Architecting highly resilient applications on Azure Mahesh Krishnan

Architecting highly resilient applications on Azure Mahesh Krishnan ARC 427

What comes out of the box? Multiple instances of services Elasticity on demand Distributed caching Storage redundancy (local or geo) Traffic Manager for location transparency Queue based decoupling Automation and Scripting Infrastructure Database replication Diagnostics and Telemetry

Characteristics Statistics workload Relevance of information Permanent Variations in load Minor variations over time Impact of temporary Amortized Impact failure Fallback Strategy Degrade to stale data Live scores workload Temporary Spike during matches; “ 0” at other times Major impact during games ? ? ?

Modelling Availability Establish SLAs based on Workload and Lifecycle model Availability % Downtime/year Downtime/month 90% - One nine 36. 5 days 72 hours 99% - Two nines 3. 65 days 7. 2 hours 99. 9% - Three nines ~8 hours 45 minutes ~43 minutes 99. 95% ~4 hours 22 minutes ~22 minutes 99. 99% - Four nines ~52 minutes ~4 minutes

Patterns for building the application Resiliency patterns Redundancy patterns Recovery patterns

Resiliency pattern – Async everything

Resiliency patterns for handling errors Try, Try Again Ignore the failure Deny the request Fake it! Go to Plan B

Resiliency pattern – Circuit breakers

Redundancy patterns Multiple copies within the same DC Multiple redundancies across multiple DCs Multiple redundancies across multiple providers

Recovery patterns Backup/Restore Idempotency considerations Compensators

Considerations Automate all the things Measure all the things Monitor all the things

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