How to Use Kubernetes LeaderElection for Custom Controller High Availability

In Kubernetes, high availability and fault tolerance are essential for system reliability. For controllers, LeaderElection is a mechanism that ensures only one instance of a controller operates on a specific task at a time in a multi-replica deployment. This blog delves into the concept of LeaderElection, its importance, implementation, and best practices.

---

What is Kubernetes LeaderElection?

LeaderElection is a process where multiple replicas of a controller or service coordinate to elect a single leader that performs the primary tasks, while others remain on standby. If the leader fails, another instance is elected to ensure continuity.

Why is LeaderElection Necessary?

• Prevents duplicate work: Without a leader, multiple controller replicas could simultaneously act on the same resource, leading to conflicts or inconsistencies.
• Ensures high availability: If the leader fails, a new one is promptly elected, maintaining uninterrupted operation.

Kubernetes LeaderElection Best Practices: Achieving Reliable Controller Management

---

How LeaderElection Works

LeaderElection relies on coordination primitives provided by Kubernetes, typically using ConfigMaps or Leases stored in the API server.

1. Lease-based LeaderElection:
   The leader acquires a lease by updating a resource (like a ConfigMap or Lease object) with its identity and timestamp.
2. Health checks:
   The leader continuously updates its lease to indicate it is active.
3. Failover:
   If the leader fails to update the lease within the specified timeout, other candidates compete to acquire the lease.

---

Key Components of LeaderElection

1. LeaderElectionConfiguration

A configuration block for enabling leader election in custom controllers or operators.

Example configuration:

leaderElection: true
leaderElectionID: "my-custom-controller-leader-election"
leaderElectionNamespace: "kube-system"
leaseDuration: 15s
renewDeadline: 10s
retryPeriod: 2s

2. Leases API

The Lease resource in the coordination.k8s.io API group is often used for LeaderElection.

Example Lease Object:

apiVersion: coordination.k8s.io/v1
kind: Lease
metadata:
  name: my-leader-election-lease
  namespace: kube-system
spec:
  holderIdentity: instance-1
  leaseDurationSeconds: 15
  renewTime: 2024-01-01T00:00:00Z

---

How to Implement LeaderElection in Go

LeaderElection can be added to custom controllers using the Kubernetes client-go library.

Setup Code for LeaderElection

1. Import Required Libraries:

import (
    "context"
    "fmt"
    "os"
    "time"

    "k8s.io/client-go/kubernetes"
    "k8s.io/client-go/rest"
    "k8s.io/client-go/tools/leaderelection"
    "k8s.io/client-go/tools/leaderelection/resourcelock"
)

2. Create a Resource Lock:
   The resourcelock package provides abstractions for Lease or ConfigMap-based locks.

config, err := rest.InClusterConfig()
if err != nil {
    panic(err)
}

clientset, err := kubernetes.NewForConfig(config)
if err != nil {
    panic(err)
}

lock, err := resourcelock.New(
    resourcelock.LeasesResourceLock,
    "kube-system", // Namespace
    "my-controller", // Lease name
    clientset.CoreV1(),
    clientset.CoordinationV1(),
    resourcelock.ResourceLockConfig{
        Identity: os.Hostname(),
    },
)
if err != nil {
    panic(err)
}

3. Start LeaderElection:

leaderelection.RunOrDie(context.TODO(), leaderelection.LeaderElectionConfig{
    Lock:          lock,
    LeaseDuration: 15 * time.Second,
    RenewDeadline: 10 * time.Second,
    RetryPeriod:   2 * time.Second,
    Callbacks: leaderelection.LeaderCallbacks{
        OnStartedLeading: func(ctx context.Context) {
            fmt.Println("Started leading")
            // Your controller's main logic
        },
        OnStoppedLeading: func() {
            fmt.Println("Stopped leading")
        },
        OnNewLeader: func(identity string) {
            if identity == os.Hostname() {
                fmt.Println("I am the leader now")
            } else {
                fmt.Printf("New leader elected: %s\n", identity)
            }
        },
    },
})

---

Testing LeaderElection

1. Deploy your controller with multiple replicas:

   spec:
     replicas: 3

2. Verify logs to see which instance becomes the leader.
3. Simulate leader failure by terminating the leader pod and observe failover.

