HealthLog/tests/integration/dashboard-summary-reads.test.ts

/**
 * v1.38.5 — the dashboard's two per-type reads, against a real Postgres.
 *
 * Both were rewritten for their query plans, not for their results: the
 * latest-reading read moved from `DISTINCT ON (type)` to a LATERAL join
 * over the type list, and the sparkline read gained the `type` predicate
 * its rollup key needs. Neither change may move a single row.
 *
 * So the reference implementation is the OLD SQL, kept verbatim in this
 * file and run side by side with the shipped readers over the same
 * fixture. The rows have to match exactly — value, ordering, and the
 * types that are present at all.
 *
 * The fixture deliberately carries the three cases that could break the
 * rewrite: a type whose only rows are tombstoned (must drop out of BOTH
 * results, as an inner join and a filtered group both do), a type with
 * no rows at all (same), and a rollup table holding coarser
 * granularities the DAY read must not pick up.
 *
 * Plan shape is asserted by the unit companion
 * (`src/__tests__/dashboard-summary-read-plan-guard.test.ts`) rather than
 * here: a testcontainer fixture is far too small for the planner to
 * choose the same access paths a real account produces, so an EXPLAIN
 * assertion at this size would pin nothing.
 */
import { beforeEach, describe, expect, it } from "vitest";

import { getPrismaClient, truncateAllTables } from "./setup";
import {
  allTimeRollupBoundary,
  readAllTimeCounts,
  readLatestEver,
  readSparkBuckets,
  type LatestEverRow,
  type SparklineRow,
} from "@/lib/dashboard/summary-reads";
import { measurementTypeEnum } from "@/lib/validations/measurement";
import { recomputeUserRollups } from "@/lib/rollups/measurement-rollups";
import type {
  MeasurementType,
  RollupGranularity,
} from "@/generated/prisma/client";

const SPARK_DAYS = 7;
const TYPES = [...measurementTypeEnum.options] as MeasurementType[];

/** The pre-v1.38.5 latest-reading read, kept as the parity reference. */
function legacyLatestEver(userId: string): Promise<LatestEverRow[]> {
  return getPrismaClient().$queryRaw<LatestEverRow[]>`
    SELECT DISTINCT ON (m."type")
      m."type"                                  AS type,
      m."value"::double precision               AS value,
      m."measured_at"                           AS measured_at
    FROM measurements m
    WHERE m."user_id" = ${userId}
      AND m."deleted_at" IS NULL
    ORDER BY m."type", m."measured_at" DESC
  `;
}

/** The pre-v1.38.5 sparkline read, without the `type` predicate. */
function legacySparkBuckets(userId: string): Promise<SparklineRow[]> {
  return getPrismaClient().$queryRaw<SparklineRow[]>`
    SELECT type, bucket_start, mean, count, sum_value
    FROM (
      SELECT
        r."type"                                  AS type,
        r."bucket_start"                          AS bucket_start,
        r."mean"::double precision                AS mean,
        r."count"::int                            AS count,
        r."sum_value"::double precision           AS sum_value,
        ROW_NUMBER() OVER (
          PARTITION BY r."type"
          ORDER BY r."bucket_start" DESC
        ) AS rn
      FROM measurement_rollups r
      WHERE r."user_id" = ${userId}
        AND r."granularity" = 'DAY'
    ) sub
    WHERE rn <= ${SPARK_DAYS}
    ORDER BY type, bucket_start ASC
  `;
}

const DAY = 86_400_000;

async function seedAccount(): Promise<string> {
  const prisma = getPrismaClient();
  const user = await prisma.user.create({
    data: {
      username: "summary-reads-user",
      email: "summary-reads-user@example.test",
      role: "USER",
    },
  });

