HealthKit Queries - Deep Dive

Expert guidance for constructing HealthKit queries with focus on accuracy, performance, and edge case handling.

When to Use This Skill

•Implementing new HealthKit data queries
•Debugging "why is my data off by X?" issues
•Optimizing query performance for large datasets
•Handling day/hour boundary edge cases
•Implementing background delivery and observer queries
•Dealing with timezone/DST issues in HealthKit data

Core Query Types

HKSampleQuery - Direct Sample Access

When to use: Simple queries, custom aggregation logic, widget contexts where you need full control

Pattern:

swift

let predicate = HKQuery.predicateForSamples(
    withStart: startDate,
    end: endDate,
    options: .strictStartDate  // Samples that START in range (not overlap)
)

let samples = try await withCheckedThrowingContinuation { continuation in
    let query = HKSampleQuery(
        sampleType: quantityType,
        predicate: predicate,
        limit: HKObjectQueryNoLimit,  // Get all matching samples
        sortDescriptors: nil  // Sort in memory if needed
    ) { _, samples, error in
        if let error = error {
            continuation.resume(throwing: error)
            return
        }
        continuation.resume(returning: samples as? [HKQuantitySample] ?? [])
    }
    healthStore.execute(query)
}

// Manual aggregation
var hourlyTotals: [Date: Double] = [:]
for sample in samples {
    let hourStart = calendar.dateInterval(of: .hour, for: sample.startDate)?.start
        ?? sample.startDate
    let calories = sample.quantity.doubleValue(for: .kilocalorie())
    hourlyTotals[hourStart, default: 0] += calories
}

Pros:

•Full control over aggregation logic
•Works in widget extensions (no long-running queries)
•Can implement custom grouping (by hour, by source, etc.)
•Straightforward error handling

Cons:

•Manual aggregation required
•More verbose than statistics queries
•Need to handle empty intervals yourself

Real example from HealthTrends:

swift

// From HealthKitQueryService.swift:85-111
private func fetchHourlyData(from startDate: Date, to endDate: Date, type: HKQuantityType) async throws -> [HourlyEnergyData] {
    let predicate = HKQuery.predicateForSamples(withStart: startDate, end: endDate, options: .strictStartDate)

    var hourlyTotals: [Date: Double] = [:]

    let samples = try await withCheckedThrowingContinuation { continuation in
        let query = HKSampleQuery(sampleType: type, predicate: predicate, limit: HKObjectQueryNoLimit, sortDescriptors: nil) { _, samples, error in
            if let error = error {
                continuation.resume(throwing: error)
                return
            }
            continuation.resume(returning: samples as? [HKQuantitySample] ?? [])
        }
        healthStore.execute(query)
    }

    // Group by hour
    for sample in samples {
        let hourStart = calendar.dateInterval(of: .hour, for: sample.startDate)?.start ?? sample.startDate
        let calories = sample.quantity.doubleValue(for: .kilocalorie())
        hourlyTotals[hourStart, default: 0] += calories
    }

    return hourlyTotals.map { HourlyEnergyData(hour: $0.key, calories: $0.value) }
        .sorted { $0.hour < $1.hour }
}

HKStatisticsCollectionQuery - Built-in Time Series Aggregation

When to use: App context (not widgets), standard time intervals, built-in aggregation

Pattern:

swift

// Define anchor and interval
let calendar = Calendar.current
let anchorDate = calendar.startOfDay(for: Date())  // Anchor at midnight
let interval = DateComponents(hour: 1)  // 1-hour intervals

let query = HKStatisticsCollectionQuery(
    quantityType: activeEnergyType,
    quantitySamplePredicate: nil,
    options: .cumulativeSum,  // For cumulative metrics like calories
    anchorDate: anchorDate,
    intervalComponents: interval
)

// Set initial results handler
query.initialResultsHandler = { query, results, error in
    guard let results = results else {
        if let error = error {
            print("Query failed: \(error)")
        }
        return
    }

    // Enumerate statistics
    results.enumerateStatistics(from: startDate, to: endDate) { statistics, stop in
        if let sum = statistics.sumQuantity() {
            let calories = sum.doubleValue(for: .kilocalorie())
            print("\(statistics.startDate): \(calories) cal")
        }
    }
}

healthStore.execute(query)

Pros:

