Scheduling API use
How the page schedules main-thread work: scheduler.yield / postTask vs setTimeout churn.
At a glance the headline numbers for Scheduling API use
How the page schedules main-thread work: scheduler.yield / postTask vs setTimeout churn.
2.1% of scheduling calls use scheduler.yield. 47.3% chunk work with setTimeout.
The scheduling API use mix who uses what, and how fast each group loads
Scheduling API use. On the fleet: 47.3% settimeout, 46.4% raf, 3.4% requestidlecallback.
By count setTimeout leads (47.3%); by bytes it is postTask (0.0%). computed
Passing INP per bucket every category and count level at once - color is the pass rate
Each row is a category, each column its own count bucket (few on the left, many on the right); the cell is the share of those sites passing INP.
SetInterval swings the hardest: 92% of sites pass INP with few, 80% with many. computed
Few vs many - does quantity cost INP? the pass rate with few vs many of each category
Per category: the pass rate among pages with FEW of it (hollow ring) against pages with MANY (solid dot), worst trend first. Thin buckets are excluded from the endpoints.
More SetInterval costs the most: the INP pass rate falls from 92% with few to 80% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
Long tasks block every interaction that arrives while they run. The fix is yielding: split the work and give the browser a chance to handle input in between. How a site yields matters. scheduler.yield hands control back and resumes with priority. postTask schedules work with an explicit priority. A setTimeout chain yields too, but blindly: no priority, and every link in the chain adds delay.
Seeing the modern scheduler APIs on a site is a strong signal: someone engineered the INP instead of inheriting it.
Chrome field data from 189,915 sites, representing millions of real page loads. How we measured.
Live queries (2) — admin only
SELECT 1 AS ok FROM site_metric_bags LIMIT 0;WITH flat AS (
SELECT b.cat, b.n, b.size, s.crux."phone".inp AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'inp.scheduling'
AND b.cat IS NOT NULL
),
totals AS (
SELECT cat,
SUM(n) AS fleet_n,
SUM(size) AS fleet_size,
COUNT(*) FILTER (WHERE n IS NOT NULL) AS sample,
COUNT(*) FILTER (WHERE n > 0) AS with_any,
quantile_disc(n, 0.95) AS p95,
COUNT(*) AS n_rows
FROM flat
GROUP BY cat
HAVING COUNT(*) >= (SELECT COUNT(DISTINCT origin) FROM site_metric_bags WHERE path = 'inp.scheduling') * 0.005
),
widths AS (
SELECT *,
GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)) AS cap,
LEAST(GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)), 12 - 1) AS regular_buckets
FROM totals
),
widths2 AS (
SELECT *,
GREATEST(1, CAST(ceil(cap * 1.0 / regular_buckets) AS INTEGER)) AS width
FROM widths
),
binned AS (
SELECT f.cat, f.cwv_val, w.fleet_n, w.fleet_size, w.sample, w.with_any, w.width, w.regular_buckets,
CASE WHEN f.n IS NULL THEN NULL
ELSE LEAST(CAST(FLOOR(f.n * 1.0 / w.width) AS INTEGER), w.regular_buckets)
END AS bucket_idx
FROM flat f
JOIN widths2 w ON f.cat = w.cat
)
SELECT cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets,
bucket_idx,
COUNT(*) AS n,
quantile_disc(cwv_val, 0.5) AS median,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL) AS total_cwv,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL AND cwv_val <= 200) AS good
FROM binned
WHERE bucket_idx IS NOT NULL
GROUP BY cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets, bucket_idx
ORDER BY cat, bucket_idx;