Use Case

Database Optimization on Kubernetes with StormForge

Ensure databases running on Kubernetes are tuned and optimized before deployment. Improve performance of both relational and NoSQL databases while minimizing cost and resource usage.

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Optimizing databases for performance and efficiency is a challenge even in traditional environments. Running databases, whether relational or NoSQL, on Kubernetes adds a new level of difficulty. Performance and efficiency are affected by a wide variety of factors, starting with the choice of database but also including decisions on storage, caching, I/O and more.

Sub-optimal defaults.

Database makers provide installation guides with default settings that are meant to reduce risk and support calls, not to deliver efficient, optimized performance. Most organizations leave these defaults in place “to be safe” even though it may use more resources, and cost more to run, than is necessary.

Database tuning complexity.

Whether running an embedded, NoSQL database like Cassandra or MongoDB, or a more traditional RDBMS type database like Oracle or MySQL, there are a number of configuration decisions that must be made. Each of these decisions affects the performance and resource usage. Understanding the optimal settings is difficult for most organizations, especially because running databases on Kubernetes is still new and not well understood by most.

Difficulty understanding performance trade-offs.

This complexity makes it hard for application and database teams to understand the trade-offs between performance, resource utilization, and data accuracy. Teams lack the insights needed to make architectural and configuration decisions to ensure efficiency and performance.

Screenshot of a StormForge Experiment Results window, demonstrating that they can be analyzed and visualized to fully understand system behavior.

Experiment results can be visualized and analyzed to fully understand system behavior.

StormForge uses a process of rapid experimentation to ensure databases running on Kubernetes are tuned and optimized before deployment. Used in a non-production environment, experimentation helps organizations analyze, understand, and optimize their databases and applications for a wide range of scenarios. Experimentation-based tuning provides deep insights into the trade-offs of different database types and configurations to help drive smart business decisions.

Scientific approach.

Use experimentation and machine learning to understand what variables affect database performance, resource utilization, and cost-efficiency.

Machine learning-powered.

Tune key database parameters using machine learning to automatically configure databases for optimal performance and efficiency.

Make smart architectural decisions.

Non-prod experimentation helps teams make smart decisions. Which database, which storage option, and what configuration will provide the best trade-offs between cost and performance?