You are preparing to conduct a load test on your Cloud Run service by using JMeter. You need to orchestrate the steps and services to use for an effective load test and analysis. You want to follow Google-recommended practices. What should you do?
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.
1. Load Generation:
• Compute Engine instance is preferred over local machine for scalability and network proximity to Google Cloud services.
• Install Apache JMeter on the Compute Engine instance to generate controlled load on the Cloud Run service.
2. Logging and Data Capture:
• Create a log sink to BigQuery for efficient structured, real-time ingestion of Cloud Run logs and metrics.
• BigQuery is optimal for handling large volumes of data and supporting complex queries for performance analytics.
3. Analysis and Visualization:
• Use Looker Studio (formerly Data Studio) to create interactive dashboards on top of BigQuery.
• This enables custom, real-time analysis of latency, error rates, throughput, etc.
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saratk1984
1 month, 2 weeks agoanshad666
6 months, 3 weeks ago