Capacity Planning for Tyk Deployments

Guide to capacity planning for Tyk deployments. Learn how to estimate resource requirements, plan for growth, and optimize infrastructure for different traffic patterns and deployment scenarios.

Capacity Planning for Tyk Deployments

This guide provides a systematic approach to capacity planning for Tyk deployments, helping you estimate resource requirements, plan for growth, and optimize your infrastructure for different traffic patterns and deployment scenarios.

Capacity Planning Fundamentals

Importance of Capacity Planning

Effective capacity planning for Tyk deployments is essential for:

• Performance assurance: Ensuring your API platform meets performance requirements
• Cost optimization: Right-sizing infrastructure to avoid over-provisioning
• Growth accommodation: Planning for future traffic increases
• Reliability: Preventing resource exhaustion and service degradation
• Budget planning: Forecasting infrastructure costs accurately

Capacity Planning Process

Follow this systematic process for capacity planning:

1. Gather requirements: Identify performance targets and constraints
2. Estimate traffic: Forecast API request volumes and patterns
3. Calculate resources: Determine required infrastructure resources
4. Test and validate: Verify capacity estimates through testing
5. Implement monitoring: Track actual usage against capacity
6. Review regularly: Adjust capacity plans as needs evolve

Key Metrics for Capacity Planning

Focus on these key metrics when planning capacity:

• Requests per second (RPS): Total API request volume
• Peak RPS: Maximum request rate during peak periods
• Request latency: Response time requirements
• Concurrent connections: Number of simultaneous connections
• Data transfer: Network bandwidth requirements
• Analytics volume: Amount of analytics data generated
• Storage needs: Database and log storage requirements

Component Resource Requirements

Gateway Resource Requirements

Tyk Gateway resource requirements depend on several factors:

• CPU: Scales with request volume and complexity

  • Base: 2 CPU cores for up to 1,000 RPS
  • Add 2 cores for each additional 1,000 RPS
  • Add 1-2 cores for complex middleware or plugins
  • Add 1-2 cores for SSL termination at high volumes

• Memory: Scales with connections and configuration

  • Base: 2GB RAM for up to 1,000 RPS
  • Add 1GB for each additional 1,000 RPS
  • Add 1-2GB for extensive API definitions
  • Add 1-2GB for response caching
  • Add 1GB for detailed analytics

• Network: Scales with request volume and payload size

  • Calculate based on: RPS × Average payload size × 2 (request/response)
  • Add 20-30% overhead for TLS and protocol overhead

Dashboard Resource Requirements

Tyk Dashboard resource requirements:

• CPU:

  • Base: 2 CPU cores for up to 50 concurrent users
  • Add 1 core for each additional 50 users
  • Add 1 core for Portal with high traffic

• Memory:

  • Base: 4GB RAM
  • Add 2GB for extensive use of the Dashboard
  • Add 2GB for high-traffic Developer Portal

Pump Resource Requirements

Tyk Pump resource requirements:

• CPU:

  • Base: 1 CPU core for up to 1,000 RPS
  • Add 1 core for each additional 2,000 RPS
  • Add 1 core for complex aggregation or multiple pumps

• Memory:

  • Base: 2GB RAM
  • Add 1GB for each additional 2,000 RPS
  • Add 1GB for multiple pump destinations

Redis Resource Requirements

Redis resource requirements:

• Memory:

  • Base: 2GB RAM
  • Key storage: ~1GB per 1 million keys
  • Analytics: ~1GB per 5,000 RPS (depends on purge delay)
  • Add 50% overhead for Redis operations

• CPU:

  • Base: 2 CPU cores for up to 5,000 RPS
  • Add 2 cores for each additional 5,000 RPS

Database Resource Requirements

MongoDB/PostgreSQL resource requirements:

• Disk:

  • Base: 50GB
  • Analytics growth: ~1GB per million requests (varies by detail level)
  • Consider SSD for performance-critical deployments

