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.

For years Real User Measurement (RUM) has been the gold standard for how to measure the performance of web applications. And the reason for it is quite simple: there is no better measure for how users are experiencing your site, than the users’ actual experiences themselves.

Have you ever wondered why WebPageTest can sometimes show that a repeat view loaded with less bytes downloaded, while also triggering warnings related to browser caching? It can seem like the test is reporting an issue that does not exist, but in fact it’s often a sign of a more serious issue that should be investigated. Often the issue is not the lack of caching, but rather lack of control over how your content is cached.

Over the past few weeks the topic of security related HTTP headers has come up in numerous discussions – both with customers I work with as well as other colleagues that are trying to help improve the security posture of their customers. I’ve often felt that these headers were underutilized, and a quick test on Scott Helme’s excellent securityheaders.io site usually proves this to be true. I decided to take a deeper look at how these headers are being used on a large scale.

In January 2017, Facebook wrote about a new Cache-Control directive – immutable – which was designed to tell supported browsers not to attempt to revalidate an object on a normal reload during it’s freshness lifetime. Firefox 49 implemented it, while Chrome went ahead with a different approach by changing the behavior of the reload button. Additionally it seems that WebKit has also implemented the immutable directive since then.

On Nov 14th, Mozilla released Firefox Quantum. On launch day, I personally felt that the new version was rendering pages faster and I heard anecdotal reports indicating the same. There have also been a few benchmarks which seem to show that this latest Firefox version is getting content to screens faster than its predecessor. But I wanted to try a different approach to measurement.

Since the HTTP Archive is capturing the timing information on each request, I thought it would be interesting to correlate request timings (ie, when an object was loaded) with page timings. The idea is that we can categorize resources that were loaded before or after and event.

I thought it would be interesting to explore how some of the page metrics we use to analyze web performance compare with each other. In the HTTP Archive “pages” table, metrics such as TTFB, renderStart, VisuallyComplete, onLoad and fullyLoaded are tracked. And recently some of the newer metrics such as Time to Interactive, First Meaningful Paint, First Contentful paint, etc exist in the HAR file tables.

As of July 2017, the “average” page weight is 3MB. @Tammy wrote an excellent blog post about HTTP Archive page stats and trends. Last year @igrigorik published an analysis on page weight using CDF plots. And of course, we can view the trends over time on the HTTP Archive trends page. Since this is all based on HTTP Archive data, I thought I’d start a thread here to continue the discussion on how to gauge the increase in page weight over time.

One of the exciting new features in Chrome Dev Tools is the addition of 3rd party product names integrated into Dev Tools. I was interested in exploring how this works, and whether any of this data can be used to do further research via the HTTP Archive.