ChameleonDB: a Key-value Store for Optane Persistent Memory

The emergence of Intel's Optane DC persistent memory (Optane Pmem) draws much interest in building persistent key-value (KV) stores to take advantage of its high throughput and low latency. A major challenge in the efforts stems from the fact that Optane Pmem is essentially a hybrid storage device with two distinct properties. On one hand, it is a high-speed byte-addressable device similar to DRAM. On the other hand, the write to the Optane media is conducted at the unit of 256 bytes, much like a block storage device. Existing KV store designs for persistent memory do not take into account of the latter property, leading to high write amplification and constraining both write and read throughput. In the meantime, a direct re-use of a KV store design intended for block devices, such as LSM-based ones, would cause much higher read latency due to the former property. In this paper, we propose ChameleonDB, a KV store design specifically for this important hybrid memory/storage device by considering and exploiting these two properties in one design. It uses LSM tree structure to efficiently admit writes with low write amplification. It uses an in-DRAM hash table to bypass LSM-tree's multiple levels for fast reads. In the meantime, ChameleonDB may choose to opportunistically maintain the LSM multi-level structure in the background to achieve short recovery time after a system crash. ChameleonDB's hybrid structure is designed to be able to absorb sudden bursts of a write workload, which helps avoid long-tail read latency. Our experiment results show that ChameleonDB improves write throughput by 3.3x and reduces read latency by around 60% compared with a legacy LSM-tree based KV store design. ChameleonDB provides performance competitive even with KV stores using fully in-DRAM index by using much less DRAM space. Compared with CCEH, a persistent hash table design, ChameleonDB provides 6.4x higher write throughput.