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test(rollups): stop the summary parity tests going red after midnight UTC

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The two summary tests seeded readings counting back from the current
instant, a few more minutes earlier each, and compared the composed mean
with a plain AVG. Between midnight and about 01:15 UTC the earliest
minutes slid into the day before, a day gained a reading in another hour,
and for pulse, whose day is a mean of hourly means, the composed mean
left the plain AVG. The fixtures now count back from noon UTC yesterday,
and the expected mean is the one each type documents: AVG, or the mean
of hourly-mean days.
This commit is contained in:
Marc-André Bombeck committed 2026-10-11 12:54:37 +02:00
1 parent 2dc313980b
commit 118494b91d
1 file changed
+87 -48
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tests/integration/measurement-rollups.test.ts
+87 -48
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@@ -35,6 +35,7 @@ import {
} from "@/lib/rollups/measurement-rollups";
import { aggregateBuckets } from "@/lib/rollups/measurement-read";
import { buildComprehensiveAggregate } from "@/lib/insights/comprehensive-aggregator";
import { windowWeighting } from "@/lib/measurements/day-statistic";
vi.mock("@/lib/db-compat", () => ({
ensureDbCompatibility: vi.fn().mockResolvedValue(undefined),
@@ -65,6 +66,81 @@ afterEach(() => {
vi.restoreAllMocks();
});
const DAY_MS = 24 * 60 * 60 * 1000;
/**
* The instant the summary fixtures count back from: noon UTC yesterday.
*
* They used to count back from `Date.now()`, with each reading a few more
* minutes earlier than the last. Run between midnight and about 01:15 UTC,
* the earliest of those minutes slid across midnight into the day before,
* so a day gained a reading in another hour, and for a type whose day is a
* mean of hourly means (pulse) the composed mean moved off the plain AVG the
* tests compared it with: the suite went red for an hour every night. Noon
* leaves every offset inside its own day, so the layout is the same at any
* time the suite runs.
*/
function fixtureAnchor(): number {
const now = new Date();
return (
Date.UTC(now.getUTCFullYear(), now.getUTCMonth(), now.getUTCDate()) -
DAY_MS +
12 * 60 * 60 * 1000
);
}
/**
* Live SQL over the raw rows for the summary's count / min / max / mean,
* with the mean each type documents: a plain AVG over readings, or, for a
* type whose day is a mean of hourly means (`windowWeighting(type) ===
* "day"`), the mean of those per-day values.
*/
async function liveSummaryRows(userId: string, since: Date) {
const prisma = getPrismaClient();
const rows = await prisma.$queryRaw<
Array<{
type: string;
count: bigint;
min_value: number;
max_value: number;
mean_value: number;
day_mean_value: number;
}>
>`
WITH hourly AS (
SELECT m."type", date_trunc('day', m."measured_at") AS day,
AVG(m."value") AS v
FROM measurements m
WHERE m."user_id" = ${userId} AND m."measured_at" >= ${since}
GROUP BY m."type", date_trunc('day', m."measured_at"),
date_trunc('hour', m."measured_at")
),
daily AS (
SELECT "type", day, AVG(v) AS v FROM hourly GROUP BY "type", day
),
by_day AS (
SELECT "type", AVG(v)::double precision AS day_mean_value
FROM daily GROUP BY "type"
)
SELECT
m."type"::text AS type,
COUNT(*) AS count,
MIN(m."value")::double precision AS min_value,
MAX(m."value")::double precision AS max_value,
AVG(m."value")::double precision AS mean_value,
d.day_mean_value AS day_mean_value
FROM measurements m
JOIN by_day d ON d."type" = m."type"
WHERE m."user_id" = ${userId} AND m."measured_at" >= ${since}
GROUP BY m."type", d.day_mean_value
`;
return rows.map((row) => ({
...row,
expectedMean:
windowWeighting(row.type) === "day" ? row.day_mean_value : row.mean_value,
}));
}
describe("measurement rollups — integration", () => {
it("writes a DAY rollup row that matches the inserted measurements", async () => {
const prisma = getPrismaClient();
@@ -298,8 +374,8 @@ describe("measurement rollups — integration", () => {
// type families exercise the per-type composition path. The
// values are deliberately fractional so the round2 contract gets
// a workout.
