Use this as a starting point to size a product environment.
Adjust the values for your environment as needed based on your load tests.
== Performance recommendations
[WARNING]
====
* Performance will be lowered when scaling to more Pods (due to additional overhead) and using a cross-datacenter setup (due to additional traffic and operations).
* Increased cache sizes can improve the performance when {project_name} instances running for a longer time.
This will decrease response times and reduce IOPS on the database.
Still, those caches need to be filled when an instance is restarted, so do not set resources too tight based on the stable state measured once the caches have been filled.
* In containers, Keycloak allocates 70% of the memory limit for heap based memory. It will also use approximately 300 MB of non-heap-based memory.
To calculate the requested memory, use the calculation above. As memory limit, subtract the non-heap memory from the value above and divide the result by 0.7.
* Leave 200% extra head-room for CPU usage to handle spikes in the load.
This ensures a fast startup of the node, and sufficient capacity to handle failover tasks like, for example, re-balancing Infinispan caches, when one node fails.
Performance of {project_name} dropped significantly when its Pods were throttled in our tests.
(Allow for three times the CPU requested to handle peaks, startups and failover tasks, and also refresh token handling which we don't have numbers on, yet)
* Database seeded with 100,000 users and 100,000 clients.
* Infinispan caches at default of 10,000 entries, so not all clients and users fit into the cache, and some requests will need to fetch the data from the database.
* PostgreSQL deployed inside the same OpenShift with ephemeral storage.
+
Using a database with persistent storage will have longer database latencies, which might lead to longer response times; still, the throughput should be similar.