•HealthKit handles aggregation (correct, efficient)
•Natural time interval alignment
•Handles empty intervals gracefully (returns nil quantity)
•Can act as long-running query with statisticsUpdateHandler

Cons:

•More complex setup (anchor dates, intervals)
•Less flexible for custom aggregation
•Not suitable for widgets (long-running queries)

Choosing Statistics Options:

swift

// For CUMULATIVE metrics (calories burned, steps, distance)
options: .cumulativeSum

// For DISCRETE metrics (heart rate, blood pressure)
options: [.discreteAverage, .discreteMin, .discreteMax]

// To separate by data source (Apple Watch vs iPhone)
options: [.cumulativeSum, .separateBySource]

// CANNOT combine cumulative and discrete
// ❌ [.cumulativeSum, .discreteAverage]  // ERROR

Understanding Anchor Dates:

The anchor date defines when each interval starts. For 1-hour intervals:

•Anchor at 3:00 AM → intervals start at 3 AM, 4 AM, 5 AM...
•Anchor at midnight → intervals start at 12 AM, 1 AM, 2 AM...

The exact date doesn't matter - only the time component. These are equivalent:

•2020-01-01 03:00
•2025-11-26 03:00

Both produce intervals starting at 3 AM daily.

Authorization & Privacy

Requesting Authorization

swift

func requestAuthorization() async throws {
    guard HKHealthStore.isHealthDataAvailable() else {
        throw HealthKitError.notAvailable
    }

    let activeEnergyType = HKObjectType.quantityType(forIdentifier: .activeEnergyBurned)!
    let activitySummaryType = HKObjectType.activitySummaryType()

    let typesToRead: Set<HKObjectType> = [activeEnergyType, activitySummaryType]
    let typesToWrite: Set<HKSampleType> = [activeEnergyType]

    try await healthStore.requestAuthorization(toShare: typesToWrite, read: typesToRead)

    // For read permissions, HealthKit NEVER reveals if user granted access (privacy)
    // Assume success if no error thrown
    isAuthorized = true
}

Privacy Protection:

•HealthKit never tells you if read permission was denied
•Apps only see data they wrote themselves (if denied)
•Check authorization status before writes:

swift

let status = healthStore.authorizationStatus(for: activeEnergyType)

switch status {
case .notDetermined:
    // Haven't requested yet - call requestAuthorization()
    try await requestAuthorization()
case .sharingDenied:
    // User denied - show error, can't save
    throw HealthKitError.authorizationDenied
case .sharingAuthorized:
    // Can save samples
    try await healthStore.save(samples)
@unknown default:
    break
}

Guest User Mode (Vision Pro)

swift

// In Guest User sessions, writes fail with specific error
do {
    try await healthStore.save(sample)
} catch let error as HKError where error.code == .errorNotPermissibleForGuestUserMode {
    // Silently ignore for passive saves, or show alert for explicit actions
    print("Cannot save in Guest User mode")
}

Date & Time Edge Cases

Midnight Boundaries

Critical insight: HealthKit samples can span midnight. Always consider boundary conditions.

swift

// ❌ WRONG: Might miss samples that start before midnight and end after
let today = calendar.startOfDay(for: Date())
let predicate = HKQuery.predicateForSamples(withStart: today, end: Date(), options: .strictStartDate)

// ✅ CORRECT: strictStartDate means "sample starts in range"
// This is usually what you want for daily queries
let predicate = HKQuery.predicateForSamples(
    withStart: startOfDay,
    end: Date(),
    options: .strictStartDate  // Sample.startDate >= startOfDay
)

// If you need samples that OVERLAP the interval (rare):
let predicate = HKQuery.predicateForSamples(
    withStart: startOfDay,
    end: Date(),
    options: []  // Sample overlaps [startOfDay, now]
)

Handling midnight transitions in widgets:

swift

// From DailyActiveEnergyWidget.swift:138-162
// Problem: Widget needs to show zero-state at midnight without querying HealthKit
// Solution: Create deterministic midnight entry

let midnight = calendar.nextDate(after: currentDate, matching: DateComponents(hour: 0), matchingPolicy: .nextTime)

if midnight < next15MinUpdate {
    // Create zero-state entry - we KNOW today resets to 0
    let midnightEntry = EnergyWidgetEntry(
        date: midnight,
        todayTotal: 0,  // Known state!
        averageAtCurrentHour: 0,
        projectedTotal: currentEntry.projectedTotal,  // Keep for reference
        moveGoal: currentEntry.moveGoal,
        todayHourlyData: [HourlyEnergyData(hour: midnight, calories: 0)],
        averageHourlyData: currentEntry.averageHourlyData
    )

