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perf(insights): batch the medication-compliance and per-type latest queries (#125)

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Two N+1 patterns the v1.4 discovery audit caught — the same shape v1.3
fixed in /api/insights/comprehensive but in adjacent code paths:

1. **`extractFeatures` (used by every AI-insight route)** issued one
   `prisma.medicationIntakeEvent.findMany` per active medication,
   in-loop. Replaced with a single windowed query
   (`medicationId IN (...)` + `scheduledFor >= 90 days`) and an
   in-memory Map group, mirroring the V3 fix in `/api/insights/
   comprehensive`. Latency now scales with total recent intakes, not
   `medications × intakes-per-medication`. The 90-day cutoff matches
   the longest window `calculateCompliance` consults.

2. **`/api/insights/targets`** issued one `prisma.measurement.findFirst`
   per `MeasurementType` (×~10) to fetch each type's most recent
   value. Replaced with a single `findMany({ distinct: ["type"],
   orderBy: { measuredAt: "desc" } })`. One round-trip instead of N.

Behaviour identical, just fewer round-trips. Tests stay at 371/371.

Note: the eager Recharts import in `app/insights/page.tsx` (the audit
also flagged it as a bundle bloat that neutralises the existing
`dynamic()` wrappers) is its own refactor — every ScatterChart usage
inside the page needs to be lifted into its own component before
dynamic-loading helps. Tracked for a follow-up perf PR so this one
stays scoped to query-side wins.

