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NoSQL databases have become an important component of many big data andreal-time web applications. Their distributed nature and scalability make theman ideal data storage repository for a variety of use cases. While NoSQLdatabases are delivered with a default ''off-the-shelf'' configuration, theyoffer configuration settings to adjust a database's behavior and performance toa specific use case and environment. The abundance and oftentimes imperceptibleinter-dependencies of configuration settings make it difficult to optimize andperformance-tune a NoSQL system. There is no one-size-fits-all configurationand therefore the workload, the physical design, and available resources needto be taken into account when optimizing the configuration of a NoSQL database.This work explores Machine Learning as a means to automatically tune a NoSQLdatabase for optimal performance. Using Random Forest and Gradient BoostingDecision Tree Machine Learning algorithms, multiple Machine Learning modelswere fitted with a training dataset that incorporates properties of the NoSQLphysical configuration (replication and sharding). The best models were thenemployed as surrogate models to optimize the Database Management System'sconfiguration settings for throughput and latency using a Black-boxOptimization algorithm. Using an Apache Cassandra database, multipleexperiments were carried out to demonstrate the feasibility of this approach,even across varying physical configurations. The tuned DBMS configurationsyielded throughput improvements of up to 4%, read latency reductions of up to43%, and write latency reductions of up to 39% when compared to the defaultconfiguration settings.

NoSQL Database Tuning through Machine Learning