Sizing Red Hat Quay requires analyzing a complex architecture of ten distinct components, with five directly controlling image pull throughput. To create reproducible benchmarks, the engineering team tested a self-managed Red Hat OpenShift deployment on Amazon Web Services using two Quay pod replicas. The researchers simulated client workloads by fetching multi-layered test images and tracking resource consumption through Prometheus and Grafana dashboards.
Key points
Quay pull throughput relies directly on five core elements, meaning any misconfigured com…: Quay pull throughput relies directly on five core elements, meaning any misconfigured component can choke overall registry performance.
Teams can leverage the open source quay-performance-scripts framework to run standardized…: Teams can leverage the open source quay-performance-scripts framework to run standardized tests and pinpoint bottlenecks in their own clusters.
Red Hat Quay is a container image registry that stores, distributes, and helps secure container images across on-premise, cloud, and hybrid environments. When a large enterprise customer needed to validate Quay's performance capabilities for their deployment, we turned this sizing request into a systematic method for profiling every component in the architecture. Here is how we uncovered hidden bottlenecks across storage, client runtimes, and Red Hat OpenShift node limits, and how you can benchmark your own Quay cluster.
The request
A large enterprise customer operating Red Hat Quay across 160 nodes spanning 2 edge clusters was experiencing pull failures under load. Their question was straightforward: How many concurrent pulls can Quay handle before it breaks?
We wanted to go beyond answering for 1 customer. The goal was to produce general sizing recommendations for a standard Quay deployment—numbers any customer or technical account manager (TAM) could use as a baseline, with the understanding that real-world performance would differ based on their specific environment, deployment choices, and resource allocation.
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