skip to main content

Search for: All records

Creators/Authors contains: "Spillane, Richard"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. 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.
  2. Modern NVMe solid state drives offer significantly higher bandwidth and lower latency than prior storage devices. Cur- rent 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 STBe-tree) that exposes I/O and CPU concurrency and re- duces write amplification without sacrificing query perfor- mance. STBe-tree combines ideas from log-structured merge trees and Be-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 6–10⇥ on insertions and 2–2.6⇥ on point queries, while matching RocksDB on small range queries. Furthermore, SplinterDB reduces write amplification by 2⇥ compared to RocksDB.