Towards Ex Vivo Testing of MapReduce Applications

TLDR: This work proposes an automatic test framework implementing a novel testing approach called Ex Vivo that can identify a fault in a few seconds, then the program can be stopped, not only avoiding an incorrect output, but also saving money, time and energy of production resources.

Abstract: Big Data programs are those that process large data exceeding the capabilities of traditional technologies. Among newly proposed processing models, MapReduce stands out as it allows the analysis of schema-less data in large distributed environments with frequent infrastructure failures. Functional faults in MapReduce are hard to detect in a testing/preproduction environment due to its distributed characteristics. We propose an automatic test framework implementing a novel testing approach called Ex Vivo. The framework employs data from production but executes the tests in a laboratory to avoid side-effects on the application. Faults are detected automatically without human intervention by checking if the same data would generate different outputs with different infrastructure configurations. The framework (MrExist) is validated with a real-world program. MrExist can identify a fault in a few seconds, then the program can be stopped, not only avoiding an incorrect output, but also saving money, time and energy of production resources.