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+/**
+ * v1.4.39 W-WMY — WEEK / MONTH / YEAR rollup readers.
+ *
+ * Background — the read-side gap
+ * ------------------------------
+ * Every measurement write fans out via `recomputeBucketsForMeasurement`
+ * into a synchronous DAY upsert plus pg-boss-queued WEEK / MONTH / YEAR
+ * recomputes. The boot-time backfill (`enqueueBootTimeRollupBackfill`)
+ * mints the full WEEK / MONTH / YEAR history once per uncovered user.
+ *
+ * As of v1.4.38 the only reader that consults `measurement_rollups`
+ * (`rollup-read.ts`, plus the per-fast-path branches in
+ * `summaries-slice`, `comprehensive-aggregator`, etc.) caps at DAY and
+ * a trailing 90-day window. The WEEK / MONTH / YEAR buckets sit in
+ * Postgres as pure write amplification — populated every write,
+ * surfaced nowhere on read. The v1.4.38 perf audit
+ * (`.planning/round-v1438-perf-analysis.md` §2 + §5 P6) calls this out
+ * as the largest unused investment in the rollup tier.
+ *
+ * What this module adds
+ * ---------------------
+ * Helpers that read the trailing N-bucket window for a single
+ * `(userId, type)` pair at a chosen granularity, plus an auto-router
+ * that picks the largest granularity that still resolves the requested
+ * window. The shape returned is identical to `readRollupBuckets` so
+ * callers can interleave WEEK / MONTH / YEAR rows with DAY rows
+ * downstream without branching on the source granularity.
+ *
+ * Compositional contract
+ * ----------------------
+ * `count / min / max / mean / sumValue` are linearly composable across
+ * any granularity — aggregating these stats over WEEK buckets returns
+ * the same numbers as aggregating the underlying DAY buckets or the
+ * raw measurements. `sd / slope / r2` are NOT linearly composable; the
+ * routed callers either fall back to live SQL for those or accept the
+ * per-bucket stat as-is. The auto-router is a granularity selector
+ * only — it does not attempt to reconstruct slope across coarser
+ * buckets.
+ *
+ * Coverage-miss semantics
+ * -----------------------
+ * Each reader returns `null` when the requested granularity yields
+ * zero rows for `(userId, type, since)`. The caller decides whether
+ * that is a real "no data" case (the user has not logged this type in
+ * the window) or a coverage miss the boot-backfill / worker has not
+ * caught up on. The default policy is to fall back to a finer
+ * granularity (MONTH → WEEK → DAY) before giving up — see
+ * `readBestGranularityRollups`.
+ *
+ * Scope vs. `rollup-read.ts`
+ * --------------------------
+ * `rollup-read.ts` carries the existing DAY-only aggregator. This
+ * module is deliberately separate so the WEEK / MONTH / YEAR wiring
+ * is reviewable in isolation and so the v1.4.39 W-SUM agent's
+ * concurrent edits to the writer side don't collide with the reader
+ * additions.
+ */
+import type {
+  MeasurementType,
+  RollupGranularity,
+} from "@/generated/prisma/client";
+
+import { prisma } from "@/lib/db";
+
+/**
+ * Normalised bucket row returned by every WMY reader. Mirrors the
+ * shape `readRollupBuckets` returns plus the `sumValue` column the
+ * v1.4.39 W-SUM agent added to `MeasurementRollup`. `sumValue` is
+ * `null` for rows that pre-date the column's introduction; the boot
+ * backfill fills it in alongside the other stats.
+ */
+export interface RollupBucketRow {
+  bucketStart: Date;
+  count: number;
+  mean: number;
+  sd: number | null;
+  slope: number | null;
+  r2: number | null;
+  sumValue: number | null;
+  minValue: number;
+  maxValue: number;
+}
+
+/**
+ * Per-granularity "this is the smallest window the granularity can
+ * meaningfully resolve" floor. Used by `readBestGranularityRollups`
+ * to route a requested window into the coarsest tier that still
+ * carries enough buckets for the caller to do something useful.
+ *
+ *   - DAY     → any window — 90 daily buckets for a 90-day window
+ *               is already trivially cheap and the trend resolution
+ *               is canonical
+ *   - WEEK    → > 90 days (~13 weekly buckets for a quarter)
+ *   - MONTH   → > 180 days (~6 monthly buckets, enough trend signal
+ *               for a half-year view; smaller windows benefit more
+ *               from DAY-bucket granularity than coarse-grained
+ *               averaging)
+ *   - YEAR    → > 730 days (≥ 2 yearly buckets — anything below 2 y
+ *               collapses to one bucket and carries no slope signal)
+ *
+ * The pinned routing the v1.5 multi-year trend card relies on:
+ *   90 d → DAY   (90 buckets)
+ *   365 d → MONTH (12 buckets)
+ *   1095 d → YEAR (3 buckets)
+ *
+ * The floors are conservative on purpose — coarser tiers only
+ * activate when the row-count savings actually justify trading the
+ * finer trend resolution.
+ */
+const GRANULARITY_FLOORS: Array<{
+  granularity: RollupGranularity;
+  minWindowDays: number;
+}> = [
+  { granularity: "YEAR", minWindowDays: 731 },
+  { granularity: "MONTH", minWindowDays: 181 },
+  { granularity: "WEEK", minWindowDays: 91 },
+  { granularity: "DAY", minWindowDays: 0 },
+];
+
+/**
+ * Read the trailing WEEK buckets for `(userId, type)` since `since`.
+ * Returns `null` when no buckets exist in the window — the caller
+ * decides whether to fall back to a finer granularity or to live SQL.
