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Adding Rank Magnitude to the CrUX Report in BigQuery
Adding Rank Magnitude to the CrUX Report in BigQuery.

Starting with the February 2021 dataset, we’re adding an experimental metric to the CrUX report in BigQuery which distinguishes the popularity of origins by orders of magnitude: The top 1k origins, top 10k, top 100k, top 1M, ...

Let’s see how this looks in practice:

  experimental.popularity.rank AS rank_magnitude,
  COUNT(DISTINCT origin) AS num_origins
Row rank_magnitude num_origins
1 1,000 1,000
2 10,000 9,000
3 100,000 90,000
4 1,000,000 900,000
15 10,000,000 7,264,371

For the February 2021 global data set, we get 5 buckets. As expected, in row 1, we see that there are 1000 origins with rank magnitude 1000 - the 1k most popular origins by our metric. Row 2 may look surprising, indicating that there are only 9k origins in the top 10k set; this is because the origins in row 1 are also part of the top 10k set. To select the top 10k origins, one needs to specify experimental.popularity.rank <= 10000 when querying.

The dataset also contains country specific rank magnitude. For example, this query lists the 10k origins that are most popular in Germany.

FROM `chrome-ux-report.country_de.202102`
WHERE experimental.popularity.rank <= 10000

To touch on the potential of our new popularity metric, let’s see how popularity segments of the web differ with respect to the first contentful paint metric (FCP). For the purpose of this query, we consider 1 second a fast user experience.

  SUM(fcp.density)/count(distinct origin)
  UNNEST(first_contentful_paint.histogram.bin) AS fcp
  fcp.start < 1000 AND experimental.popularity.rank <= 1000

For the origins with experimental.popularity.rank <= 1000, the query sums all histogram bucket densities for FCP metric values smaller than 1000ms and divides it by the number of origins - that is, it calculates the average percentage of fast FCP loads for the 1k most popular origins. In this query, all origins have equal weight, so arguably this is not perfect. But let’s see whether the result is sensitive to changing the rank magnitude, by altering the where clause to specify experimental.popularity.rank <= 10000. We do this for 10k, 100k, and so on:

Rank magnitude of origins Percentage of FCP < 1s, averaged over origins
1.000  53.6%
10,000 49.6%
100,000 45.9%
1,000,000 43.2%
10,000,000 39.9%

This indicates that a faster user experience on the web is correlated with being more popular.

In the October 2022 dataset this was further split out by half-rank steps. Rerunning the first query for this dataset shows the half-steps and the number of origins in each rank magnitude::

  experimental.popularity.rank AS rank_magnitude,
  COUNT(DISTINCT origin) AS num_origins
Row rank_magnitude num_origins
1 1,000 1,000
2 5,000 4,000
3 10,000 5,000
4 50,000 40,000
5 100,000 50,000
6 500,000 400,000
7 1,000,000 500,000
8 5,000,000 4,000,000
9 10,000,000 5,000,000
10 50,000,000 7,637,195

Learn more about using CrUX on BigQuery and browse the CrUX Cookbook for more example queries. Share your queries if you like, and let us know what you find.