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chore(scripts): add a seed for an account the size of a multi-year import
`scripts/seed-large-account.ts` inserts about 1.25 million measurements for one user, shaped like an Apple Health export plus the iOS sync: dense heart rate, HRV and SpO2, the high-frequency mean types, export `stats:` daily rows, recent per-sample cumulative rows and nightly sleep stages. Everything is generated in Postgres, so a run takes seconds, and it ends with ANALYZE. It is how the maintenance jobs in #1031 were reproduced and measured; never point it at a database holding real data.
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/**
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* Seed one account shaped like a multi-year Apple Health `export.xml` import,
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* for reproducing the maintenance jobs at the size a real heavy account
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* reaches (issue #1031: about 1.26 million measurement rows).
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*
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* DATABASE_URL=postgresql://… pnpm dlx tsx scripts/seed-large-account.ts \
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* [--user bench-large] [--years 4] [--pulse-seconds 165]
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*
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* Never point this at a database that holds real data: it inserts rows under
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* the given user id and, with `--reset`, deletes that user first.
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*
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* The shape follows what the import and the iOS sync actually write:
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*
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* - dense spot rows (heart rate every `--pulse-seconds`, HRV, SpO2) keyed by
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* a hashed sample id, the tier the dense-intraday retention folds;
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* - high-frequency mean rows (respiratory rate, gait metrics, audio
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* exposure), the tier the daily-mean consolidation folds;
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* - one `stats:` daily row per cumulative type and day (what the export
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* import writes for steps, energy, distance and flights);
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* - per-sample cumulative rows with opaque sample ids for the most recent
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* 90 days (what the iOS sync writes, and what the cumulative drain folds);
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* - sleep-stage segments every night.
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*
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* Everything is generated inside Postgres with `generate_series`, so a million
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* rows take seconds rather than a million round trips. `ANALYZE` runs at the
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* end: a planner reading pre-seed statistics picks plans no production table
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* of this size would get.
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*/
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import { prisma } from "@/lib/db";
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function arg(name: string, fallback: string): string {
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const i = process.argv.indexOf(`--${name}`);
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return i >= 0 && process.argv[i + 1] ? process.argv[i + 1]! : fallback;
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}
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const USER_ID = arg("user", "bench-large");
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const YEARS = Number(arg("years", "4"));
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const PULSE_SECONDS = Number(arg("pulse-seconds", "165"));
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const RESET = process.argv.includes("--reset");
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/** A spot series: `perDay` evenly spaced rows per day over the whole span. */
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interface SpotSeries {
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type: string;
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unit: string;
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perDay: number;
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base: number;
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spread: number;
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/** Days back from now the series covers. Defaults to the whole span. */
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days?: number;
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/** externalId prefix; `stats:` rows are generated separately. */
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idPrefix: string;
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}
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async function insertSpot(s: SpotSeries, days: number): Promise<number> {
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const step = Math.floor(86_400 / s.perDay);
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const count = Math.floor((days * 86_400) / step);
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// Jitter stays below the step so every measured_at is distinct per type
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// (the (user, type, measured_at, source, sleep_stage) unique index).
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const jitter = Math.max(1, Math.min(59, step - 1));
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return prisma.$executeRawUnsafe(
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`
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INSERT INTO measurements
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(id, user_id, type, value, unit, source, measured_at, external_id, created_at, updated_at)
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SELECT
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'seed-' || $1 || '-' || $2 || '-' || g,
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$1,
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$2::measurement_type,
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round(($3 + $4 * sin(g / 37.0) + ($4 / 2) * ((g * 7919) % 100) / 100.0)::numeric, 2),
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$5,
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'APPLE_HEALTH',
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date_trunc('minute', now()) - make_interval(secs => $6 * $7::bigint)
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+ make_interval(secs => g * $6 + (g * 31) % $8),
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$9 || md5($2 || g::text),
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now(), now()
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FROM generate_series(0, $7::bigint - 1) AS g
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`,
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USER_ID,
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s.type,
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s.base,
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s.spread,
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s.unit,
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step,
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count,
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jitter,
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s.idPrefix,
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);
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}
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async function main(): Promise<void> {
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const days = Math.round(YEARS * 365);
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if (RESET) {
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await prisma.$executeRawUnsafe(`DELETE FROM users WHERE id = $1`, USER_ID);
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}
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await prisma.$executeRawUnsafe(
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`INSERT INTO users (id, username, timezone, updated_at)
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VALUES ($1, $1, 'Europe/Berlin', now())
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ON CONFLICT (id) DO NOTHING`,
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USER_ID,
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);
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const pulsePerDay = Math.floor(86_400 / PULSE_SECONDS);
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const spot: SpotSeries[] = [
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{
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type: "PULSE",
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unit: "bpm",
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perDay: pulsePerDay,
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base: 62,
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spread: 25,
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idPrefix: "hk-",
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},
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{
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type: "HEART_RATE_VARIABILITY",
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unit: "ms",
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perDay: 12,
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base: 45,
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spread: 20,
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idPrefix: "hk-",
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},
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{
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type: "OXYGEN_SATURATION",
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unit: "%",
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perDay: 24,
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base: 96,
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spread: 2,
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idPrefix: "hk-",
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},
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{
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type: "RESPIRATORY_RATE",
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unit: "breaths/min",
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perDay: 48,
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base: 14,
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spread: 3,
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idPrefix: "hk-",
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},
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{
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type: "WALKING_SPEED",
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unit: "km/h",
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perDay: 30,
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base: 4.8,
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spread: 0.8,
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idPrefix: "hk-",
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},
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{
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type: "WALKING_STEP_LENGTH",
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unit: "cm",
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perDay: 30,
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base: 72,
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spread: 6,
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idPrefix: "hk-",
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},
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{
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type: "WALKING_ASYMMETRY",
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unit: "%",
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perDay: 30,
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base: 3,
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spread: 2,
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idPrefix: "hk-",
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},
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{
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type: "WALKING_DOUBLE_SUPPORT",
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unit: "%",
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perDay: 30,
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base: 28,
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spread: 3,
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idPrefix: "hk-",
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},
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{
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type: "AUDIO_EXPOSURE_ENV",
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unit: "dB",
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perDay: 60,
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base: 62,
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spread: 12,
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idPrefix: "hk-",
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},
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// The iOS sync's per-sample cumulative rows: dense, recent, opaque ids.