    // Schedule reload 1 minute after midnight for fresh data
    let reloadTime = calendar.date(byAdding: .minute, value: 1, to: midnight)!
    return Timeline(entries: [currentEntry, midnightEntry], policy: .after(reloadTime))
}

DST & Timezone Handling

Always use Calendar.current for date arithmetic:

swift

// ✅ CORRECT: Respects DST and timezone
let hourStart = calendar.dateInterval(of: .hour, for: date)?.start

// ❌ WRONG: Breaks during DST transitions
let hourStart = Date(timeIntervalSince1970: (date.timeIntervalSince1970 / 3600).rounded(.down) * 3600)

DST Edge Case - Hour 2 AM doesn't exist on spring forward:

swift

// Spring forward: 2 AM doesn't exist, jumps to 3 AM
let march12_2023 = Date(/* 2023-03-12 */)

// Query from midnight to 3 AM - will miss the "missing" hour
// HealthKit handles this correctly, but your aggregation might not!

// ✅ CORRECT: Let Calendar handle it
for hour in 0..<24 {
    if let hourDate = calendar.date(byAdding: .hour, value: hour, to: startOfDay) {
        // This will skip 2 AM on spring forward
    }
}

Cumulative Data Across Days

Pattern for averaging cumulative patterns across multiple days:

swift

// From HealthKitManager.swift:366-459
private func fetchCumulativeAverageHourlyPattern(from startDate: Date, to endDate: Date, type: HKQuantityType) async throws -> [HourlyEnergyData] {
    let samples = /* fetch all samples in range */

    // Step 1: Group by day and hour
    var dailyCumulativeData: [Date: [Int: Double]] = [:]  // [dayStart: [hour: calories]]

    for sample in samples {
        let dayStart = calendar.startOfDay(for: sample.startDate)
        let hour = calendar.component(.hour, from: sample.startDate)
        let calories = sample.quantity.doubleValue(for: .kilocalorie())

        if dailyCumulativeData[dayStart] == nil {
            dailyCumulativeData[dayStart] = [:]
        }
        dailyCumulativeData[dayStart]![hour, default: 0] += calories
    }

    // Step 2: Convert each day to cumulative
    var dailyCumulative: [Date: [Int: Double]] = [:]

    for (dayStart, hourlyData) in dailyCumulativeData {
        var runningTotal: Double = 0
        var cumulativeByHour: [Int: Double] = [:]

        for hour in 0..<24 {
            runningTotal += hourlyData[hour] ?? 0
            cumulativeByHour[hour] = runningTotal
        }

        dailyCumulative[dayStart] = cumulativeByHour
    }

    // Step 3: Average across days for each hour
    var averageCumulativeByHour: [Int: Double] = [:]

    for hour in 0..<24 {
        var totalForHour: Double = 0
        var count = 0

        for (_, cumulativeByHour) in dailyCumulative {
            if let cumulativeAtHour = cumulativeByHour[hour], cumulativeAtHour > 0 {
                totalForHour += cumulativeAtHour
                count += 1
            }
        }

        averageCumulativeByHour[hour] = count > 0 ? totalForHour / Double(count) : 0
    }

    // Step 4: Convert to timeline data with timestamps at END of each hour
    let startOfToday = calendar.startOfDay(for: Date())
    var hourlyData: [HourlyEnergyData] = []

    hourlyData.append(HourlyEnergyData(hour: startOfToday, calories: 0))  // Midnight = 0

    for hour in 0..<24 {
        let timestamp = calendar.date(byAdding: .hour, value: hour + 1, to: startOfToday)!
        let avgCumulative = averageCumulativeByHour[hour] ?? 0
        hourlyData.append(HourlyEnergyData(hour: timestamp, calories: avgCumulative))
    }

    return hourlyData
}

Why this matters:

•Averaging NON-cumulative hourly data gives you "average hourly burn"
•Averaging CUMULATIVE hourly data gives you "typical progress throughout day"
•HealthTrends needs the latter for "are you ahead or behind?" comparisons

