Design patterns and plan for developing high available azure applications
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El documento detalla patrones de diseño y estrategias para desarrollar aplicaciones de alta disponibilidad en Azure, enfatizando la importancia de la disponibilidad medida como porcentaje de tiempo funcional. Incluye patrones específicos como monitoreo de punto de salud, nivelación de carga basada en colas, control de recursos, y técnica de reintentos para enfrentar fallas temporales. Se proporciona orientación sobre su implementación en Azure y se discuten factores que afectan la disponibilidad como latencia de red y rendimiento de sistemas subyacentes.
Design patterns and plan for developing high available azure applications
- 1. Design Patterns and Plan for developing High Available Azure Applications Himanshu Sahu Mindfire Solutions himanshus@mindfiresolutions.com
- 2. Availability Availability Availability defines the proportion of time that the system is functional and working. It will be affected by system errors, infrastructure problems, malicious attacks, and system load. It is usually measured as a percentage of uptime. Cloud applications typically provide users with a service level agreement (SLA), which means that applications must be designed and implemented in a way that maximizes availability.
- 3. Why Design Patterns Design patterns can help to solve a specific and commonly occurring problems that may be encountered when building applications that run in the cloud. Problem Areas in the Cloud Availability , Data management, Design and Implementation, Messaging, Management and Monitoring, Performance and Scalability, Resiliency, Security
- 4. Design Patterns and Plan for developing High Available Azure Applications Different Patterns for High Availability 3.1 Health Endpoint Monitoring Pattern 3.2 Queue-based Load Leveling Pattern 3.2 Throttling Pattern 3.3 Retry Pattern 3.4 Multiple Datacenter Deployment Guidance
- 5. Health Endpoint Monitoring Pattern Implement functional checks within an application that external tools can access through exposed endpoints at regular intervals. This pattern helps to verify that applications and services are performing correctly. Many factors that affect cloud-hosted applications are network latency, the performance and availability of the underlying compute and storage systems, and the network bandwidth between them. The service may fail entirely or partially due to any of these factors. Therefore, you must verify at regular intervals that the service is performing correctly to ensure the required level of availability—which might be part of your Service Level Agreement (SLA).
- 6. Health Endpoint Monitoring Pattern Solutions Implement health monitoring by sending requests to an endpoint on the application. A health monitoring check typically combines two factors: the checks (if any) performed by the application or service in response to the request to the health verification endpoint, and analysis of the result by the tool or framework that is performing the health verification check.
- 7. Health Endpoint Monitoring Pattern
- 8. Health Endpoint Monitoring Pattern When to Use this Pattern • Monitoring websites and web applications to verify availability. • Monitoring websites and web applications to check for correct operation. • Monitoring middle-tier or shared services to detect and isolate a failure that could disrupt other applications. • To complement existing instrumentation within the application, such as performance counters and error handlers. Health verification checking does not replace the requirement for logging and auditing in the application. Instrumentation can provide valuable information for an existing framework that monitors counters and error logs to detect failures or other issues. However, it cannot provide information if the application is unavailable.
- 9. Health Endpoint Monitoring Pattern How to do in Azure • Use the built-in features of Microsoft Azure, such as the Management Services or Traffic Manager. • Use a third party service (NewRelic, and Statuscake) • Create a custom utility or a service that runs on your own or on a hosted server.
- 10. Queue-Based Load Leveling Pattern Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads that may otherwise cause the service to fail or the task to time out. This pattern can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service. Temporal decoupling Load leveling Load balancing Loose coupling
- 11. Queue-Based Load Leveling Pattern
- 12. Queue-Based Load Leveling Pattern When to Use this Pattern • This pattern is ideally suited to any type of application that uses services that may be subject to overloading. • This pattern might not be suitable if the application expects a response from the service with minimal latency.
- 13. Queue-Based Load Leveling Pattern
- 14. Queue-Based Load Leveling Pattern
- 15. Queue-Based Load Leveling Pattern How to do in Azure • Using Windows Azure Storage Queue or Windows Azure Service Bus
- 16. Throttling Pattern Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service. This pattern can allow the system to continue to function and meet service level agreements, even when an increase in demand places an extreme load on resources. Many factors that affect cloud-hosted applications are network latency, the performance and availability of the underlying compute and storage systems, and the network bandwidth between them. The service may fail entirely or partially due to any of these factors. Therefore, you must verify at regular intervals that the service is performing correctly to ensure the required level of availability—which might be part of your Service Level Agreement (SLA).
- 17. Throttling Pattern Solutions Auto Scaling Queue-Based Load Leveling Pattern Priority Queue Pattern Rejecting requests from an individual user who has already accessed system APIs more than n times per second over a given period of time. Disabling or degrading the functionality of selected nonessential services so that essential services can run unimpeded with sufficient resources.
- 19. Throttling Pattern When to Use this Pattern To ensure that a system continues to meet service level agreements. To prevent a single tenant from monopolizing the resources provided by an application. To handle bursts in activity. To help cost-optimize a system by limiting the maximum resource levels needed to keep it functioning.
- 20. Retry Pattern Enable an application to handle anticipated, temporary failures when it attempts to connect to a service or network resource by transparently retrying an operation that has previously failed in the expectation that the cause of the failure is transient. An application that communicates with elements running in the cloud must be sensitive to the transient faults (that means Faults which are self-correcting such as Network failure, temporarry serivice available or time-out error due to busy server ) that can occur in this environment.
- 21. Retry Pattern Solutions Implement retry logic when trying to connect any service in cloud. Implement retry logic when trying to connect SQL Azure Azure Service Bus Azure Storage Azure Caching Service
- 22. Retry Pattern Solutions The Transient Fault Handling Application Block The Transient Fault Handling Application Block includes the following retry strategies FixedInterval Incremental ExponentialBackoff The FixedInterval retry strategy retries an operation a fixed number of times at fixed intervals.
- 23. Retry Pattern Solutions The Incremental retry strategy retries an operation a fixed number of times at intervals that increase by the same amount each time. For example, at two-second, four-second, six-second, and eight- second intervals. The ExponentialBackoff retry strategy retries an operation a fixed number of times at intervals that increase by a greater number each time. For example, at two-second, four-second, eight-second, and sixteen-second intervals. This retry strategy also introduces a small amount of random variation into the intervals.
- 24. Retry Pattern Solutions Connection Resiliency / Retry Logic (EF6 onwards) Connection Resiliency refers to the ability for EF to automatically retry any commands that fail due to connection throttling, or to instability in the network causing intermittent timeouts and other transient errors.
- 25. Multiple Datacenter Deployment Guidance Growing capacity over time. Providing global reach with minimum latency for users Maintaining performance and availability Providing additional instances for resiliency Providing a facility for disaster recovery
- 26. Multiple Datacenter Deployment Guidance Solutions Manual Re-Routing On Application Failure Automated Re-Routing On Application Failure Re-Routing with Azure Traffic Manager( Built-In Azure Service)
- 28. Questions?
- 29. Thank you!