Troubleshooting Scenario Questions

kubectl ssh api-pod-7d4f9 -n production

Why it fails: kubectl ssh is not a real kubectl subcommand; the Kubernetes CLI has no built-in SSH functionality because Pods are not VMs and typically lack sshd. To get a shell in a container you must use `kubectl exec -it <pod> -n <namespace> -- <command>`, which uses the container runtime's exec API rather than a network SSH daemon. Even if `kubectl ssh` existed, it would not know how to negotiate container-level access, so this command would fail with an unknown command error.

kubectl run debug --image=busybox --attach=api-pod-7d4f9

Why it fails: `kubectl run` is used to create a new workload resource (a Pod, Deployment, or Job) from an image — it does not attach to an existing Pod. The `--attach` flag on `kubectl run` only controls whether the CLI attaches to the newly created container's output, and it requires a command to run; it cannot target another Pod like `api-pod-7d4f9`. Therefore this command would create a separate busybox Pod (or Deployment) rather than giving you a shell inside the existing API Pod, which is not what you want for debugging that specific Pod.

gcloud container exec api-pod-7d4f9 --namespace=production -- bash

Why it fails: `gcloud container exec` is not a valid command in the Google Cloud CLI; `gcloud container` manages GKE clusters (e.g., `gcloud container clusters list`, `gcloud container clusters get-credentials`), but it does not provide a direct way to execute commands inside a Pod. Pod-level access is always performed through `kubectl`, which talks to the Kubernetes API server after you have authenticated to the cluster. To achieve the same result, you would first run `gcloud container clusters get-credentials <cluster>` and then use `kubectl exec -it api-pod-7d4f9 -n production -- bash`.

Cloud Monitoring

Why it fails: Cloud Monitoring specializes in time-series metrics, uptime checks, dashboards, and alerting policies — not raw log viewing. While it can display logs-based metrics derived from Cloud Run log entries, the actual log messages themselves live in Cloud Logging. To inspect the logs generated by a Cloud Run service, you must use Cloud Logging's Logs Explorer, not Cloud Monitoring.

Error Reporting

Why it fails: Error Reporting ingests and aggregates only exception and error signals, using machine learning to cluster them into meaningful groups with stack traces, so it is not a general log viewer. It consumes error-like entries from Cloud Logging but is neither the storage location nor the interface for browsing the full set of Cloud Run logs. A typical Cloud Run service also emits info and debug logs that Error Reporting ignores entirely.

Cloud Trace

Why it fails: Cloud Trace measures latency and traces end-to-end request paths across services, recording spans with timing metadata rather than log content. It can show how long each call took in a Cloud Run request, but it will not show you the textual or structured logs your service emitted. For Cloud Run log viewing, Cloud Trace is a complementary debugging tool, not the destination for logs.

VPC firewall rules are blocking egress traffic.

Why it fails: VPC firewall rules blocking egress traffic would prevent all outbound packets from leaving the instance, but this instance has no external IP address assigned (no accessConfigs in the output). Even with permissive egress firewall rules, the instance cannot reach the internet because its packets lack a public source IP for return traffic. The output does not indicate any firewall deny logs; it simply shows an absent external connectivity path.

The instance needs a Cloud NAT gateway for outbound connectivity.

Why it fails: Cloud NAT is a managed service that enables outbound internet from instances without external IPs, but it is a solution, not the root cause of the connectivity failure. The command output shows the instance has no external IP, which is the immediate problem; Cloud NAT could be configured as a workaround, but the lack of a public IP is what the output reveals. Additionally, Cloud NAT would require proper routing and firewall rules, but the underlying missing component is the external IP assignment.

The subnetwork is misconfigured.

Why it fails: The subnetwork is misconfigured would affect settings like the IP address range, gateway, or route propagation, but the output shows a valid subnet with a proper internal IP assigned to the instance. A subnet misconfiguration typically impacts all instances in that subnet or causes IP address conflicts, not the absence of an external IP. The instance's interface has a private IP from a healthy subnet, so the issue lies solely in the missing public IP, not the subnet itself.

Use Cloud Trace to analyze the latency of each Pub/Sub message

Why it fails: Cloud Trace is designed for end-to-end distributed tracing of HTTP-based request latencies, not for Pub/Sub message flow. Pub/Sub does not automatically propagate trace contexts through its API, and Cloud Trace cannot capture the latency of an individual message from publish to acknowledgment unless your subscriber code explicitly wraps the processing with a custom trace span. Even then, it would only give per-request timing, not throughput or backlog health, so it is the wrong tool for this systemic slowness diagnosis.

