To visualize cache misses over time for an application that logs each miss, what is the best approach?

Creating a logs-based metric in Cloud Logging and a dashboard for that metric in Cloud Monitoring is the best approach for visualizing cache misses over time. This method allows you to aggregate log data efficiently and create specific metrics that reflect the frequency of cache misses.

By establishing a logs-based metric, you can directly quantify the number of cache misses occurring over a given period. Cloud Monitoring can then be employed to visualize this metric in real-time or historical dashboards. This setup not only facilitates effective monitoring but also allows for alerts and notifications based on predefined thresholds, enabling quicker responses to performance issues.

In contrast, linking Cloud Logging to Google Data Studio would require additional steps for visualization without providing the same level of real-time monitoring or metric aggregation capabilities available through Cloud Monitoring. Configuring Cloud Profiler focuses more on performance profiling and may not directly visualize events over time like cache misses. Similarly, using BigQuery as a sink for Cloud Logging involves creating a more complex process to filter and aggregate data before visualization, which may introduce delays and could complicate real-time analysis.