Scalability

This guide summarizes OpsKnight's capacity targets, real-world load scenarios, and the configuration knobs that unlock higher scale. Numbers below assume recommended database pooling and sensible infrastructure sizing.

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System Capacity Overview

User Capacity

Metric	Capacity	Notes
Total Registered Users	10,000+	Database can handle this easily
Concurrent Active Users	200-300	With SSE streams open
Peak Concurrent Users	400-500	With proper DB pool config

Incident Handling

Metric	Per Minute	Per Hour	Notes
New Incidents Created	200-300	12,000+	Via API or integrations
Incidents Processed (Escalations)	150-200	9,000+	Parallel processing (5 concurrent)
Incident Updates	500+	30,000+	Status changes, notes, etc.

Notification Capacity

Channel	Per Minute	Per Hour	Notes
Email	100	6,000	Rate limited to avoid spam flags
SMS	50	3,000	Twilio rate limits
Push	200	12,000	Web push is fast
Slack	100	6,000	Slack API limits
Webhooks	100	6,000	Per destination
Total Combined	500-600	30,000+	Across all channels

Real-Time Streams (SSE)

Metric	Capacity
Concurrent SSE Connections	400-500
DB Queries (with caching)	20-30/sec (was 200-300)
Data Freshness	3-5 seconds

Background Job Processing

Job Type	Per Minute	Notes
Escalation Jobs	200+	Parallel batches of 5
Notification Jobs	300+	Parallel batches of 10
Total Jobs	500+	With 100 job limit per cycle

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Real-World Scenarios

Scenario 1: Normal Operations

50 concurrent users
10 incidents/hour
~100 notifications/hour
System runs at <10% capacity

Scenario 2: Busy Day

150 concurrent users
50 incidents/hour
~500 notifications/hour
System runs at ~30% capacity

Scenario 3: Major Outage

300 concurrent users
200 incidents in 10 minutes
~2000 notifications in 10 minutes
System handles it (may see 5-10 sec delays)

Scenario 4: Stress Test

500 concurrent users
500 incidents/minute
5000 notifications/minute
System at capacity, some queuing

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Quick Reference Card

┌─────────────────────────────────────────┐
│         OpsKnight Capacity              │
├─────────────────────────────────────────┤
│ Concurrent Users:     200-500           │
│ Incidents/min:        200-300           │
│ Notifications/min:    500-600           │
│ Escalations/min:      150-200           │
│ SSE Connections:      400-500           │
│ DB Queries/sec:       50-100 (cached)   │
└─────────────────────────────────────────┘

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Critical Configuration

Database Connection Pool

The database connection pool is critical for handling concurrent users. Without proper configuration, the system will fail at ~50 concurrent users.

Why Connection Pooling Matters

• Default pool size is 10 - This is insufficient for production
• Each SSE stream, API request, and background job needs a connection
• Without pooling: 50 concurrent users = connection exhaustion
• With pooling: 500+ concurrent users possible

Configuration

Add these parameters to your DATABASE_URL:

# Production (200-500 concurrent users)
DATABASE_URL="postgresql://user:pass@host:5432/db?connection_limit=40&pool_timeout=30"

# High-scale (500+ concurrent users)
DATABASE_URL="postgresql://user:pass@host:5432/db?connection_limit=80&pool_timeout=30"

Parameter	Value	Description
connection_limit	40	Max connections per app instance
pool_timeout	30	Seconds to wait for available connection

PostgreSQL Server Tuning

For the database server itself, ensure these settings in postgresql.conf:

# Connection Settings
max_connections = 200          # Total connections across all clients
shared_buffers = 256MB         # 25% of available RAM (for 1GB system)
effective_cache_size = 768MB   # 75% of available RAM

# For small systems (1-2 CPU cores)
work_mem = 16MB
maintenance_work_mem = 128MB

# Connection handling
tcp_keepalives_idle = 600
tcp_keepalives_interval = 30
tcp_keepalives_count = 10

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Performance Optimizations Implemented

1. SSE Caching Layer

• File: src/lib/realtime-cache.ts
• Impact: 10x reduction in database queries
• How: Caches dashboard metrics and incident lists for 3-5 seconds

2. Transaction Isolation Optimization

• File: src/lib/db-utils.ts
• Impact: 10x less contention, fewer deadlocks
• How: Uses ReadCommitted for event ingestion, Serializable only for critical updates

3. Parallel Job Processing

• Files: src/lib/cron-scheduler.ts, src/lib/jobs/queue.ts
• Impact: 5x faster job processing
• How: Processes jobs in parallel batches of 10-15

4. Circuit Breaker Pattern

• File: src/lib/circuit-breaker.ts
• Impact: Prevents cascade failures
• How: Fails fast when external services (email, SMS) are down

5. Notification Queue with Batching

• File: src/lib/notification-queue.ts
• Impact: 1000+ notifications/min capacity
• How: Batches notifications, deduplicates, and rate limits per channel

6. Rate Limiter with TTL Cleanup

• File: src/lib/rate-limit.ts
• Impact: Prevents memory leaks
• How: Cleans expired entries every 60 seconds