Datalog and Recursive Query Processing

Green, Todd; Huang, Shan; Loo, Boon Thau; Zhou, Wenchao

doi:10.1561/1900000017

Cited by 46 publications

(19 citation statements)

References 86 publications

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“…A major goal of our system is to unify the programming model for applications that automate and enhance decision making by using a single, expressive, declarative language that can be used by domain experts to understand and evolve the application. To achieve this goal, we have developed LogiQL, an extended form of Datalog [29,2,19] that is expressive enough to allow coding of entire applications (including queries and views; stored procedures; reactive rules and triggers; and statistical and mathematical modeling).…”

Section: Design Principlesmentioning

confidence: 99%

Design and Implementation of the LogicBlox System

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Cate

Green

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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188

175

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The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a "self-service" model. Our perspective in this area is informed by over twenty years of experience building dozens of missioncritical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.

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Section: Design Principlesmentioning

confidence: 99%

Design and Implementation of the LogicBlox System

Aref

Cate

Green

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Self Cite

188

175

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“…Datalog has already been used in cloud‐related research and industry activities,() but none of them were designed to address the data aggregation in NFV environments. The OpenStack policy framework Congress uses Datalog, in this case, as the language to describe the policy rules in an OpenStack environment. In Congress, the state of each service is represented as a collection of tables expressed as Datalog facts.…”

Section: Related Workmentioning

confidence: 99%

“…To address the challenges of automatic and flexible decomposition and aggregation of performance metrics in a recursive NFV architecture, we argue for a declarative logic‐based language. The query language proposed in this paper is based on Datalog to provide recursive query capabilities. Query evaluation with Datalog is based on first‐order logic and is thus sound and complete.…”

Section: The Recursive Query Languagementioning

confidence: 99%

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Automatic data aggregation for recursively modeled NFV services

2017

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Summary Network function virtualization (NFV) allows to model network services as graphs interconnecting virtual network functions (VNFs), which may include nested VNFs, modeled as subgraphs of VNFs or end points. To query the performance data of network services modeled as abstract and high‐level graphs is challenging because of the recursivity of the NFV architecture and the elasticity and dynamicity provided by the NFV infrastructure. We propose to use Datalog, a declarative logic programming language, to build a framework that supports efficient data aggregation for performance metrics of recursively modeled network services. We present our recursive query language for automatic and flexible decomposition and aggregation of NFV performance metrics and describe example use cases for both compute and network metrics. We also describe the design and implementation of a proof‐of‐concept query engine using the language. Our performance evaluation shows that the total query latency is dominated by retrieval times of performance metrics from infrastructure databases, and the time for execution and automatic decomposition of high‐level queries by the query engine itself increases linearly with the size of the service graph up to 1000 nodes. Hence, our evaluations show that the query engine scales well, bounded mainly by the limited execution capabilities of our test environment. Furthermore, it can handle multiple concurrent queries up to the concurrency limits of the backend database in use. The proposed query language and engine are thus effective in recursively retrieving performance metrics of NFV environments supporting large‐scale service graphs and large numbers of query requests.

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“…Datalog is a logic programming language that is frequently used for program analyses [Avgustinov et al 2016;Green et al 2013; Smaragdakis and Bravenboer 2011; Whaley and Lam 2004]. Incremental Datalog solvers efficiently update query results based on code changes [Gupta and Mumick 1995].…”

Section: Introductionmentioning

confidence: 99%

Incrementalizing lattice-based program analyses in Datalog

Szabó

Bergmann

Erdweg

et al. 2018

Proc. ACM Program. Lang.

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Program analyses detect errors in code, but when code changes frequently as in an IDE, repeated re-analysis from-scratch is unnecessary: It leads to poor performance unless we give up on precision and recall. Incremental program analysis promises to deliver fast feedback without giving up on precision or recall by deriving a new analysis result from the previous one. However, Datalog and other existing frameworks for incremental program analysis are limited in expressive power: They only support the powerset lattice as representation of analysis results, whereas many practically relevant analyses require custom lattices and aggregation over lattice values. To this end, we present a novel algorithm called DRed L that supports incremental maintenance of recursive lattice-value aggregation in Datalog. The key insight of DRed L is to dynamically recognize increasing replacements of old lattice values by new ones, which allows us to avoid the expensive deletion of the old value. We integrate DRed L into the analysis framework IncA and use IncA to realize incremental implementations of strong-update points-to analysis and string analysis for Java. As our performance evaluation demonstrates, both analyses react to code changes within milliseconds. CCS Concepts: • Software and its engineering → Automated static analysis;

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Datalog and Recursive Query Processing

Cited by 46 publications

References 86 publications

Design and Implementation of the LogicBlox System

Design and Implementation of the LogicBlox System

Automatic data aggregation for recursively modeled NFV services

Incrementalizing lattice-based program analyses in Datalog

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