Descrição: Quando o seu crescimento ou a demanda por seus sistemas excederem, ou se prevê que excedam, sua capacidade atual - esse é um "bom" problema. No entanto, isso pode ser tanto uma ameaça à confiabilidade do seu sistema quanto qualquer outro fator.
Nesta sessão, mergulhe nos conceitos básicos de planejamento de capacidade e estimativa de custos, incluindo as ferramentas que o Azure fornece para ajudar com ambos.
Terminamos com uma discussão e demonstração de como os Traders Tailwind aplicaram criteriosamente os recursos de dimensionamento do Azure. Aprenda como satisfazer seus clientes e uma demanda crescente, mesmo quando "desafiado" pelo sucesso.
10. iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
Single points of failure
11. iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
Pre-provisioned capacity
12. iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
Unknown costs
13. iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
Tailwind Traders architecture
14. When may we need to scale?
Sometimes we
can plan for scale
Sometimes we’re
hit by surprise
Hopefully, the system
just takes care of it
20. Do I need to do capacity
planning in the cloud?
21. iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Mobile for
frontend
Backend for
frontend
Website
26. Azure Cosmos DB capacity planning—organic growth
2530
5100
7435
10081
0
2000
4000
6000
8000
10000
12000
July August September October
Users
Example Tailwind Traders usage
Users
27. Azure Cosmos DB capacity planning—organic growth
151 198 254 300
5000 5000 5000 5000
0
1000
2000
3000
4000
5000
6000
July August September October
Storage Capacity
Example Tailwind Traders usage
Storage
28. Azure Cosmos DB capacity planning—organic growth
0
100
200
300
1000 1000 1000 1000
0
200
400
600
800
1000
1200
July August September October
RU/s Usage Provisioned RU/s
Example Tailwind Traders usage
Throughput
32. Azure Cosmos DB capacity planning—organic growth
151 198 254 300 400
5000 5000 5000 5000 5000
0
1000
2000
3000
4000
5000
6000
July August September October November
Storage Capacity
After marketing event
Storage
Example Tailwind Traders usage
33. Azure Cosmos DB capacity planning—organic growth
0
100
200
300
500
1000 1000 1000 1000 1000
0
200
400
600
800
1000
1200
July August September October November
RU/s usage Provisioned RU/s
Example Tailwind Traders usage
Throughput
After marketing event
Sufficient capacity
43. You work closely with developers
to make your systems more scalable
44. Tailwind Traders architecture
iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
Products
54. Azure Region
Scaling our products service: DB sharding
Availability Zone AAvailability Zone B
A B C A B C
Read replica
55. Why we may need to shard
Too much data Too much throughput Physical isolation
56. What’s next?
Going global?
Do we need to go global?
Can we leverage a CDN?
Would independent
systems be more suitable?
What consistency do
we need for the data?
57. Tailwind Traders architecture
iOS and Android
client applications
Rewards app
Azure Kubernetes Service
Stock
Profile
Products
My coupons
Cart
Popular
products
Website
Mobile for
frontend
Backend for
frontend
58. Summary:
planning
for growth
Are you aware of your service limits
Can you plan for scale
using capacity planning?
Can you predict your future cloud spend?
59. Summary:
scale
Could you leverage an inherently
scalable cloud service instead?
Should we fix the bug?
Or scale our resources?
Are you able to scale horizontally?
Can you decouple your
system using queues?
Will caching help?
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65. Resources
Session Code on GitHub
Session Resources
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All Event Resources
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aka.ms/OPS50
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