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 thesis, 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 a Spark cluster and only paying for the execution of individual Spark programs. We describe the design, implementation, and performance of Flint, along with the challenges associated with serverless analytics.
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Youngbin Kim
(2018).
Serverless Data Analytics with Flint. UWSpace.
http://hdl.handle.net/10012/13681
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