| dc.description.abstract | RocksDB is one of the most widely used embeddable persistent key-value stores
available open-source. Its configurability, performance and workload flexibility have
been essential factors that differentiate it from contenders. The data structure,
Log Structured Merge Trees (LSM-trees), used in RocksDB differs from the more
traditional B+ tree especially by offering better write throughput. However, the
LSM-trees themselves do not provide a full-grown solution to all workloads, hence
why there exist so many different databases implementing their own versions of the
data structure.
Auto-tuning databases is in the wind, with examples like Oracle Autonomous
Database and Peloton offering next to no configuration. RocksDB has also
recently received tuning features like dynamically changing level sizes for Leveled
Compaction and an auto-tuning rate limiter. However, RocksDB is known for
dominating background activity by default and requires configuration for optimal
performance for different workloads. This thesis evaluates an implementation of
a compaction auto-tuner for RocksDB and presenting positive write performance
gains during high write load. The research did also attract positive attention from
the RocksDB developers at Facebook. | |