Observer Queries & Background Delivery

Setting Up Observer Queries

swift

// Register for background delivery (call once at app launch)
func enableBackgroundDelivery() async throws {
    try await healthStore.enableBackgroundDelivery(
        for: activeEnergyType,
        frequency: .hourly  // .immediate, .hourly, .daily, .weekly
    )
}

// Create observer query (call in application(_:didFinishLaunchingWithOptions:))
let observerQuery = HKObserverQuery(
    sampleType: activeEnergyType,
    predicate: nil
) { query, completionHandler, error in
    guard error == nil else {
        print("Observer query failed: \(error!)")
        completionHandler()  // MUST call even on error
        return
    }

    // Fetch new data
    Task {
        try? await fetchEnergyData()

        // Reload widget
        WidgetCenter.shared.reloadAllTimelines()

        // MUST call completion handler
        completionHandler()
    }
}

healthStore.execute(observerQuery)

Critical:

•Call completionHandler() even on errors
•Set up observer queries in application(_:didFinishLaunchingWithOptions:)
•Background delivery wakes your app, runs observer handler, then suspends

Disabling Background Delivery

swift

// Call when user disables feature or revokes permissions
func disableBackgroundDelivery() async throws {
    try await healthStore.disableBackgroundDelivery(for: activeEnergyType)
}

Performance Optimization

Caching Strategies

Pattern from HealthTrends: Cache slow queries (average data), refresh fast queries (today)

swift

// Widget: Hybrid approach - query today, cache average
// From DailyActiveEnergyWidget.swift:192-293

// Today's data: Fast, changes frequently → always query
let todayData = try await healthKit.fetchTodayHourlyTotals()

// Average data: Slow, changes rarely → cache for 6 hours
if cacheManager.shouldRefresh() {
    let (total, hourlyData) = try await healthKit.fetchAverageData()
    let cache = AverageDataCache(
        averageHourlyPattern: hourlyData,
        projectedTotal: total,
        cachedAt: Date(),
        cacheVersion: 1
    )
    try? cacheManager.save(cache)
} else {
    // Use cached average data
    let cache = cacheManager.load()
}

Why this works:

•Today's total changes every few minutes → must be fresh
•30-day average changes minimally → safe to cache
•Widgets have strict time limits (~30 seconds)

Predicate Optimization

swift

// ✅ EFFICIENT: Filter at query time
let predicate = NSCompoundPredicate(andPredicateWithSubpredicates: [
    HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate),
    HKQuery.predicateForObjects(from: [HKSource.default()])  // Only our app's data
])

// ❌ INEFFICIENT: Filter after fetching
let samples = try await fetchAllSamples()
let filtered = samples.filter { $0.sourceRevision.source == HKSource.default() }

Batch Queries for Multiple Metrics

swift

// ✅ EFFICIENT: Parallel queries with async let
async let activeEnergy = fetchActiveEnergy()
async let heartRate = fetchHeartRate()
async let steps = fetchSteps()

let (energy, hr, stepCount) = try await (activeEnergy, heartRate, steps)

// ❌ INEFFICIENT: Sequential queries
let energy = try await fetchActiveEnergy()
let hr = try await fetchHeartRate()
let steps = try await fetchSteps()

Debugging Common Issues

"My data is off by one hour"

Cause: Confusing hour START vs hour END timestamps

swift

// Problem: What timestamp represents "calories burned during hour 7-8 AM"?

// ❌ Hour START (7 AM): Ambiguous - is this cumulative up to 7 AM or during 7-8 AM?
HourlyEnergyData(hour: sevenAM, calories: 250)

// ✅ Hour END (8 AM): Clear - cumulative total AT 8 AM
HourlyEnergyData(hour: eightAM, calories: 250)

// From HealthKitManager.swift:268-269
let timestamp = calendar.date(byAdding: .hour, value: 1, to: data.hour)!
cumulativeData.append(HourlyEnergyData(hour: timestamp, calories: runningTotal))

"Widget shows stale data after midnight"

Cause: Cached data from previous day

swift

// ✅ Check if cached data is from different day
let calendar = Calendar.current
let cachedEntry = loadCachedEntry(forDate: date)

if let lastDataPoint = cachedEntry.todayHourlyData.last,
   !calendar.isDate(lastDataPoint.hour, inSameDayAs: date) {
    // Return zero-state for new day
    return EnergyWidgetEntry(
        date: date,
        todayTotal: 0,
        todayHourlyData: [HourlyEnergyData(hour: calendar.startOfDay(for: date), calories: 0)],
        // ... keep average data
    )
}

