A demo web application for learning observability with Grafana and performance testing with k6.
QuickPizza is a demo web application that generates random pizza combinations, specifically designed as a learning tool for observability and performance testing. It helps developers and SREs practice instrumenting applications, collecting telemetry data, and running load tests using the Grafana stack and k6. The project provides pre-configured setups to visualize metrics, logs, traces, and profiles in Grafana.
Developers, SREs, and DevOps engineers who want to learn or teach observability practices, performance testing with k6, and monitoring with Grafana in a hands-on, practical environment.
It offers a fully instrumented, real-world-like application out of the box, eliminating the setup overhead and allowing users to focus on learning observability concepts and tools. The project is maintained by Grafana, ensuring best practices and integration with the latest Grafana ecosystem features.
Demo app for learning observability with Grafana and performance testing with k6.
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Pre-configured to emit metrics, logs, traces, and profiling data following OpenTelemetry conventions, providing a ready-to-use sandbox for learning without manual setup.
Includes k6 scripts covering foundations, browser testing, and extensions, allowing practical performance testing alongside observability practices.
Supports both monolithic and microservices modes with Docker Compose files, demonstrating different observability patterns like distributed tracing.
Offers pre-built dashboards and drilldown apps for visualizing telemetry in Grafana OSS or Cloud, reducing configuration time for learners.
Heavily tied to Grafana tools like Alloy, Tempo, and Loki, making it difficult to adapt for other monitoring stacks without significant rework.
Requires Docker, Docker Compose, and k6 installations, and running the full local stack demands substantial system resources, which can be daunting for newcomers.
As a demo app focused on pizza generation, it lacks the complexity and features of production applications, which might not fully prepare users for instrumenting their own code.