Posted in: Azure architecture design patterns

Azure architecture design patterns

azure architecture design patterns

Handle faults that might take a variable amount of time to recover from, when connecting to a remote service or resource.

This can improve the stability and resiliency of an application. In a distributed environment, calls to remote resources and services can fail due to transient faults, such as slow network connections, timeouts, or the resources being overcommitted or temporarily unavailable.

These faults typically correct themselves after a short period of time, and a robust cloud application should be prepared to handle them by using a strategy such as the Retry pattern. However, there can also be situations where faults are due to unanticipated events, and that might take much longer to fix. These faults can range in severity from a partial loss of connectivity to the complete failure of a service.

In these situations it might be pointless for an application to continually retry an operation that is unlikely to succeed, and instead the application should quickly accept that the operation has failed and handle this failure accordingly.

Additionally, if a service is very busy, failure in one part of the system might lead to cascading failures.

For example, an operation that invokes a service could be configured to implement a timeout, and reply with a failure message if the service fails to respond within this period. However, this strategy could cause many concurrent requests to the same operation to be blocked until the timeout period expires.

These blocked requests might hold critical system resources such as memory, threads, database connections, and so on. Consequently, these resources could become exhausted, causing failure of other possibly unrelated parts of the system that need to use the same resources.

In these situations, it would be preferable for the operation to fail immediately, and only attempt to invoke the service if it's likely to succeed.

Note that setting a shorter timeout might help to resolve this problem, but the timeout shouldn't be so short that the operation fails most of the time, even if the request to the service would eventually succeed. Allowing it to continue without waiting for the fault to be fixed or wasting CPU cycles while it determines that the fault is long lasting.

The Circuit Breaker pattern also enables an application to detect whether the fault has been resolved. If the problem appears to have been fixed, the application can try to invoke the operation.

The purpose of the Circuit Breaker pattern is different than the Retry pattern. The Retry pattern enables an application to retry an operation in the expectation that it'll succeed. The Circuit Breaker pattern prevents an application from performing an operation that is likely to fail. An application can combine these two patterns by using the Retry pattern to invoke an operation through a circuit breaker. However, the retry logic should be sensitive to any exceptions returned by the circuit breaker and abandon retry attempts if the circuit breaker indicates that a fault is not transient.

A circuit breaker acts as a proxy for operations that might fail. The proxy should monitor the number of recent failures that have occurred, and use this information to decide whether to allow the operation to proceed, or simply return an exception immediately.

Azure Application Architecture Guide

The proxy can be implemented as a state machine with the following states that mimic the functionality of an electrical circuit breaker:.

Closed : The request from the application is routed to the operation. The proxy maintains a count of the number of recent failures, and if the call to the operation is unsuccessful the proxy increments this count. If the number of recent failures exceeds a specified threshold within a given time period, the proxy is placed into the Open state.Lambda architecture is a popular pattern in building Big Data pipelines. It is designed to handle massive quantities of data by taking advantage of both a batch layer also called cold layer and a stream-processing layer also called hot or speed layer.

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The following are some of the reasons that have led to the popularity and success of the lambda architecture, particularly in big data processing pipelines. The ability to process data at high speed in a streaming context is necessary for operational needs, such as transaction processing and real-time reporting. Typically, batch processing, involving massive amounts of data, and related correlation and aggregation is important for business reporting.

This is to understand how the business is performing, what the trends are, and what corrective or additive measure can be executed to improve business or customer experience. One of the triggers that lead to the very existence of lambda architecture was to make the most of the technology and tool set available.

Similarly, very fast layers such as cache databases, NoSQL, streaming technology allows fast operational analytics on smaller data sets but cannot do massive scale correlation and aggregation and other analytics operations such as Online Analytical Processing like a batch system can.

Additionally, in the market you will find people who are highly skilled in batch systems, and often they do not have the same depth of skills in stream processing, and vice versa.

The following is one of the many representative Lambda architecture on Azure for building Big Data pipelines. Figure 1: Lambda architecture for big data processing represented by Azure products and services.

Note, other Azure and or ISV solutions can be placed in the mix if needed based on specific requirements. As stated in the previous section, lambda architecture resolves some business challenges. Various parts of the business have different needs in terms of speed, level of granularity and mechanism to consume data. Finally, it ensures people with skills dealing with transaction and speed layer can work in parallel and together with people with skills in batch processing.

Although immensely successful and widely adopted across many industries and a defacto architectural pattern for big data pipelines, it comes with its own challenges. Here are a few:. Transient data silos: Lambda pipelines often creates silos that could may cause some challenges in the business.

