Kubernetes networking can feel like a hidden mystery. One moment, your Pods talk to each other just fine. Then suddenly, you get slammed with “i/o timeout” errors, and your apps start crashing left and right.
Honestly, it isn’t your fault. Kubernetes hides so much behind the scenes—stuff like CNI overlays, iptables, and Service virtual IPs.
Even experienced engineers end up in the dark. The real problem usually isn’t one thing that broke. It’s a snag somewhere in the tangled path between CNI, kube-proxy, and CoreDNS.
This guide gets straight to the point. We’ll walk through Kubernetes networking, layer by layer, so you can actually see what’s going on. No more guessing games or scrambling in a crisis. Just move from panicked troubleshooting to a calm, step-by-step process.
You’ll pick up expert strategies to tackle DNS issues, service discovery, CNI snags, and learn how to use advanced monitoring tools. Time to get your Kubernetes troubleshooting under control.
KEY TAKEAWAYS
- Classify errors into layers to detect systematically.
- Most common mystery failures originate in core DNS overload or misconfigured ndots.
- Generic HTTP errors are usually not the app but a failure in the ingress controller.
Kubernetes is tricky. One day things are calm, the next you are watching Pods time out and wondering what changed. Usually nothing obvious did. The setup looks clean, the configs look fine, but traffic still stops. It is not your fault. Kubernetes networking is one of those parts that looks easy until you have to fix it while everything is on fire.
Everything starts with the CNI plugin. That is the piece that lets Pods talk inside the cluster and reach the outside world. It’s invisible until something leaks. Each CNI behaves a bit differently. Calico, Cilium, and Flannel all have their own habits.
The big cloud platforms have their own versions too, like Amazon VPC CNI, Azure CNI, and GKE CNI. Each one handles routing and security in its own style. Once you figure out which one your cluster uses and how it behaves, troubleshooting gets much easier.
Pods come and go, but applications still need a steady address. That’s where Services come in. They create a consistent access point – an internal IP that doesn’t vanish every time a Pod restarts.
There are a few flavors:
Kubernetes uses EndpointSlice objects to monitor all of the healthy backends. The system is informing you that the list is empty if you have ever received the “no endpoints available” issue.
Next up is kube-proxy, the node-level traffic cop. It sets up routing rules using either iptables or IPVS to move packets from Service IPs to Pod IPs. When something suddenly starts timing out, kube-proxy logs are often where you start digging.
DNS is handled by CoreDNS, and sometimes boosted by NodeLocal DNSCache for speed. If CoreDNS sneezes, the whole cluster catches a cold. “No such host” errors, mysterious latency spikes, random gRPC resets – all classic signs of DNS drama.
Finally, there’s the entryway to your cluster: the Ingress controller, or the newer Gateway API if you’re running something modern. These handle the fancy stuff – TLS termination, HTTP routing, and external traffic management.
Nine times out of ten, this is the problem when someone reports that “the app is down” yet Pods appear to be operating OK. Those dreaded 502s or 503s might be caused by misconfigured rules, mismatched certificates, or a missing annotation.
To troubleshoot Kubernetes networking, you need to picture how data actually flows.
If you’re running something fancy like Cilium, you’re using eBPF datapaths that bypass iptables altogether. That means different tools, different debugging mindset. Once you get used to it, though, you get better visibility and faster traffic handling.
When things break in Kubernetes, it’s easy to start guessing. That’s how most people lose hours. The trick is to slow down and look at the symptom and not the noise. Every network failure has a pattern that, once you can recognize which pattern you’re dealing with makes triaging fast and methodical instead of random panic.
Here’s how to think about it in layers.
DNS problems are one of the top reasons teams end up chasing ghosts. A single CoreDNS hiccup can ripple through your entire cluster. When you see “no such host” errors, start by checking the basics – your search domains, ndots configuration, and whether NodeLocal DNSCache is forwarding queries correctly.
Sometimes it’s not even DNS. You might have a Service selector mismatch, where the Service points to Pods that don’t actually exist under that label. The result looks the same: empty EndpointSlices, nothing routing, and a perfectly healthy app that nobody can find.