---

Best Practices for LeaderElection

1. Use short timeouts carefully:
   Setting a very short lease duration or renew deadline may lead to unnecessary failovers due to temporary network issues.

2. Avoid leader-specific data persistence:
   If the leader persists state, ensure it is accessible to other instances after a failover.

3. Monitor LeaderElection health:
   Use metrics and logs to monitor the status of LeaderElection in your cluster.

4. Leverage Kubernetes RBAC:
   Secure the resources (e.g., Lease or ConfigMap) used for LeaderElection to prevent unauthorized access.

---

Example Use Cases for LeaderElection

1. Custom Operators:
   Ensures only one operator instance performs resource reconciliation.

2. Backup Jobs:
   Ensures only one instance performs a backup at a time.

3. Distributed Systems Coordination:
   Facilitates leader selection in distributed systems for tasks like coordination or consensus.

---

Conclusion

LeaderElection is a vital mechanism in Kubernetes for ensuring high availability and preventing conflicts in multi-replica deployments. By following this guide, you can implement LeaderElection in your custom controllers, enhancing their reliability and fault tolerance.

What use cases do you have in mind for LeaderElection? Share your thoughts in the comments!

Understanding Java Stream Gatherers with examples

Java 22 Stream Gatherers

Java Streams have revolutionized the way we process data. With their clean, declarative style, Streams allow you to work on collections with minimal boilerplate. But the real magic lies in "gatherers"—the tools that let you collect, group, and aggregate data into meaningful results. Let’s dive deep into the world of Java Stream gatherers, understand their potential, and explore how to wield them effectively.

What Are Stream Gatherers?

Stream gatherers are mechanisms to accumulate or "gather" the results of Stream operations into collections, strings, maps, or even custom data structures. At the heart of this process is the Collector interface and the powerful Collectors utility class, which provides out-of-the-box gatherers.

How Gatherers Work in Java Streams

The Stream.collect() method is the gateway to gathering data. This method requires a Collector, which defines how the elements in the stream are processed and gathered.

Components of a Collector:

1. Supplier: Provides a container to hold the gathered data.
2. Accumulator: Defines how each element is added to the container.
3. Combiner: Combines two containers, especially in parallel streams.
4. Finisher: Transforms the accumulated data into the desired final result.
5. Characteristics: Defines behavior like immutability or concurrency.

The Built-In Gatherers in Collectors

Java's Collectors class provides a variety of pre-built gatherers to solve common problems.

1. Gathering into Collections

The most straightforward gatherers are those that collect stream elements into a collection.

• To List:

List<String> names = List.of("Alice", "Bob", "Charlie")
    .stream()
    .collect(Collectors.toList());

• To Set:

Set<String> uniqueNames = List.of("Alice", "Bob", "Alice")
    .stream()
    .collect(Collectors.toSet());

• To Specific Condition:

Set<String> uniqueNames = List.of("Alice", "Bob", "Alice")
    .stream()
    .collect(Collectors.toSet());

2. Gathering into a Map

Maps are powerful, but beware of duplicate keys.

• Basic Mapping:

Map<Integer, String> nameMap = List.of("Alice", "Bob", "Charlie")
    .stream()
    .collect(Collectors.toMap(String::length, Function.identity()));

• Handling Duplicates:

Map<Integer, String> nameMap = List.of("Alice", "Anna", "Bob")
    .stream()
    .collect(Collectors.toMap(
        String::length, 
        Function.identity(), 
        (existing, replacement) -> existing // Handle duplicates
    ));

3. Gathering by Grouping

Grouping allows you to categorize elements based on a classifier function.

