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The process of data analysis, especially in GUI-based analytics systems, ishighly exploratory. The user iteratively refines a workflow multiple timesbefore arriving at the final workflow. In such an exploratory setting, it isvaluable to the user if the initial results of the workflow are representativeof the final answers so that the user can refine the workflow without waitingfor the completion of its execution. Partitioning skew may lead to theproduction of misleading initial results during the execution. In this paper,we explore skew and its mitigation strategies from the perspective of theresults shown to the user. We present a novel framework called Reshape that canadaptively handle partitioning skew in pipelined execution. Reshape employs atwo-phase approach that transfers load in a fine-tuned manner to mitigate skewiteratively during execution, thus enabling it to handle changes in input-datadistribution. Reshape has the ability to adaptively adjust skew-handlingparameters, which reduces the technical burden on the users. Reshape supports avariety of operators such as HashJoin, Group-by, and Sort. We implementedReshape on top of two big data engines, namely Amber and Flink, to demonstrateits generality and efficiency, and report an experimental evaluation using realand synthetic datasets.

Reshape: Adaptive Result-aware Skew Handling for Exploratory Analysis on Big Data