High Performance Architecture Patterns

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Trabalhar com milhares de requisições e mensagens por dia já é um desafio para arquitetura, imagina parte do software ter requisitos de alta disponibilidade e alta velocidade. Nesta palestra irei falar em padrões de projeto para confiabilidade e escalabilidade que irão garantir que o seu software escale e garanta a consistência ao seu usuário. Entre esses padrões, há arquitetura de caches, uso de APIs e integrações e padrões de como encapsular a sua plataforma para que se proteja de falhas e sobrecargas.

Technology

High Performance
Architecture Patterns
Celso Crivelaro
@celsocrivelaro

$ whoami > Celso Crivelaro
- Engineer and MSc in Computer Engineering
- Dev Ruby / Python / Java
@celsocrivelaro
http://crivelaro.me

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

Disk
Memory
Band-
width
Processor
time
Logs
GC
Open
Connections
Deadlocks
Stop all disk activities
Swap
Slowness
Services stop
responding
Queues Amount
Workers
Slowness

Integration Fails always
Internal
External
DB API Storage Network
API ISP HopCloud

Threads Timeouts to get rid of deadlocls
Internal Little timeout
External Depends: The least possible

Panel / API
Painel Web Internal API - HTTP
Resources
Connection
Pool
App Server
Business
Rules
Integrations

App Internal APIApp Panel
Painel Web API - HTTP
Resources
Conn.
Pool
App
Server
Business
Rules
Integrations
Resources
Conn.
Pool
App
Server
Business
Rules
Integrations

Availability
Consistency
Tolerance
to network
partition

Availability
Consistency
Partition
Tolerance
to network
partition
whether system is
available or not

Availability
Consistency
Tolerance
to network
partition
Same data in all nodes

Availability
Consistency
Tolerance
to network
partition
Tolerante when a network
fails

Availability
Consistency
Partition
Tolerance
Postgres

Availability
Consistency
Partition
Tolerance
DNS

Availability
Consistency
Partition
Tolerance
Protocols

Evaluate which one is
acceptable to your
platform

App
Connection
Resource
Connection
Connection
Connection
Connection

Controlled Timeouts
Controlled Resource Consumption
Avoid opening/closing connections
overhead

Default Cache Strategy
App
Cache
Source
1
2
3

Slow Processing
Fast processing
Asynchronous Processing
Authentication
Message
Transformation
MTA ProvidersUser
Message
Persistence
Consolidation

Test your limits...
Load Test
Stress Test
Peak Test

Thank you!
Celso Crivelaro
@celsocrivelaro
celsocrivelaro@gmail.com

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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

10.

11.

12. High Availability

13. Resource Consumption

14.

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

17. Integrations always fails!

18. Integration Fails always Internal External DB API Storage Network API ISP HopCloud

19. Map hidden integrations E.g. DNS

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 ...

27. Isolate Critical Components

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

30. Less Components == Less Problems

31.

32. Redundancy

33. Health Check

34.

35.

36. Scalability

37. CAP Theorem

38. Availability Consistency Tolerance to network partition

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