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Kube-Burner

Kube-Burner is a Kubernetes workload generation and performance benchmarking tool (performance testing) designed to create, run, and measure synthetic workloads on Kubernetes clusters.

  • Kubernetes cluster performance and scalability benchmarking (performance testing).
  • Configurable synthetic workload generation using templated Kubernetes objects (workload orchestration).
  • Metric collection and storage integration with backends such as Prometheus and Elasticsearch (observability).
  • Execution of cyclic or batch test runs, including create and delete operations for Kubernetes resources (test automation).
  • Support for configurable test scenarios and tunable parameters for resource, timing, and concurrency profiles (performance engineering).

More About Kube-Burner

Kube-Burner is a Kubernetes-focused workload generation and benchmarking tool (performance testing) designed to create and run reproducible synthetic workloads against Kubernetes clusters. It targets use cases where platform, site reliability, and performance engineering teams need to evaluate how a cluster behaves under varied resource loads, object counts, and operational patterns. The project centers on running controlled experiments that deploy and tear down Kubernetes resources at scale while capturing telemetry relevant to performance and capacity planning.

At its core, Kube-Burner uses configuration-driven test scenarios (test automation) defined in YAML to specify which Kubernetes objects to create, how many instances to deploy, and how operations are scheduled over time. It relies on Go templates (configuration management) to parameterize Kubernetes manifests, enabling reuse of object definitions with different sizes, labels, namespaces, or resource requests. Kube-Burner executes create, read, update, and delete operations against the Kubernetes Application Programming Interface (API), which allows users to model application lifecycles, churn patterns, and cluster management workflows.

The project integrates with metric backends such as Prometheus and Elasticsearch (observability) to collect and store performance data generated during runs. It gathers cluster and node metrics, as well as data about Kube-Burner job execution, and pushes or scrapes this information through configured endpoints. This observability integration helps enterprises correlate Kubernetes object operations with Central Processing Unit (CPU), memory, and API server behavior, and supports time-series analysis of load profiles.

Kube-Burner supports both batch and cyclic execution modes (performance testing), allowing one-time tests or repeated runs with configurable intervals. Users can tune concurrency, object counts, namespaces, and timing parameters, which enables construction of scenarios that resemble application onboarding, peak traffic events, or ongoing multi-tenant cluster use. The tool can also manage namespaces and clean up test resources, which fits into automated pipelines and continuous performance testing setups.

In enterprise environments, Kube-Burner is used with Kubernetes distributions and cloud platforms (infrastructure operations) to validate scalability targets, compare cluster configurations, and test cluster upgrades or configuration changes under load. It operates within the broader cloud-native ecosystem (cloud-native infrastructure), aligning with CNCF technologies such as Kubernetes and Prometheus for metrics. Its configuration-driven approach, templated manifests, and metric backend support position it within categories such as performance testing, workload orchestration, and observability tooling for Kubernetes platform operations.