If DNS checks out but traffic still won’t move, shift your focus to kube-proxy. It’s responsible for wiring up virtual IPs using iptables or IPVS. When those rules drift out of sync with what the cluster expects, your Services can look fine on paper but silently drop packets.
A common culprit here is stale conntrack entries – they hang onto old Pod IPs even after those Pods are gone. Half your requests work, half timeout, and suddenly you’re wondering if you have a ghost in the machine. Spoiler: it’s just conntrack not letting go.
If new Pods won’t start or can’t reach anything, the problem often lives in your CNI plugin. The most common one? IP address exhaustion. Once your IPAM pool runs dry, Pods can’t get addresses and just sit in “ContainerCreating.”
Another quiet killer is overlapping CIDR ranges between your Pod network and your VPC or on-prem subnets. That overlap can cause traffic to disappear into a routing black hole. Then there’s MTU mismatches. If your overlay network packets are too big for the underlay to handle, they get fragmented or dropped without warning. Everything looks “up” but nothing moves.
This one catches even seasoned engineers. You roll out a new NetworkPolicy, everything deploys fine, and then half your traffic stops flowing. A missing “allow” rule in a default-deny setup can block valid communication without showing obvious errors.
Hybrid and cloud setups add an extra layer of confusion. Inside Kubernetes, everything might look perfect, yet somewhere above it, a firewall or security rule is silently blocking traffic. You can check your NetworkPolicies all day with kubectl get networkpolicy and still miss it. When that happens, the only real way forward is to trace traffic both directions. Start inside the cluster, then move outward until you find where things stop.
Ingress controllers have their own personalities. NGINX behaves differently than Traefik or Envoy. Common failures could stem from path rewrite mistakes (i.e /api/v1/users got mangled into v1/users), backend annotation mismatches, or certificate problems generating endless TLS handshake failures.
Layer 7 issues often reside with generic HTTP status codes (502, 503). Dig into the Ingress controller logs and you’ll usually find the real error: upstream timeouts, connection refused messages, or certificate validation failures. That’s where the actual problem lives.
What it means: Your Pod can’t reach another Pod or external endpoint within the timeout window.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl exec -it <pod> — curl -v http://<target>
kubectl get networkpolicy -n <namespace>
kubectl describe networkpolicy <policy-name>
ip route show
iptables -L -n -v
ping -M do -s 1472 <target-ip>
Fixes:
First, Prevention: Check on NetworkPolicies in staging before moving to production. Use admission controllers to enforce MTU values and run periodic connectivity tests.
What it means: The Service exists, but it has no healthy Pods to route traffic to.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl get svc <service-name> -o yaml
kubectl get pods –show-labels -n <namespace>
kubectl get endpointslices -n <namespace>
kubectl describe endpointslice <name>
kubectl get pods -n <namespace>
kubectl describe pod <pod-name>
Fixes:
Prevention: Use automated testing to validate Service-to-Pod label matching. Monitor EndpointSlice readiness in your observability stack.
What it means: Your Ingress controller or service mesh tried to forward a request, but the backend connection failed.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl logs -n ingress-nginx <ingress-controller-pod>
kubectl get svc <service-name> -o yaml
kubectl get pod <pod-name> -o yaml
kubectl port-forward <pod-name> 8080:8080
curl localhost:8080
Fixes:
Prevention: Include port validation in CI/CD. Use end-to-end smoke tests after deployments.
What it means: DNS lookup failed. The hostname doesn’t exist or can’t be resolved.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl exec -it <pod> — nslookup kubernetes.default
kubectl exec -it <pod> — nslookup google.com
kubectl get pods -n kube-system | grep coredns
kubectl logs -n kube-system <coredns-pod>
kubectl get configmap coredns -n kube-system -o yaml
kubectl exec -it <pod> — cat /etc/resolv.conf
Fixes:
Prevention: Monitor CoreDNS query latency and SERVFAIL rates. Set up automated scaling based on query volume.
What it means: The target Pod exists and is reachable, but nothing is listening on the expected port.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl exec -it <pod> — netstat -tuln
kubectl exec -it <pod> — ps aux
kubectl logs <pod-name>
Fixes:
Prevention: Always define proper readiness probes. Use health check endpoints that verify the app is actually serving traffic.
