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Auto-tuning RocksDB

Warlo, Hans-Wilhelm Kirsch
Master thesis
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URI
http://hdl.handle.net/11250/2506148
Date
2018
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  • Institutt for datateknologi og informatikk [3870]
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.
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NTNU

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