• Basic Grouping:

Map<Integer, List<String>> groupedByLength = List.of("Alice", "Bob", "Anna")
    .stream()
    .collect(Collectors.groupingBy(String::length));

• Grouping with Downstream Collectors:

Map<Integer, Set<String>> groupedWithSet = List.of("Alice", "Anna", "Bob")
    .stream()
    .collect(Collectors.groupingBy(
        String::length, 
        Collectors.toSet()
    ));

4. Partitioning

Partitioning splits data into two groups based on a predicate.

Map<Boolean, List<Integer>> partitioned = IntStream.range(1, 10).boxed()
    .collect(Collectors.partitioningBy(n -> n % 2 == 0));

Advanced Techniques with Gatherers

1. Custom Collectors

If built-in gatherers don’t fit your needs, you can create a custom Collector.

Example: Custom Collector for Concatenation

Collector<String, StringBuilder, String> concatenator = Collector.of(
    StringBuilder::new,
    StringBuilder::append,
    StringBuilder::append,
    StringBuilder::toString
);

String result = List.of("Java", "Streams", "Gatherers")
    .stream()
    .collect(concatenator);

2. Parallel Streams and Gatherers

Parallel streams use the combiner step to merge intermediate results. Proper implementation ensures thread safety.

Example: Safe Parallel Summation

int sum = IntStream.range(1, 100)
    .parallel()
    .reduce(0, Integer::sum); // Associative and thread-safe

3. Combining Multiple Gatherers

Sometimes, you need to gather data in multiple ways simultaneously.

Example: Statistics and Grouping Together

Map<Boolean, Long> stats = IntStream.range(1, 100).boxed()
    .collect(Collectors.partitioningBy(
        n -> n % 2 == 0, 
        Collectors.counting()
    ));

Common Pitfalls and How to Avoid Them

1. Duplicate Keys in toMap

Pitfall: Duplicate keys result in an IllegalStateException.

Solution: Provide a merge function to resolve conflicts.

2. Memory Overhead in joining()

Pitfall: Large streams result in high memory consumption.

Solution: Break the stream into chunks or use efficient file writing techniques.

3. Misuse of Parallel Streams

Pitfall: Parallelizing non-thread-safe collectors leads to race conditions.

Solution: Stick to built-in collectors like toList() for parallel streams.

Interactive Examples for Practice

Q1: Gather All Even Numbers

Try this:

List<Integer> evenNumbers = IntStream.range(1, 20).boxed()
    .filter(n -> n % 2 == 0)
    .collect(Collectors.toList());

What do you think the result will be?

Q2: Group Names by Their First Letter

Map<Character, List<String>> groupedNames = List.of("Alice", "Anna", "Bob", "Charlie")
    .stream()
    .collect(Collectors.groupingBy(name -> name.charAt(0)));

Can you predict the output?

Real-World Use Cases

1. Processing Logs

Group logs by severity levels and count occurrences:

Map<String, Long> logCounts = logs.stream()
    .collect(Collectors.groupingBy(Log::getSeverity, Collectors.counting()));


2. Generating Reports

Partition employee data into full-time and part-time groups:

Map<Boolean, List<Employee>> partitionedEmployees = employees.stream()
    .collect(Collectors.partitioningBy(Employee::isFullTime));

3. Building Dashboards

Aggregate sales data by region:

Map<String, Double> salesByRegion = sales.stream()
    .collect(Collectors.groupingBy(Sale::getRegion, Collectors.summingDouble(Sale::getAmount)));


Conclusion

Java Stream gatherers offer immense flexibility and power. By understanding their nuances and mastering both built-in and custom collectors, you can write clean, efficient, and expressive data-processing pipelines. Whether you're aggregating statistics, generating reports, or building dashboards, gatherers are your go-to tool for transforming streams into meaningful results.

How to Fix 'RBAC Permissions Denied' Errors in Kubernetes

Role-Based Access Control (RBAC) is an integral part of Kubernetes security, ensuring that users and applications access only the resources they are authorized to. However, encountering RBAC Permissions Denied errors can be frustrating and disruptive. This article will explore common causes of these errors and provide a systematic approach to resolve them.

