A COMPREHENSIVE PERFORMANCE EVALUATION OF DATA STORES IN CONTEXT WITH APPLICATION MANAGEMENT TOOLS

Abstract

As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.business informatics. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s, as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created and it is required to analyze a vast amount of data. A number of companies have developed sophisticated monitoring tools for data analysis. For specialized APIs, can monitor single invocations. To maximize the benefit of data monitoring, the data has to be stored for an extended period of time for ulterior analysis. This new wave of big data analytics imposes new challenges especially for the application performance monitoring systems. The monitoring data has to be stored in a system that can sustain the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the advent of modern key-value stores, a variety of data storage systems have emerged that are built with a focus on scalability and high data rates as predominant in this monitoring use case. In this work, we present our experience and a comprehensive performance evaluation of six modern (open-source) data stores in the context of application performance monitoring. We evaluated these systems with data and workloads that can be found in application performance monitoring, as well as, on-line advertisement, power monitoring, and many other use cases. We present our insights not only as performance results but also as lessons learned and our experience relating to the setup and configuration complexity of these data stores in an industry setting.