High Performance Architecture Patterns
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This document discusses high performance architecture patterns for building scalable systems. It covers patterns like isolating critical components, reducing components to reduce problems, implementing redundancy through health checks, evaluating data consistency versus availability based on the CAP theorem, using pools of resources, implementing controlled timeouts and resource consumption, caching data closer to applications, different load balancing strategies, adopting decoupled and asynchronous architectures, discovering bottlenecks through testing, and implementing telemetry through tools like Grafana and InfluxDB. The document provides an overview of essential patterns for architecting highly performant and scalable systems.
High Performance Architecture Patterns
- 1. High Performance Architecture Patterns Celso Crivelaro @celsocrivelaro
- 2. $ whoami > Celso Crivelaro - Engineer and MSc in Computer Engineering - Dev Ruby / Python / Java @celsocrivelaro http://crivelaro.me
- 5. SMTP Locaweb - Transaction email platform - Fast and high quality email sending - Live reports - Integration by SMTP Protocol and HTTP API - 2,5 Millions of messages per day - + 2 years
- 7. Platforms
- 15. Disk Memory Band- width Processor time Logs GC Open Connections Deadlocks Stop all disk activities Swap Slowness Services stop responding Queues Amount Workers Slowness
- 16. Integrations
- 18. Integration Fails always Internal External DB API Storage Network API ISP HopCloud
- 20. Fail Fast
- 21. Failing Fast reduces transient errors
- 22. Timeouts
- 23. Never trust in default timeouts
- 24. Threads Timeouts to get rid of deadlocls Internal Little timeout External Depends: The least possible
- 25. Retry
- 26. 1 2 3 5 8 n Fibonacci Proportion ...
- 28. Panel / API Painel Web Internal API - HTTP Resources Connection Pool App Server Business Rules Integrations
- 29. App Internal APIApp Panel Painel Web API - HTTP Resources Conn. Pool App Server Business Rules Integrations Resources Conn. Pool App Server Business Rules Integrations
- 32. Redundancy
- 33. Health Check
- 36. Scalability
- 37. CAP Theorem
- 39. Availability Consistency Partition Tolerance to network partition whether system is available or not
- 40. Availability Consistency Tolerance to network partition Same data in all nodes
- 41. Availability Consistency Tolerance to network partition Tolerante when a network fails
- 45. Evaluate which one is acceptable to your platform
- 48. Controlled Timeouts Controlled Resource Consumption Avoid opening/closing connections overhead
- 49. Cache
- 50. Make your data closer to your app
- 51. When use cache App API DB
- 53. Hot Cache Strategy App Cache Source 2 1
- 54. Load Balacing
- 58. Slow Processing Fast processing Asynchronous Processing Authentication Message Transformation MTA ProvidersUser Message Persistence Consolidation
- 61. Test your limits... Load Test Stress Test Peak Test
- 62. Telemetry
- 66. Indexes
- 67. Sharding
- 68. References