• Memory:

  • Base: 4GB RAM
  • Add 4GB for high analytics volume
  • Add 2GB for query optimization

Sizing Guidelines

Small Deployment Sizing

Suitable for development, testing, or small production environments:

• Traffic characteristics:

  • Up to 1,000 RPS
  • Up to 100 APIs
  • Up to 10 administrative users

• Component sizing:

  • Gateway: 2 CPU cores, 2-4GB RAM (2+ instances)
  • Dashboard: 2 CPU cores, 4GB RAM (1-2 instances)
  • Redis: 2 CPU cores, 4GB RAM
  • MongoDB: 2 CPU cores, 4GB RAM, 50GB storage
  • Pump: 1 CPU core, 2GB RAM

Medium Deployment Sizing

Suitable for moderate production environments:

• Traffic characteristics:

  • 1,000-5,000 RPS
  • 100-500 APIs
  • 10-50 administrative users

• Component sizing:

  • Gateway: 4-8 CPU cores, 4-8GB RAM (3+ instances)
  • Dashboard: 4 CPU cores, 8GB RAM (2 instances)
  • Redis: 4 CPU cores, 8GB RAM (with replication)
  • MongoDB: 4 CPU cores, 8GB RAM, 100GB storage (with replication)
  • Pump: 2 CPU cores, 4GB RAM (2 instances)

Large Deployment Sizing

Suitable for large-scale production environments:

• Traffic characteristics:

  • 5,000-20,000 RPS
  • 500+ APIs
  • 50+ administrative users

• Component sizing:

  • Gateway: 8-16 CPU cores, 16-32GB RAM (5+ instances)
  • Dashboard: 8 CPU cores, 16GB RAM (2-3 instances)
  • Redis: 8 CPU cores, 16-32GB RAM (Cluster or Sentinel)
  • MongoDB: 8 CPU cores, 16-32GB RAM, 500GB+ storage (replica set)
  • Pump: 4 CPU cores, 8GB RAM (3+ instances)

Traffic Pattern Analysis

Identifying Traffic Patterns

Analyze your API traffic patterns to inform capacity planning:

• Peak vs. average analysis:

  • Identify peak traffic periods (time of day, day of week)
  • Calculate peak-to-average ratio
  • Plan capacity for peak plus buffer (typically 50-100%)

• Geographic distribution:

  • Map user locations and traffic sources
  • Consider regional deployment for global user bases
  • Account for regional traffic patterns

• Request characteristics:

  • Analyze request payload sizes
  • Identify resource-intensive endpoints
  • Understand authentication patterns

Planning for Peak Traffic

Strategies for handling peak traffic:

• Capacity buffer: Plan for 2-3x average traffic
• Auto-scaling: Implement dynamic scaling based on load
• Traffic shaping: Consider rate limiting and quotas
• Caching: Implement caching for popular resources
• Graceful degradation: Plan for non-critical feature disabling

Growth Forecasting

Techniques for forecasting growth:

• Historical trend analysis: Extrapolate from past growth
• Business driver correlation: Link API traffic to business metrics
• Scenario planning: Develop multiple growth scenarios
• Regular reassessment: Update forecasts quarterly

Capacity Testing

Load Testing Methodology

Implement effective load testing:

1. Define test scenarios:

   • Realistic user journeys
   • Representative API call patterns
   • Proper authentication and authorization

2. Create test environment:

   • Production-like configuration
   • Isolated from production
   • Realistic data volumes

3. Execute incremental tests:

   • Start at low volume
   • Gradually increase to target load
   • Continue to failure point
   • Measure resource utilization throughout