---

Understanding RBAC in Kubernetes

RBAC controls access to Kubernetes resources based on roles and bindings.

• Roles: Define a set of permissions for resources within a namespace.
• ClusterRoles: Similar to Roles but applicable cluster-wide.
• RoleBinding: Binds a Role to a user, group, or service account within a namespace.
• ClusterRoleBinding: Binds a ClusterRole to a user, group, or service account cluster-wide.

When a request is made, Kubernetes checks if the user (or service account) has the necessary permissions via RBAC policies. If the required permissions are missing, Kubernetes returns a 403 Forbidden error with a message like "RBAC: Permissions Denied."

How to Fix 'RBAC Permissions Denied' Errors in Kubernetes
@copyright tech693.com

---

Diagnosing the Error

Before fixing the issue, you need to diagnose the problem accurately. Here's how:

1. Identify the Request Source

Determine whether the request originates from:

• A user using kubectl or the Kubernetes API.
• A pod using its service account.

2. Examine the Error Message

Run the command that triggered the error and review the output. For instance:

kubectl get pods --namespace dev

If denied, you'll see something like:

Error from server (Forbidden): pods is forbidden: User "user@example.com" cannot list resource "pods" in API group "" in the namespace "dev"

Key details to note:

• User or service account: e.g., user@example.com.
• Resource: e.g., pods.
• Namespace: e.g., dev.
• Verb: e.g., list.

3. Check Current Bindings

List the RBAC bindings for the user or service account:

kubectl get rolebinding,clusterrolebinding --all-namespaces | grep "user@example.com"

---

Steps to Fix 'RBAC Permissions Denied' Errors

Step 1: Review Current RBAC Policies

Use kubectl auth can-i to simulate the permissions:

kubectl auth can-i list pods --namespace dev

This command outputs whether the user or service account has the required permission.

---

Step 2: Grant Necessary Permissions

Case 1: Missing Role or RoleBinding

If no RoleBinding exists for the user, create one.

1. Create a Role: Define a Role for the required namespace:
   
   

2. 
   apiVersion: rbac.authorization.k8s.io/v1
   kind: Role
   metadata:
     namespace: dev
     name: pod-reader
   rules:
   - apiGroups: [""]
     resources: ["pods"]
     verbs: ["get", "list", "watch"]
   
   

   Save this as role.yaml and apply it:

3. 
   kubectl apply -f role.yaml
   
   

4. Create a RoleBinding: Bind the Role to the user or service account:
   
   

   
   apiVersion: rbac.authorization.k8s.io/v1
   kind: RoleBinding
   metadata:
     namespace: dev
     name: pod-reader-binding
   subjects:
   - kind: User
     name: user@example.com
     apiGroup: rbac.authorization.k8s.io
   roleRef:
     kind: Role
     name: pod-reader
     apiGroup: rbac.authorization.k8s.io
   
   

   Save as rolebinding.yaml and apply:

kubectl apply -f rolebinding.yaml 

Case 2: Cluster-Wide Permissions Needed

If the error involves cluster-wide resources, use a ClusterRole and ClusterRoleBinding.

1. Create a ClusterRole:
   

   
   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRole
   metadata:
     name: cluster-admin-read-only
   rules:
   - apiGroups: [""]
     resources: ["*"]
     verbs: ["get", "list"]
   
   

   Apply it:
   
   

   
   kubectl apply -f clusterrole.yaml
   
   

2. Create a ClusterRoleBinding:
   

   
   apiVersion: rbac.authorization.k8s.io/v1
   kind: ClusterRoleBinding
   metadata:
     name: cluster-read-only-binding
   subjects:
   - kind: User
     name: user@example.com
     apiGroup: rbac.authorization.k8s.io
   roleRef:
     kind: ClusterRole
     name: cluster-admin-read-only
     apiGroup: rbac.authorization.k8s.io
   
   

   Apply the configuration:

   
   kubectl apply -f clusterrolebinding.yaml
   
   

---

Step 3: Validate the Changes

Re-run the kubectl auth can-i command:

kubectl auth can-i list pods --namespace dev

This should now return yes.