+ */
+export async function readWeekRollups(
+  userId: string,
+  type: MeasurementType,
+  since: Date,
+): Promise<RollupBucketRow[] | null> {
+  return readGranularity(userId, type, "WEEK", since);
+}
+
+/**
+ * Read the trailing MONTH buckets for `(userId, type)` since `since`.
+ * Returns `null` on coverage miss.
+ */
+export async function readMonthRollups(
+  userId: string,
+  type: MeasurementType,
+  since: Date,
+): Promise<RollupBucketRow[] | null> {
+  return readGranularity(userId, type, "MONTH", since);
+}
+
+/**
+ * Read the trailing YEAR buckets for `(userId, type)` since `since`.
+ * Returns `null` on coverage miss.
+ */
+export async function readYearRollups(
+  userId: string,
+  type: MeasurementType,
+  since: Date,
+): Promise<RollupBucketRow[] | null> {
+  return readGranularity(userId, type, "YEAR", since);
+}
+
+/**
+ * Pick the largest granularity that can still resolve the requested
+ * `windowDays` window — WEEK for >14 d, MONTH for >62 d, YEAR for
+ * >730 d. Falls back to a finer granularity on coverage miss so a
+ * user with WEEK / MONTH coverage but no YEAR buckets (e.g. a tenant
+ * who joined less than two years ago) still gets a usable trend
+ * series.
+ *
+ * Returns the granularity the helper resolved against plus the row
+ * shape `readWeekRollups` / `readMonthRollups` / `readYearRollups`
+ * return. `null` when no granularity yields any buckets in the
+ * window — the caller is expected to short-circuit to "no data" or
+ * to live SQL.
+ */
+export async function readBestGranularityRollups(
+  userId: string,
+  type: MeasurementType,
+  windowDays: number,
+): Promise<{
+  granularity: RollupGranularity;
+  rows: RollupBucketRow[];
+} | null> {
+  if (!Number.isFinite(windowDays) || windowDays <= 0) return null;
+  const since = new Date(Date.now() - windowDays * 24 * 60 * 60 * 1000);
+  // Walk the floors from coarsest to finest and return the first
+  // granularity whose floor the window clears AND which has coverage
+  // for `(userId, type, since)`. The `if rows == null` fall-through
+  // is what makes the helper resilient to partial coverage.
+  for (const floor of GRANULARITY_FLOORS) {
+    if (windowDays < floor.minWindowDays) continue;
+    const rows = await readGranularity(userId, type, floor.granularity, since);
+    if (rows && rows.length > 0) {
+      return { granularity: floor.granularity, rows };
+    }
+  }
+  return null;
+}
+
+/**
+ * Linearly compose `count / min / max / mean / sum` across an array
+ * of bucket rows. Mirrors `rollup-read.ts:aggregateBuckets` but
+ * carries `sumValue` so cumulative-metric callers (steps, energy,
+ * distance) can read the window total without re-deriving from
+ * `mean * count`. SD / slope / r2 are intentionally omitted — those
+ * stats do not compose across coarser buckets and the consumers that
+ * need them stay on live SQL.
+ */
+export function aggregateWmyBuckets(rows: RollupBucketRow[]): {
+  count: number;
+  min: number | null;
+  max: number | null;
+  mean: number | null;
+  sum: number | null;
+} {
+  if (rows.length === 0) {
+    return { count: 0, min: null, max: null, mean: null, sum: null };
+  }
+  let totalCount = 0;
+  let sumWeighted = 0;
+  let sumCumulative = 0;
+  let sawSum = false;
+  let min = Infinity;
+  let max = -Infinity;
+  for (const row of rows) {
+    totalCount += row.count;
+    sumWeighted += row.count * row.mean;
+    if (row.sumValue !== null && Number.isFinite(row.sumValue)) {
+      sumCumulative += row.sumValue;
+      sawSum = true;
+    }
+    if (row.minValue < min) min = row.minValue;
+    if (row.maxValue > max) max = row.maxValue;
+  }
+  if (totalCount === 0) {
+    return { count: 0, min: null, max: null, mean: null, sum: null };
+  }
+  return {
+    count: totalCount,
+    min: Number.isFinite(min) ? min : null,
+    max: Number.isFinite(max) ? max : null,
+    mean: sumWeighted / totalCount,
+    sum: sawSum ? sumCumulative : null,
+  };
+}
+
+/**
+ * Internal — shared `findMany` projection. Returns `null` when the
+ * window has zero rows so the caller can branch on coverage miss
+ * without a separate count round-trip.
+ */
+async function readGranularity(
+  userId: string,
+  type: MeasurementType,
+  granularity: RollupGranularity,
+  since: Date,
+): Promise<RollupBucketRow[] | null> {
+  // Bounded `findMany`: `(userId, type, granularity, bucketStart)`
+  // is the composite primary key so the planner picks the index path
+  // every time. `bucketStart >= since` is the same shape
+  // `readRollupBuckets` uses; we don't carry an upper bound because
+  // the helpers are always reading "trailing window to now".
+  const rows = await prisma.measurementRollup.findMany({
+    where: {
+      userId,
+      type,
+      granularity,
+      bucketStart: { gte: since },
+    },
+    orderBy: { bucketStart: "asc" },
+    select: {
+      bucketStart: true,
+      count: true,
+      mean: true,
+      sd: true,
+      slope: true,
+      r2: true,
+      sumValue: true,
+      minValue: true,
+      maxValue: true,
+    },
+  });
+  if (rows.length === 0) return null;
+  return rows.map((r) => ({
+    bucketStart: r.bucketStart,
+    count: r.count,
+    mean: r.mean,
+    sd: r.sd,
+    slope: r.slope,
+    r2: r.r2,
+    sumValue: r.sumValue,
+    minValue: r.minValue,
+    maxValue: r.maxValue,
+  }));
+}