How to scale infrastructure and systems for growth
Build technical infrastructure that scales with your business without breaking. Plan capacity, architect for growth, optimize performance, and maintain reliability as load increases 10x, 100x, or 1000x.
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0 of 8 steps completedStep-by-Step Instructions
1 Step 1: Establish baseline performance and capacity
Step 1: Establish baseline performance and capacity
Measure current system performance: response times, throughput, resource utilization, error rates. Document current capacity limits: concurrent users, transactions per second, data volume. Identify bottlenecks through load testing. Understand what breaks first as load increases.
2 Step 2: Model future growth and requirements
Step 2: Model future growth and requirements
Forecast growth based on business plans: user acquisition, feature launches, geographic expansion. Model technical requirements: compute, storage, bandwidth, database capacity. Plan for spiky traffic (launch days, seasonal peaks). Build capacity roadmap with trigger points for scaling.
Designing Data-Intensive Applications by Martin Kleppmann
Essential book on scalable system architecture
3 Step 3: Architect for horizontal scalability
Step 3: Architect for horizontal scalability
Design systems to scale out (add more servers) not just up (bigger servers). Make services stateless so any instance can handle any request. Use load balancers to distribute traffic. Implement caching layers. Design database architecture for sharding and replication.
4 Step 4: Implement auto-scaling and elasticity
Step 4: Implement auto-scaling and elasticity
Configure auto-scaling rules based on metrics: CPU usage, queue depth, request rate. Scale up during peaks, scale down during troughs to control costs. Test auto-scaling behavior under load. Set sensible limits to prevent runaway scaling costs.
5 Step 5: Optimize database performance at scale
Step 5: Optimize database performance at scale
Add indexes for common queries. Implement read replicas to distribute query load. Use connection pooling. Archive old data. Consider database sharding for massive scale. Implement caching (Redis, Memcached) to reduce database hits. Monitor slow queries and optimize.
6 Step 6: Build comprehensive monitoring and alerting
Step 6: Build comprehensive monitoring and alerting
Monitor all critical metrics: application performance, infrastructure health, user experience, business metrics. Set up alerts for anomalies. Create dashboards for on-call engineers. Implement distributed tracing to debug issues across microservices. Use APM tools.
7 Step 7: Plan and execute load testing
Step 7: Plan and execute load testing
Regularly test system behavior under expected peak load and beyond. Simulate realistic traffic patterns. Identify breaking points. Test failure scenarios: database failover, service degradation, network partitions. Fix issues before they hit production. Make load testing part of CI/CD.
8 Step 8: Implement disaster recovery and high availability
Step 8: Implement disaster recovery and high availability
Deploy across multiple availability zones or regions. Implement database backup and restore procedures. Test disaster recovery plans regularly. Design for graceful degradation: what features can be disabled under extreme load? Document runbooks for common incidents.
