Two-Stage Stochastic View Selection for Data-Analysis Queries

Huang, Rong; Chirkova, Rada; Fathi, Yahya

doi:10.1007/978-3-642-32741-4_11

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…There has been much research on physical database design problems, such as the automatic selection of materialized views [42,51,65,72], indices [27,54,61,69], or both [7,31,46,76]. Also, most modern databases come with designer tools, e.g., Tuning Wizard in Microsoft SQL Server [7], IBM DB2's Design Advisor [76], Teradata's Index Wizard [20], and Oracle's SQL Tuning Adviser [31].…”

Section: Related Workmentioning

confidence: 99%

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Mozafari

Goh

Yoon

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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A fundamental problem in database systems is choosing the best physical design, i.e., a small set of auxiliary structures that enable the fastest execution of future queries. Almost all commercial databases come with designer tools that create a number of indices or materialized views (together comprising the physical design) that they exploit during query processing. Existing designers are what we call nominal; that is, they assume that their input parameters are precisely known and equal to some nominal values. For instance, since future workload is often not known a priori, it is common for these tools to optimize for past workloads in hopes that future queries and data will be similar. In practice, however, these parameters are often noisy or missing. Since nominal designers do not take the influence of such uncertainties into account, they find designs that are sub-optimal and remarkably brittle. Often, as soon as the future workload deviates from the past, their overall performance falls off a cliff. Thus, we propose a new type of database designer that is robust against parameter uncertainties, so that overall performance degrades more gracefully when future workloads deviate from the past. Users express their risk tolerance by deciding on how much nominal optimality they are willing to trade for attaining their desired level of robustness against uncertain situations. To the best of our knowledge, this paper is the first to adopt the recent breakthroughs in robust optimization theory to build a practical framework for solving one of the most fundamental problems in databases, replacing today's brittle designs with robust designs that guarantee a predictable and consistent performance.

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Section: Related Workmentioning

confidence: 99%

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Mozafari

Goh

Yoon

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…There exist many recent and influential works on tuning data structures, intermediate result recycling and query matching, as well as eager/lazy aggregation. Specifically, there has been a plethora of work on automatic selection of materialized views [16], [22], [28], [31], indices [30], [40], or both [5] and most modern databases come with tuning tools. All these works, however, assume known workloads.…”

Section: Related Workmentioning

confidence: 99%

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Olma

Papapetrou

Appuswamy

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget, and offer substantial performance improvement for predictable workloads, but fail when new query types appear, i.e., due to shifting user interests. To the other extreme, online AQP engines assume that query workloads are unpredictable, and therefore build all samples at query time, without reusing samples (or parts of them) across queries. Clearly, both extremes miss out on different opportunities for optimizing performance and cost. In this paper, we present Taster, a self-tuning, elastic, online AQP engine that synergistically combines the benefits of online and offline AQP. Taster performs online approximation by injecting synopses (samples and sketches) into the query plan, while at the same time it strategically materializes and reuses synopses across queries, and continuously adapts them to changes in the workload and to the available storage resources. Our experimental evaluation shows that Taster adapts to shifting workload and to varying storage budgets, and always matches or significantly outperforms the state-of-the-art performing AQP approaches (online or offline).

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Two-Stage Stochastic View Selection for Data-Analysis Queries

Cited by 2 publications

References 13 publications

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Taster: Self-Tuning, Elastic and Online Approximate Query Processing

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