The reporting at the speed layer that the operations team is dealing with, may be different for the aggregate batch layer that the management teams are working with. Such creases may eventually iron out, but it has the potential of causing some inconsistencies. With the technological breakthrough at Microsoft, particularly in Azure Cosmos DBthis is now possible.

Azure Cosmos DB is a globally distributed, multi-model database. With Cosmos DB you can independently scale throughput and storage across any number of Azure's geographic regions. It offers throughput, latency, availability, and consistency guarantees with comprehensive service level agreements SLAs.

Here are some of the key features that renders Cosmos DB as a suitable candidate for implementing the proposed reference architecture where the speed later and the batch layer merges into a single layer.

The following is a diagrammatic representation of the emerging big data pipeline that we have been discussing in this blog:. Figure 2: Emerging architectural pattern implemented using Cosmos DB for Big Data pipelines as an evolution of the traditional lambda architecture. Hence, by leveraging Cosmos DB features, particularly the change feed architecture, this emerging pattern can resolve many of the common use-cases.

This in turn, gives all the benefits of the lambda architecture, and resolves some of complexities that lambda introduces. More and more customers adopting this and resulting in a successful community, and success of this new pattern and increased adoption of Azure Cosmos DB.

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Blog Big Data. The emerging big data architectural pattern. Why lambda?Architecture diagrams, reference architectures, example scenarios, and solutions for common workloads on Azure. Move AI models to the edge with a solution architecture that includes Azure Stack.

A step-by-step workflow will help you harness the power of edge AI when disconnected from the internet. Developers could use knowledge mining to help attorneys quickly identify entities of importance from discovery documents and flag important ideas across documents. This reference architecture shows how to apply neural style transfer to a video, using Azure Machine Learning.

Build a scalable solution for batch scoring models on a schedule in parallel using Azure Machine Learning.

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Build a scalable solution for batch scoring an Apache Spark classification model on a schedule using Azure Databricks. Perform batch scoring with R models using Azure Batch and a data set based on retail store sales forecasting. How to build an enterprise-grade conversational bot chatbot using the Azure Bot Framework.

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In industries where bidding competition is fierce, or when the diagnosis of a problem must be quick or in near real-time, companies can use knowledge mining to avoid costly mistakes. Together, the Azure Bot Service and Language Understanding service enable developers to create conversational interfaces for various scenarios like banking, travel, and entertainment.

For example, a hotel's concierge can use a bot to enhance traditional e-mail and phone call interactions by validating a customer via Azure Active Directory and using Cognitive Services to better contextually process customer requests using text and voice.

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The Speech recognition service can be added to support voice commands. Knowledge mining with a search index makes it easy for customers and employees to locate what they are looking for faster. Knowledge mining can help organizations to scour thousands of pages of sources to create an accurate bid. Customer Churn Prediction uses Cortana Intelligence Suite components to predict churn probability and helps find patterns in existing data associated with the predicted churn rate.

Knowledge mining can help customer support teams quickly find the right answer for a customer inquiry or assess customer sentiment at scale. Learn how to use Azure Machine Learning to predict failures before they happen with real-time assembly line data.

Knowledge mining through a search index makes it easy for end customers and employees to locate what they are looking for faster. This reference architecture shows how to conduct distributed training of deep learning models across clusters of GPU-enabled VMs using Azure Machine Learning. This solution provides an Azure-based smart solution, leveraging external open-source tools, that determines the optimal energy unit commitments from various types of energy resources for an energy grid.

Azure Bot Service can be easily combined with Language Understanding to build powerful enterprise productivity bots, allowing organizations to streamline common work activities by integrating external systems, such as Office calendar, customer cases stored in Dynamics CRM and much more.

The QnA Maker tool makes it super easy for the content owners to maintain their knowledge base of QnAs.The distributed nature of cloud applications requires a messaging infrastructure that connects the components and services, ideally in a loosely coupled manner in order to maximize scalability.

Asynchronous messaging is widely used, and provides many benefits, but also brings challenges such as the ordering of messages, poison message management, idempotency, and more. You may also leave feedback directly on GitHub. Skip to main content. Exit focus mode. Pattern Summary Asynchronous Request-Reply Decouple backend processing from a frontend host, where backend processing needs to be asynchronous, but the frontend still needs a clear response. Claim Check Split a large message into a claim check and a payload to avoid overwhelming a message bus.

