Tips & Tricks Statnive Live · Parhum Khoshbakht

A Million Page Views a Minute, on a Single Server: How Statnive Live Was Engineered for Scale

How a Go binary, ClickHouse rollups, and a 687-byte tracker handle a million page views a minute on one 8-core server — without slowing your site down.

Analytics performance is a site-speed problem

Most articles about analytics performance focus on the backend — how many events per second the server can handle. That’s the wrong number to lead with. The number a site owner actually pays for is what your analytics script does to your visitors’ page-load times — and through that to Core Web Vitals, conversion rate, and SEO.

Google’s Core Web Vitals (INP replaced FID on 12 March 2024) — LCP, INP, CLS — are a ranking signal. Mobile JavaScript parsing is roughly 2–5× slower than desktop, which means a 50 KB analytics script on desktop can become a 200 KB-equivalent parse cost on a phone. Render-blocking analytics scripts are the single biggest performance killer in the genre.

Statnive Live was engineered with that asymmetry in mind. The headline numbers — 200 M events/day per node, a 687-byte tracker, sub-500 ms p99 query latency — are all in service of one goal: the analytics layer never becomes the reason your checkout slows down. This post walks through how, with file paths so you can verify every claim.

This is the closing piece in our four-part statnive.live pre-launch series. Where we make a measurable claim, you’ll find the file or command that proves it.

The 687-byte tracker

The Statnive Live tracker measured 1,394 bytes minified / 687 bytes gzipped on 2026-04-28. Those aren’t aspirational numbers — they’re the bytes Go’s go:embed directive carved into the binary, and you can re-derive them in any clone of the repo:

$ wc -c internal/tracker/dist/tracker.js
    1394 internal/tracker/dist/tracker.js

$ gzip -9 -c internal/tracker/dist/tracker.js | wc -c
     687

The numbers don’t drift, because the file is go:embed-ded into the binary — you can’t ship a different tracker than the one in your repo without rebuilding. And they can’t bloat unnoticed: a Go test in internal/tracker/tracker_test.go enforces the budget at 1,500 bytes minified / 700 bytes gzipped, and fails the build if either threshold is exceeded:

const (
    maxMinifiedBytes = 1500
    maxGzippedBytes  = 700
)

The same test forbids the entire shape of a non-trivial tracker — XMLHttpRequest, localStorage, sessionStorage, indexedDB, document.cookie, plaintext URLs, CDN imports — by string-grep against the embedded bundle. If a refactor accidentally pulled in a bigger transport library, or a new feature reached for localStorage, CI rejects the PR.

For comparison, the GA4 gtag.js script is roughly 110 KB compressed, and Plausible’s published number is 135 KB gzipped for the same script. Pick either: Statnive Live’s tracker is two orders of magnitude smaller — well over 50× lighter than GA4 regardless of which figure you anchor on.

The transport is sendBeacon plus fetch keepalive — both fire-and-forget, neither blocks the main thread. The structure is an IIFE in vanilla JS; there’s no framework, because 1,394 bytes won’t fit one. The tracker ships first-party via go:embed: no external CDN, no DNS lookup outside the operator’s domain, no third-party tag manager. The air-gap-validator rule in CI rejects any tracker change that would re-introduce an external reference.

The ingest path — fire-and-forget, WAL-first

A site owner’s contract with the tracker is “this should never block my page”. The server’s contract with the tracker is “this should never lose your event”. Statnive Live’s ingest pipeline is built around making both contracts cheap.

Every ingest request goes through a Write-Ahead Log before the handler responds 202. The handler waits for fsync — but on a 100 ms group-commit ticker, not per-event, because per-event fsync would cap throughput at ~100 events/s on commodity disk and we need ~7 K EPS sustained on the SaaS floor. The WAL is tidwall/wal (MIT-licensed, vendored), opened with NoSync: true; the 100 ms ticker handles durability. The handler waits via AppendAndWait before sending its 202 ack. If sync ever fails, the process exits — analytics is not the place to silently corrupt history.

The handler caps request bodies at 8 KB via Go’s http.MaxBytesReader:

const (
    maxBodyBytes  = 8 * 1024  // 8 KB MaxBytesReader
    maxArrayItems = 10        // batch at most 10 events per request
    uaMinLen      = 16
    uaMaxLen      = 500
)

Before the WAL, a fast-reject gate drops the obvious junk at HTTP 204 — User-Agent length outside 16–500, non-ASCII UA, IP-as-UA, UUID-as-UA, prefetch headers (X-Purpose, X-Moz). Those requests never reach enrichment, the WAL, or the rollups. ClickHouse async-insert exists, but only on a separate /ingest-fallback endpoint — never on the hot /api/event path.

Rate limiting is CGNAT-aware: requests from mobile-carrier ASNs get a compound (ip, site_id) key at 1 K req/s sustained / 2 K burst, while everyone else falls back to 100 req/s per IP. A per-site_id global cap of 25 K req/s prevents one customer from saturating the host. The CGNAT awareness matters because a phone-network gateway can sit behind a single IP — a naive per-IP limit would blackhole thousands of legitimate visitors on the same carrier.