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{
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type: "ACTIVITY_STEPS",
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unit: "steps",
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perDay: 100,
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base: 90,
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spread: 60,
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days: 90,
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idPrefix: "uuid-",
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},
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{
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type: "ACTIVE_ENERGY_BURNED",
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unit: "kcal",
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perDay: 400,
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base: 1.2,
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spread: 0.9,
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days: 90,
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idPrefix: "uuid-",
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},
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{
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type: "WALKING_RUNNING_DISTANCE",
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unit: "m",
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perDay: 100,
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base: 70,
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spread: 50,
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days: 90,
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idPrefix: "uuid-",
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},
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{
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type: "WEIGHT",
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unit: "kg",
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perDay: 1,
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base: 82,
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spread: 1.5,
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idPrefix: "hk-",
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},
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];
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let total = 0;
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for (const s of spot) {
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const n = await insertSpot(s, s.days ?? days);
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total += n;
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console.log(`${s.type.padEnd(28)} ${String(n).padStart(9)}`);
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}
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// One export-estimate `stats:` row per cumulative type and local day, at
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// local noon — what `flushCumulativeBuckets` in the export import writes.
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for (const [type, hk, unit, base] of [
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["ACTIVITY_STEPS", "HKQuantityTypeIdentifierStepCount", "steps", 8000],
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[
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"ACTIVE_ENERGY_BURNED",
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"HKQuantityTypeIdentifierActiveEnergyBurned",
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"kcal",
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550,
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],
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[
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"WALKING_RUNNING_DISTANCE",
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"HKQuantityTypeIdentifierDistanceWalkingRunning",
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"m",
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6000,
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],
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["FLIGHTS_CLIMBED", "HKQuantityTypeIdentifierFlightsClimbed", "count", 9],
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] as const) {
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const n = await prisma.$executeRawUnsafe(
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`
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INSERT INTO measurements
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(id, user_id, type, value, unit, source, measured_at, external_id,
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aggregation_provenance, created_at, updated_at)
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SELECT
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'seed-' || $1 || '-' || $2 || '-stats-' || d,
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$1, $2::measurement_type, $5 + (d * 7919) % ($5 / 2), $4, 'APPLE_HEALTH',
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((current_date - d)::timestamp + interval '12 hours')
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AT TIME ZONE 'Europe/Berlin' AT TIME ZONE 'UTC',
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'stats:' || $3 || ':' || to_char(current_date - d, 'YYYY-MM-DD'),
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'EXPORT_XML_SOURCE_MAX', now(), now()
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FROM generate_series(91, $6::int) AS d
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`,
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USER_ID,
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type,
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hk,
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unit,
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base,
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days,
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);
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total += n;
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console.log(`${`${type} (stats:)`.padEnd(28)} ${String(n).padStart(9)}`);
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}
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// Sleep: 30 segments a night from 23:00 local, 16 minutes apart, rotating
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// through the stages. Distinct measured_at per segment.
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const sleep = await prisma.$executeRawUnsafe(
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`
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INSERT INTO measurements
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(id, user_id, type, value, unit, source, measured_at, external_id,
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sleep_stage, created_at, updated_at)
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SELECT
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'seed-' || $1 || '-sleep-' || d || '-' || s,
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$1, 'SLEEP_DURATION', 16, 'minutes', 'APPLE_HEALTH',
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((current_date - d)::timestamp - interval '1 hour' + s * interval '16 minutes')
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AT TIME ZONE 'Europe/Berlin' AT TIME ZONE 'UTC',
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'hk-' || md5('sleep' || d || '-' || s),
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(ARRAY['CORE','DEEP','CORE','REM','AWAKE','CORE']::sleep_stage[])[1 + s % 6],
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now(), now()
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FROM generate_series(1, $2::int) AS d, generate_series(0, 29) AS s
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`,
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USER_ID,
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days,
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);
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total += sleep;
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console.log(`${"SLEEP_DURATION".padEnd(28)} ${String(sleep).padStart(9)}`);
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await prisma.$executeRawUnsafe(`ANALYZE measurements`);
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console.log(`${"total".padEnd(28)} ${String(total).padStart(9)}`);
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}
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main()
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.catch((err) => {
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console.error(err);
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process.exitCode = 1;
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})
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.finally(() => prisma.$disconnect());
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