A generalization of the differential approach to recursive query evaluation

In recent years there has been interest in achieving applicationlevel consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. A more powerful approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent.In logic languages such as Bloom, CALM analysis can automatically verify that program modules achieve consistency without coordination.In this paper we present Bloom L , an extension to Bloom that takes inspiration from both these traditions. Bloom L generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use Bloom L to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how Bloom L encourages the safe composition of small, easy-to-analyze lattices into larger programs.

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“…Semi-naive evaluation improves upon naive evaluation by making fewer redundant derivations [7]. Let ∆ 0 represent the initial database state.…”

Section: Semi-naive Evaluationmentioning

confidence: 99%

“…For efficient incremental execution, we extend the standard Datalog semi-naive evaluation scheme [7] to support arbitrary lattices. We also describe how an existing Datalog-style engine can be extended to support lattices with relatively minor changes.…”

Section: Bloommentioning

confidence: 99%

Logic and lattices for distributed programming

Conway

Marczak

Alvaro

et al. 2012

Proceedings of the Third ACM Symposium on Cloud Computing

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“…If r 6 is active, then A(supm4, 6), A(msg, 6), or both are not empty. The following expression is the version of r 6 resulting from the semi-naive rewriting based on the differential notation proposed in [1] (for the sake of simplicity, we have omitted the join conditions and the projection list):…”

Section: The Dynamically Ordered Semi-naive Evaluationmentioning

confidence: 99%

“…DYN iterates each recursive rule individually in the same man-ner as the algorithms proposed by Kildall [15] and Cai and Paige [6]. For the semi-naive property, we transform recursive rules into equivalent ones that satisfy the semi-naive property by using the differential notation proposed by Balbin and Ramamohanarao [1], There is another differential notation proposed by Bancilhon [2], but it requires more joins than the former.…”

Section: Zz Our Approachmentioning

confidence: 99%

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Dynamically ordered semi-naive evaluation of recursive queries

Hong

Lee

Whang

1997

Information Sciences

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Conventional fixed point evaluation techniques evaluate recursions by applying all rules repeatedly using an initial set of tuples (i.e., a given extensional database instance) until no new tuples are generated, but there is no specific order in which rules are applied. We can speed up the evaluation by applying rules in an appropriate order. In this paper, we propose a new fixed point evaluation technique, called the dynamically ordered semi-naive evaluation (or simply DYN), in which the next rule to be applied is determined at run time dynamically. DYN consists of a semi-naive algorithm and a set of selection strategies, The semi-naive algorithm allows dynamic ordering of rule applications and makes tuples generated by a rule application immediately available in the subsequent rule applications. After each rule application, the selection strategies determine the next rule by considering the syntactic structure of recursion and some information about the intermediate result up to the present. We develop these selection strategies so that the total number of rule applications and joins can be reduced. Through experimental comparisons, we shows that DYN outperforms the previous evaluation techniques in terms of the total number of rule applications and joins.

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