Test the actual command to confirm the error is resolved:

kubectl get pods --namespace dev

---

Common Troubleshooting Tips

Incorrect Subject Kind

Ensure the subjects field in RoleBinding or ClusterRoleBinding matches the user type:

• Use kind: User for individual users.
• Use kind: ServiceAccount for pods.
• Use kind: Group for groups.

Namespace Mismatch

Roles are namespace-specific. Ensure the namespace in the RoleBinding matches the namespace you’re working with:

kubectl config set-context --current --namespace=dev

Overly Restrictive Rules

Review the Role's rules section. Ensure the API group, resource, and verb are correctly defined.

Use Logs for Advanced Debugging

Enable verbose output in kubectl:

kubectl get pods --namespace dev --v=9

The logs provide detailed insights into permission issues.

---

Best Practices to Avoid RBAC Errors

Principle of Least Privilege

Grant only the permissions required for the task. Avoid broad permissions like *.

Use Service Accounts for Applications

Avoid using cluster-admin roles for pods. Instead, create dedicated service accounts with specific permissions.

Regular Audits

Audit your RBAC policies periodically:

kubectl get roles,rolebindings,clusterroles,clusterrolebindings --all-namespaces

Employ Tools for RBAC Management

Use tools like RBAC Manager or kubectl-rbac for easier management.

---

Conclusion

Fixing RBAC Permissions Denied errors in Kubernetes requires a clear understanding of roles, bindings, and user contexts. By systematically diagnosing and addressing the issue, you can ensure your cluster remains secure while enabling seamless access for users and applications. Following the best practices outlined in this guide will help you maintain robust and manageable RBAC policies.

Ephemeral Containers in Kubernetes: Understanding the What, Why, and How

Mastering Ephemeral Containers in Kubernetes: A Complete Guide to Debugging and Troubleshooting Pods 

Kubernetes has transformed the way we manage and orchestrate containerized applications, offering scalability, resilience, and robust automation. However, the dynamic and distributed nature of Kubernetes environments often leads to challenges in debugging and troubleshooting running workloads. Enter Ephemeral Containers — a powerful feature designed to bridge the gap between troubleshooting and running containerized applications without disrupting production workloads.

This blog explores what ephemeral containers are, their purpose, how they work, and some practical use cases to illustrate their significance.

---

What Are Ephemeral Containers?

Ephemeral containers are a special type of container in Kubernetes designed explicitly for troubleshooting and debugging. Unlike regular containers, they are temporary, non-persistent, and injected into an existing pod without restarting it. This allows developers and operators to diagnose issues in a live pod without affecting the ongoing workload.

Key Characteristics:

1. Temporary Nature: Ephemeral containers exist only for the duration of a debugging session and are removed once their purpose is fulfilled.
2. Non-Disruptive: They do not require the pod to restart, ensuring zero disruption to the running application.
3. Debugging Focus: They are primarily intended for attaching debuggers, running diagnostic tools, or executing troubleshooting commands.
4. No Lifecycle Management: Kubernetes does not manage their lifecycle like regular containers (e.g., they are not restarted if they fail).

Ephemeral containers became generally available in Kubernetes 1.23, evolving from an experimental feature to a valuable tool for production-grade clusters.

---

Why Do We Need Ephemeral Containers?

Debugging issues in Kubernetes is inherently complex due to the following reasons:

• Immutable Container Design: Containers are typically immutable, making it challenging to add debugging tools after deployment.
• Dynamic Environments: Kubernetes environments are highly dynamic, with pods being frequently scaled up or down.
• Isolation: Pods are isolated from the host system, making it harder to access underlying files or processes.