"Query returns no data but I know there are samples"

Debug steps:

•Check authorization:

swift

let status = healthStore.authorizationStatus(for: quantityType)
print("Auth status: \(status)")  // Should be .sharingAuthorized for writes

•Verify date range:

swift

print("Query range: \(startDate) to \(endDate)")
print("Sample dates: \(samples.map { $0.startDate })")

•Check predicate options:

swift

// .strictStartDate: Sample starts in range
// []: Sample overlaps range (start OR end in range)
let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)

•Inspect sample sources:

swift

for sample in samples {
    print("\(sample.startDate): \(sample.quantity) from \(sample.sourceRevision.source.name)")
}

"Average calculation seems wrong at late hours (11 PM - midnight)"

Cause: Incomplete days, zero-value filtering

swift

// Problem: At 11 PM on Nov 26, averaging "cumulative by 11 PM" across last 30 days
// But some days might not have data at 11 PM yet, or have zero

// ✅ Filter out zero values to avoid skewing average
for (_, cumulativeByHour) in dailyCumulative {
    if let cumulativeAtHour = cumulativeByHour[hour], cumulativeAtHour > 0 {
        totalForHour += cumulativeAtHour
        count += 1
    }
}
averageCumulativeByHour[hour] = count > 0 ? totalForHour / Double(count) : 0

See: HealthKitManager.swift:420-427

"Widget fails to load with database inaccessible error"

Cause: Device locked, HealthKit protected

swift

do {
    let samples = try await healthStore.execute(query)
} catch let error as HKError where error.code == .errorDatabaseInaccessible {
    // Device is locked - use cached data
    return loadCachedEntry()
}

Activity Summary Queries

For fetching Move goals:

swift

func fetchMoveGoal() async throws -> Double {
    let calendar = Calendar.current
    let now = Date()

    // Create predicate for today
    var dateComponents = calendar.dateComponents([.year, .month, .day], from: now)
    dateComponents.calendar = calendar
    let predicate = HKQuery.predicateForActivitySummary(with: dateComponents)

    let activitySummary = try await withCheckedThrowingContinuation { continuation in
        let query = HKActivitySummaryQuery(predicate: predicate) { _, summaries, error in
            if let error = error {
                continuation.resume(throwing: error)
                return
            }
            continuation.resume(returning: summaries?.first)
        }
        healthStore.execute(query)
    }

    if let summary = activitySummary {
        let goal = summary.activeEnergyBurnedGoal.doubleValue(for: .kilocalorie())
        // Only update if valid (goal might be 0 if Fitness app not set up)
        return goal > 0 ? goal : cachedGoal
    }

    return cachedGoal  // Fallback
}

Widget-Specific Patterns

Timeline Generation with Midnight Handling

swift

func getTimeline(in context: Context, completion: @escaping (Timeline<Entry>) -> Void) {
    Task {
        let currentEntry = await loadFreshEntry()
        var entries = [currentEntry]

        let calendar = Calendar.current
        let next15Min = calendar.date(byAdding: .minute, value: 15, to: Date())!
        let midnight = calendar.nextDate(after: Date(), matching: DateComponents(hour: 0), matchingPolicy: .nextTime)!

        if midnight < next15Min {
            // Midnight coming soon - add zero-state entry
            let midnightEntry = createMidnightEntry(/* ... */)
            entries.append(midnightEntry)

            // Reload 1 minute after midnight
            let reloadTime = calendar.date(byAdding: .minute, value: 1, to: midnight)!
            completion(Timeline(entries: entries, policy: .after(reloadTime)))
        } else {
            // Normal 15-minute refresh
            completion(Timeline(entries: entries, policy: .after(next15Min)))
        }
    }
}

Fallback Strategy for Failed Queries

swift

// From DailyActiveEnergyWidget.swift:200-228
do {
    todayData = try await healthKit.fetchTodayHourlyTotals()
} catch {
    // Check if cached data is stale (from previous day)
    let cachedEntry = loadCachedEntry()

    if isFromPreviousDay(cachedEntry) {
        // Return zero-state instead of stale data
        return EnergyWidgetEntry(/* zero state */)
    }

