Predicting Hadoop misconfigurations using machine learning

Distributed applications are popular for heavy workloads where the resources of a single machine are not sufficient. These distributed applications come with many parameters to tune so that cluster resources can be effectively utilized. However, any misconfiguration of the available parameters may result in suboptimal performance of one or more machines in the cluster. These events may go unnoticed or can result in crashes. This problem of misconfigured parameters has no straightforward solution due to the variety of parameters and vastly different workloads being processed. In this article, we propose a methodology for machine learning-based detection of misconfigurations. We collect data mined from system resource utilization, Hadoop logs, and job-level metrics to train a model using decision tree and support vector machine. The models are used to identify whether a set of configuration parameters could result in a crash or a slowdown for a specific workload. The approach explained in this article can be extended to other distributed big data applications, such as Spark, Hive, Pig, and so on.