Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs


Konstantinos Kanellis, Ramnatthan Alagappan, and Shivaram Venkataraman, University of Wisconsin – Madison
Best Presentation Award Finalist


To achieve high performance, recent research has shown that it is important to automatically tune the configuration knobs present in database systems. However, as database systems usually have 100s of knobs, auto-tuning frameworks spend a significant amount of time exploring the large configuration space and need to repeat this as workloads change. Given this challenge, we ask a more fundamental question of how many knobs do we need to tune in order to achieve good performance. Surprisingly, we find that with YCSB workload-A on Cassandra, tuning just five knobs can achieve 99% of the performance achieved by the best configuration that is obtained by tuning many knobs. We also show that our results hold across workloads and applies to other systems like PostgreSQL, motivating the need for tools that can automatically filter out the knobs that need to be tuned. Based on our results, we propose an initial design for accelerating auto-tuners and detail some future research directions.

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@inproceedings {254280,
author = {Konstantinos Kanellis and Ramnatthan Alagappan and Shivaram Venkataraman},
title = {Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs},
booktitle = {12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20)},
year = {2020},
url = {},
publisher = {USENIX Association},
month = jul

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