Parallel-Correctness and Containment for Conjunctive Queries with Union and Negation

Geck, Gaetano; Ketsman, Bas; Neven, Frank; Schwentick, Thomas

doi:10.1145/3329120

Cited by 6 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We note that Proposition 5.6 continues to hold true in the presence of union and inequalities (under a suitable definition of minimal valuation for unions of CQs) leading to the same complexity bounds as stated in Theorem 5.7 [9].…”

Section: Pci(cq P) Inputmentioning

confidence: 56%

“…Query containment asks whether for two queries Q and Q , it holds that Q(I) ⊆ Q (I), for all instances I. It is shown in [9] that query containment for CQ ¬ is coNEXPTIME-complete, implying coNEXPTIME-hardness for parallel-correctness as well. The result regarding containment of CQ ¬ confirms the observation in [13] that the Π p 2 -completeness result for query containment for CQ ¬ mentioned in [20] only holds for fixed database schemas (or a fixed arity bound, for that matter).…”

Section: Xn)mentioning

confidence: 99%

See 1 more Smart Citation

Reasoning on data partitioning for single-round multi-join evaluation in massively parallel systems

et al. 2017

Self Cite

View full text Add to dashboard Cite

Evaluating queries over massive amounts of data is a major challenge in the big data era. Modern massively parallel systems, like e.g. Spark, organize query answering as a sequence of rounds each consisting of a distinct communication phase followed by a computation phase. The communication phase redistributes data over the available servers, while in the subsequent computation phase each server performs the actual computation on its local data. There is a growing interest in single-round algorithms for evaluating multiway joins where data is first reshuffled over the servers and then evaluated in a parallel but communication-free way. As the amount of communication induced by a reshuffling of the data is a dominating cost in such systems, we introduce a framework for reasoning about data partitioning to detect when we can avoid the data reshuffling step. Specifically, we formalize the decision problems parallel-correctness and transfer of parallel-correctness, provide semantical characterizations, and obtain tight complexity bounds.

show abstract

Section: Pci(cq P) Inputmentioning

confidence: 56%

Section: Xn)mentioning

confidence: 99%

Reasoning on data partitioning for single-round multi-join evaluation in massively parallel systems

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Together with Proposition 5.7, the above proposition implies that a Datalog program where the body of each rule contains only edb relations is parallel-correct if and only if it supports every minimal rule instantiation, or equivalently if and only it supports every essential rule instantiation. Notice that this class of Datalog programs corresponds to a program that computes a set of UCQs, and thus the above result captures the characterization of parallel-correctness for CQs and UCQs in [6,15]. We should emphasize here that [6,15] consider only economic policies where P assigns every fact to every server, while a general economic policy can assign facts to any subset of servers.…”

Section: Pc(l E) Inputmentioning

confidence: 77%

“…Notice that this class of Datalog programs corresponds to a program that computes a set of UCQs, and thus the above result captures the characterization of parallel-correctness for CQs and UCQs in [6,15]. We should emphasize here that [6,15] consider only economic policies where P assigns every fact to every server, while a general economic policy can assign facts to any subset of servers. (x, y), is trivially minimal, useful and essential.…”

Section: Pc(l E) Inputmentioning

confidence: 78%

Distribution Policies for Datalog

2019

Self Cite

View full text Add to dashboard Cite

Modern data management systems extensively use parallelism to speed up query processing over massive volumes of data. This trend has inspired a rich line of research on how to formally reason about the parallel complexity of join computation. In this paper, we go beyond joins and study the parallel evaluation of recursive queries. We introduce a novel framework to reason about multi-round evaluation of Datalog programs, which combines implicit predicate restriction with distribution policies to allow expressing a combination of data-parallel and query-parallel evaluation strategies. Using our framework, we reason about key properties of distributed Datalog evaluation, including parallel-correctness of the evaluation strategy, disjointness of the computation effort, and bounds on the number of communication rounds.

show abstract

“…Thus, the equivalence between two SQL queries is in general undecidable [47]. Extensive research has been done to study the complexity of containment and equivalence of fragments of SQL queries under bag semantics and set semantics [8,10,19,27,29,42,52]. We list the results in Figure 9.…”

Section: Query Rewritingmentioning

confidence: 99%

HoTTSQL: proving query rewrites with univalent SQL semantics

Chu

Weitz

Cheung

et al. 2017

Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

Every database system contains a query optimizer that performs query rewrites. Unfortunately, developing query optimizers remains a highly challenging task. Part of the challenges comes from the intricacies and rich features of query languages, which makes reasoning about rewrite rules difficult. In this paper, we propose a machine-checkable denotational semantics for SQL, the de facto language for relational database, for rigorously validating rewrite rules. Unlike previously proposed semantics that are either non-mechanized or only cover a small amount of SQL language features, our semantics covers all major features of SQL, including bags, correlated subqueries, aggregation, and indexes. Our mechanized semantics, called HoTT SQL, is based on K-Relations and homotopy type theory, where we denote relations as mathematical functions from tuples to univalent types. We have implemented HoTT SQL in Coq, which takes only fewer than 300 lines of code and have proved a wide range of SQL rewrite rules, including those from database research literature (e.g., magic set rewrites) and real-world query optimizers (e.g., subquery elimination). Several of these rewrite rules have never been previously proven correct. In addition, while query equivalence is generally undecidable, we have implemented an automated decision procedure using HoTT SQL for conjunctive queries: a well-studied decidable fragment of SQL that encompasses many real-world queries.

show abstract

Parallel-Correctness and Containment for Conjunctive Queries with Union and Negation

Cited by 6 publications

References 24 publications

Reasoning on data partitioning for single-round multi-join evaluation in massively parallel systems

Reasoning on data partitioning for single-round multi-join evaluation in massively parallel systems

Distribution Policies for Datalog

HoTTSQL: proving query rewrites with univalent SQL semantics

Contact Info

Product

Resources

About