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Arc: An IR for Batch and Stream Programming
KTH Royal Institute of Technology, Sweden.
KTH Royal Institute of Technology, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-9351-8508
KTH Royal Institute of Technology, Sweden.
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2019 (English)In: Proceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages, Association for Computing Machinery , 2019, p. 53-58Conference paper, Published paper (Refereed)
Abstract [en]

In big data analytics, there is currently a large number of data programming models and their respective frontends such as relational tables, graphs, tensors, and streams. This has lead to a plethora of runtimes that typically focus on the efficient execution of just a single frontend. This fragmentation manifests itself today by highly complex pipelines that bundle multiple runtimes to support the necessary models. Hence, joint optimization and execution of such pipelines across these frontend-bound runtimes is infeasible. We propose Arc as the first unified Intermediate Representation (IR) for data analytics that incorporates stream semantics based on a modern specification of streams, windows and stream aggregation, to combine batch and stream computation models. Arc extends Weld, an IR for batch computation and adds support for partitioned, out-of-order stream and window operators which are the most fundamental building blocks in contemporary data streaming.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2019. p. 53-58
Series
DBPL 2019
Keywords [en]
stream processing, data analytics, intermediate representation
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-49108DOI: 10.1145/3315507.3330199OAI: oai:DiVA.org:ri-49108DiVA, id: diva2:1476721
Conference
17th ACM SIGPLAN International Symposium on Database Programming Languages
Available from: 2020-10-15 Created: 2020-10-15 Last updated: 2023-06-07Bibliographically approved

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Carbone, ParisHaridi, Seif

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