    // Same day - safe to use cached data
    return cachedEntry
}

Testing Strategies

Preview Data Generation

swift

// Generate realistic preview data
static func generateSampleTodayData() -> [HourlyEnergyData] {
    let calendar = Calendar.current
    let now = Date()
    let startOfDay = calendar.startOfDay(for: now)
    let currentHour = calendar.component(.hour, from: now)

    var data: [HourlyEnergyData] = []
    var cumulative: Double = 0

    // Midnight point
    data.append(HourlyEnergyData(hour: startOfDay, calories: 0))

    // Completed hours with realistic burn patterns
    for hour in 0..<currentHour {
        let calories = generateRealisticCalories(for: hour)
        cumulative += calories
        let timestamp = calendar.date(byAdding: .hour, value: hour + 1, to: startOfDay)!
        data.append(HourlyEnergyData(hour: timestamp, calories: cumulative))
    }

    return data
}

private static func generateRealisticCalories(for hour: Int) -> Double {
    switch hour {
    case 0..<6:   return Double.random(in: 5...15)     // Sleep
    case 7:       return Double.random(in: 150...250)  // Morning workout
    case 9..<12:  return Double.random(in: 25...50)    // Morning activity
    case 12..<14: return Double.random(in: 30...60)    // Lunch/midday
    default:      return Double.random(in: 20...40)
    }
}

Testing with Sample Data

swift

// Generate sample HealthKit data for development
func generateSampleData() async throws {
    let calendar = Calendar.current
    let now = Date()
    var samples: [HKQuantitySample] = []

    // Generate 60 days of data
    for dayOffset in 0..<60 {
        guard let dayStart = calendar.date(byAdding: .day, value: -dayOffset, to: calendar.startOfDay(for: now)) else {
            continue
        }

        // For today, only generate up to current hour
        let maxHour = dayOffset == 0 ? calendar.component(.hour, from: now) : 23

        for hour in 0...maxHour {
            guard let hourStart = calendar.date(byAdding: .hour, value: hour, to: dayStart),
                  hourStart <= now else {
                continue
            }

            let calories = generateRealisticCalories(for: hour)
            let quantity = HKQuantity(unit: .kilocalorie(), doubleValue: calories)
            let sample = HKQuantitySample(
                type: activeEnergyType,
                quantity: quantity,
                start: hourStart,
                end: calendar.date(byAdding: .hour, value: 1, to: hourStart)!
            )
            samples.append(sample)
        }
    }

    try await healthStore.save(samples)
}

Best Practices Summary

•Always use Calendar.current for date arithmetic (handles DST/timezones)
•Use .strictStartDate for daily queries (samples that START in range)
•Cache slow queries (30-day averages), refresh fast queries (today)
•Handle midnight boundaries explicitly in widgets (create zero-state entries)
•Filter zero values when averaging to avoid skewing results
•Check cached data dates before using (might be from previous day)
•Use async let for parallel queries (activeEnergy, heartRate, steps)
•Call completion handlers in observer queries, even on errors
•Validate date ranges when debugging (print start/end, sample dates)
•Use HKSampleQuery in widgets (not long-running statistics queries)

References

•Apple Docs: Executing Statistics Collection Queries
•Apple Docs: Authorizing Access to Health Data
•Apple Docs: Executing Observer Queries
•HealthTrends: HealthKitManager.swift, HealthKitQueryService.swift, DailyActiveEnergyWidget.swift

healthkit-queries

HealthKit Queries - Deep Dive

When to Use This Skill

Core Query Types

HKSampleQuery - Direct Sample Access

HKStatisticsCollectionQuery - Built-in Time Series Aggregation

Authorization & Privacy

Requesting Authorization

Guest User Mode (Vision Pro)

Date & Time Edge Cases

Midnight Boundaries

DST & Timezone Handling

Cumulative Data Across Days

Observer Queries & Background Delivery

Setting Up Observer Queries

Disabling Background Delivery

Performance Optimization

Caching Strategies

Predicate Optimization

Batch Queries for Multiple Metrics

Debugging Common Issues

"My data is off by one hour"

"Widget shows stale data after midnight"

"Query returns no data but I know there are samples"

"Average calculation seems wrong at late hours (11 PM - midnight)"

"Widget fails to load with database inaccessible error"

Activity Summary Queries

Widget-Specific Patterns

Timeline Generation with Midnight Handling

Fallback Strategy for Failed Queries

Testing Strategies

Preview Data Generation

Testing with Sample Data

Best Practices Summary

References

See Also