Example load testing tool configuration (k6):

import http from 'k6/http';
import { check, sleep } from 'k6';

export let options = {
  stages: [
    { duration: '5m', target: 100 }, // Ramp up to 100 RPS
    { duration: '10m', target: 100 }, // Stay at 100 RPS
    { duration: '5m', target: 500 }, // Ramp up to 500 RPS
    { duration: '10m', target: 500 }, // Stay at 500 RPS
    { duration: '5m', target: 1000 }, // Ramp up to 1000 RPS
    { duration: '10m', target: 1000 }, // Stay at 1000 RPS
    { duration: '5m', target: 0 }, // Ramp down to 0 RPS
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'], // 95% of requests must complete within 500ms
    http_req_failed: ['rate<0.01'], // Less than 1% of requests can fail
  },
};

export default function() {
  let response = http.get('https://api.example.com/resource', {
    headers: { 'Authorization': 'Bearer ' + __ENV.API_KEY },
  });
  
  check(response, {
    'status is 200': (r) => r.status === 200,
    'response time < 200ms': (r) => r.timings.duration < 200,
  });
  
  sleep(1);
}

Performance Benchmarks

Establish performance benchmarks:

• Gateway throughput: Requests per second per instance
• Latency profile: Response time at different load levels
• Resource utilization: CPU, memory, network at different loads
• Scaling efficiency: Performance gain per additional instance
• Maximum capacity: Breaking point for different configurations

Example benchmark results:

Configuration	RPS per Instance	p95 Latency	Max Concurrent Connections
2 CPU, 4GB RAM	1,000	150ms	5,000
4 CPU, 8GB RAM	2,500	120ms	10,000
8 CPU, 16GB RAM	5,000	100ms	20,000
16 CPU, 32GB RAM	9,000	90ms	40,000

Implementation Example: E-commerce API Platform

This example demonstrates capacity planning for an e-commerce API platform with variable traffic patterns.

Requirements:

• Normal traffic: 2,000 RPS average
• Peak traffic: 10,000 RPS during sales events
• Latency requirements: 95% of requests under 200ms
• Availability target: 99.99% uptime
• Growth projection: 50% year-over-year

Capacity Analysis:

1. Traffic Pattern Analysis:

   • Daily peak: 2x average (4,000 RPS)
   • Sale events: 5x average (10,000 RPS)
   • Geographic distribution: 70% North America, 20% Europe, 10% Asia
   • Peak hours: 9 AM - 5 PM in each region

2. Resource Calculation:

   • Gateway: 20 CPU cores, 40GB RAM total (5 instances)
   • Redis: 8 CPU cores, 16GB RAM (Sentinel with 3 nodes)
   • MongoDB: 8 CPU cores, 16GB RAM, 200GB storage (replica set)
   • Dashboard: 4 CPU cores, 8GB RAM (2 instances)
   • Pump: 4 CPU cores, 8GB RAM (2 instances)

3. Scaling Strategy:

   • Auto-scaling Gateway instances: 5-15 based on load
   • Pre-scaling before known sale events
   • Regional deployment in North America and Europe
   • Caching for product catalog APIs (high-read, low-write)

Results:

• Successfully handled 12,000 RPS during Black Friday sale
• Maintained 99.99% uptime throughout the year
• 95th percentile latency remained under 150ms
• Scaled efficiently to accommodate 60% year-over-year growth
• Optimized infrastructure costs by 25% through right-sizing

Best Practices

Documentation

• Document capacity requirements for each component
• Record assumptions used in capacity planning
• Maintain historical performance data
• Update documentation as requirements change

Regular Review

• Review capacity plans quarterly
• Adjust based on actual usage patterns
• Update forecasts based on business changes
• Perform capacity testing after significant changes

Capacity Buffer

• Plan for 2-3x average traffic
• Consider seasonal variations
• Account for unexpected traffic spikes
• Balance buffer against cost considerations

Testing Cadence

• Perform baseline capacity tests quarterly
• Test before major releases or changes
• Conduct stress tests to identify breaking points
• Validate capacity after infrastructure changes

Next Steps

• Scaling Strategies
• Performance Tuning
• Monitoring and Alerting