
:guide-id: concepts-threads
:guide-title: Concepts for configuring thread pools
:guide-summary: Understand these concepts to avoid resource exhaustion and congestion
:guide-priority: 999
:guide-tile-visible: false
:version: 26.0.2

include::../attributes.adoc[]

[[concepts-threads]]
= Concepts for configuring thread pools

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This section is intended when you want to understand the considerations and best practices on how to configure thread pools connection pools for {project_name}.
For a configuration where this is applied, visit link:{links_high-availability_deploy-keycloak-kubernetes_url}[{links_high-availability_deploy-keycloak-kubernetes_name}].

== Concepts

=== Quarkus executor pool

{project_name} requests, as well as blocking probes, are handled by an executor pool. Depending on the available CPU cores, it has a maximum size of 50 or more threads.
Threads are created as needed, and will end when no longer needed, so the system will scale up and down automatically.
{project_name} allows configuring the maximum thread pool size by the link:{links_server_all-config_url}?q=http-pool-max-threads[`http-pool-max-threads`] configuration option. See link:{links_high-availability_deploy-keycloak-kubernetes_url}[{links_high-availability_deploy-keycloak-kubernetes_name}] for an example.

When running on Kubernetes, adjust the number of worker threads to avoid creating more load than what the CPU limit allows for the Pod to avoid throttling, which would lead to congestion.
When running on physical machines, adjust the number of worker threads to avoid creating more load than the node can handle to avoid congestion.
Congestion would result in longer response times and an increased memory usage, and eventually an unstable system.

Ideally, you should start with a low limit of threads and adjust it accordingly to the target throughput and response time.
When the load and the number of threads increases, the database connections can also become a bottleneck.
Once a request cannot acquire a database connection within 5 seconds, it will fail with a message in the log like `Unable to acquire JDBC Connection`.
The caller will receive a response with a 5xx HTTP status code indicating a server side error.

If you increase the number of database connections and the number of threads too much, the system will be congested under a high load with requests queueing up, which leads to a bad performance.
The number of database connections is configured via the link:{links_server_all-config_url}?q=db-pool[`Database` settings `db-pool-initial-size`, `db-pool-min-size` and `db-pool-max-size`] respectively.
Low numbers ensure fast response times for all clients, even if there is an occasionally failing request when there is a load spike.

=== JGroups connection pool

NOTE: This currently applies to single-site setups only. In a multi-site setup with an external {jdgserver_name} this is no longer a restriction.

The combined number of executor threads in all {project_name} nodes in the cluster should not exceed the number of threads available in JGroups thread pool to avoid the error `org.jgroups.util.ThreadPool: thread pool is full`.
To see the error the first time it happens, the system property `jgroups.thread_dumps_threshold` needs to be set to `1`, as otherwise the message appears only after 10000 requests have been rejected.

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Use metrics to monitor the total JGroup threads in the pool and for the threads active in the pool.
When using TCP as the JGroups transport protocol, the metrics `vendor_jgroups_tcp_get_thread_pool_size` and `vendor_jgroups_tcp_get_thread_pool_size_active` are available for monitoring. When using UDP, the metrics `vendor_jgroups_udp_get_thread_pool_size` and `vendor_jgroups_udp_get_thread_pool_size_active` are available.
This is useful to monitor that limiting the Quarkus thread pool size keeps the number of active JGroup threads below the maximum JGroup thread pool size.

[#load-shedding]
=== Load Shedding

By default, {project_name} will queue all incoming requests infinitely, even if the request processing stalls.
This will use additional memory in the Pod, can exhaust resources in the load balancers, and the requests will eventually time out on the client side without the client knowing if the request has been processed.
To limit the number of queued requests in {project_name}, set an additional Quarkus configuration option.

Configure `http-max-queued-requests` to specify a maximum queue length to allow for effective load shedding once this queue size is exceeded.
Assuming a {project_name} Pod processes around 200 requests per second, a queue of 1000 would lead to maximum waiting times of around 5 seconds.

When this setting is active, requests that exceed the number of queued requests will return with an HTTP 503 error.
{project_name} logs the error message in its log.

[#probes]
=== Probes

{project_name}'s liveness probe is non-blocking to avoid a restart of a Pod under a high load.

// Developer's note: See KeycloakReadyHealthCheck for the details of the blocking/non-blocking behavior
The overall health probe and the readiness can probe in some cases block to check the connection to the database, so they might fail under a high load.
Due to this, a Pod can become non-ready under a high load.

=== OS Resources

In order for Java to create threads, when running on Linux it needs to have file handles available.
Therefore, the number of open files (as retrieved as `ulimit -n` on Linux) need to provide head-space for {project_name} to increase the number of threads needed.
Each thread will also consume memory, and the container memory limits need to be set to a value that allows for this or the Pod will be killed by Kubernetes.


