SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
PNUTS: Yahoo!'s hosted data serving platform
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Proceedings of the VLDB Endowment
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Proceedings of the 1st ACM symposium on Cloud computing
ZooKeeper: wait-free coordination for internet-scale systems
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
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ACM SIGMOD Record
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Performance Evaluation of Range Queries in Key Value Stores
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
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BigBench: towards an industry standard benchmark for big data analytics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Poster: MADES - a multi-layered, adaptive, distributed event store
Proceedings of the 7th ACM international conference on Distributed event-based systems
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As the complexity of enterprise systems increases, the need for monitoring and analyzing such systems also grows. A number of companies have built sophisticated monitoring tools that go far beyond simple resource utilization reports. For example, based on instrumentation and specialized APIs, it is now possible to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail enables more precise forms of analysis and prediction but comes at the price of high data rates (i.e., big data). 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 as part of CA Technologies initiative. 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.