Host-managed shingled magnetic recording drives (HMSMR) give a capacity advantage to harness the explosive growth of data. Applications where data is sequentially written
and randomly read, such as key-value stores based on Log-Structured Merge Trees (LSM-trees), make the HMSMR an ideal solution due to its capacity, predictable performance,
and economical cost. However, building an LSMtree based KV store on HM-SMR drives presents severe challenges in maintaining the performance and space efficiency
due to the redundant cleaning processes for applications and storage devices (i.e., compaction and garbage collections). To eliminate the overhead of on-disk garbage collections (GC) and improve compaction efficiency, this paper presents GearDB, a GC-free KV store tailored for HMSMR drives. GearDB proposes three new techniques: a new
on-disk data layout, compaction windows, and a novel gear compaction algorithm. We implement and evaluate GearDB with LevelDB on a real HM-SMR drive. Our extensive experiments have shown that GearDB achieves both good performance and space efficiency, i.e., on average 1:71 faster than LevelDB in random write with a space efficiency of 89.9%.
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A Set-aware Key-Value Store on Shingled Magnetic Recording Drives with Dynamic Band
Key-value (KV) stores play an increasingly critical role in supporting diverse large-scale applications in modern data centers hosting terabytes of KV items which even might reside on a single server due to virtualization purpose. The combination of ever growing volume of KV items and storage/application consolidation is driving a trend of high storage density for KV stores. Shingled Magnetic Recording (SMR) represents a promising technology for increasing disk capacity, but it comes at a cost of poor random write performance and severe I/O amplification. Applications/software working with SMR devices need to be designed and optimized in an SMR-friendly manner.
In this work, we present SEALDB, a Log-Structured Merge tree (LSM-tree) based key-value store that is specifically op- timized for and works well with SMR drives via adequately addressing the poor random writes and severe I/O amplification issues. First, for LSM-trees, SEALDB concatenates SSTables of each compaction, and groups them into sets. Taking sets as the basic unit for compactions, SEALDB improves compaction efficiency by mitigating random I/Os. Second, SEALDB creates varying size bands on HM-SMR drives, named dynamic bands. Dynamic bands not only accommodate the storage of sets, but also eliminate the auxiliary write amplification from SMR drives.
We demonstrate the advantages of SEALDB via extensive experiments in various workloads. Overall, SEALDB delivers impressive performance improvement. Compared with LevelDB, SEALDB is 3.42× faster on random load due to improved compaction efficiency and eliminated auxiliary write amplification on SMR drives.
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- NSF-PAR ID:
- 10065072
- Date Published:
- Journal Name:
- the 32nd IEEE International Parallel and Distributed Processing Symposium (IPDPS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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