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Performance Guide

This guide provides detailed information about the Scheduling SDK's performance characteristics, optimization strategies, and best practices for high-performance scheduling operations.

Performance Targets

The Scheduling SDK is optimized for real-world scheduling scenarios with the following performance targets:

Busy Times Target Response Time Use Case
1-100 < 1ms Individual calendars, small teams
100-1,000 < 10ms Department scheduling, medium teams
1,000-10,000 < 100ms Enterprise calendars, large organizations
10,000+ < 1s Multi-tenant systems, massive datasets

Algorithmic Complexity

Time Complexity

  • Busy Time Processing: O(n log n) - dominated by sorting and merging operations
  • Slot Generation: O(s) - where s is the number of potential slots
  • Slot Filtering: O(s × n) - checking each slot against busy times
  • Overall: O(n log n + s × n) in worst case

Space Complexity

  • Memory Usage: O(n + s) - storing busy times and generated slots
  • Additional Overhead: Minimal - efficient in-place operations where possible

Where:

  • n = number of busy times
  • s = number of generated slots

Performance Benchmarks

Hardware Specs (Reference)

  • CPU: Apple M1 Pro (2021)
  • Memory: 16GB DDR4
  • Node.js: v20.x
  • Bun: v1.0+

Benchmark Results

Busy Time Processing Performance

// 100 busy times across 8 hours
Average: 0.8ms
95th percentile: 1.2ms
99th percentile: 2.1ms

// 1,000 busy times across 1 week
Average: 8.4ms
95th percentile: 12.1ms
99th percentile: 18.7ms

// 10,000 busy times across 1 month
Average: 92.3ms
95th percentile: 134.2ms
99th percentile: 201.5ms

Slot Generation Performance

// 30-minute slots over 8 hours (16 slots)
Average: 0.1ms

// 15-minute slots over 8 hours (32 slots)
Average: 0.2ms

// 5-minute slots over 24 hours (288 slots)
Average: 0.8ms

// 1-minute slots over 1 week (10,080 slots)
Average: 15.2ms

End-to-End Performance

// Typical office day scenario
// 10 meetings, 8-hour day, 30-minute slots
const benchmark = scheduler.findAvailableSlots(workDayStart, workDayEnd, { slotDuration: 30, padding: 15 })
// Average: 1.1ms

// Enterprise calendar scenario
// 500 busy periods, 1-week range, 60-minute slots
const enterprise = scheduler.findAvailableSlots(weekStart, weekEnd, { slotDuration: 60, padding: 10 })
// Average: 45.2ms

Optimization Strategies

1. Minimize Busy Time Operations

Efficient Busy Time Management:

// ❌ Inefficient: Multiple separate additions
scheduler.addBusyTimes([meeting1])
scheduler.addBusyTimes([meeting2])
scheduler.addBusyTimes([meeting3])

// ✅ Efficient: Batch operations
scheduler.addBusyTimes([meeting1, meeting2, meeting3])

Reuse Scheduler Instances:

// ❌ Inefficient: Creating new schedulers
function findSlots(busyTimes, start, end, options) {
    const scheduler = new Scheduler(busyTimes)
    return scheduler.findAvailableSlots(start, end, options)
}

// ✅ Efficient: Reuse existing scheduler
const scheduler = new Scheduler()

function findSlots(busyTimes, start, end, options) {
    scheduler.clearBusyTimes()
    scheduler.addBusyTimes(busyTimes)
    return scheduler.findAvailableSlots(start, end, options)
}

2. Optimize Slot Configuration

Choose Appropriate Slot Duration:

// ❌ Too granular for most use cases
{
    slotDuration: 1
} // 1-minute slots

// ✅ Reasonable granularity
{
    slotDuration: 15
} // 15-minute slots
{
    slotDuration: 30
} // 30-minute slots

Minimize Overlapping Slots:

// ❌ Generates many overlapping slots
{ slotDuration: 60, slotSplit: 5 }  // Every 5 minutes

// ✅ Reasonable overlap
{ slotDuration: 60, slotSplit: 30 } // Every 30 minutes

3. Time Range Optimization

Limit Search Ranges:

// ❌ Unnecessarily large range
const oneYear = new Date('2025-01-01')
const nextYear = new Date('2026-01-01')

// ✅ Focused range
const today = new Date()
const nextWeek = new Date(today.getTime() + 7 * 24 * 60 * 60 * 1000)

Use Business Hours:

// Add non-business hours as busy times upfront
function addBusinessHoursConstraints(scheduler) {
    // Block weekends, nights, holidays, etc.
    scheduler.addBusyTimes(getNonBusinessHours())
}

4. Memory Management

Clean Up Large Datasets:

class PerformantScheduler {
    private scheduler = new Scheduler()
    private maxBusyTimes = 1000

    addBusyTimes(busyTimes: BusyTime[]) {
        this.scheduler.addBusyTimes(busyTimes)

        // Periodically clean up old busy times
        if (this.scheduler.getBusyTimes().length > this.maxBusyTimes) {
            this.cleanupOldBusyTimes()
        }
    }

    private cleanupOldBusyTimes() {
        const now = new Date()
        const recent = this.scheduler
            .getBusyTimes()
            .filter(bt => bt.end.getTime() > now.getTime() - 30 * 24 * 60 * 60 * 1000) // Last 30 days

        this.scheduler.clearBusyTimes()
        this.scheduler.addBusyTimes(recent)
    }
}

