We present C rash M onkey and A ce , a set of tools to systematically find crash-consistency bugs in Linux file systems. C rash M onkey is a record-and-replay framework which tests a given workload on the target file system by simulating power-loss crashes while the workload is being executed, and checking if the file system recovers to a correct state after each crash. A ce automatically generates all the workloads to be run on the target file system. We build C rash M onkey and A ce based on a new approach to test file-system crash consistency: bounded black-box crash testing ( B 3 ). B 3 tests the file system in a black-box manner using workloads of file-system operations. Since the space of possible workloads is infinite, B 3 bounds this space based on parameters such as the number of file-system operations or which operations to include, and exhaustively generates workloads within this bounded space. B 3 builds upon insights derived from our study of crash-consistency bugs reported in Linux file systems in the last 5 years. We observed that most reported bugs can be reproduced using small workloads of three or fewer file-system operations on a newly created file system, and that all reported bugs result from crashes after fsync()-related system calls. C rash M onkey and A ce are able to find 24 out of the 26 crash-consistency bugs reported in the last 5 years. Our tools also revealed 10 new crash-consistency bugs in widely used, mature Linux file systems, 7 of which existed in the kernel since 2014. Additionally, our tools found a crash-consistency bug in a verified file system, FSCQ. The new bugs result in severe consequences like broken rename atomicity, loss of persisted files and directories, and data loss.

Key-value stores such as LevelDB and RocksDB offer excellent write throughput, but suffer high write amplification. The write amplification problem is due to the Log-Structured Merge Trees data structure that underlies these key-value stores. To remedy this problem, this paper presents a novel data structure that is inspired by Skip Lists, termed Fragmented Log-Structured Merge Trees (FLSM). FLSM introduces the notion of guards to organize logs, and avoids rewriting data in the same level. We build PebblesDB, a high-performance key-value store, by modifying HyperLevelDB to use the FLSM data structure. We evaluate PebblesDB using micro-benchmarks and show that for write-intensive workloads, PebblesDB reduces write amplification by 2.4-3x compared to RocksDB, while increasing write throughput by 6.7x. We modify two widely-used NoSQL stores, MongoDB and HyperDex, to use PebblesDB as their underlying storage engine. Evaluating these applications using the YCSB benchmark shows that throughput is increased by 18-105% when using PebblesDB (compared to their default storage engines) while write IO is decreased by 35-55%.