Adaptive optimizations of recursive queries in teradata

Ghazal, Ahmad; Seid, Dawit Yimam; Crolotte, Alain; Al-Kateb, Mohammed

doi:10.1145/2213836.2213966

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The reason is that the cost of 20 relational joins on the large Twitter table exceeds the temporary-memory limits of VoltDB, and time-out the queries on a commercial disk-based RDBMS after 5 hours of execution. Also, as the number of self-joins increases in the Native Relational-Core approach, the relational optimizer may not be able to select the best join algorithm due to inaccurate cardinality estimations of the in- termediate results (see [24] for details). The relational engine is already very efficient in performing filtering predicates.…”

Section: Reachability Queries With Filtering Predicatesmentioning

confidence: 99%

Empowering In-Memory Relational Database Engines with Native Graph Processing

Hassan¹,

Kuznetsova²,

Jeong³

et al. 2017

Preprint

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The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates in a biological network. The maturity of RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing, where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this paper, we propose to treat graphs as first-class citizens inside the relational engine so that operations on graphs are executed natively inside the RDBMS. We realize our approach inside VoltDB, an open-source in-memory relational database, and name this realization GRFusion. The SQL and the query engine of GRFusion are empowered to declaratively define graphs and execute cross-data-model query plans formed by graph and relational operators, resulting in up to four orders-ofmagnitude in query-time speedup w.r.t. state-of-the-art approaches.

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Section: Reachability Queries With Filtering Predicatesmentioning

confidence: 99%

Empowering In-Memory Relational Database Engines with Native Graph Processing

Hassan¹,

Kuznetsova²,

Jeong³

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…On the same principle, query execution is continuously monitored, run-time statistics are collected and the parallelism degree or the partition key choice is dynamically modified. Re-optimization is also applied for recursive queries in [28], where the estimation errors may be propagated to later iterations. The authors proposed two mechanisms.…”

Section: Re-optimizationmentioning

confidence: 99%

Robust Query Optimization Methods With Respect to Estimation Errors

2015

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The quality of a query execution plan chosen by a Cost-Based Optimizer (CBO) depends greatly on the estimation accuracy of input parameter values. Many research results have been produced on improving the estimation accuracy, but they do not work for every situation. Therefore, "robust query optimization" was introduced, in an effort to minimize the sub-optimality risk by accepting the fact that estimates could be inaccurate. In this survey, we aim to provide an overview of robust query optimization methods by classifying them into different categories, explaining the essential ideas, listing their advantages and limitations, and comparing them with multiple criteria.

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“…General purpose large graph management has drawn great research interest [26], [27], [28], [29], [30], [31], [32], [33], [34], [35]. Early work [36] illustrates the challenging issues of large graph management.…”

Section: Related Workmentioning

confidence: 99%

Efficient Parallel Processing of Distance Join Queries Over Distributed Graphs

Zhang

Chen

Wang

2015

IEEE Trans. Knowl. Data Eng.

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Distance join queries have recently been recognized as a particularly useful operation over graph data, since they capture graph similarity in a meaningful way. Consequently, they have been studied extensively in recent years [1], [2]. However, current methods are designed for centralized systems, and rely on the graph embedding for effective pruning and indexing. As graph sizes become very large and graph data must be deployed in the distributed environment, these techniques become impractical. In this work, we propose a solution for efficient parallel processing of distance join queries over distributed large graphs. There have been emerging efforts devoted to managing large graphs in distributed and parallel systems. Programming models like Pregel [3] and iterative computing framework like HaLoop [4] have been proposed to handle queries over distributed graphs. However, they are designed in the perspective of functionality instead of the query efficiency. In this work, we define an optimization problem: combining the iterative join and the graph exploration method to minimize the evaluation time of distance join queries. Without sacrificing a system's scalability, our technique exploits a light-weight vertex centric encoding schema built on a distance-aware partition of the entire graph. Extensive experiments over both real and synthetic large graphs show that, by employing an adaptive query plan generation and scheduling method, we can effectively reduce the redundant message passing and I/O costs. Compared to simply using iterative join or graph exploration method, our solution achieves as many as one order of magnitude of time saving for the query evaluation.

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Adaptive optimizations of recursive queries in teradata

Cited by 9 publications

References 29 publications

Empowering In-Memory Relational Database Engines with Native Graph Processing

Empowering In-Memory Relational Database Engines with Native Graph Processing

Robust Query Optimization Methods With Respect to Estimation Errors

Efficient Parallel Processing of Distance Join Queries Over Distributed Graphs

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