Tutorial: Using BigQuery to Analyze Chrome User Experience Report Data

Last week I wrote a blog post showing some examples of how you can use the Chrome User Experience report to compare your site’s RUM data to competitors. In this post I’d like to share some brief videos to help you quickly get started exploring the data via Google BigQuery.

Accessing the Chrome User Experience Report (CrUX) Data

The Chrome User Experience Report data is available on Google BigQuery, which is part of the Google Cloud Platform. To get started, log into Google Cloud, create a project for your CrUX work, and then navigate to the BigQuery console. Then add the chrome-ux-report dataset and explore the way the tables are structured. Here’s a short video that walks you through this process.

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Google Cloud Console: https://console.cloud.google.com

Brief CrUX Overview

Now that we’ve accessed the CrUX data, let’s explore the table structure and where you can find some resources for additional help:

Links:

CrUX Overview: https://developers.google.com/web/tools/chrome-user-experience-report/

Rick Viscomi’s CrUX Cookbook: https://github.com/rviscomi/crux-cookbook

Comparing Form Factor and Connection Type Distributions For Different Sites

Next let’s explore what we can do with the form factor and effective connection type dimensions. In this next video we’ll explore the % of these dimensions for two sites.

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Competitive Histograms

In this next video we’ll take a look at how to analyze the performance of a single metric across multiple sites in BigQuery. We’ll export the results and graph them –

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Graphing all the Metrics

Finally, let’s UNION together a bunch of queries and examine the performance of a single site by creating histograms for first paint, first contentful paint, DOM Content Loaded and onLoad.

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Conclusion

I hope this helps you get started digging into the CrUX data. As I mentioned in my earlier blog post on CrUX, Akamai is working on building this functionality into mPulse so that it will be easy for our customers to quickly analyze this data in the future. But for now these videos should give you a starting point to explore the data via BigQuery.