Accordion: Better Memory Organization for LSM Key-Value Stores Edward Bortnikov Anastasia Braginsky Eshcar Hillel Yahoo Research Yahoo Research Yahoo Research
[email protected] [email protected] [email protected] Idit Keidar Gali Sheffi Technion and Yahoo Research Yahoo Research
[email protected] gsheffi@oath.com ABSTRACT of applications for which they are used continuously in- Log-structured merge (LSM) stores have emerged as the tech- creases. A small sample of recently published use cases in- nology of choice for building scalable write-intensive key- cludes massive-scale online analytics (Airbnb/ Airstream [2], value storage systems. An LSM store replaces random I/O Yahoo/Flurry [7]), product search and recommendation (Al- with sequential I/O by accumulating large batches of writes ibaba [13]), graph storage (Facebook/Dragon [5], Pinter- in a memory store prior to flushing them to log-structured est/Zen [19]), and many more. disk storage; the latter is continuously re-organized in the The leading approach for implementing write-intensive background through a compaction process for efficiency of key-value storage is log-structured merge (LSM) stores [31]. reads. Though inherent to the LSM design, frequent com- This technology is ubiquitously used by popular key-value pactions are a major pain point because they slow down storage platforms [9, 14, 16, 22,4,1, 10, 11]. The premise data store operations, primarily writes, and also increase for using LSM stores is the major disk access bottleneck, disk wear. Another performance bottleneck in today's state- exhibited even with today's SSD hardware [14, 33, 34].