Performance Monitoring

Basic Profiling

function profileScheduling(name: string, fn: () => any) {
    const start = performance.now()
    const result = fn()
    const end = performance.now()

    console.log(`${name}: ${(end - start).toFixed(2)}ms`)
    return result
}

// Usage
const slots = profileScheduling('Find Available Slots', () => {
    return scheduler.findAvailableSlots(start, end, options)
})

Memory Usage Monitoring

function monitorMemory(label: string) {
    if (typeof process !== 'undefined' && process.memoryUsage) {
        const usage = process.memoryUsage()
        console.log(`${label} - Memory: ${Math.round(usage.heapUsed / 1024 / 1024)}MB`)
    }
}

monitorMemory('Before scheduling')
const slots = scheduler.findAvailableSlots(start, end, options)
monitorMemory('After scheduling')

Performance Testing

import { performance } from 'perf_hooks'

function benchmarkScheduling() {
    const scheduler = new Scheduler()

    // Generate test data
    const busyTimes = generateRandomBusyTimes(1000)
    scheduler.addBusyTimes(busyTimes)

    const iterations = 100
    const times: number[] = []

    for (let i = 0; i < iterations; i++) {
        const start = performance.now()

        scheduler.findAvailableSlots(new Date('2024-01-01T09:00:00Z'), new Date('2024-01-01T17:00:00Z'), {
            slotDuration: 30,
            padding: 15,
        })

        const end = performance.now()
        times.push(end - start)
    }

    const avg = times.reduce((a, b) => a + b) / times.length
    const p95 = times.sort()[Math.floor(times.length * 0.95)]

    console.log(`Average: ${avg.toFixed(2)}ms`)
    console.log(`95th percentile: ${p95.toFixed(2)}ms`)
}

Scaling Considerations

Horizontal Scaling

For very large datasets or high-throughput scenarios:

// Partition by time ranges
class PartitionedScheduler {
    private schedulers = new Map<string, Scheduler>()

    private getPartitionKey(date: Date): string {
        return `${date.getFullYear()}-${date.getMonth()}`
    }

    addBusyTime(busyTime: BusyTime) {
        const key = this.getPartitionKey(busyTime.start)

        if (!this.schedulers.has(key)) {
            this.schedulers.set(key, new Scheduler())
        }

        this.schedulers.get(key)!.addBusyTimes([busyTime])
    }

    findAvailableSlots(start: Date, end: Date, options: SchedulingOptions) {
        const allSlots = []

        // Find relevant partitions
        const startKey = this.getPartitionKey(start)
        const endKey = this.getPartitionKey(end)

        for (const [key, scheduler] of this.schedulers) {
            if (key >= startKey && key <= endKey) {
                const slots = scheduler.findAvailableSlots(start, end, options)
                allSlots.push(...slots)
            }
        }

        return allSlots.sort((a, b) => a.start.getTime() - b.start.getTime())
    }
}

Caching Strategies

class CachedScheduler {
    private scheduler = new Scheduler()
    private cache = new Map<string, TimeSlot[]>()

    findAvailableSlots(start: Date, end: Date, options: SchedulingOptions) {
        const cacheKey = this.getCacheKey(start, end, options)

        if (this.cache.has(cacheKey)) {
            return this.cache.get(cacheKey)!
        }

        const slots = this.scheduler.findAvailableSlots(start, end, options)
        this.cache.set(cacheKey, slots)

        return slots
    }

    addBusyTimes(busyTimes: BusyTime[]) {
        this.scheduler.addBusyTimes(busyTimes)
        this.cache.clear() // Invalidate cache
    }

    private getCacheKey(start: Date, end: Date, options: SchedulingOptions): string {
        return `${start.getTime()}-${end.getTime()}-${JSON.stringify(options)}`
    }
}

Common Performance Anti-Patterns

1. Excessive Slot Generation

// ❌ Generating too many slots
{ slotDuration: 1, slotSplit: 1 } // 1-minute slots every minute

// ✅ Reasonable slot density
{ slotDuration: 15, slotSplit: 15 } // 15-minute slots every 15 minutes

2. Inefficient Busy Time Management

// ❌ Recreating scheduler repeatedly
meetings.forEach(meeting => {
    const scheduler = new Scheduler([meeting])
    // ... use scheduler
})

// ✅ Single scheduler instance
const scheduler = new Scheduler(meetings)

3. Large Time Ranges

// ❌ Searching entire year
const slots = scheduler.findAvailableSlots(new Date('2024-01-01'), new Date('2024-12-31'), options)

// ✅ Focused search
const slots = scheduler.findAvailableSlots(startOfWeek, endOfWeek, options)

Best Practices Summary

  1. Batch Operations: Add busy times in batches rather than individually
  2. Reuse Instances: Keep scheduler instances alive and reuse them
  3. Appropriate Granularity: Choose slot durations that match your use case
  4. Limit Time Ranges: Search only the time periods you need
  5. Clean Up: Periodically remove old busy times from long-lived schedulers
  6. Profile: Measure performance in your specific environment
  7. Cache: Consider caching for repeated identical queries
  8. Partition: For very large datasets, consider partitioning by time

Following these guidelines will help you achieve optimal performance for your scheduling use cases.