Co-authored-by: Marc-André Bombeck <noreply@anthropic.com>
This commit is contained in:
Marc-André BombeckandMarc-André Bombeck authored and GitHub committed 2026-05-08 12:11:57 +02:00
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commit f9ff450e08
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+136 -47
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src/app/api/insights/targets/route.ts
+12 -8
View File
@@ -109,20 +109,24 @@ export const GET = apiHandler(async () => {
select: { type: true, value: true, measuredAt: true },
});
// Also get the absolute latest measurement per type (even if older than 30 days)
// Also get the absolute latest measurement per type (even if older than 30 days).
// Single grouped query replaces the previous N×findFirst loop — DISTINCT ON
// (Postgres) emulated via a windowed orderBy + take-1 per type would still be
// N round-trips, so we sort once on the wide query and pick per-type below.
const latestEverByType = await prisma.measurement.findMany({
where: { userId, type: { in: types } },
orderBy: { measuredAt: "desc" },
distinct: ["type"],
select: { type: true, value: true },
});
const latestByType: Record<string, number | null> = {};
const avg30ByType: Record<string, number | null> = {};
for (const t of types) {
// Latest value
const latest = await prisma.measurement.findFirst({
where: { userId, type: t },
orderBy: { measuredAt: "desc" },
select: { value: true },
});
const latest = latestEverByType.find((row) => row.type === t);
latestByType[t] = latest?.value ?? null;
// 30-day average
const recentOfType = recentMeasurements.filter((m) => m.type === t);
if (recentOfType.length > 0) {
const sum = recentOfType.reduce((acc, m) => acc + m.value, 0);
src/lib/insights/features.ts
+124 -39
View File
@@ -97,7 +97,7 @@ export interface AggregatedFeatures {
ratePressureProduct?: {
rpp7: number | null;
rpp30: number | null;
risk: 'normal' | 'elevated' | null;
risk: "normal" | "elevated" | null;
};
bodyCompositionDivergence?: {
weightStable: boolean;
@@ -109,7 +109,7 @@ export interface AggregatedFeatures {
winterAvgSys: number | null;
summerAvgSys: number | null;
delta: number | null;
significance: 'normal' | 'elevated' | null;
significance: "normal" | "elevated" | null;
};
correlations?: {
weightVsSystolic: CorrelationResult | null;
@@ -176,7 +176,8 @@ export interface RawFeatures extends AggregatedFeatures {
function stdDev(values: number[]): number | null {
if (values.length < 2) return null;
const mean = values.reduce((s, v) => s + v, 0) / values.length;
const variance = values.reduce((s, v) => s + (v - mean) ** 2, 0) / (values.length - 1);
const variance =
values.reduce((s, v) => s + (v - mean) ** 2, 0) / (values.length - 1);
return Math.round(Math.sqrt(variance) * 10) / 10;
}
@@ -237,7 +238,11 @@ function avgInWindow(
function computeHistoricalComparison(
records: Array<{ value: number; measuredAt: Date }>,
now: number,
): { current7dAvg: number | null; previous30dAvg: number | null; change: number | null } {
): {
current7dAvg: number | null;
previous30dAvg: number | null;
change: number | null;
} {
const current7dAvg = avgInWindow(records, now, 7, 0);
const previous30dAvg = avgInWindow(records, now, 37, 7);
const change =
@@ -355,19 +360,28 @@ export async function extractFeatures(
let pctInTarget: number | null = null;
if (bpTargets) {
const sysByTime = new Map(sysData.map(m => [m.measuredAt.getTime(), m.value]));
const sysByTime = new Map(
sysData.map((m) => [m.measuredAt.getTime(), m.value]),
);
let inTargetCount = 0;
let pairedCount = 0;
for (const dia of diaData) {
const sysVal = sysByTime.get(dia.measuredAt.getTime());
if (sysVal === undefined) continue;
pairedCount++;
if (sysVal >= bpTargets.sysLow && sysVal <= bpTargets.sysHigh &&
dia.value >= bpTargets.diaLow && dia.value <= bpTargets.diaHigh) {
if (
sysVal >= bpTargets.sysLow &&
sysVal <= bpTargets.sysHigh &&
dia.value >= bpTargets.diaLow &&
dia.value <= bpTargets.diaHigh
) {
inTargetCount++;
}
}
pctInTarget = pairedCount > 0 ? Math.round((inTargetCount / pairedCount) * 100) : null;
pctInTarget =
pairedCount > 0
? Math.round((inTargetCount / pairedCount) * 100)
: null;
}
features.bloodPressure = {
@@ -385,12 +399,16 @@ export async function extractFeatures(
slopeDia30: diaSummary?.slope30?.slope ?? null,
sdSys30: (() => {
const fromMs = now - 30 * 24 * 60 * 60 * 1000;
const vals = sysData.filter(m => m.measuredAt.getTime() >= fromMs).map(m => m.value);
const vals = sysData
.filter((m) => m.measuredAt.getTime() >= fromMs)
.map((m) => m.value);
return stdDev(vals);
})(),
sdDia30: (() => {
const fromMs = now - 30 * 24 * 60 * 60 * 1000;
const vals = diaData.filter(m => m.measuredAt.getTime() >= fromMs).map(m => m.value);
const vals = diaData
.filter((m) => m.measuredAt.getTime() >= fromMs)
.map((m) => m.value);
return stdDev(vals);
})(),
pulsePressure30: (() => {
@@ -556,15 +574,20 @@ export async function extractFeatures(
moodVsPulse: computeCorr(moodPoints, pulsePoints),
moodVsSystolic: computeCorr(moodPoints, sysPoints),
moodVsWeight: computeCorr(moodPoints, weightPoints),
sleepVsPulse: sleepData.length > 0 ? computeCorr(sleepPoints, pulsePoints) : null,
sleepVsSystolic: sleepData.length > 0 ? computeCorr(sleepPoints, sysPoints) : null,
sleepVsPulse:
sleepData.length > 0 ? computeCorr(sleepPoints, pulsePoints) : null,
sleepVsSystolic:
sleepData.length > 0 ? computeCorr(sleepPoints, sysPoints) : null,
};
// Rate-Pressure Product (RPP) — myocardial oxygen demand indicator
if (features.pulse && features.bloodPressure) {
const rpp7 =
features.pulse.avg7 !== null && features.bloodPressure.avgSys30 !== null
? Math.round(features.pulse.avg7 * (avgInWindow(sysData, now, 7) ?? features.bloodPressure.avgSys30))
? Math.round(
features.pulse.avg7 *
(avgInWindow(sysData, now, 7) ?? features.bloodPressure.avgSys30),
)
: null;