Traditional debugging methods, such as adding sidecar containers or modifying pod specs, can be disruptive and time-consuming. Ephemeral containers address these issues by providing an on-the-fly mechanism to inspect and debug running pods.

---

How Do Ephemeral Containers Work?

Ephemeral containers leverage the existing pod's infrastructure to run a temporary debugging container. They are injected using the Kubernetes kubectl debug command, which dynamically updates the pod's specification to include the ephemeral container.

Key Steps:

1. Command Execution:

   
   kubectl debug -it <pod-name> --image=<debugging-image>
   
   

   This command adds an ephemeral container to the specified pod.

2. Debugging Environment: The ephemeral container runs the specified debugging image, such as busybox, alpine, or a custom image with pre-installed diagnostic tools.

3. Interaction: Operators can access the ephemeral container's terminal to inspect logs, run diagnostic commands, or analyze network traffic.

4. Removal: Once the debugging session is over, the ephemeral container is automatically cleaned up.

Example YAML:

Here’s an example of how an ephemeral container can be defined programmatically:

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  containers:
  - name: app-container
    image: nginx
  ephemeralContainers:
  - name: debug-container
    image: busybox
    command:
    - sh
    - "-c"
    - "sleep 1d"

---

Use Cases for Ephemeral Containers

1. Debugging Application Failures

Ephemeral containers are ideal for inspecting application behavior without redeploying or restarting the pod. For example:

• Diagnosing why an application is unable to connect to a database.
• Debugging a configuration issue causing the application to crash.

2. Inspecting Network Traffic

Operators can use ephemeral containers to analyze network traffic, DNS resolution, or firewall configurations within a pod. Tools like tcpdump or curl can be executed from the ephemeral container.

3. Adding Debugging Tools

Sometimes, the base container image lacks debugging utilities. Ephemeral containers allow the injection of a debugging image with tools like strace, lsof, or top.

4. Troubleshooting Resource Utilization

Monitoring CPU, memory, or disk usage of a running pod can be challenging. Ephemeral containers provide the flexibility to add diagnostic scripts or utilities for detailed insights.

5. Debugging Cluster-Level Issues

For system pods running Kubernetes components (e.g., kube-proxy or coredns), ephemeral containers can help troubleshoot issues related to cluster networking or DNS resolution.

---

Best Practices

1. Use Minimal Images: Choose lightweight debugging images like alpine or busybox to minimize resource usage and attack surface.

2. Restrict Access: Control who can deploy ephemeral containers using Kubernetes Role-Based Access Control (RBAC) to prevent misuse.

3. Automate Cleanup: Ensure ephemeral containers are removed after the debugging session to maintain a clean cluster state.

4. Logging and Monitoring: Log the usage of ephemeral containers for auditing purposes.

5. Predefined Debugging Images: Maintain a repository of pre-approved debugging images with essential tools.

---

Limitations of Ephemeral Containers

While ephemeral containers are powerful, they come with certain limitations:

1. No Restart Policy: Kubernetes does not manage the lifecycle of ephemeral containers.
2. Not Suitable for Persistent Work: Ephemeral containers are designed for temporary tasks and should not be used as a substitute for regular containers.
3. Limited Resource Guarantees: They share resources with the parent pod, which might lead to contention in resource-constrained environments.

---

Future of Debugging in Kubernetes

As Kubernetes evolves, tools like ephemeral containers will continue to play a critical role in improving observability and debugging capabilities. They pave the way for more sophisticated debugging workflows, including automated diagnostics and seamless integration with third-party tools.

---

Conclusion

Ephemeral containers are a game-changer for troubleshooting Kubernetes applications, offering a non-disruptive, flexible, and efficient way to debug live workloads. Their ability to inspect, analyze, and resolve issues without requiring pod restarts makes them indispensable for Kubernetes administrators and developers alike.

By embracing ephemeral containers and incorporating them into your Kubernetes workflows, you can ensure faster resolution of issues and maintain the high availability and reliability of your applications.

