skip to main content


Title: GearDB: A GC-free Key-Value Store on HM-SMR Drives with Gear Compaction
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%.  more » « less
Award ID(s):
1717660 1702474
NSF-PAR ID:
10106223
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
19th USENIX Conf. on File and Storage Technologies (FAST)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. Log-based data management systems use storage as if it were an append-only medium, transforming random writes into sequential writes, which delivers significant benefits when logs are persisted on hard disks. Although solid-state drives (SSDs) offer improved random write capabilities, sequential writes continue to be advan- tageous due to locality and space efficiency. However, the inherent properties of flash-based SSDs induce major disadvantages when used with a random write block interface, causing write amplifica- tion, uneven wear, log stacking, and garbage collection overheads. To eliminate these disadvantages, Zoned Namespace (ZNS) SSDs have recently been introduced. They offer increased capacity, re- duced write amplification, and open up data placement and garbage collection to the host through zones, which have sequential-write semantics and must be explicitly reset. We explain how the new ZNS Zone Append primitive, which sup- ports pushing fine-grained data placement onto the device, along with our proposal for “Group Append”, which enables sub-block sized appends, could benefit log-structured data management sys- tems. We explore advantages of ZNS SSDs with Zone Append, Group Append, and computational storage in four log-based data management areas: (i) log-based file systems, (ii) LSM trees such as RocksDB, (iii) database systems, and (iv) event logs/shared logs. Furthermore, we propose research directions for each of these data management systems using ZNS SSDs. 
    more » « less
  3. The emergence of Hybrid Shingled Magnetic Recording (H-SMR) allows dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. H-SMR is promising for its ability to manage the performance/capacity trade-off on the disk platters and to adaptively support different application scenarios in large-scale storage systems. However, there is little research on how to efficiently manage data and space in such H-SMR drives. In this paper, we present ZoneAlloy, an elastic data and space management scheme for H-SMR drives, to explore the benefit of using such drives. ZoneAlloy initially allocates CMR space for the application and then gradually converts the disk format from CMR to SMR to create more space for the application. ZoneAlloy controls the overhead of the format conversion on the application I/O with our quantized migration mechanism. When data is stored in an SMR area, ZoneAlloy reduces the SMR update overhead using H-Buffer and Zone-Swap. H-Buffer is a small host-controlled CMR space that absorbs the SMR updates and migrates those updates back to the SMR space in batches to bring down the SMR update cost. Zone-Swap dynamically swaps "hot" data from the SMR space to the CMR space to further alleviate the SMR update problem. Evaluation results based on MSR-Cambridge traces demonstrate that ZoneAlloy can reduce the average I/O latency and limit the performance degradation of the application I/O during format conversion. 
    more » « less
  4. Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR. 
    more » « less
  5. Modern NVMe solid state drives offer significantly higher bandwidth and low latency than prior storage devices. Current key-value stores struggle to fully utilize the bandwidth of such devices. This paper presents SplinterDB, a new key-value store explicitly designed for NVMe solid state drives. SplinterDB is designed around a novel data structure (the STBε-tree), that exposes I/O and CPU concurrency and reduces write amplification without sacrificing query performance. STBε-tree combines ideas from log-structured merge trees and Bε-trees to reduce write amplification and CPU costs of compaction. The SplinterDB memtable and cache are designed to be highly concurrent and to reduce cache misses. We evaluate SplinterDB on a number of micro- and macro-benchmarks, and show that SplinterDB outperforms RocksDB, a state-of-the-art key-value store, by a factor of 6–10x on insertions and 2–2.6x on point queries, while matching RocksDB on small range queries. Furthermore, SplinterDB reduces write amplification by 2x compared to RocksDB. 
    more » « less