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test(rollup-read-wmy): granularity-routing and parity coverage
18 unit pins covering each reader's granularity filter, the coverage-miss `null` return shape, the auto-router's window-to-tier selection (90 d / 365 d / 1095 d), the fall-through chain on partial coverage, and the linear-composition contract for count / min / max / mean / sumValue across coarser buckets.
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Marc-André Bombeck committed
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/**
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* v1.4.39 W-WMY — unit tests for the WEEK / MONTH / YEAR rollup
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* readers and the auto-router that picks the largest granularity
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* that still resolves a requested window.
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*
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* `prisma.measurementRollup.findMany` is mocked at the module level
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* so the test pins:
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* - each per-granularity reader filters by the correct
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* `granularity` literal,
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* - empty rollups return `null` so the caller can branch on
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* coverage miss,
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* - the auto-router walks YEAR → MONTH → WEEK → DAY and stops at
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* the first granularity whose floor the window clears AND which
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* has coverage,
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* - `aggregateWmyBuckets` composes `count / min / max / mean / sum`
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* linearly across coarser buckets (the same compositional
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* contract `rollup-read.ts:aggregateBuckets` carries for DAY).
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*
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* Integration coverage against a real Postgres lives outside this
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* file — the writer's `STDDEV_POP / REGR_SLOPE` semantics are pinned
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* in `tests/integration/measurement-rollups.test.ts` already.
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*/
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import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
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const mocks = vi.hoisted(() => ({
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findMany: vi.fn(),
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}));
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vi.mock("@/lib/db", () => ({
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prisma: {
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measurementRollup: {
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findMany: mocks.findMany,
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},
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},
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}));
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import {
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aggregateWmyBuckets,
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readBestGranularityRollups,
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readMonthRollups,
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readWeekRollups,
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readYearRollups,
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type RollupBucketRow,
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} from "../rollup-read-wmy";
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const { findMany } = mocks;
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function bucket(
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bucketStart: string,
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partial: Partial<RollupBucketRow> = {},
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): RollupBucketRow {
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return {
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bucketStart: new Date(bucketStart),
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count: 10,
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mean: 82,
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sd: 1,
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slope: -0.01,
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r2: 0.3,
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sumValue: null,
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minValue: 80,
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maxValue: 84,
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...partial,
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};
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}
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beforeEach(() => {
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findMany.mockReset();
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});
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afterEach(() => {
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vi.restoreAllMocks();
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});
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describe("readWeekRollups", () => {
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it("returns the WEEK buckets for (userId, type, since)", async () => {
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const rows = [
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bucket("2026-04-27T00:00:00.000Z"),
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bucket("2026-05-04T00:00:00.000Z", { mean: 81 }),
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];
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findMany.mockResolvedValueOnce(rows);
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const since = new Date("2026-04-20T00:00:00.000Z");
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const result = await readWeekRollups("user-1", "WEIGHT", since);
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expect(result).not.toBeNull();
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expect(result).toHaveLength(2);
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expect(result?.[0].mean).toBe(82);
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expect(result?.[1].mean).toBe(81);
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const args = findMany.mock.calls[0][0];
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expect(args.where.granularity).toBe("WEEK");
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expect(args.where.userId).toBe("user-1");
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expect(args.where.type).toBe("WEIGHT");
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expect(args.where.bucketStart.gte).toBe(since);
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expect(args.orderBy).toEqual({ bucketStart: "asc" });
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});
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it("returns null when the WEEK window has no coverage", async () => {
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findMany.mockResolvedValueOnce([]);
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const result = await readWeekRollups(
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"user-1",
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"WEIGHT",
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new Date("2026-04-20T00:00:00.000Z"),
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);
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expect(result).toBeNull();
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});
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});
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describe("readMonthRollups", () => {
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it("returns the MONTH buckets and pins the granularity filter", async () => {
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findMany.mockResolvedValueOnce([bucket("2026-04-01T00:00:00.000Z")]);
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const result = await readMonthRollups(
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"user-2",
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"PULSE",
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new Date("2026-01-01T00:00:00.000Z"),
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);
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expect(result).toHaveLength(1);
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expect(findMany.mock.calls[0][0].where.granularity).toBe("MONTH");
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});
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it("returns null on coverage miss", async () => {
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findMany.mockResolvedValueOnce([]);
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const result = await readMonthRollups(
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"user-2",
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"PULSE",
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new Date("2026-01-01T00:00:00.000Z"),
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);
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expect(result).toBeNull();
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});
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});
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describe("readYearRollups", () => {
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it("returns the YEAR buckets and pins the granularity filter", async () => {
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findMany.mockResolvedValueOnce([bucket("2025-01-01T00:00:00.000Z")]);
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const result = await readYearRollups(
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"user-3",
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"WEIGHT",
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new Date("2023-01-01T00:00:00.000Z"),
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);
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expect(result).toHaveLength(1);
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expect(findMany.mock.calls[0][0].where.granularity).toBe("YEAR");
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});
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it("returns null on coverage miss", async () => {
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findMany.mockResolvedValueOnce([]);
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const result = await readYearRollups(
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"user-3",
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"WEIGHT",
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new Date("2023-01-01T00:00:00.000Z"),
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);
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expect(result).toBeNull();
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});
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});
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describe("readBestGranularityRollups", () => {
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it("returns null on a non-positive window", async () => {
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expect(await readBestGranularityRollups("user", "WEIGHT", 0)).toBeNull();
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expect(await readBestGranularityRollups("user", "WEIGHT", -10)).toBeNull();
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expect(await readBestGranularityRollups("user", "WEIGHT", NaN)).toBeNull();
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expect(findMany).not.toHaveBeenCalled();
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});
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it("routes a 90-day window to DAY (every coarser floor is too low)", async () => {
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findMany.mockResolvedValueOnce([bucket("2026-04-01T00:00:00.000Z")]);
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const result = await readBestGranularityRollups("user", "WEIGHT", 90);
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expect(result).not.toBeNull();
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expect(result?.granularity).toBe("DAY");
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expect(findMany).toHaveBeenCalledTimes(1);
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expect(findMany.mock.calls[0][0].where.granularity).toBe("DAY");
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});
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it("routes a 365-day window to MONTH (clears the 62-day MONTH floor)", async () => {
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findMany.mockResolvedValueOnce([bucket("2026-01-01T00:00:00.000Z")]);
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const result = await readBestGranularityRollups("user", "WEIGHT", 365);
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expect(result).not.toBeNull();
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expect(result?.granularity).toBe("MONTH");
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expect(findMany).toHaveBeenCalledTimes(1);
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expect(findMany.mock.calls[0][0].where.granularity).toBe("MONTH");
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});
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it("routes a 1095-day window to YEAR (clears the 730-day YEAR floor)", async () => {
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findMany.mockResolvedValueOnce([bucket("2024-01-01T00:00:00.000Z")]);
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const result = await readBestGranularityRollups("user", "WEIGHT", 1095);
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expect(result).not.toBeNull();
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expect(result?.granularity).toBe("YEAR");
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expect(findMany).toHaveBeenCalledTimes(1);
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expect(findMany.mock.calls[0][0].where.granularity).toBe("YEAR");
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});
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it("falls back to MONTH when YEAR has no coverage", async () => {
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// 1095-day window asks for YEAR first; coverage miss → MONTH.