  // Live readings across several types, several rows each, so the
  // "latest wins" pick has something to choose between.
  const live: MeasurementType[] = [
    "WEIGHT",
    "PULSE",
    "BLOOD_PRESSURE_SYS",
    "ACTIVITY_STEPS",
    "SLEEP_DURATION",
  ];
  const base = Date.UTC(2026, 0, 1);
  const rows = live.flatMap((type, ti) =>
    Array.from({ length: 12 }, (_, i) => ({
      userId: user.id,
      type,
      value: 50 + ti * 10 + i,
      unit: "x",
      source: "MANUAL" as const,
      measuredAt: new Date(base + i * DAY + ti * 3_600_000),
      deletedAt: null as Date | null,
    })),
  );
  // BODY_FAT exists only as tombstones: both reads must drop it.
  rows.push(
    ...Array.from({ length: 4 }, (_, i) => ({
      userId: user.id,
      type: "BODY_FAT" as MeasurementType,
      value: 20 + i,
      unit: "%",
      source: "MANUAL" as const,
      measuredAt: new Date(base + i * DAY + 7_200_000),
      deletedAt: new Date(base + 30 * DAY),
    })),
  );
  // A live type whose newest row is tombstoned — the read must fall back
  // to the newest surviving row, not report the deleted one.
  rows.push(
    {
      userId: user.id,
      type: "BLOOD_GLUCOSE" as MeasurementType,
      value: 95,
      unit: "mg/dL",
      source: "MANUAL" as const,
      measuredAt: new Date(base + 5 * DAY),
      deletedAt: null,
    },
    {
      userId: user.id,
      type: "BLOOD_GLUCOSE" as MeasurementType,
      value: 999,
      unit: "mg/dL",
      source: "MANUAL" as const,
      measuredAt: new Date(base + 9 * DAY),
      deletedAt: new Date(base + 30 * DAY),
    },
  );
  await prisma.measurement.createMany({ data: rows });

  // DAY buckets past the SPARK_DAYS window, plus coarser granularities
  // the DAY read must ignore.
  const bucketRows = live.flatMap((type, ti) =>
    Array.from({ length: 11 }, (_, i) => ({
      userId: user.id,
      type,
      granularity: "DAY" as RollupGranularity,
      bucketStart: new Date(base + i * DAY),
      source: "MANUAL" as const,
      count: 3,
      mean: 60 + ti + i,
      minValue: 50,
      maxValue: 70,
      sumValue: 180 + ti,
    })),
  );
  bucketRows.push(
    ...(["WEEK", "MONTH", "YEAR"] as const).flatMap((granularity) =>
      live.map((type, ti) => ({
        userId: user.id,
        type,
        granularity,
        bucketStart: new Date(base + ti * DAY),
        source: "MANUAL" as const,
        count: 9,
        mean: 1000 + ti,
        minValue: 900,
        maxValue: 1100,
        sumValue: 9000 + ti,
      })),
    ),
  );
  await prisma.measurementRollup.createMany({ data: bucketRows });

  return user.id;
}

beforeEach(async () => {
  await truncateAllTables(getPrismaClient());
});

describe("dashboard summary reads — parity with the pre-v1.38.5 SQL", () => {
  it("returns the identical latest-reading rows the DISTINCT ON returned", async () => {
    const userId = await seedAccount();

    const [legacy, shipped] = await Promise.all([
      legacyLatestEver(userId),
      readLatestEver(getPrismaClient(), userId, TYPES),
    ]);

    expect(shipped).toEqual(legacy);
    // Not a vacuous pass: the fixture has to have produced rows.
    expect(shipped.length).toBe(6);
    // Tombstone-only type is absent from both; a type with no rows too.
    expect(shipped.map((r) => r.type)).not.toContain("BODY_FAT");
    expect(shipped.map((r) => r.type)).not.toContain("VO2_MAX");
    // The newest LIVE glucose row wins, not the newer tombstoned one.
    expect(shipped.find((r) => r.type === "BLOOD_GLUCOSE")?.value).toBe(95);
  });

  it("returns the identical sparkline buckets the type-less read returned", async () => {
    const userId = await seedAccount();

    const [legacy, shipped] = await Promise.all([
      legacySparkBuckets(userId),
      readSparkBuckets(getPrismaClient(), userId, TYPES, SPARK_DAYS),
    ]);

    expect(shipped).toEqual(legacy);
    // Five types × the trailing SPARK_DAYS buckets, and nothing coarser.
    expect(shipped.length).toBe(5 * SPARK_DAYS);
    expect(shipped.every((r) => r.mean < 1000)).toBe(true);
  });

  it("orders both reads the way the callers' per-type maps expect", async () => {
    const userId = await seedAccount();

    const latest = await readLatestEver(getPrismaClient(), userId, TYPES);
    // Enum declaration order, exactly as `ORDER BY m."type"` produced it.
    const enumRank = new Map(TYPES.map((t, i) => [t, i]));
    const ranks = latest.map((r) => enumRank.get(r.type) ?? -1);
    expect(ranks).toEqual([...ranks].sort((a, b) => a - b));

