Log-Structured Merge Key-Value stores (LSM KVs) are designed to offer good write performance, by capturing client writes in memory, and only later flushing them to storage. Writes are later compacted into a tree-like data structure on disk to improve read performance and to reduce storage space use. It has been widely documented that compactions severely hamper throughput. Various optimizations have successfully dealt with this problem. These techniques include, among others, rate-limiting flushes and compactions, selecting among compactions for maximum effect, and limiting compactions to the highest level by so-called fragmented LSMs. In this article, we focus on latencies rather than throughput. We first document the fact that LSM KVs exhibit high tail latencies. The techniques that have been proposed for optimizing throughput do not address this issue, and, in fact, in some cases, exacerbate it. The root cause of these high tail latencies is interference between client writes, flushes, and compactions. Another major cause for tail latency is the heterogeneous nature of the workloads in terms of operation mix and item sizes whereby a few more computationally heavy requests slow down the vast majority of smaller requests. We introduce the notion of an Input/Output (I/O) bandwidth scheduler for an LSM-based KV store to reduce tail latency caused by interference of flushing and compactions and by workload heterogeneity. We explore three techniques as part of this I/O scheduler: (1) opportunistically allocating more bandwidth to internal operations during periods of low load, (2) prioritizing flushes and compactions at the lower levels of the tree, and (3) separating client requests by size and by data access path. SILK+ is a new open-source LSM KV that incorporates this notion of an I/O scheduler.

In concurrent systems without automatic garbage collection, it is challenging to determine when it is safe to reclaim memory, especially for lock-free data structures. Existing concurrent memory reclamation schemes are either fast but do not tolerate process delays, robust to delays but with high overhead, or both robust and fast but narrowly applicable.This paper proposes QSense, a novel concurrent memory reclamation technique. QSense is a hybrid technique with a fast path and a fallback path. In the common case (without process delays), a high-performing memory reclamation scheme is used (fast path). If process delays block memory reclamation through the fast path, a robust fallback path is used to guarantee progress. The fallback path uses hazard pointers, but avoids their notorious need for frequent and expensive memory fences.QSense is widely applicable, as we illustrate through several lock-free data structure algorithms. Our experimental evaluation shows that QSense has an overhead comparable to the fastest memory reclamation techniques, while still tolerating prolonged process delays.

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