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Answer: Set up a Compute Engine instance, install JMeter on the instance, create a log sink to BigQuery, and use Looker Studio to analyze the results.
To follow Google-recommended practices for load testing a Cloud Run service with JMeter: 1. **Compute Engine Instance**: Running JMeter on a Compute Engine instance ensures sufficient resources and avoids limitations of local execution (eliminating Option A). 2. **Log Sink to BigQuery**: Exporting logs to BigQuery enables efficient querying and analysis of large datasets (eliminating Options B and C, which use Cloud Storage). 3. **Looker Studio**: Looker Studio (formerly Data Studio) is Google's recommended tool for visualizing and analyzing data from BigQuery, unlike Looker (a separate BI platform). Option D combines these best practices: JMeter on Compute Engine, logs in BigQuery, and analysis via Looker Studio.
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How should you orchestrate the steps and services for conducting an effective load test on your Cloud Run service using JMeter while adhering to Google-recommended best practices?
A
Install JMeter on your local machine, create a log sink to BigQuery, and use Looker to analyze the results.
B
Set up a Compute Engine instance, install JMeter on the instance, create a log sink to a Cloud Storage bucket, and use Looker Studio to analyze the results.
C
Set up a Compute Engine instance, install JMeter on the instance, create a log sink to a Cloud Storage bucket, and use Looker to analyze the results.
D
Set up a Compute Engine instance, install JMeter on the instance, create a log sink to BigQuery, and use Looker Studio to analyze the results.
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