WiscKey

391 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

PebblesDB: Building Key-Value Stores using Fragmented Log-Structured Merge Trees (2017, background): “WiscKey [37] improves performance by not storing the values in the LSM structure.”
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems (2020, background): “Wis-cKey (Lu et al., 2017) separates keys and values to minimize read/write ampliﬁcations.”
Serverless Computation with OpenLambda (2016, background): “Read amplifications for range reads can range from 1× to 50× [34] on different replicas; an integrated scheduler could potentially coordinate with database shards to track these varying costs.”
SCORE (2018, background): “…one hand, SSDs are widely used to serve read-critical workloads with limited random writes [19, 40, 55, 60, 72, 75] and many popular applications and file systems are designed to transform random writes to sequential writes (such as log-structured file systems and key-value stores) [37, 43, 52].”