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We present a non-intrusive approach to robust query processing that can be used on top of any SQL execution engine. To reduce the risk of selecting highly sub-optimal query plans, we execute multiple plans in parallel. Query processing finishes once the first of these plans finishes execution. Plans are selected to be complementary in terms of the intermediate results they generate. This increases robustness to cardinality estimation errors, making cost prediction hard, that concern a subset of candidate results. We present multiple cost-based approaches to selecting plans for robust execution. The first approach uses a simple cost model, based on diversity of intermediate results. The second approach features a probabilistic model, approximating expected execution overheads, given uncertainty on true intermediate result sizes. We present greedy and exhaustive algorithms to select optimal plans according to those cost models. The experiments demonstrate that executing multiple plans in parallel is preferable over executing single plans that are occasionally sub-optimal, as well as over several baselines.
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DEX: Query Execution in a Delta-based Storage System
The increasing reliance on robust data-driven decision-making across many domains has made it necessary for data management systems to manage many thousands to millions of versions of datasets, acquired or constructed at various stages of analysis pipelines over time. Delta encoding is an effective and widely-used solution to compactly store a large number of datasets, that simultaneously exploits redundancies across them and keeps the average retrieval cost of reconstructing any dataset low. However, supporting any kind of rich retrieval or querying functionality, beyond single dataset checkout, is challenging in such storage engines. In this paper, we initiate a systematic study of this problem, and present DEX, a novel stand-alone delta-oriented execution engine, whose goal is to take advantage of the already computed deltas between the datasets for efficient query processing. In this work, we study how to execute checkout, intersection, union and t-threshold queries over record-based files; we show that processing of even these basic queries leads to many new and unexplored challenges and trade-offs. Starting from a query plan that confines query execution to a small set of deltas, we introduce new transformation rules based on the algebraic properties of the deltas, that allow us to explore the search space of alternative plans. For the case of checkout, we present a dynamic programming algorithm to efficiently select the optimal query plan under our cost model, while we design efficient heuristics to select effective plans that vastly outperform the base checkout-then-query approach for other queries. A key characteristic of our query execution methods is that the computational cost is primarily dependent on the size and the number of deltas in the expression (typically small), and not the input dataset versions (which can be very large). We have implemented DEX prototype on top of git, a widely used version control system. We present an extensive experimental evaluation on synthetic data with diverse characteristics, that shows that our methods perform exceedingly well compared to the baseline.
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- Award ID(s):
- 1650755
- PAR ID:
- 10041784
- Date Published:
- Journal Name:
- 2017 ACM International Conference on Management of Data (SIGMOD)
- Page Range / eLocation ID:
- 171 to 186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3035918.3064056
