LIPIcs.SNAPL.2015.63.pdf
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Yedalog: Exploring Knowledge at Scale
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1st Summit on Advances in Programming Languages (SNAPL 2015)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: Summit on Advances in Programming Languages (SNAPL) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2015-04-30
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Brian Chin, Daniel von Dincklage, Vuk Ercegovac, Peter Hawkins, Mark S. Miller, Franz Och, Christopher Olston, and Fernando Pereira. Yedalog: Exploring Knowledge at Scale. In 1st Summit on Advances in Programming Languages (SNAPL 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 32, pp. 63-78, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)
https://doi.org/10.4230/LIPIcs.SNAPL.2015.63
Abstract
With huge progress on data processing frameworks, human programmers are frequently the bottleneck when analyzing large repositories of data. We introduce Yedalog, a declarative programming language that allows programmers to mix data-parallel pipelines and computation seamlessly in a single language. By contrast, most existing tools for data-parallel computation embed a sublanguage of data-parallel pipelines in a general-purpose language, or vice versa. Yedalog extends Datalog, incorporating not only computational features from logic programming, but also features for working with data structured as nested records. Yedalog programs can run both on a single machine, and distributed across a cluster in batch and interactive modes, allowing programmers to mix different modes of execution easily.
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- Datalog
- MapReduce
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