We present BigKV, a key-value cache specifically designed for caching large objects in an all-flash array (AFA). The design of BigKV is centered around the unique property of a cache: since it contains a copy of the data, exact bookkeeping of what is in the cache is not critical for correctness. By ignoring hash collisions, approximating metadata information, and allowing data loss from failures, BigKV significantly increases the cache hit ratio and keeps more useful objects in the system. Experiments on a real AFA show that our design increases the throughput by 3.1× on average and reduces the average and tail latency by 57% and 81%, respectively.

Key-value store based on a log-structured merge-tree (LSM-tree) is preferable to hash-based key-value store, because an LSM-tree can support a wider variety of operations and show better performance, especially for writes. However, LSM-tree is difficult to implement in the resource constrained environment of a key-value SSD (KV-SSD), and, consequently, KV-SSDs typically use hash-based schemes. We present PinK , a design and implementation of an LSM-tree-based KV-SSD, which compared to a hash-based KV-SSD, reduces 99th percentile tail latency by 73%, improves average read latency by 42%, and shows 37% higher throughput. The key idea in improving the performance of an LSM-tree in a resource constrained environment is to avoid the use of Bloom filters and instead, use a small amount of DRAM to keep/pin the top levels of the LSM-tree. We also find that PinK is able to provide a flexible design space for a wide range of KV workloads by leveraging the read-write tradeoff in LSM-trees.