Patterns and Best Practices #
This document describes patterns and best practices, to build and run operators, and how to implement them in terms of the Java Operator SDK (JOSDK).
See also best practices in Operator SDK.
Implementing a Reconciler #
Reconcile All The Resources All the Time #
The reconciliation can be triggered by events from multiple sources. It could be tempting to check the events and reconcile just the related resource or subset of resources that the controller manages. However, this is considered an anti-pattern for operators because the distributed nature of Kubernetes makes it difficult to ensure that all events are always received. If, for some reason, your operator doesn’t receive some events, if you do not reconcile the whole state, you might be operating with improper assumptions about the state of the cluster. This is why it is important to always reconcile all the resources, no matter how tempting it might be to only consider a subset. Luckily, JOSDK tries to make it as easy and efficient as possible by providing smart caches to avoid unduly accessing the Kubernetes API server and by making sure your reconciler is only triggered when needed.
Since there is a consensus regarding this topic in the industry, JOSDK does not provide
event access from
Reconciler implementations anymore starting with version 2 of the framework.
EventSources and Caching #
As mentioned above during a reconciliation best practice is to reconcile all the dependent resources
managed by the controller. This means that we want to compare a desired state with the actual
state of the cluster. Reading the actual state of a resource from the Kubernetes API Server
directly all the time would mean a significant load. Therefore, it’s a common practice to
instead create a watch for the dependent resources and cache their latest state. This is done
following the Informer pattern. In Java Operator SDK, informers are wrapped into an
to integrate it with the eventing system of the framework. This is implemented by the
A new event that triggers the reconciliation is only propagated to the
Reconciler when the actual
resource is already in cache.
Reconciler implementations therefore only need to compare the
desired state with the observed one provided by the cached resource. If the resource cannot be
found in the cache, it therefore needs to be created. If the actual state doesn’t match the
desired state, the resource needs to be updated.
Since all resources should be reconciled when your
Reconciler is triggered and reconciliations
can be triggered multiple times for any given resource, especially when retry policies are in
place, it is especially important that
Reconciler implementations be idempotent, meaning that
the same observed state should result in exactly the same outcome. This also means that
operators should generally operate in stateless fashion. Luckily, since operators are usually
managing declarative resources, ensuring idempotency is usually not difficult.
Sync or Async Way of Resource Handling #
Depending on your use case, it’s possible that your reconciliation logic needs to wait a
non-insignificant amount of time while the operator waits for resources to reach their desired
state. For example, you
Reconciler might need to wait for a
Pod to get ready before
performing additional actions. This problem can be approached either synchronously or
The asynchronous way is to just exit the reconciliation logic as soon as the
determines that it cannot complete its full logic at this point in time. This frees resources to
process other primary resource events. However, this requires that adequate event sources are
put in place to monitor state changes of all the resources the operator waits for. When this is
done properly, any state change will trigger the
Reconciler again and it will get the
opportunity to finish its processing
The synchronous way would be to periodically poll the resources’ state until they reach their
desired state. If this is done in the context of the
reconcile method of your
implementation, this would block the current thread for possibly a long time. It’s therefore
usually recommended to use the asynchronous processing fashion.
Why have Automatic Retries? #
Automatic retries are in place by default and can be configured to your needs. It is also
possible to completely deactivate the feature, though we advise against it. The main reason
configure automatic retries for your
Reconciler is due to the fact that errors occur quite
often due to the distributed nature of Kubernetes: transient network errors can be easily dealt
with by automatic retries. Similarly, resources can be modified by different actors at the same
time so it’s not unheard of to get conflicts when working with Kubernetes resources. Such
conflicts can usually be quite naturally resolved by reconciling the resource again. If it’s
done automatically, the whole process can be completely transparent.
Managing State #
Thanks to the declarative nature of Kubernetes resources, operators that deal only with Kubernetes resources can operator in a stateless fashion, i.e. they do not need to maintain information about the state of these resources, as it should be possible to completely rebuild the resource state from its representation (that’s what declarative means, after all). However, this usually doesn’t hold true anymore when dealing with external resources and it might be necessary for the operator to keep track of this external state so that it is available when another reconciliation occurs. While such state could be put in the primary resource’s status sub-resource, this could become quickly difficult to manage if a lot of state needs to be tracked. It also goes against the best practice that a resource’s status should represent the actual resource state, when its spec represents the desired state. Putting state that doesn’t striclty represent the resource’s actual state is therefore discouraged. Instead, it’s advised to put such state into a separate resource meant for this purpose such as a Kubernetes Secret or ConfigMap or even a dedicated Custom Resource, which structure can be more easily validated.