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Update to July
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‎sql/2025/ecommerce/core_web_vitals_by_platform.sql‎

Lines changed: 72 additions & 72 deletions
Original file line numberDiff line numberDiff line change
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#standardSQL
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# CrUX Core Web Vitals performance of Ecommerce vendors by device (fid was upated to inp, and is non optinal now)
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CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good / (good + needs_improvement + poor) >= 0.75
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);
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CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good + needs_improvement + poor > 0
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);
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SELECT
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client,
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ecomm,
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COUNT(DISTINCT origin) AS origins,
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# Origins with good LCP divided by origins with any LCP.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))
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) AS pct_good_lcp,
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# Origins with good INP divided by origins with any inp.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_inp, avg_inp, slow_inp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_inp, avg_inp, slow_inp), origin, NULL))
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) AS pct_good_inp,
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# Origins with good CLS divided by origins with any CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))
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) AS pct_good_cls,
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# Origins with good LCP, inp, and CLS divided by origins with any LCP, inp, and CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(
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IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
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IS_GOOD(fast_inp, avg_inp, slow_inp) AND
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IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL
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)),
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COUNT(DISTINCT IF(
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IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
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IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AND
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IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL
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))
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) AS pct_good_cwv
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FROM
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`chrome-ux-report.materialized.device_summary`
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JOIN (
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SELECT
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_TABLE_SUFFIX AS client,
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url,
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app AS ecomm
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FROM
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`httparchive.technologies.2025_06_01_*`
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WHERE
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category = 'Ecommerce' AND
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(
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app != 'Cart Functionality' AND
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app != 'Google Analytics Enhanced eCommerce'
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)
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)
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ON
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CONCAT(origin, '/') = url AND
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IF(device = 'desktop', 'desktop', 'mobile') = client
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WHERE
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date = '2025-06-01'
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GROUP BY
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client,
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ecomm
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ORDER BY
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origins DESC
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#standardSQL
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# CrUX Core Web Vitals performance of Ecommerce vendors by device (fid was upated to inp, and is non optinal now)
3+
CREATE TEMP FUNCTION IS_GOOD(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
4+
good / (good + needs_improvement + poor) >= 0.75
5+
);
6+
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CREATE TEMP FUNCTION IS_NON_ZERO(good FLOAT64, needs_improvement FLOAT64, poor FLOAT64) RETURNS BOOL AS (
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good + needs_improvement + poor > 0
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);
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11+
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SELECT
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client,
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ecomm,
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COUNT(DISTINCT origin) AS origins,
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# Origins with good LCP divided by origins with any LCP.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_lcp, avg_lcp, slow_lcp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp), origin, NULL))
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) AS pct_good_lcp,
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# Origins with good INP divided by origins with any inp.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(fast_inp, avg_inp, slow_inp), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(fast_inp, avg_inp, slow_inp), origin, NULL))
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) AS pct_good_inp,
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# Origins with good CLS divided by origins with any CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL)),
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COUNT(DISTINCT IF(IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL))
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) AS pct_good_cls,
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# Origins with good LCP, inp, and CLS divided by origins with any LCP, inp, and CLS.
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SAFE_DIVIDE(
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COUNT(DISTINCT IF(
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IS_GOOD(fast_lcp, avg_lcp, slow_lcp) AND
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IS_GOOD(fast_inp, avg_inp, slow_inp) AND
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IS_GOOD(small_cls, medium_cls, large_cls), origin, NULL
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)),
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COUNT(DISTINCT IF(
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IS_NON_ZERO(fast_lcp, avg_lcp, slow_lcp) AND
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IS_NON_ZERO(fast_inp, avg_inp, slow_inp) AND
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IS_NON_ZERO(small_cls, medium_cls, large_cls), origin, NULL
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))
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) AS pct_good_cwv
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FROM
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`chrome-ux-report.materialized.device_summary`
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JOIN (
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SELECT
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_TABLE_SUFFIX AS client,
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url,
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app AS ecomm
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FROM
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`httparchive.technologies.2025_06_01_*`
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WHERE
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category = 'Ecommerce' AND
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(
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app != 'Cart Functionality' AND
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app != 'Google Analytics Enhanced eCommerce'
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)
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)
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ON
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CONCAT(origin, '/') = url AND
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IF(device = 'desktop', 'desktop', 'mobile') = client
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WHERE
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date = '2025-07-01'
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GROUP BY
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client,
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ecomm
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ORDER BY
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origins DESC

‎sql/2025/ecommerce/median_lighthouse_score_ecommsites.sql‎

Lines changed: 43 additions & 43 deletions
Original file line numberDiff line numberDiff line change
@@ -1,43 +1,43 @@
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WITH technologies AS (
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SELECT
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client,
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page,
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category,
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technology,
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rank,
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lighthouse,
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COUNT(DISTINCT page) OVER (PARTITION BY client) AS total_websites
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FROM `httparchive.all.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(categories) AS category
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WHERE
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date = '2025-06-01' AND
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is_root_page = TRUE
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)
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SELECT
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client,
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rank,
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technology,
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ARRAY_AGG(DISTINCT category) AS categories,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.performance.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_performance,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.accessibility.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_accessibility,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.seo.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_seo,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.best-practices.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_best_practices,
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ANY_VALUE(total_websites) AS total_websites,
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COUNT(DISTINCT page) AS number_of_websites,
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COUNT(DISTINCT page) / ANY_VALUE(total_websites) AS percent_of_websites
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FROM technologies
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WHERE
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category = 'Ecommerce' AND
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(
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technology != 'Cart Functionality' AND
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technology != 'Google Analytics Enhanced eCommerce'
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)
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GROUP BY
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client,
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rank,
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technology
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ORDER BY
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client,
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number_of_websites DESC
1+
WITH technologies AS (
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SELECT
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client,
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page,
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category,
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technology,
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rank,
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lighthouse,
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COUNT(DISTINCT page) OVER (PARTITION BY client) AS total_websites
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FROM `httparchive.all.pages`,
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UNNEST(technologies) AS tech,
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UNNEST(categories) AS category
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WHERE
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date = '2025-07-01' AND
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is_root_page = TRUE
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)
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SELECT
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client,
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rank,
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technology,
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ARRAY_AGG(DISTINCT category) AS categories,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.performance.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_performance,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.accessibility.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_accessibility,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.seo.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_seo,
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APPROX_QUANTILES(CAST(JSON_EXTRACT_SCALAR(lighthouse, '$.categories.best-practices.score') AS NUMERIC), 1000)[OFFSET(500)] AS median_best_practices,
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ANY_VALUE(total_websites) AS total_websites,
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COUNT(DISTINCT page) AS number_of_websites,
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COUNT(DISTINCT page) / ANY_VALUE(total_websites) AS percent_of_websites
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FROM technologies
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WHERE
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category = 'Ecommerce' AND
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(
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technology != 'Cart Functionality' AND
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technology != 'Google Analytics Enhanced eCommerce'
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)
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GROUP BY
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client,
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rank,
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technology
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ORDER BY
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client,
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number_of_websites DESC

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