongui Kim, Beomseok Nam
http://doi.org/10.5626/JOK.2020.47.7.635
With the release of Intel"s Optane DC Persistent Memory, non-volatile memory, offering higher capacity than DRAM and showing higher performance than SSD and HDD, is in the spotlight as the next generation of storage devices. In this paper, we propose the Persistent Skip List DataBase (PSL-DB), a key-value store system optimized for the Optane DCPM in app-direct mode. PSL-DB uses a byte-addressable skip list that significantly reduces the I/O traffic as it avoids redundant writes. PSL-DB also does not sacrifice write performance for read performance as it does not degrade the write performance via artificial governors. In our experiments using Intel Optane DC Persistent Memory, PSL-DB shows significantly higher query processing throughput than legacy LevelDB that stores SSTables in Optane DC PM.
Distributed Storage System for Reducing Write Amplification on Non-Volatile Memory
http://doi.org/10.5626/JOK.2020.47.2.129
Recently, research on non-volatile memory, such as 3DXpoint, in distributed storage systems has received considerable interest from both academia and industry. However, in order to utilize these state-of-the-art non-volatile memory devices effectively in distributed storage systems, there is a need for improvements in traditional architectures of HDD/SSD-based storage systems. This is because current distributed storage system structures use a dedicated space for journaling to make up for slow storage performance. Also, considering the performance characteristics of non-volatile memory, which are similar to that of DRAM, current distributed storage system structures are not only inefficient in terms of overall performance but also cause write amplification. In this paper, we propose an architecture that mitigates the effects of write amplification in non-volatile memory-based distributed storage systems. To evaluate the proposed architecture and scheme, we have conducted diverse experiments in a CEPH storage system environment. Through these experiments, we have confirmed that the DAXNJ structure proposed in this paper decreases write amplification by 61% during 1M object write operations and increases the overall system performance by 15%.
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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