Data For: Improving Kieker'S Scalability By Employing Linked Read-Optimized And Write-Optimized Nosql Storage

We show, how polyglot persistence increase Kieker's scalability by employing separate read-optimized and write-optimized noSQL storage. For this purpose we extended Kieker to store its monitoring output in Apache Cassandra , which is a write-optimized wide-column noSQL database. For the analysis of the generated monitoring output we are using ElasticSearch, a read-optimized document store noSQL storage. We are interlinking read-optimized and write-optimized noSQL storage within our Regression Benchmarking Execution Environment (RBEE). To ensure scalability we are employing a container infrastructure. The mentioned noSQL storages and the linker are operating within one single Docker container which scales horizontally. For generating reference values, we instrumented a Java SE application with Kieker's file system writer and measured throughput and method's execution times. Consecutively, we instrumented the same Java SE application with our Apache Cassandra writer and measured throughput and method's execution time. Finally, we compared measurement results of Kieker's file system writer with the measurement results of our Apache Cassandra writer.