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As the complexity of enterprise systems increases, the need for monitoringand analyzing such systems also grows. A number of companies have builtsophisticated monitoring tools that go far beyond simple resource utilizationreports. For example, based on instrumentation and specialized APIs, it is nowpossible to monitor single method invocations and trace individual transactionsacross geographically distributed systems. This high-level of detail enablesmore precise forms of analysis and prediction but comes at the price of highdata rates (i.e., big data). To maximize the benefit of data monitoring, thedata 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 theapplication performance monitoring systems. The monitoring data has to bestored in a system that can sustain the high data rates and at the same timeenable an up-to-date view of the underlying infrastructure. With the advent ofmodern key-value stores, a variety of data storage systems have emerged thatare built with a focus on scalability and high data rates as predominant inthis monitoring use case. In this work, we present our experience and acomprehensive performance evaluation of six modern (open-source) data stores inthe context of application performance monitoring as part of CA Technologiesinitiative. We evaluated these systems with data and workloads that can befound in application performance monitoring, as well as, on-line advertisement,power monitoring, and many other use cases. We present our insights not only asperformance results but also as lessons learned and our experience relating tothe setup and configuration complexity of these data stores in an industrysetting.

Solving Big Data Challenges for Enterprise Application Performance Management