Raw IP is never persisted. It enters the pipeline only for the GeoIP lookup, then is discarded before the batch writer sees the row. The audit log is also IP-free by design — the rate-limiter still keys on the IP for the limiting decision, but the audit-log serialization loses it. The gdpr-code-review rule enforces this in CI.

The query path — three rollups + HyperLogLog

The dashboard never queries raw events. All dashboard reads come from rollup tables — that’s Architecture Rule 1, enforced by a CI choke-point. The raw events_raw table is write-only, except for funnel windows that call windowFunnel() with an hour-cached result.

The three v1 rollups are AggregatingMergeTree views, all keyed on site_id first:

  • hourly_visitors — ENGINE = AggregatingMergeTree() PARTITION BY toYYYYMM(hour) ORDER BY (site_id, hour)
  • daily_pages — ORDER BY (site_id, day, pathname)
  • daily_sources — ORDER BY (site_id, day, channel, referrer_name, utm_source, utm_medium)

Visitor cardinality is HyperLogLog via AggregateFunction(uniqCombined64, FixedString(16)) — about 0.5% error, sub-linear memory. The FixedString(16) is a BLAKE3-128 hash, truncated to 16 bytes; identity is BLAKE3(daily_salt || identity_input), with the daily salt derived as HMAC(master_secret, site_id || YYYY-MM-DD), rotated daily and never persisted. Same visitor, different hash each day — and the rollups carry only the hash state, never the input.

Every dashboard query funnels through one helper:

// whereTimeAndTenant emits the WHERE clause every read query MUST start
// with: site_id = ? AND <timeColumn> >= ? AND <timeColumn> < ?.
// site_id is the first WHERE term so the (site_id, …) ORDER BY prefix
// can prune partitions cleanly.
func whereTimeAndTenant(f *Filter, timeColumn string) (string, []any) {
    clause := fmt.Sprintf("WHERE site_id = ? AND %s >= ? AND %s < ?",
        timeColumn, timeColumn)
    return clause, []any{f.SiteID, f.From, f.To}
}

A CI rule rejects any new query that bypasses whereTimeAndTenant or doesn’t start with WHERE site_id = ?. That sounds nitpicky; in practice it’s the difference between cleanly pruned partitions and a multi-tenant ClickHouse scanning everyone’s data on every dashboard render.

Nullable(...) is banned for analytics columns — its measured cost is 10–200% on aggregations (project doc 20 measured 2× on Nullable(Int8)). The rollups use DEFAULT '' and DEFAULT 0 instead, which keeps both write and merge paths fast.

The numbers

The published proof strip on the /live page lists four:

  • 600 B gzipped tracker (the rounded marketing version of 687 B)
  • 200 M events/day per node
  • <500 ms p99
  • EU/EEA-only data

Honest annotations on each:

  • Tracker: measured 1,394 B min / 687 B gz on 2026-04-28; budget 1,500 B / 700 B gz, asserted in CI.
  • 200 M events/day: design ceiling, not measured production. Source: project doc 19’s Hetzner-class envelope; the SaaS floor is a Hetzner AX42 (8 cores / 64 GB) with headroom. 200 M/day = ~2,300 EPS sustained, well within ClickHouse’s published throughput envelope (Cloudflare runs an 11 M-rows/sec ingestion across 36 nodes; Plausible migrated from PostgreSQL because ClickHouse is mandatory above ~1 M events/day).
  • <500 ms p99: design ceiling, not measured production. The Phase-11a production p99 will publish after public signup ships; the ProofStrip claim is a graduation-gate threshold, not a measurement.
  • EU/EEA-only data: processed in Nuremberg, Germany on a Netcup VPS 2000 G12 NUE — measured in the sense that there’s an integration test running the binary under iptables -P OUTPUT DROP that proves there’s no required egress.

The dashboard sits inside a 16 KB gzipped initial-JS budget, asserted by size-limit against the built index-*.js chunk. The lazy chart chunk is bounded at 25 KB, lazy panel chunks at 10 KB, CSS at 5 KB / 3 KB. You can re-run the gate locally:

$ npm --prefix web run bundle-gate

The analytics-invariant SLOs the test gate enforces:

  • Event loss ≤ 0.05% server / ≤ 0.5% client
  • Duplicates ≤ 0.1%
  • Attribution correctness ≥ 99.5%
  • Consent / PII leaks = 0
  • TTFB overhead ≤ +10% / +25 ms

Every threshold is release-blocking, asserted on every PR by CI, and additionally enforced during the per-phase 72-hour soak + 6-scenario chaos matrix before any production cutover. Whatever the next big spike looks like, it has to clear those gates before it ships.

The honest trade-off — 1-hour delay

The 1-hour delay is the part of Statnive Live that some readers won’t like, so let’s name it up front. Architecture Rule 3 reads:

1-hour delay, NOT real-time — saves 98% query cost. Never build 5-min real-time.

