Compression mix
Text-response compression across requests: brotli, gzip, zstd, or none.
At a glance the headline numbers for Compression mix
Text-response compression across requests: brotli, gzip, zstd, or none.
25.5% of text responses use Brotli. 49.6% ship uncompressed.
The compression mix mix who uses what, and how fast each group loads
Compression mix. On the fleet: 49.6% none, 25.5% br, 21.7% gzip.
None leads by count (49.6%) and by bytes (80.0%). computed
Passing LCP 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 LCP.
Gzip swings the hardest: 84% of sites pass LCP with few, 58% with many. computed
Few vs many - does quantity cost LCP? 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 Gzip costs the most: the LCP pass rate falls from 84% with few to 58% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
HTML, CSS, JavaScript, SVG and JSON shrink to a fraction of their size with gzip or brotli. Every CDN can apply it at the edge. An uncompressed HTML document delays the TTFB. An uncompressed stylesheet delays rendering, and with it the LCP.
Compression cuts transfer time, not execution time. A compressed JavaScript bundle still costs the same CPU to parse and run, so compression helps TTFB and LCP, not INP. Also check the right files: images, video and woff2 fonts are already compressed. The gains are in your text responses.
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".lcp AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'network.compression'
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 = 'network.compression') * 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 <= 2500) 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;