What it means: DNS queries are slow or randomly failing, degrading application performance.
Symptoms:
Likely Causes:
Debugging Steps:
kubectl exec -it <pod> — time nslookup kubernetes.default
kubectl top pods -n kube-system | grep coredns
Fixes:
Prevention: Set DNS latency SLOs (e.g., p95 < 50ms). Monitor and alert on SERVFAIL rates.
What it means: Outbound traffic from Pods is being throttled or dropped, often due to NAT exhaustion.
Symptoms:
Likely Causes:
Fixes:
Prevention: Set up egress SLOs, track NAT utilization, and isolate noisy apps early.
Managed Observability Solutions: For teams looking to streamline their Kubernetes monitoring without building everything in-house, platforms like Palark.com offer integrated observability stacks that combine metrics, logs, and traces specifically tuned for Kubernetes networking diagnostics.
By the time your customers notice latency, the problem’s already been brewing for a while. The best engineers don’t just fix networking issues, they see them forming before anything breaks. Kubernetes gives you plenty of signals; the key is knowing which ones actually matter.
Raw metrics show that something’s wrong. Logs and traces tell you why.
Once you start correlating these sources, you’ll notice the same pattern over and over: every “mystery outage” leaves breadcrumbs long before the dashboard goes red.
If your cluster uses Cilium, you already have one of the best visibility tools around. Hubble lets you watch traffic as it moves through the system, from basic network packets to full application requests. It shows what gets through, what gets blocked, and the reason behind it. Once you see how traffic actually flows, troubleshooting becomes a lot less mysterious.
Calico has its own flow logs too, which are great for audit trails and incident replay, especially useful for regulated environments where you need to explain what actually happened, not just that it did.
In high-scale production environments, combining NodeLocal DNSCache with flow visibility tools can significantly reduce mean time to recovery. When engineers can see network verdicts instantly, they stop guessing and start fixing.
You can troubleshoot all day, or you can design a system that doesn’t fail in the first place. Most networking disasters are the result of assumptions that didn’t scale. Here’s how to get ahead of them.
Default-deny NetworkPolicies sound great in theory, but they’ll wreck your day if you forget to add the right allow rules. Start permissive, then tighten things gradually.
Automate it with cert-manager, set clear rotation SLOs, and make sure certificate expiry alerts actually reach your team.
Every millisecond counts in distributed systems.
Performance issues rarely announce themselves. They creep in. Keeping these basics in check keeps your clusters fast and predictable.
A network that can’t pick itself up after a stumble isn’t resilient—it’s just setting you up for a bigger mess down the line.
Design readinessProbes that reflect real dependencies like DNS, databases, and caches. Set health budget SLOs and error budgets to control deployment velocity, that keeps change fatigue from becoming outage fatigue.
Document your runbooks for recurring incidents. The best teams run GameDays where they simulate chaos: drop DNS, block egress, tweak MTU, and time their recovery. It’s the only way to know how your system behaves when things really go sideways.
Troubleshooting Kubernetes? Networking issues aren’t some distant possibility—they’re inevitable. Distributed systems are always a bit chaotic. But with solid playbooks, good observability, and sharp design, you can turn that chaos into something you can actually predict and manage.
When teams adopt structured troubleshooting instead of improvisation, incidents stop feeling random. The commands and diagnostic flows outlined here turn those dreaded “network unknowns” into clear, repeatable routines. Layer in consistent Kubernetes monitoring, detailed audit logs, and baseline performance metrics, and you’ll start cutting mean time to recovery in half.
The difference between firefighting and foresight often comes down to visibility and standardized run books. Teams that invest in proper observability and documented procedures find themselves spending less time in emergency mode and more time building features.
Ans: Just run kubectl exec -it
Ans: Basically, Pods are coming and going too often—flapping, really. That usually points to instability, running out of resources, or a crashing app.
Ans: Most of the time, it’s NetworkPolicies getting in the way, or there’s a routing mess like the wrong MTU, and packets just disappear.
Ans: Start by giving your Pod and Service CIDRs plenty of headroom—aim for at least double what you think you’ll need.