Choreography Have each component of the system participate in the decision-making process about the workflow of a business transaction, instead of relying on a central point of control. Competing Consumers Enable 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.

azure architecture design patterns

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. Publisher-Subscriber Enable an application to announce events to multiple interested consumers asynchronously, without coupling the senders to the receivers.

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.

Sequential Convoy Process a set of related messages in a defined order, without blocking processing of other groups of messages. Related Articles Is this page helpful?

Design patterns for microservices

Yes No. Any additional feedback? Skip Submit. Send feedback about This page. This page. Submit feedback. There are no open issues. View on GitHub. Is this page helpful? Decouple backend processing from a frontend host, where backend processing needs to be asynchronous, but the frontend still needs a clear response.

Have each component of the system participate in the decision-making process about the workflow of a business transaction, instead of relying on a central point of control.This guide presents a structured approach for designing applications on Azure that are scalable, resilient, and highly available. It is based on proven practices that we have learned from customer engagements. The cloud is changing how applications are designed. Instead of monoliths, applications are decomposed into smaller, decentralized services.

These services communicate through APIs or by using asynchronous messaging or eventing. Applications scale horizontally, adding new instances as demand requires.

These trends bring new challenges. Application state is distributed. Operations are done in parallel and asynchronously. Applications must be resilient when failures occur.

Deployments must be automated and predictable. Monitoring and telemetry are critical for gaining insight into the system. This guide is designed to help you navigate these changes.

The Azure Application Architecture Guide is organized as a series of steps, from the architecture and design to implementation. For each step, there is supporting guidance that will help you with the design of your application architecture. The first decision point is the most fundamental. What kind of architecture are you building? It might be a microservices architecture, a more traditional N-tier application, or a big data solution. We have identified several distinct architecture styles.

There are benefits and challenges to each. Knowing the type of architecture you are building, now you can start to choose the main technology pieces for the architecture. The following technology choices are critical:.

Compute refers to the hosting model for the computing resources that your applications run on. For more information, see Choose a compute service. Data stores include databases but also storage for message queues, caches, logs, and anything else that an application might persist to storage.

For more information, see Choose a data store. Messaging technologies enable asynchronous messages between components of the system. For more information, see Choose a messaging service.We introduced the topic of design patterns in this previous postthen we discussed how design patterns apply specifically to the AWS cloud.

azure architecture design patterns

Sample code and an infographic depicting all the patterns are also available. The book contains 24 design patterns, 10 guidance topics, and 10 sample applications. These, in fact, represent the basic orientation for developing applications in the cloud:.

For each pattern, the book provides a short description, some context and a problem along with its solution, issues and considerations, A use case, sample code with Cand finally the patterns and related guidance. Deploy static content to a cloud-based storage service that can deliver these directly to the client. This pattern can reduce the requirement for potentially expensive compute instances. Web applications typically include some elements of static content.

This static content may include HTML pages and other resources such as images and documents that are available to the client, either as part of an HTML page such as inline images, style sheets, and client-side JavaScript files or as separate downloads such as PDF documents.

Although web servers are well tuned to optimize requests through efficient dynamic page code execution and output caching, they must still handle requests to download static content. This absorbs processing cycles that could often be put to better use. The cost of cloud-hosted storage is typically much less than for compute instances. When hosting some parts of an application in a storage service, the main considerations are related to the deployment of the application and to securing resources that are not intended to be available to anonymous users.

The sample code simulates the solution of pattern also giving us useful snippet of Azure code.

Cloud Design Patterns

As you can see, Cloud Design Patterns for Azure is rich in useful content. In particular, I highly recommend a quick read of Autoscaling Guidance. I began to deal with the Cloud since as a private researcher, then as an entrepreneur. I have "baptized" many to the Cloud. My Nicknames: Mr. Cloud or Santa Cloud at Christmas time. Free content on Cloud Academy More and more customers are relying on our technology and content to keep upskilling their people in these months, and we are doing our best to keep supporting them.Security is the capability of a system to prevent malicious or accidental actions outside of the designed usage, and to prevent disclosure or loss of information.

Cloud applications are exposed on the Internet outside trusted on-premises boundaries, are often open to the public, and may serve untrusted users. Applications must be designed and deployed in a way that protects them from malicious attacks, restricts access to only approved users, and protects sensitive data. You may also leave feedback directly on GitHub.

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Pattern Summary Federated Identity Delegate 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. Related Articles Is this page helpful?

Yes No. Any additional feedback? Skip Submit. Send feedback about This page. This page. Submit feedback. There are no open issues. View on GitHub. Is this page helpful? 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.

Use a token or key that provides clients with restricted direct access to a specific resource or service.


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