You will load-test a Cloud Run service and need second-by-second log analysis of traffic spikes with minimal effort. How should you analyze the logs?

Creating a BigQuery log sink with an inclusion filter for the specific Cloud Run service is the most efficient and scalable solution for second-by-second log analysis during a load test. BigQuery is designed for high-volume, real-time analytics and allows for complex SQL queries to quickly identify patterns and spikes in traffic. Estimating performance from summary monitoring charts is insufficient for detailed, second-by-second analysis. Cloud Monitoring's default log console is good for real-time viewing but lacks the analytical power and query capabilities of BigQuery for in-depth analysis. Exporting logs to Pub/Sub and then using Dataflow to push them into Cloud SQL introduces unnecessary complexity and latency, and Cloud SQL is not optimized for the same scale of analytical queries as BigQuery.