Persistent memory (PM) brings important opportunities for improving data storage including the widely used hash tables. However, PM is not friendly to small writes, which causes existing PM hashes to suffer from high hardware write amplification. Hybrid memory offers the performance and concurrency of DRAM and the durability and capacity of PM, but existing hybrid memory hashes cannot deliver high performance, low DRAM footprint, and fast recovery at the same time. This paper proposes WALSH, a flat hash with novel log-structured separate chaining designs to optimize the performance while ensuring low DRAM footprint and fast recovery. To address the overhead of hash resizing and garbage collection (GC), WALSH further proposes partial resizing/GC mechanisms and a 4-phase protocol for concurrent hash operations. As a result, WALSH is the first flat index for hybrid memory with embedded write aggregation ability. A comprehensive evaluation shows that WALSH substantially outperforms state-of-the-art hybrid memory hashes; e.g., its insert throughput is up to 2.4X that of related works while saving more than 87% of DRAM. WALSH also provides efficient recovery; e.g., it can recover a dataset with 1 billion objects in just a few seconds.
Our extensive experiments reveal that existing key-value stores (KVSs) achieve high performance at the expense of a huge memory footprint that is often impractical or unacceptable. Even with the emerging ultra-fast byte-addressable persistent memory (PM), KVSs fall far short of delivering the high performance promised by PM's superior I/O bandwidth. To find the root causes and bridge the huge performance/memory-footprint gap, we revisit the architectural features of two representative indexing mechanisms (single-stage and multi-stage) and propose a three-stage KVS called FluidKV. FluidKV effectively consolidates these indexes by fast and seamlessly running incoming key-value request stream from the write-concurrent frontend stage to the memory-efficient backend stage across an intermediate stage. FluidKV also designs important enabling techniques, such as thread-exclusive logging, PM-friendly KV-block structures, and dual-grained indexes, to fully utilize both parallel-processing and high-bandwidth capabilities of ultra-fast storage hardware while reducing the overhead. We implemented a FluidKV prototype and evaluated it under a variety of workloads. The results show that FluidKV outperforms the state-of-the-art PM-aware KVSs, including ListDB and FlatStore with different indexes, by up to 9× and 3.9× in write and read throughput respectively, while cutting up to 90% of the DRAM footprint.
more » « less- PAR ID:
- 10567329
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- Volume:
- 17
- Issue:
- 6
- ISSN:
- 2150-8097
- Page Range / eLocation ID:
- 1377 to 1390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.14778/3648160.3648177
