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Troubleshooting Cloud Run Function Behavior
In this Google Cloud lab, you will troubleshoot a failing event-driven Cloud Run function that processes order files dropped into a Cloud Storage bucket. Using Cloud Logging, runtime metrics, and the function's configuration and identity, you will isolate why invocations are failing, remediate access, runtime configuration, and event-handling problems, while validating that each fix restores correct invocation behavior and the expected operational signals. The work mirrors a realistic on-call task where a deployed function has stopped producing output, and you must recover it without changing its code.
Lab Info
Table of Contents
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Challenge
Inspect the event-driven invocation path and restore event delivery
- Inspect logs and runtime metrics to confirm the function is not being invoked even though files are being uploaded.
- Examine the event-source path, including the Eventarc trigger and its Pub/Sub-backed delivery, and observe how events are redelivered while invocation keeps failing.
- Isolate the event-handling condition that prevents the event source from successfully invoking the function.
- Remediate the condition so the event source can invoke the function.
- Upload a sample file and validate that the function is now invoked.
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Challenge
Diagnose and remediate the access failure on the function's runtime identity
- Inspect logs and invocation results to isolate a permission error that occurs while the function is executing.
- Identify that the function's runtime service account cannot write to the output resource.
- Remediate by granting the runtime service account the required role on the output bucket.
- Upload a sample file and validate that a processed record is written and the permission error clears.
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Challenge
Diagnose and remediate the runtime configuration failure
- Use runtime metrics and logs to isolate a configuration limit that causes failed or degraded execution on a larger input.
- Identify the insufficient memory setting on the function.
- Remediate by updating the function's runtime configuration to the required value, which produces a new revision.
- Upload the larger sample file and validate successful execution and healthy metrics.
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