    const spark = await readSparkBuckets(
      getPrismaClient(),
      userId,
      TYPES,
      SPARK_DAYS,
    );
    for (const type of new Set(spark.map((r) => r.type))) {
      const stamps = spark
        .filter((r) => r.type === type)
        .map((r) => r.bucket_start.getTime());
      expect(stamps).toEqual([...stamps].sort((a, b) => a - b));
    }
  });
});

describe("dashboard all-time counts — parity with the live count", () => {
  /** The pre-rollup read: one index entry per live reading. */
  async function legacyAllTime(userId: string) {
    const groups = await getPrismaClient().measurement.groupBy({
      by: ["type"],
      where: { userId, type: { in: TYPES }, deletedAt: null },
      _count: { _all: true },
    });
    return new Map(groups.map((g) => [g.type, g._count._all]));
  }

  it("counts what the live count counts, from the rollups", async () => {
    const prisma = getPrismaClient();
    const user = await prisma.user.create({
      data: { username: "all-time-counts", email: "all-time@example.test" },
    });
    const now = new Date();
    const boundary = allTimeRollupBoundary(now);

    // A watch-dense pulse history from two sources: a reading every four
    // minutes for two years, written set-wise the way an import lands it.
    const twoYears = 2 * 365 * DAY;
    for (const [source, offsetMinutes] of [
      ["APPLE_HEALTH", 0],
      ["WITHINGS", 2],
    ] as const) {
      await prisma.$executeRawUnsafe(
        `INSERT INTO measurements
           (id, user_id, type, value, unit, source, measured_at, created_at, updated_at)
         SELECT md5($1 || $2 || g::text), $1, 'PULSE'::"measurement_type",
                60 + (g % 30), 'bpm', $2::"measurement_source",
                $3::timestamptz + make_interval(mins => g * 4 + $4), now(), now()
           FROM generate_series(0, $5::int) AS g`,
        user.id,
        source,
        new Date(now.getTime() - twoYears),
        offsetMinutes,
        Math.floor(twoYears / (4 * 60_000)) - 1,
      );
    }
    // Weigh-ins: some older than the fold window, one tombstoned.
    await prisma.measurement.createMany({
      data: [
        ...[1, 2, 3].map((years) => ({
          userId: user.id,
          type: "WEIGHT" as MeasurementType,
          value: 80 + years,
          unit: "kg",
          measuredAt: new Date(boundary.getTime() - years * 30 * DAY),
        })),
        ...[3, 2, 1].map((days) => ({
          userId: user.id,
          type: "WEIGHT" as MeasurementType,
          value: 79,
          unit: "kg",
          measuredAt: new Date(now.getTime() - days * DAY),
          deletedAt: days === 2 ? now : null,
        })),
      ],
    });
    await recomputeUserRollups(user.id, { granularities: ["DAY"] });
    // A type written after the fold, never bucketed: counted live.
    await prisma.measurement.createMany({
      data: [5, 4].map((days) => ({
        userId: user.id,
        type: "BLOOD_GLUCOSE" as MeasurementType,
        value: 98,
        unit: "mg/dL",
        measuredAt: new Date(now.getTime() - days * DAY),
      })),
    });

    let started = performance.now();
    const legacy = await legacyAllTime(user.id);
    const legacyMs = performance.now() - started;
    started = performance.now();
    const shipped = await readAllTimeCounts(prisma, user.id, TYPES, now);
    const shippedMs = performance.now() - started;
    console.log(
      `all-time counts over ${legacy.get("PULSE")} pulse rows: live ${legacyMs.toFixed(0)} ms, rollups ${shippedMs.toFixed(0)} ms`,
    );

    expect(new Map(shipped.map((r) => [r.type, r.count]))).toEqual(legacy);
    expect(legacy.get("PULSE")).toBeGreaterThan(500_000);
    expect(legacy.get("WEIGHT")).toBe(5);
    expect(legacy.get("BLOOD_GLUCOSE")).toBe(2);
    // Over half a million rows written and folded into DAY buckets: about
    // 35 s on a CI runner when the file ran alone. With three files sharing
    // the runner's Postgres it took 1.7 times as long (measured with the
    // database capped at two CPUs, HEALTHLOG_INTEGRATION_PG_CPUS=2: 14.6 s
    // alone, 25.3 s beside the sharing matrix and the restore memory test),
    // past the suite's 60 s default. The work is the point of the case, so
    // the budget is sized to it: twice the contended estimate.
  }, 150_000);
});