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Interactive Debugging for Big Data Analytics
Muhammad Ali Gulzar, Xueyuan Han, Matteo Interlandi, and Shaghayegh Mardani, University of California, Los Angeles; Sai Deep Tetali, Google, Inc.; Todd Millstein and Miryung Kim, University of California, Los Angeles
An abundance of data in many disciplines has accelerated the adoption of distributed technologies such as Hadoop and Spark, which provide simple programming semantics and an active ecosystem. However, the current cloud computing model lacks the kinds of expressive and interactive debugging features found in traditional desktop computing. We seek to address these challenges with the development of BIGDEBUG, a framework providing interactive debugging primitives and tool-assisted fault localization services for big data analytics. We showcase the data provenance and optimized incremental computation features to effectively and efficiently support interactive debugging, and investigate new research directions on how to automatically pinpoint and repair the root cause of errors in large-scale distributed data processing.
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