Question

Your team runs a microservices application on GKE with structured JSON logs flowing into Cloud Logging. During a partial outage, on-call engineers need to correlate error logs from the payment service with slow upstream requests across a 6-hour window, aggregating by HTTP status code and computing p95 latency per service. Ad-hoc filtering in the Logs Explorer is too slow and cannot perform these aggregations. What is the most appropriate approach to enable this analysis with minimal additional infrastructure?

Accepted Answer

Enable Log Analytics on the log bucket and use SQL queries in the Log Analytics interface to aggregate and join log fields.

Answer

Create a log-based metric for each service and status code, then build a Cloud Monitoring dashboard to visualize the aggregations.

Answer

Export all logs to Cloud Storage via a log sink and run BigQuery external table queries against the JSON files.

Answer

Increase the Logs Explorer query time range and add more label filters to narrow the results manually.