The “98%” is grounding against a hypothetical 5-minute pipeline on the same stack — keeping rollup writes cheap, keeping per-site rollup footprint under 100 KB/day/site (3 v1 rollups; up to 6 in v1.1), keeping dashboard queries serving from compact aggregates instead of scanning hot tables. If you check analytics once an hour or once a day, the 1-hour delay is invisible. If you need sub-minute feedback for a live-event spike monitor, Live is the wrong tool — pick a real-time analytics product, accept the ~50× higher query cost, and move on.

The Realtime panel still exists, and surfaces active-in-the-last-hour off the same hourly_visitors rollup that everything else reads. There is no separate 5-minute pipeline behind it, on purpose. The trade-off is the centerpiece of the architecture, not a hidden cost.

What this means for your site

The architecture above is what makes the site-owner story uneventful:

The tracker can’t block your checkout. sendBeacon plus fetch keepalive is fire-and-forget — even if the analytics origin is offline, the page still navigates and the customer still pays. Verify by killing the analytics endpoint and watching the page work.

Core Web Vitals impact is bounded by 687 bytes plus an inline IIFE. That’s well under any documented “render-blocking” threshold for the genre. We benchmarked the WordPress plugin’s tracker LCP impact in a separate post; we haven’t yet published a measured LCP-delta for the Live tracker, and we won’t claim one we don’t have.

Server-side overhead lives on a separate origin. The tracker posts to a Statnive Live endpoint, not to your web app. The 100 ms WAL fsync ticker buys ~7 K EPS sustained on the SaaS floor — nothing about that competes with your application’s PHP, Node, or Rails request budget.

Common questions

Will it scale to 10 M page views per day?

Yes. 10 M PV/day is roughly 115 events/second sustained — well under the 200 M/day design ceiling (~2,300 EPS sustained) on a single 8-core / 32 GB box. If you outgrow that on one node, the migrations already use {{if .Cluster}} Go-templates so the single-node → Distributed transition is a config flip, not a re-platform.

Can I run it on shared hosting?

No. ClickHouse needs a real server (8 cores / 32 GB minimum). For shared hosting, the WordPress plugin is the right answer — it stores in your existing MySQL/MariaDB and has zero new ops surface.

How does this compare to GA4’s 110 KB script?

GA4’s gtag.js is somewhere between 110 KB compressed (Stape) and 135 KB gzipped (Plausible) depending on payload version. Statnive Live’s tracker is 687 B gzipped. Well over 50× smaller, regardless of which GA4 number you pick. Mobile parse-time difference dominates; on a mid-range Android phone, the tracker disappears into noise.

What hardware does the SaaS plan run on?

The published SaaS floor is a Hetzner AX42 (8 cores / 64 GB). The active SaaS production VPS is a Netcup VPS 2000 G12 NUE in Nuremberg, Germany — EU/EEA-only processing, no Chapter V transfer. Article 3 covers the contractual side; Article 2 covers the regulatory side.

How is the size budget enforced?

Two CI gates run on every PR. (a) go test ./internal/tracker/... enforces the tracker 1,500 B / 700 B gz budget plus the forbidden-token rejection. (b) npm --prefix web run bundle-gate runs size-limit against all five dashboard entries in web/.size-limit.json. Both are part of make ci-local, which the GitHub Actions workflow runs end-to-end against a real ClickHouse in 8–12 minutes.

Show the receipts

Every claim above is reproducible from a clone of statnive-live:

# Tracker size budget — 1,500 B min / 700 B gz, asserted by Go test
$ wc -c internal/tracker/dist/tracker.js
    1394 internal/tracker/dist/tracker.js
$ gzip -9 -c internal/tracker/dist/tracker.js | wc -c
     687
$ go test ./internal/tracker/...
ok      github.com/statnive/statnive.live/internal/tracker      0.32s

# Dashboard bundle budget — five size-limit entries
$ npm --prefix web run bundle-gate

# Whole gate — ClickHouse + integration + smoke + e2e (~8–12 min)
$ make ci-local

The same commands run in GitHub Actions on every PR. There is no separate “release benchmark” — if a PR breaks a budget, it doesn’t merge. If a release breaks an SLO under the 72-hour soak, it doesn’t ship. Engineering for a million page views a minute looks unglamorous in person; mostly it’s CI gates, fast-reject filters, and rollup tables, and very little of it is heroic.

The bottom line

The analytics stack you ship in 2026 is mostly judged by what it does to your site, not what it does for it. Statnive Live’s design picks make the trade explicit: a 687-byte first-party tracker, a 1-hour-delay rollup pipeline that saves 98% of the query cost real-time would have demanded, and a CI-asserted set of SLOs that block a release before they ever reach you. We don’t claim production-measured p99 numbers we haven’t shipped yet, and we don’t claim LCP deltas we haven’t benchmarked yet — but every number above traces to a file path you can verify.

Statnive Live is coming soon at statnive.com/live. This four-part series is the slow-build introduction: WP plugin vs Statnive Live for the decision tree, GDPR-compliant analytics in 2026 for the regulatory side, own your analytics data for the deployment-shape side, and this post for the engineering side. The features page is the one-pager. If a number in this post turns out to be wrong, write to me — every claim has a file or a command behind it, and we’d rather correct one than ship a polished half-truth.

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