SILT: a memory-efficient, high-performance key-value store

By Hyeontaek Lim, Bin Fan, D. Andersen, Michael Kaminsky. Symposium on Operating Systems Principles, 2011

343 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/54509d22d936bc62a71b0f475d559dfea359c1b9

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In Computer Networks and Communications

Citations in the corpus

bLSM: a general purpose log structured merge tree (2012, background): “7 bytes per key to achieve a read amplification of 1 [21].”
LinkBench: a database benchmark based on the Facebook social graph (2013, background): “[7] H. Lim, B. Fan, D. G. Andersen, and M. Kaminsky, SILT: a memory-e.cient, high-performance key-value store, in Proc.”
PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees (2017, background): “Great progress has been made in improving different aspects of key-value stores such as memory efficiency [9, 17, 34, 42, 58] and energy efficiency [6].”
MemC3: Compact and Concurrent MemCache with Dumber Caching and Smarter Hashing (2013, neutral): “We aim to remove all mutexes on the GET path to boost the concurrency of Memcached.”
NetCache: Balancing Key-Value Stores with Fast In-Network Caching (2017, background): “Given the high-throughput and low-latency requirements of large-scale Internet services, key-value storage systems are shifting to in-memory designs [1, 15, 16, 22, 23, 26, 27, 29, 30, 34, 36, 38, 42, 43].”
In-Memory Big Data Management and Processing: A Survey (2015, background): “With the advancement of hardware, hybrid systems which incorporate nonvolatile memories (NVMs) (e.g., SCM, PCM, SSD, Flash memory) [30], [31], [32], [118], [127], [153], [154], [155], [156] become a natural solution for achieving the speed.”
Scaling Memcache at Facebook (2013, neutral): “Improving single server cache performance is an active research area [9, 10, 28, 25].”
Characterizing, Modeling, and Benchmarking RocksDB Key-Value Workloads at Facebook (2020, neutral): “Other KV-store studies, such as SILT [31], Dynamo [14], FlashStore [12], and SkimpyStash [13], evaluate designs and implementations with some real-world workloads.”
WiscKey (2016, background): “, 16B) [Anand et al. 2010; Andersen et al. 2009; Atikoglu et al. 2015; Debnath et al. 2010; Lim et al. 2011], though value sizes can vary widely (e.”
Using RDMA efficiently for key-value services (2014, methodology): “Cuckoo hash tables are an attractive choice for building fast key-value systems [9, 31, 17] because, with K hash functions (usually, K is 2 or 3), they require only K memory lookups for GET operations, plus an additional pointer dereference if the values are not stored in the table itself.”
Cuckoo Filter: Practically Better Than Bloom (2014, methodology): “Our previous study in building high-speed and memoryefficient key-value stores [17, 11] and software-based Ethernet switches [26] all applied cuckoo hash tables as internal data structures.”
Horton Tables: Fast Hash Tables for In-Memory Data-Intensive Computing (2016, background): “Additional approaches attempt to fuse or use other data structures in tandem with hash tables to enable faster lookups [48, 61, 65].”