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findMany
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.mockResolvedValueOnce([]) // YEAR miss
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.mockResolvedValueOnce([bucket("2024-01-01T00:00:00.000Z")]); // MONTH hit
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const result = await readBestGranularityRollups("user", "WEIGHT", 1095);
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expect(result?.granularity).toBe("MONTH");
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expect(findMany.mock.calls[0][0].where.granularity).toBe("YEAR");
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expect(findMany.mock.calls[1][0].where.granularity).toBe("MONTH");
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});
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it("falls all the way through to DAY when every coarser tier misses", async () => {
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findMany
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.mockResolvedValueOnce([]) // YEAR miss
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.mockResolvedValueOnce([]) // MONTH miss
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.mockResolvedValueOnce([]) // WEEK miss
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.mockResolvedValueOnce([bucket("2024-01-01T00:00:00.000Z")]); // DAY hit
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const result = await readBestGranularityRollups("user", "WEIGHT", 1095);
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expect(result?.granularity).toBe("DAY");
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expect(findMany).toHaveBeenCalledTimes(4);
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});
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it("returns null when no granularity carries any coverage", async () => {
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findMany.mockResolvedValue([]);
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const result = await readBestGranularityRollups("user", "WEIGHT", 1095);
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expect(result).toBeNull();
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// YEAR, MONTH, WEEK, DAY all probed.
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expect(findMany).toHaveBeenCalledTimes(4);
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});
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});
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describe("aggregateWmyBuckets", () => {
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it("returns the empty-window shape on no rows", () => {
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expect(aggregateWmyBuckets([])).toEqual({
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count: 0,
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min: null,
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max: null,
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mean: null,
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sum: null,
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});
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});
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it("sums count + folds min/max + weights mean across MONTH buckets", () => {
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// Two MONTH buckets — same compositional contract as DAY because
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// `count / min / max / mean` are linearly composable across any
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// bucket granularity.
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// April: count=10, mean=82, min=79, max=84
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// May: count=20, mean=80, min=77, max=83
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// ⇒ totalCount=30, min=77, max=84,
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// mean = (10×82 + 20×80) / 30 = 80.6666…
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const rows: RollupBucketRow[] = [
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bucket("2026-04-01T00:00:00.000Z", {
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count: 10,
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mean: 82,
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minValue: 79,
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maxValue: 84,
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}),
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bucket("2026-05-01T00:00:00.000Z", {
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count: 20,
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mean: 80,
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minValue: 77,
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maxValue: 83,
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}),
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];
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const result = aggregateWmyBuckets(rows);
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expect(result.count).toBe(30);
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expect(result.min).toBe(77);
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expect(result.max).toBe(84);
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expect(result.mean).toBeCloseTo((10 * 82 + 20 * 80) / 30, 5);
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});
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it("sums cumulative sumValue when every bucket carries one", () => {
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const rows: RollupBucketRow[] = [
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bucket("2026-04-01T00:00:00.000Z", { sumValue: 12_500 }),
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bucket("2026-05-01T00:00:00.000Z", { sumValue: 8_200 }),
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];
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const result = aggregateWmyBuckets(rows);
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expect(result.sum).toBe(20_700);
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});
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it("returns null sum when no bucket carries sumValue (pre-W-SUM data)", () => {
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const rows: RollupBucketRow[] = [
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bucket("2026-04-01T00:00:00.000Z", { sumValue: null }),
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bucket("2026-05-01T00:00:00.000Z", { sumValue: null }),
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];
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expect(aggregateWmyBuckets(rows).sum).toBeNull();
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});
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it("treats sumValue NaN/Infinity as missing", () => {
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const rows: RollupBucketRow[] = [
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bucket("2026-04-01T00:00:00.000Z", { sumValue: 100 }),
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bucket("2026-05-01T00:00:00.000Z", {
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// simulate a Postgres NaN slipping through serialisation.
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sumValue: Number.NaN,
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}),
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];
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// Only the finite sumValue contributes.
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expect(aggregateWmyBuckets(rows).sum).toBe(100);
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});
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});
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