Get our free extension to see links to code for papers anywhere online!Free add-on: code for papers everywhere!Free add-on: See code for papers anywhere!

Add to Chrome

Add to Firefox

Add to Edge

Wangda Zhang

Deploying a Steered Query Optimizer in Production at Microsoft

Oct 24, 2022
Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao, Nasim Ghazanfari Karlen Lie, Marc Friedman, Rafah Hosn, Hiren Patel, Alekh Jindal

Figure 1 for Deploying a Steered Query Optimizer in Production at Microsoft

Figure 2 for Deploying a Steered Query Optimizer in Production at Microsoft

Figure 3 for Deploying a Steered Query Optimizer in Production at Microsoft

Figure 4 for Deploying a Steered Query Optimizer in Production at Microsoft

Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workload, and make major strides in pushing previous research ideas to production deployment. Along the way we solve several operational challenges including, making steering actions more manageable, keeping the costs of steering within budget, and avoiding unexpected performance regressions in production. Our resulting system, QQ-advisor, essentially externalizes the query planner to a massive offline pipeline for better exploration and specialization. We discuss various aspects of our design and show detailed results over production SCOPE workloads at Microsoft, where the system is currently enabled by default.

* Proceedings of the 2022 International Conference on Management of Data 2022 Jun 10 (pp. 2299-2311)

Via

Access Paper or Ask Questions

Conditionally Risk-Averse Contextual Bandits

Oct 24, 2022
Mónika Farsang, Paul Mineiro, Wangda Zhang

Figure 1 for Conditionally Risk-Averse Contextual Bandits

Figure 2 for Conditionally Risk-Averse Contextual Bandits

Figure 3 for Conditionally Risk-Averse Contextual Bandits

Figure 4 for Conditionally Risk-Averse Contextual Bandits

We desire to apply contextual bandits to scenarios where average-case statistical guarantees are inadequate. Happily, we discover the composition of reduction to online regression and expectile loss is analytically tractable, computationally convenient, and empirically effective. The result is the first risk-averse contextual bandit algorithm with an online regret guarantee. We state our precise regret guarantee and conduct experiments from diverse scenarios in dynamic pricing, inventory management, and self-tuning software; including results from a production exascale cloud data processing system.

Via

Access Paper or Ask Questions