As the data collected by enterprises grows in scale, there is a growing trend of performing data analytics on large datasets. Batch processing systems that can handle petabyte scale of data, such as Hadoop, have flourished and gained traction in the industry. As the results of batch analytics have been used to continuously improve front-facing user experience, there is a growing interest in pushing the processing latency down. This trend has fueled a resurgence in the development and usage of execution engines that can process continuous queries.

There is a growing trend of performing analysis on large datasets using workflows composed of MapReduce jobs connected through producer-consumer relationships based on data. This trend has spurred the development of a number of interfaces---ranging from program-based to query-based interfaces---for generating MapReduce workflows. Studies have shown that the gap in performance can be quite large between optimized and unoptimized workflows. However, automatic cost-based optimization of MapReduce workflows remains a challenge due to the multitude of interfaces, large size of the execution plan space, and the frequent unavailability of all types of information needed for optimization. We introduce a comprehensive plan space for MapReduce workflows generated by popular workflow generators. We then propose Stubby , a cost-based optimizer that searches selectively through the subspace of the full plan space that can be enumerated correctly and costed based on the information available in any given setting. Stubby enumerates the plan space based on plan-to-plan transformations and an efficient search algorithm. Stubby is designed to be extensible to new interfaces and new types of optimizations, which is a desirable feature given how rapidly MapReduce systems are evolving. Stubby's efficiency and effectiveness have been evaluated using representative workflows from many domains.