System Architecture
Enterprise architecture focused on scalable, maintainable, and secure systems, developed through advanced education and practical experience.
Core Principles
Modular Design
- Independent components
- Easy maintenance
- Simplified testing
Scalability
- Horizontal scaling
- Vertical scaling
- Auto-scaling
Security
- Multiple security layers
- Regular audits
- Compliance standards
Interoperability
- Standard protocols
- API-first design
- Integration patterns
Technology Stack
Frontend
Backend
Database
DevOps
Design Patterns
Microservices
Service Boundaries
- Domain-driven design
- Bounded contexts
- Service independence
Event-Driven
- Message queues
- Event sourcing
- Pub/sub patterns
API Gateway
- Request routing
- Load balancing
- API versioning
Data Architecture
Event Sourcing
- Audit trails
- State reconstruction
- Event replay
CQRS
- Optimized queries
- Scalable writes
- Data consistency
Linux Infrastructure
System Administration
Server Management
Systemd Services
- Custom service units for application deployment
- Dependency management for complex services
- Automatic service recovery and monitoring
Process Control
- cgroups v2 for resource isolation
- systemd-nspawn for lightweight containers
- LXC for system containers
Resource Optimization
- CPU pinning for performance-critical applications
- NUMA-aware process placement
- Memory ballooning for dynamic resource allocation
Network Configuration
Network Management
- NetworkManager for dynamic configuration
- systemd-networkd for static setups
- netplan for cloud environments
Security
- iptables/nftables for packet filtering
- firewalld for dynamic firewall management
- ufw for simplified firewall configuration
DNS & Routing
- BIND for authoritative DNS
- dnsmasq for local DNS caching
- Unbound for recursive DNS resolution
Linux Tools & Automation
System Monitoring
Metrics Collection
- Prometheus with Node Exporter for system metrics
- cAdvisor for container monitoring
- AlertManager for notification management
Log Management
- ELK Stack for centralized logging
- Filebeat for log shipping
- Logstash for log processing
Performance Analysis
- collectd for system statistics
- telegraf for metrics collection
- netdata for real-time monitoring
Automation & Scripting
Configuration Management
- Ansible for infrastructure automation
- Puppet for system configuration
- Custom scripts for specific tasks
Deployment Automation
- CI/CD pipelines with GitHub Actions
- Custom deployment scripts
- Container orchestration with Kubernetes
Monitoring Automation
- Custom monitoring scripts
- Automated alerting
- Performance reporting
Performance Optimization
Caching Strategies
Multi-level Caching
Implementing caching at multiple levels for optimal performance:
- Browser caching for static assets
- CDN caching for global distribution
- Application-level caching
- Database query caching
Cache Invalidation
Managing cache consistency and updates:
- Time-based expiration
- Event-based invalidation
- Version-based caching
- Cache warming strategies
Linux Performance
System Tuning
Kernel Optimization
- vm.swappiness for memory management
- vm.dirty_ratio for I/O performance
- net.core.somaxconn for network connections
Memory Management
- Huge pages for large memory allocations
- Transparent huge pages for automatic optimization
- Memory zones for NUMA systems
I/O Optimization
- I/O scheduler selection
- Block device tuning
- Filesystem optimization
Monitoring Tools
System Monitoring
- top/htop for process monitoring
- glances for system overview
- bpftrace for dynamic tracing
Performance Analysis
- perf for CPU profiling
- ftrace for kernel tracing
- eBPF for custom monitoring
Resource Monitoring
- iotop for I/O monitoring
- nethogs for network usage
- vmstat for memory statistics
Future Architecture
Emerging Technologies
Serverless Architecture
Moving towards event-driven, serverless computing:
- Function-as-a-Service (FaaS)
- Event-driven processing
- Pay-per-use pricing
Edge Computing
Bringing computation closer to data sources:
- Edge devices
- Local processing
- Reduced latency
AI/ML Integration
Incorporating artificial intelligence and machine learning:
- Predictive analytics
- Automated decision making
- Pattern recognition
Scalability Roadmap
Performance Targets
- Response time optimization
- Throughput improvement
- Resource utilization
Capacity Planning
- Resource forecasting
- Growth projections
- Infrastructure scaling
Technology Evolution
- New technology adoption
- Legacy system migration
- Architecture updates