Infographic describing BayesQO, an offline, multi-iteration learned query optimizer. On the left, it shows a Variational Autoencoder (VAE) being pretrained to reconstruct query plans from vectors, using orange-colored plan diagrams. The decoder part of the VAE is retained. In the center and right, the image shows Bayesian optimization being performed in the learned vector space: new vectors are decoded into query plans, tested for latency, and refined iteratively. At the bottom, a library of optimized query plans is used to train a robot labeled โLLM,โ which can then generate new plans directly. The caption reads: "We get a fast query, but also a library of high-quality plans. We can train an LLM to speed up the process for next time!" The image credits Jeff Tao et al., SIGMOD '25, and links to https://rm.cab/bayesqo
For that one query that must go ๐๐๐๐๐๐ฆ ๐๐๐ ๐ก, BayesQO (by Jeff Tao) finds superoptimized plans using Bayesian optimization in a learned plan space. Itโs costly, but the results can train an LLM to speed things up next time.
๐https://rm.cab/bayesqo
03.06.2025 19:34 โ ๐ 3 ๐ 0 ๐ฌ 0 ๐ 0
Infographic describing LimeQO, a workload-level, offline, learned query optimizer. On the left, it shows a workload consisting of multiple queries (qโ to qโ), each with a default execution time (3s, 9s, 12s, 22s respectively). On the right, alternate plans (hโ, hโ, hโ) show varying execution times for each query, with some entries missing (represented by question marks). For example, qโ takes 1s under hโ, much faster than the 3s default. A specific callout highlights that for qโ, plan hโ reduced the time from 12s to 3s, but took 18s to find, resulting in a benefit of 9s gained / 18s search. The image poses the question: โWhere should we explore next to maximize benefit?โ The image credits Zixuan Yi et al., SIGMOD '25, and provides a link: https://rm.cab/limeqo
LimeQO (by Zixuan Yi), a ๐ค๐๐๐๐๐๐๐-๐๐๐ฃ๐๐ approach to query optimization, can use neural networks or simple linear methods to find good query hints significantly faster than a random or brute force search.
๐https://rm.cab/limeqo
03.06.2025 19:34 โ ๐ 2 ๐ 0 ๐ฌ 1 ๐ 0
OLAP workloads are dominated by repetitive queries -- how can we optimize them?
A promising direction is to do ๐ผ๐ณ๐ณ๐น๐ถ๐ป๐ฒ query optimization, allowing for a much more thorough plan search.
Two new SIGMOD papers! ๐งต
03.06.2025 19:34 โ ๐ 6 ๐ 0 ๐ฌ 1 ๐ 0