const now = Date.now();
const dayMs = 24 * 60 * 60 * 1000;
const now = fixtureAnchor();
const dayMs = DAY_MS;
const seedRows: Array<{
userId: string;
type: "WEIGHT" | "PULSE";
@@ -336,26 +412,7 @@ describe("measurement rollups — integration", () => {
// the source of truth — the read-swap must produce the same
// count/min/max/mean byte-for-byte.
const ninetyDaysAgo = new Date(Date.now() - 90 * dayMs);
const live = await prisma.$queryRaw<
Array<{
type: string;
count: bigint;
min_value: number;
max_value: number;
mean_value: number;
}>
>`
SELECT
m."type"::text AS type,
COUNT(*) AS count,
MIN(m."value")::double precision AS min_value,
MAX(m."value")::double precision AS max_value,
AVG(m."value")::double precision AS mean_value
FROM measurements m
WHERE m."user_id" = ${user.id}
AND m."measured_at" >= ${ninetyDaysAgo}
GROUP BY m."type"
`;
const live = await liveSummaryRows(user.id, ninetyDaysAgo);
const round2 = (n: number) => Math.round(n * 100) / 100;
@@ -366,9 +423,10 @@ describe("measurement rollups — integration", () => {
expect(summary.count).toBe(Number(liveRow.count));
expect(summary.min).toBe(round2(liveRow.min_value));
expect(summary.max).toBe(round2(liveRow.max_value));
// The composed weighted mean equals AVG(value) over the same
// rows; both run through `round2`.
expect(summary.mean).toBe(round2(liveRow.mean_value));
// The composed mean equals the type's documented mean over the same
// rows (AVG, or the mean of hourly-mean days); both run through
// `round2`.
expect(summary.mean).toBe(round2(liveRow.expectedMean));
}
});
@@ -741,8 +799,8 @@ describe("measurement rollups — integration", () => {
// 60 readings over 30 days, two types, deliberately spread so
// anomalies + slopes + windowed avgs all have signal.
const now = Date.now();
const dayMs = 24 * 60 * 60 * 1000;
const now = fixtureAnchor();
const dayMs = DAY_MS;
const seedRows: Array<{
userId: string;
type: "WEIGHT" | "PULSE";
@@ -777,26 +835,7 @@ describe("measurement rollups — integration", () => {
// Parallel live aggregation over the same 90-day window using the
// exact heavy-aggregate shape the cold-mount fallback runs.
const ninetyDaysAgo = new Date(now - 90 * dayMs);
const live = await prisma.$queryRaw<
Array<{
type: string;
count: bigint;
min_value: number;
max_value: number;
mean_value: number;
}>
>`
SELECT
m."type"::text AS type,
COUNT(*) AS count,
MIN(m."value")::double precision AS min_value,
MAX(m."value")::double precision AS max_value,
AVG(m."value")::double precision AS mean_value
FROM measurements m
WHERE m."user_id" = ${user.id}
AND m."measured_at" >= ${ninetyDaysAgo}
GROUP BY m."type"
`;
const live = await liveSummaryRows(user.id, ninetyDaysAgo);
const aggregate = await buildComprehensiveAggregate(user.id);
@@ -811,7 +850,7 @@ describe("measurement rollups — integration", () => {
expect(summary.count).toBe(Number(liveRow.count));
expect(summary.min).toBe(round2(liveRow.min_value));
expect(summary.max).toBe(round2(liveRow.max_value));
expect(summary.mean).toBe(round2(liveRow.mean_value));
expect(summary.mean).toBe(round2(liveRow.expectedMean));
// Narrow aggregate fills the non-composable windowed columns.
// Slope tuples and avg7/avg30 are sourced from live SQL even on
// the rollup-fresh path so the values are present (not null) as