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Cloud application architecture with Microsoft Azure
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- 56. Criteria Virtual MachinesApp Service Service Fabric Azure Functions Azure Container Service Container Instances Azure Batch Application composition Agnostic Applications, containers Services, guest executables, containers Functions Containers Containers Scheduled jobs Density Agnostic Multiple apps per instance via app service plans Multiple services per VM Serverless 1 Multiple containers per VM No dedicated instances Multiple apps per VM Minimum number of nodes 1 2 1 5 3 Serverless 1 3 No dedicated nodes 1 4 State management Stateless or Stateful Stateless Stateless or stateful Stateless Stateless or Stateful Stateless Stateless Web hosting Agnostic Built in Agnostic Not applicable Agnostic Agnostic No Can be deployed to dedicated VNet? Supported Supported 5 Supported Supported 5 Supported Not supported Supported Hybrid connectivity Supported Supported 6 Supported Supported 7 Supported Not supported Supported
- 57. Criteria Virtual Machines App ServiceService Fabric Azure Functions Azure Container Service Container Instances Azure Batch Local debugging Agnostic IIS Express, others 1 Local node cluster Visual Studio or Azure Functions CLI Local container runtime Local container runtime Not supported Programming model Agnostic Web and API applications, WebJobs for background tasks Guest executable, Service model, Actor model, Containers Functions with triggers Agnostic Agnostic Command line application Application update No built-in support Deployment slots Rolling upgrade (per service) Deployment slots Depends on orchestrator. Most support rolling updates Update container image Not applicable
- 58. Criteria Virtual Machines App ServiceService Fabric Azure Functions Azure Container Service Container Instances Azure Batch Auto-scaling VM scale sets Built-in service VM Scale Sets Built-in service Not supported Not supported N/A Load balancer Azure Load Balancer Integrated Azure Load Balancer Integrated Azure Load Balancer No built-in support Azure Load Balancer Scale limit Platform image: 1000 nodes per VMSS, Custom image: 100 nodes per VMSS 20 instances, 100 with App Service Environment 100 nodes per VMSS 200 instances per Function app 100 1 20 container groups per subscription by default. Contact customer service for increase. 2 20 core limit by default. Contact customer service for increase.
- 59. Criteria Virtual Machines App Service Service Fabric Azure Functions Azure Container Service Container Instances Azure Batch SLA SLA for Virtual Machines SLAfor App Service SLA for Service Fabric SLA for Functions SLA for Azure Container Service SLA for Container Instances SLA for Azure Batch Multi region failover Traffic manager Traffic manager Traffic manager, Multi- Region Cluster Not supporte d Traffic manager Not supporte d Not Supporte d
- 60. Criteria Virtual Machines App Service Service Fabric Azure Functions Azure Container Service Container InstancesAzure Batch SSL Configured in VM Supported Supported Supported Configured in VM Supported with sidecar container Supported Cost Windows, Li nux App Service pricing Service Fabric pricing Azure Functions pricing Azure Container Service pricing Container Instances pricing Azure Batch pricing Suitable architecture styles N-Tier, Big compute(HP C) Web- Queue- Worker, N- Tier Microservice s, Event- driven architecture Microservice s, Event- driven architecture Microservice s, Event- driven architecture Microservice s, task automation, batch jobs Big compute(HP C)
- 67. Azure SQL Database AzureDatabase for MySQL Azure Database for PostgreSQL
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- 87. SQL Data Warehouse AzureData Lake Azure Search • Time Series Databases Time Series Insights Blob Storage File Storage
- 98. Instead of running...Consider using... Active Directory Azure Active Directory Domain Services Elasticsearch Azure Search Hadoop HDInsight IIS App Service MongoDB Cosmos DB Redis Azure Redis Cache SQL Server Azure SQL Database
- 104. • Use theScalability checklist to review your design from a scalability standpoint.
