Cache Misses Over Time
Question
You support an application that stores product information in cached memory.
For every cache miss, an entry is logged in Stackdriver Logging.
You want to visualize how often a cache miss happens over time.
What should you do?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.C.
The best option for visualizing how often a cache miss happens over time in this scenario is option C: Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
Option A suggests linking Stackdriver Logging as a source in Google Data Studio and filtering the logs on the cache misses. While this would allow you to view the cache miss logs, it would not provide an easy way to visualize the data over time.
Option B suggests configuring Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs. However, Stackdriver Profiler is designed to analyze the performance of code running in production and not to monitor logging data.
Option D suggests configuring BigQuery as a sink for Stackdriver Logging and creating a scheduled query to filter the cache miss logs and write them to a separate table. While this would provide access to the data, it requires additional setup and management of BigQuery.
Option C is the best option because it utilizes Stackdriver Logging and Monitoring together to create a logs-based metric and dashboard. To create a logs-based metric, you would define a filter to identify the cache miss logs and then aggregate the results over a specified time period. The metric would then be available in Stackdriver Monitoring, where you could create a dashboard to visualize the data over time.
Overall, option C provides a simple and effective way to visualize cache miss data over time using tools that are already available in the Google Cloud Platform.