Use Cloud Debugger to inspect the subscriber code

Why it fails: Cloud Debugger lets you inspect live application state and set snapshots or logpoints without stopping the service, which is useful for debugging logic errors or variable values. However, it does not provide any metrics about Pub/Sub subscription throughput, backlog, or acknowledgement rates. The slowness you are investigating is unlikely to be caused by a single line of code; Debugger is not intended for performance analysis and cannot show queue depths or end-to-end message delivery latency.

Cloud Profiler

Why it fails: Cloud Profiler uses statistical CPU and heap sampling to build flame graphs that reveal functions consuming the most CPU time or memory. In a multi-service application, the bottleneck is often a downstream call waiting on I/O, queues, or a slow peer — not a hotspot in your own code. Profiler does not capture request-level start/end timestamps or per-hop latency, so it cannot show which service is the slow link.

Cloud Logging

Why it fails: Cloud Logging collects and stores log entries with timestamps, but it has no built-in trace propagation or span model unless you integrate Cloud Trace and add structured trace fields. You could correlate entries manually across services; however, that is brittle and lacks the precomputed latency percentiles and aggregated metrics needed to quickly isolate a slow component. For latency troubleshooting, logs are a complementary debugging aid, not the primary signal.

Cloud Debugger

Why it fails: Cloud Debugger attaches to a running application and lets you set snapshot or logpoint breakpoints that capture local variables, call stack, and execution state at a specific line, without restarting or stopping the service. It is designed for inspecting behavior and state at a moment in time, not for measuring latency distributions or aggregating timing across requests. Debugger tells you what the code is doing, not how long it takes.

Create a Cloud Monitoring alert for packet loss

Why it fails: Creating a Cloud Monitoring alert for packet loss is reactive and does not identify the root cause. Packet loss metrics may not be visible if the network path is silently dropping traffic due to VPC firewall rules, and an alert only notifies you after the fact. The correct diagnostic step is to inspect firewall rule logs or the rules themselves to see whether traffic is being denied.

Run gcloud compute ssh --dry-run

Why it fails: Running `gcloud compute ssh --dry-run` only prints the SSH command and its arguments without actually attempting a connection. It does not test network connectivity, firewall rules, or IAM permissions, so it cannot reveal why the instance is unreachable. While it can help verify local SSH configuration, it is useless for diagnosing VPC firewall issues.

Use Cloud Trace to analyze network latency

Why it fails: Cloud Trace is designed for analyzing application-level request latency in distributed systems, not for diagnosing network connectivity or firewall rule problems. It requires instrumentation in the application and does not capture packet-level events or VPC firewall decisions. Therefore, using Cloud Trace to analyze network latency would not show whether a firewall rule is blocking traffic to the Compute Engine instance.

RDP into the instance and check the system logs.

Why it fails: RDP into the instance would only work if the instance is already reachable over the network and running a Windows OS; if the boot or startup script fails, the instance may never be accessible via RDP. Even if RDP succeeded, the System Log would not show the early boot or startup script output that is needed to diagnose the deployment issue. Serial port output is the correct place to look for these logs, not RDP.

Check the instance's metadata for startup script errors.

Why it fails: The instance metadata in Compute Engine stores configuration like the startup script itself, but it does not capture or expose the script's execution output or any errors it produced. Startup script output (stdout/stderr) is written to the serial port, not to the metadata. Therefore, checking metadata for startup script errors is useless because the metadata only defines what to run, not what happened when it ran.

Recreate the instance with a new image.

Why it fails: Recreating the instance with a new image starts over from a generic baseline and would discard the current instance's disks and configuration, effectively deleting the very evidence needed for debugging. It does not reproduce the startup script or the new version's code, so the root cause (such as a bad config or missing dependency) would remain unknown. The correct approach is to inspect the existing instance's serial output, not to rebuild it.

The instance's firewall rules block egress traffic to port 80.

Why it fails: The stem explicitly states that the VPC's firewall rules allow all egress traffic, so no firewall rule is blocking port 80 or any other outbound port. Cloud NAT is compatible with permissive egress rules; it does not require a specific allow rule beyond what permits the traffic itself. Thus, this option contradicts the given facts and cannot be the cause of the connectivity failure.

The instance's service account does not have the compute.instances.update permission.

Why it fails: The instance's service account is used for authentication and authorization to Google Cloud APIs, not to control network connectivity or NAT behavior. Cloud NAT does not inspect the instance's service account when forwarding traffic. The relevant identity would be the Cloud NAT's own service account, which the deployment likely already has, so a missing compute.instances.update permission on the instance is irrelevant.

The VPC does not have a route for the instance's subnet to the internet gateway.

Why it fails: A route to the internet gateway (the default 0.0.0.0/0 route) is indeed required for Cloud NAT to forward traffic, and the scenario confirms this route exists in the VPC. Since the route is present, this option is factually incorrect as an explanation. Even when the route exists, a NAT gateway in the wrong region will still prevent the instance from reaching the internet, so the root cause remains the regional mismatch.