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- 106. % Uptime Downtime per week Downtime permonth Downtime per year 99% 1.68 hours 7.2 hours 3.65 days 99.9% 10 minutes 43.2 minutes 8.76 hours 99.95% 5 minutes 21.6 minutes 4.38 hours 99.99% 1 minute 4.32 minutes 52.56 minutes 99.999% 6 seconds 26 seconds 5.26 minutes
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- 116. Pattern Summary Cache-Aside Loaddata on demand into a cache from a data store CQRS Segregate operations that read data from operations that update data by using separate interfaces. Event Sourcing Use an append-only store to record the full series of events that describe actions taken on data in a domain. Index Table Create indexes over the fields in data stores that are frequently referenced by queries. Materialized View Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for required query operations. Sharding Divide a data store into a set of horizontal partitions or shards. Static Content Hosting Deploy static content to a cloud-based storage service that can deliver them directly to the client. Valet Key Use a token or key that provides clients with restricted direct access to a specific resource or service.
- 125. Pattern Summary Ambassador Createhelper services that send network requests on behalf of a consumer service or application. Anti-Corruption Layer Implement a façade or adapter layer between a modern application and a legacy system. Backends for Frontends Create separate backend services to be consumed by specific frontend applications or interfaces. Compute Resource Consolidation Consolidate multiple tasks or operations into a single computational unit External Configuration Store Move configuration information out of the application deployment package to a centralized location. Gateway Aggregation Use a gateway to aggregate multiple individual requests into a single request. Gateway Offloading Offload shared or specialized service functionality to a gateway proxy. Gateway Routing Route requests to multiple services using a single endpoint.
- 126. Leader Election Coordinatethe actions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. Pipes and Filters Break down a task that performs complex processing into a series of separate elements that can be reused. Sidecar Deploy components of an application into a separate process or container to provide isolation and encapsulation. Strangler Incrementally migrate a legacy system by gradually replacing specific pieces of functionality with new applications and services.
- 140. Pattern Summary Competing ConsumersEnable multiple concurrent consumers to process messages received on the same messaging channel. Pipes and Filters Break down a task that performs complex processing into a series of separate elements that can be reused. Priority Queue Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority. Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads. Scheduler Agent Supervisor Coordinate a set of actions across a distributed set of services and other remote resources.
- 144. Pattern Summary Ambassador Createhelper services that send network requests on behalf of a consumer service or application. Anti-Corruption Layer Implement a façade or adapter layer between a modern application and a legacy system. External Configuration Store Move configuration information out of the application deployment package to a centralized location. Gateway Aggregation Use a gateway to aggregate multiple individual requests into a single request. Gateway Offloading Offload shared or specialized service functionality to a gateway proxy. Gateway Routing Route requests to multiple services using a single endpoint. Health Endpoint Monitoring Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals. Sidecar Deploy components of an application into a separate process or container to provide isolation and encapsulation. Strangler Incrementally migrate a legacy system by gradually replacing specific pieces of functionality with new applications and services.
- 145. Pattern Summary Cache-Aside Loaddata on demand into a cache from a data store CQRS Segregate operations that read data from operations that update data by using separate interfaces. Event Sourcing Use an append-only store to record the full series of events that describe actions taken on data in a domain. Index Table Create indexes over the fields in data stores that are frequently referenced by queries. Materialized View Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for required query operations. Priority Queue Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority. Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads. Sharding Divide a data store into a set of horizontal partitions or shards. Static Content Hosting Deploy static content to a cloud-based storage service that can deliver them directly to the client. Throttling Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service.
- 146. Pattern Summary Bulkhead Isolateelements of an application into pools so that if one fails, the others will continue to function. Circuit Breaker Handle faults that might take a variable amount of time to fix when connecting to a remote service or resource. Compensating Transaction Undo the work performed by a series of steps, which together define an eventually consistent operation. Health Endpoint Monitoring Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals. Leader Election Coordinate the actions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances. Queue-Based Load Leveling Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads. Retry Enable an application to handle anticipated, temporary failures when it tries to connect to a service or network resource by transparently retrying an operation that's previously failed. Scheduler Agent Supervisor Coordinate a set of actions across a distributed set of services and other remote resources.
- 151. Pattern Summary Federated IdentityDelegate authentication to an external identity provider. Gatekeeper Protect applications and services by using a dedicated host instance that acts as a broker between clients and the application or service, validates and sanitizes requests, and passes requests and data between them. Valet Key Use a token or key that provides clients with restricted direct access to a specific resource or service.