Modern web services use in-memory caching extensively to increase throughput and reduce
latency. There have been several workload analyses of production systems that have fueled …

Recent work on" learned indexes" has changed the way we look at the decades-old field of
DBMS indexing. The key idea is that indexes can be thought of as" models" that predict the …

This article is an eight-year retrospective on development priorities for RocksDB, a key-value
store developed at Facebook that targets large-scale distributed systems and that is …

Healthcare information systems can reduce the expenses of treatment, foresee episodes of
pestilences, help stay away from preventable illnesses, and improve personal life …

Key-Value (KV) stores support many crucial applications and services. They perform fast in-
memory processing, but are still often limited by I/O performance. The recent emergence of …

RocksDB is a key-value store targeting large-scale distributed systems and optimized for
Solid State Drives (SSDs). This paper describes how our priorities in developing RocksDB …

Key-value stores (KVSs) have found wide application in modern software systems. For
persistence, their data resides in slow secondary storage, which requires KVSs to employ …

Distributed in-memory key-value (KV) stores are embracing the disaggregated memory (DM)
architecture for higher resource utilization. However, existing KV stores on DM employ …

Index structures in memory systems become important to improve the entire system
performance. The promising learned indexes leverage deep-learning models to …

Due to the latency difference between DRAM and non-volatile main memory (NVMM) and
the limited capacity of DRAM, incoming writes are often stalled in LSM tree-based key-value …