Step by step guide to resolve Istio 503 NC cluster_not_found on Kubernetes

How to resolve Istio 503 NC cluster_not_found on Kubernetes

The Istio 503 NC cluster_not_found error typically occurs when the service mesh cannot find a destination cluster for routing. This error is usually seen in scenarios involving Istio's Envoy proxy and can be caused by several issues, such as incorrect configuration, missing service discovery, or routing rules misconfiguration.

Here’s how you can troubleshoot and resolve the error:

---

1. Check Service Discovery

• Verify the service exists: Ensure the destination service is running in the cluster and is discoverable by Istio.
• Inspect Istio's service registry:

  
  istioctl proxy-config endpoints <pod-name>.<namespace>
  
  

• Replace <pod-name> and <namespace> with the name of the pod and its namespace. Ensure the destination service is listed.

Istio 503 nc cluster_not_found example
(created by author using Bing Image Creator)

---

2. Validate Virtual Service and Destination Rule

• Confirm that the VirtualService and DestinationRule are properly defined for the service.
• Verify that the host field matches the actual service name (including the namespace if necessary):

• 
  apiVersion: networking.istio.io/v1beta1
  kind: VirtualService
  metadata:
    name: example
  spec:
    hosts:
      - service-name.namespace.svc.cluster.local
    http:
      - route:
          - destination:
              host: service-name.namespace.svc.cluster.local
              port:
                number: 8080
  
  

---

3. Check Sidecar Injection

• Ensure that sidecar injection is enabled for the namespaces where the source and destination services are running:

  
  kubectl get namespace -L istio-injection
  
  

  • If not enabled, annotate the namespace:

    
    
    kubectl label namespace <namespace> istio-injection=enabled --overwrite
    
    

• Confirm the sidecar proxy is running:

  
  kubectl get pods -n <namespace> -o jsonpath='{.items[*].spec.containers[*].name}' | grep istio-proxy
  
  

---

4. Verify Connectivity and Endpoints

• Check if the endpoints of the destination service are correctly registered:

  
  kubectl get endpoints -n <namespace>
  

• Confirm that the pods backing the service are healthy and ready:

  
  kubectl get pods -n <namespace>
  
  

---

5. Inspect Gateway and Ingress Configuration

• If the error involves traffic coming through an Istio Gateway, ensure the Gateway is properly configured:

  
  apiVersion: networking.istio.io/v1beta1
  kind: Gateway
  metadata:
    name: example-gateway
  spec:
    selector:
      istio: ingressgateway # Use Istio ingress gateway
    servers:
      - port:
          number: 80
          name: http
          protocol: HTTP
        hosts:
          - "*"
  

• Verify the Gateway and VirtualService are linked correctly.

---

6. Check DNS Resolution

• Ensure the DNS names of the services resolve correctly inside the cluster:

  
  kubectl exec <pod-name> -n <namespace> -- nslookup <service-name>
  

---

7. Examine Logs

• Inspect logs from the sidecar proxy for the source pod:

  
  kubectl logs <pod-name> -n <namespace> -c istio-proxy
  

• Check the Istio control plane logs:

  
  kubectl logs -n istio-system -l app=istiod
  

---

8. Validate Istio Configuration Consistency

• Ensure Istio's configuration is consistent across namespaces:

  
  istioctl analyze
  
  

---

9. Sync or Restart Pods

• Restart the affected pods to re-trigger service discovery:

  
  kubectl rollout restart deployment <deployment-name> -n <namespace>
  
  

---

10. Debug Using Envoy Config

• Inspect the Envoy configuration of the sidecar proxy:

  
  istioctl proxy-config clusters <pod-name>.<namespace>
  

• Look for the expected cluster and its configuration.

---

Feel free to use comment section for further query on resolving 503 NC cluster not found error on Kubernetes

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"Istio 503 no healthy upstream"

"Cluster_not_found envoy"

"Istio retry on 503"