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Title: A staging area for in-memory computing
Author: Santamaria Mateu, Pol
Tutor: Rodero, Ivan  
Abstract: An in-memory staging area provides fast access to different applications. This research is based on evaluating the benefits of a distributed in-memory staging area applied to the field of Big data. With this purpose, a prototype is designed and proposed to verify the idea. Then, a working version comprised of the in-memory software Alluxio and the processing engine Apache Spark is deployed and evaluated. In particular, the work demonstrates the increase in performance resulting from updating the data in the in-memory staging instead of allocating space for new objects. The evaluation is conducted by running an analytic with Spark over a continuously changing dataset stored in Alluxio. The experiments reported a throughput increase of 10x when compared to storing information in a regular parallel filesystem, and an increase of 3x compared to the official deployment methodology. By updating the dataset, the Alluxio in-memory capacity stays constant at a low level compared to current deployments where its capacity decreases linearly, resulting in lower performance.
Keywords: in-memory database
big data
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 15-Jul-2018
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