LlamaTune: Sample-Efficient DBMS Configuration Tuning

Understand

Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community.

• A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) in search for high performance configurations.
• Looking at Microsoft production services operating millions of databases, sample efficiency emerged as a crucial requirement to use tuners on diverse workloads.
• This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency of existing optimizers.