const rpp30 =
features.pulse.avg30 !== null && features.bloodPressure.avgSys30 !== null
@@ -574,14 +597,17 @@ export async function extractFeatures(
features.ratePressureProduct = {
rpp7,
rpp30,
risk: rppRef !== null ? (rppRef > 12000 ? 'elevated' : 'normal') : null,
risk: rppRef !== null ? (rppRef > 12000 ? "elevated" : "normal") : null,
};
}
// Body Composition Divergence
if (features.weight && features.bodyFat) {
const weightStable = features.weight.slope30 !== null && Math.abs(features.weight.slope30) < 0.01;
const bodyFatRising = features.bodyFat.slope30 !== null && features.bodyFat.slope30 > 0;
const weightStable =
features.weight.slope30 !== null &&
Math.abs(features.weight.slope30) < 0.01;
const bodyFatRising =
features.bodyFat.slope30 !== null && features.bodyFat.slope30 > 0;
features.bodyCompositionDivergence = {
weightStable,
bodyFatRising,
@@ -590,43 +616,77 @@ export async function extractFeatures(
}
// Mood-Adherence Risk Flag
if (features.mood && features.medications && features.medications.length > 0) {
if (
features.mood &&
features.medications &&
features.medications.length > 0
) {
features.moodAdherenceRisk =
features.mood.avg7 !== null &&
features.mood.avg7 <= 2.5 &&
features.mood.trend30 === 'declining';
features.mood.trend30 === "declining";
}
// Seasonal BP Variation (only if > 180 days of data)
if (features.context.dataSpanDays > 180 && sysData.length > 0) {
const winterMonths = [11, 0, 1]; // Dec, Jan, Feb (0-indexed)
const summerMonths = [5, 6, 7]; // Jun, Jul, Aug
const winterVals = sysData.filter(m => winterMonths.includes(m.measuredAt.getMonth())).map(m => m.value);
const summerVals = sysData.filter(m => summerMonths.includes(m.measuredAt.getMonth())).map(m => m.value);
const winterAvg = winterVals.length > 0 ? Math.round((winterVals.reduce((s, v) => s + v, 0) / winterVals.length) * 10) / 10 : null;
const summerAvg = summerVals.length > 0 ? Math.round((summerVals.reduce((s, v) => s + v, 0) / summerVals.length) * 10) / 10 : null;
const delta = winterAvg !== null && summerAvg !== null ? Math.round((winterAvg - summerAvg) * 10) / 10 : null;
const winterVals = sysData
.filter((m) => winterMonths.includes(m.measuredAt.getMonth()))
.map((m) => m.value);
const summerVals = sysData
.filter((m) => summerMonths.includes(m.measuredAt.getMonth()))
.map((m) => m.value);
const winterAvg =
winterVals.length > 0
? Math.round(
(winterVals.reduce((s, v) => s + v, 0) / winterVals.length) * 10,
) / 10
: null;
const summerAvg =
summerVals.length > 0
? Math.round(
(summerVals.reduce((s, v) => s + v, 0) / summerVals.length) * 10,
) / 10
: null;
const delta =
winterAvg !== null && summerAvg !== null
? Math.round((winterAvg - summerAvg) * 10) / 10
: null;
features.seasonalVariation = {
winterAvgSys: winterAvg,
summerAvgSys: summerAvg,
delta,
significance: delta !== null ? (Math.abs(delta) > 5 ? 'elevated' : 'normal') : null,
significance:
delta !== null ? (Math.abs(delta) > 5 ? "elevated" : "normal") : null,
};
}
// Historical comparison: current 7d avg vs previous 30d avg (days 7-37)
features.historicalComparison = {};
if (weightData.length > 0) {
features.historicalComparison.weight = computeHistoricalComparison(weightData, now);
features.historicalComparison.weight = computeHistoricalComparison(
weightData,
now,
);
}
if (sysData.length > 0) {
features.historicalComparison.systolic = computeHistoricalComparison(sysData, now);
features.historicalComparison.systolic = computeHistoricalComparison(
sysData,
now,
);
}
if (diaData.length > 0) {
features.historicalComparison.diastolic = computeHistoricalComparison(diaData, now);
features.historicalComparison.diastolic = computeHistoricalComparison(
diaData,
now,
);
}
if (pulseData.length > 0) {
features.historicalComparison.pulse = computeHistoricalComparison(pulseData, now);
features.historicalComparison.pulse = computeHistoricalComparison(
pulseData,
now,
);
}
// Medications
@@ -640,22 +700,47 @@ export async function extractFeatures(
medications.map((med) => med.id),
);
features.medications = [];
for (const med of medications) {
const events = await prisma.medicationIntakeEvent.findMany({
where: { medicationId: med.id, userId },
orderBy: { scheduledFor: "desc" },
});
const mapped = events.map((e) => ({
// Single batched fetch + in-memory grouping replaces the per-medication
// findMany loop. Same shape as the v1.3.0 fix to /api/insights/comprehensive
// (the previous N+1 the v3 audit closed). 90 days is the longest window
// calculateCompliance uses below, so we don't need the full intake history.
const ninetyDaysAgo = new Date(Date.now() - 90 * 86_400_000);
const allEvents = await prisma.medicationIntakeEvent.findMany({
where: {
userId,
medicationId: { in: medications.map((med) => med.id) },
scheduledFor: { gte: ninetyDaysAgo },
},
orderBy: { scheduledFor: "desc" },
select: {
medicationId: true,
takenAt: true,
skipped: true,
scheduledFor: true,
},
});
const eventsByMed = new Map<
string,
{ takenAt: Date | null; skipped: boolean; scheduledFor: Date }[]
>();
for (const e of allEvents) {
const list = eventsByMed.get(e.medicationId) ?? [];
list.push({
takenAt: e.takenAt,
skipped: e.skipped,
scheduledFor: e.scheduledFor,
}));
});
eventsByMed.set(e.medicationId, list);
}
features.medications = medications.map((med) => {
const mapped = eventsByMed.get(med.id) ?? [];
const c7 = calculateCompliance(mapped, med.schedules, 7, med.createdAt);
const c30 = calculateCompliance(mapped, med.schedules, 30, med.createdAt);
const c90 = calculateCompliance(mapped, med.schedules, 90, med.createdAt);
features.medications.push({
return {
name: med.name,
dose: med.dose,
category: categoryMap[med.id] ?? "OTHER",
@@ -664,8 +749,8 @@ export async function extractFeatures(
compliance90: c90.rate,
streak: c30.streak,
missedLast7: c7.missed,
});
}
};
});
}
// Raw mode: add anonymized raw data points