How to Use a Single Informer to Monitor Multiple Custom Resource Definition (CRD) Changes
Monitoring multiple Custom Resource Definitions (CRDs) for changes using a single Kubernetes informer can significantly improve efficiency and reduce code complexity compared to using separate informers for each CRD. This approach leverages the power of Kubernetes' client-go library and its ability to handle multiple resources within a single informer. However, it requires careful consideration of resource filtering and event handling.
Understanding Kubernetes Informers
Before diving into the solution, it's crucial to understand how Kubernetes informers function. An informer acts as a cache and a watch mechanism for Kubernetes resources. It efficiently retrieves and tracks changes (add, update, delete) to the resources it's watching. The key here is that the informer can watch multiple resource types, but you must carefully define which resources it should watch.
The Approach: Using a Single Informer with Resource Versioning and Filtering
The most effective way to monitor multiple CRDs with a single informer involves utilizing resource versioning and careful filtering. Here's a breakdown of the process:
1. Shared Informer Factory:
Utilize the sharedInformerFactory provided by the client-go library. This factory helps manage multiple informers efficiently, sharing the underlying connection to the Kubernetes API server. This approach optimizes resource usage and reduces redundant calls.
2. Defining Resource Schemas:
Ensure you have the correct schema definitions (using client-go/rest or client-go/kubernetes) for each of the CRDs you wish to monitor. This is essential for the informer to correctly interpret the incoming events.
3. List-Watch Mechanism:
The core principle involves instructing the informer to watch for changes across all desired CRDs. This is done by registering each CRD type with the informer factory. The informer will then listen for events related to those types.
4. Event Handling and Filtering:
This is where resource versioning and filtering come into play. When an event occurs, the informer provides you with the resource's type and its current state (add, update, delete). You can efficiently filter these events based on the resourceVersion and resource type (CRD name) to isolate the changes relevant to each specific CRD. The resourceVersion helps to handle concurrent updates effectively.
5. Example Code Snippet (Conceptual):
This example is a conceptual illustration; it lacks complete error handling and would require adapting to your specific CRD structures:
import (
"context"
"fmt"
"log"
"time"
"k8s.io/client-go/informers"
"k8s.io/client-go/kubernetes"
"k8s.io/client-go/rest"
"k8s.io/client-go/tools/cache"
)
func main() {
// creates the in-cluster config
config, err := rest.InClusterConfig()
if err != nil {
panic(err.Error())
}
// creates the clientset
clientset, err := kubernetes.NewForConfig(config)
if err != nil {
panic(err.Error())
}
factory := informers.NewSharedInformerFactory(clientset, 0) // Time.Duration(0) for continuous resync
// Register informers for your CRDs. Replace with your actual CRD group, version, and plural names.
crd1Informer := factory.CustomResource(schema.GroupVersionResource{Group: "group1", Version: "version1", Resource: "crd1plural"},)
crd2Informer := factory.CustomResource(schema.GroupVersionResource{Group: "group2", Version: "version2", Resource: "crd2plural"},)
crd1Informer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
AddFunc: func(obj interface{}) {
// Handle CRD1 add event
fmt.Printf("CRD1 Added: %+v\n", obj)
},
UpdateFunc: func(oldObj, newObj interface{}) {
// Handle CRD1 update event
fmt.Printf("CRD1 Updated: %+v\n", newObj)
},
DeleteFunc: func(obj interface{}) {
// Handle CRD1 delete event
fmt.Printf("CRD1 Deleted: %+v\n", obj)
},
})
// Similarly, add event handlers for crd2Informer and other CRDs
stopCh := make(chan struct{})
defer close(stopCh)
factory.Start(stopCh)
factory.WaitForCacheSync(stopCh)
log.Println("Informers started and synced.")
select {} // Keep the program running indefinitely
}
Important Considerations:
- Error Handling: The example code snippet omits comprehensive error handling. Robust production-level code must include detailed error checks and handling.
- Resource Version Handling: Properly manage
resourceVersionto avoid missing updates or processing outdated information. - Rate Limiting: If you're monitoring a high volume of changes, consider implementing rate limiting to prevent overwhelming your application.
- Concurrency: Handle events concurrently to ensure responsiveness, particularly with a large number of CRDs or frequent updates.
By using this approach, you can efficiently monitor multiple CRDs for changes using a single informer, reducing code complexity and improving overall performance. Remember to tailor the code snippet and add comprehensive error handling and concurrency controls to suit your specific needs and environment.
