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Computer Science > Distributed, Parallel, and Cluster Computing

Title: Serverless Data Analytics with Flint

Abstract: Serverless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. In this paper, we explore a completely different part of the application space and examine the feasibility of analytical processing on big data using a serverless architecture. We present Flint, a prototype Spark execution engine that takes advantage of AWS Lambda to provide a pure pay-as-you-go cost model. With Flint, a developer uses PySpark exactly as before, but without needing an actual Spark cluster. We describe the design, implementation, and performance of Flint, along with the challenges associated with serverless analytics.
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Databases (cs.DB)
Cite as: arXiv:1803.06354 [cs.DC]
  (or arXiv:1803.06354v1 [cs.DC] for this version)

Submission history

From: Jimmy Lin [view email]
[v1] Fri, 16 Mar 2018 18:02:27 GMT (31kb,D)