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RASQL: A Powerful Language and its System for Big Data Applications

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Carlo ZanioloAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 2673 - 2676

https://doi.org/10.1145/3318464.3384677

Published: 31 May 2020 Publication History

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Abstract

There is a growing interest in supporting advanced Big Data applications on distributed data processing platforms. Most of these systems support SQL or its dialect as the query interface due to its portability and declarative nature. However, current SQL standard cannot effectively express advanced analytical queries due to its limitation in supporting recursive queries. In this demonstration, we show that this problem can be resolved via a simple SQL extension that delivers greater expressive power by allowing aggregates in recursion. To this end, we propose the Recursive-aggregate-SQL (RASQL) language and its system on top of Apache Spark to express and execute complex queries and declarative algorithms in many applications, such as graph search and machine learning. With a variety of examples, we will (i) show how complicated analytic queries can be expressed with RASQL; (ii) illustrate formal semantics of the powerful new constructs; and (iii) present a user-friendly interface to interact with the RASQL system and monitor the query results.

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Sahebolamri ABarrett LMoore SMicinski K(2023)Bring Your Own Data Structures to DatalogProceedings of the ACM on Programming Languages10.1145/36228407:OOPSLA2(1198-1223)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622840
Sun YKumar SGilray TMicinski K(2023)Communication-Avoiding Recursive Aggregation2023 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER52292.2023.00024(197-208)Online publication date: 31-Oct-2023
https://doi.org/10.1109/CLUSTER52292.2023.00024
Li YWang JLi MDas AGu JZaniolo C(2021)KDDLog:Performance and Scalability in Knowledge Discovery by Declarative Queries with Aggregates2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00113(1260-1271)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00113
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Index Terms

RASQL: A Powerful Language and its System for Big Data Applications
1. Information systems
  1. Data management systems

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SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 31 May 2020

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SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Sahebolamri ABarrett LMoore SMicinski K(2023)Bring Your Own Data Structures to DatalogProceedings of the ACM on Programming Languages10.1145/36228407:OOPSLA2(1198-1223)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622840
Sun YKumar SGilray TMicinski K(2023)Communication-Avoiding Recursive Aggregation2023 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER52292.2023.00024(197-208)Online publication date: 31-Oct-2023
https://doi.org/10.1109/CLUSTER52292.2023.00024
Li YWang JLi MDas AGu JZaniolo C(2021)KDDLog:Performance and Scalability in Knowledge Discovery by Declarative Queries with Aggregates2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00113(1260-1271)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00113
Wang JWu JLi MGu JDas AZaniolo C(2021)Formal semantics and high performance in declarative machine learning using DatalogThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00665-630:5(859-881)Online publication date: 31-May-2021
https://dl.acm.org/doi/10.1007/s00778-021-00665-6
Zaniolo CDas ALi YLi MWang J(2020)Proceedings 36th International Conference on Logic Programming (Technical Communications)Electronic Proceedings in Theoretical Computer Science10.4204/EPTCS.325.9325(35-37)Online publication date: 19-Sep-2020
https://doi.org/10.4204/EPTCS.325.9

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RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark

HIFUN - a high level functional query language for big data analytics

Big data and ICT applications: A study

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RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark

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Big data and ICT applications: A study

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