A novel, low-latency algorithm for multiple Group-By query optimization

Phan, Duy-Hung; Michiardi, Pietro

doi:10.1109/icde.2016.7498249

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Except for Xu et al [37], much work on optimizing sort algorithms for modern hardware [10,14] has focused on small tuple sizes. Grouping sets have been proposed by Gray et al [19] in 1997, and consequently there have been many proposals for optimizing the grouping order: Phan and Michiardi's [30] fairly recent paper offers a good overview. We are not aware of any research papers describing how to efficiently implement ordered-set aggregates in database systems.…”

Section: Related Workmentioning

confidence: 99%

Building Advanced SQL Analytics From Low-Level Plan Operators

Kohn

Leis

Neumann

2021

Proceedings of the 2021 International Conference on Management of Data

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Analytical queries virtually always involve aggregation and statistics. SQL offers a wide range of functionalities to summarize data such as associative aggregates, distinct aggregates, ordered-set aggregates, grouping sets, and window functions. In this work, we propose a unified framework for advanced statistics that composes all flavors of complex SQL aggregates from low-level plan operators. These operators can reuse materialized intermediate results, which decouples monolithic aggregation logic and speeds up complex multi-expression queries. The contribution is therefore twofold: our framework modularizes aggregate implementations, and outperforms traditional systems whenever multiple aggregates are combined. We integrated our approach into the high-performance database system Umbra and experimentally show that we compute complex aggregates faster than the state-of-the-art HyPer system.

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Section: Related Workmentioning

confidence: 99%

Building Advanced SQL Analytics From Low-Level Plan Operators

Kohn

Leis

Neumann

2021

Proceedings of the 2021 International Conference on Management of Data

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“…Duy-Hung Phan et al [1] they worked on methodology to the overall problem of optimizing of multiple groups by queries, so filling the gap left by current proposals can scale within the variety of synchronic queries or the quantity of attributes every query can handle. they need shown, each through an experiment and on paper, that our rule incurs in extraordinarily tiny latencies, compared to different algorithms, once producing optimized query plans.…”

Section: Literature Reviewmentioning

confidence: 99%

Query Optimization using Multiple Techniques

Wagh¹,

Nemade²

2017

IJCA

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Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. The optimizer attempts to generate the best execution plan for a SQL statement. The best execution plan is defined as the plan with the lowest cost among all considered candidate plans. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The cost is a number that represents the estimated resource usage for an execution plan. The cost computation accounts for factors of query execution such as I/O, CPU, and communication. To implement query optimization methods such as Heuristic Greedy based optimization, Iterative Improvement based cost optimization and Ant Colony optimization algorithms. Show Comparison of cost, execution time and response time between Heuristic Greedy based optimization; Ant Colony Optimization and Iterative Improvement based cost optimization algorithms.

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