How can we explain the unexpected 20% drop in average page load times for Drupal sites hosted on Pantheon Systems Platform in the last two weeks?

Introduction

The unexpected 20% drop in average page load times for Drupal sites hosted on Pantheon Systems Platform over the last two weeks is a critical issue that demands immediate attention. This performance degradation could significantly impact user experience, SEO rankings, and ultimately, customer satisfaction. I'll approach this problem systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent platform update. Has Pantheon rolled out any changes to their infrastructure or Drupal optimization features in the past month?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, a minor update was deployed two weeks ago. Impact on approach: If confirmed, we'd focus on the update's components.

• Considering the specificity of the issue to Drupal sites, I'm curious about the CMS distribution. What percentage of sites on Pantheon are Drupal-based, and has this changed recently?

Why it matters: Changes in site composition could affect overall performance metrics. Expected answer: About 60% Drupal, stable over the past quarter. Impact on approach: If stable, we'd rule out site composition changes as a factor.

• Given the nature of page load times, I'm wondering about traffic patterns. Have you noticed any significant changes in traffic volume or sources to Drupal sites in the last two weeks?

Why it matters: Traffic spikes or changes in user geography could impact load times. Expected answer: No unusual traffic patterns observed. Impact on approach: If confirmed, we'd focus more on internal factors rather than external traffic influences.

• Thinking about the measurement itself, has there been any change in how page load times are calculated or in the monitoring tools used?

Why it matters: Ensures we're comparing apples to apples in our metrics. Expected answer: No changes in measurement methodology or tools. Impact on approach: If confirmed, we can trust the data and focus on actual performance issues.