Since our public preview announcement at Microsoft Ignite 2018, every month thousands of developers worldwide have leveraged the Azure SignalR Service bindings for Azure Functions to add real-time capabilities to their serverless applications. Today, we are excited to announce the general availability of these bindings in all global regions where Azure SignalR Service is available!
SignalR Service is a fully managed Azure service that simplifies the process of adding real-time web functionality to applications over HTTP. This real-time functionality allows the service to push messages and content updates to connected clients using technologies such as WebSocket. As a result, clients are updated without the need to poll the server or submit new HTTP requests for updates.
Azure Functions provides a productive programming model based on triggers and bindings for accelerated development and serverless hosting of event-driven applications. It enables developers to build apps using the programming languages and tools of their choice, with an end-to-end developer experience that spans from building and debugging locally, to deploying and monitoring in the cloud. Combining Azure SignalR Service with Azure Functions using these bindings, you can easily push updates to the UI of your applications with just a few lines of code.
Microsoft clients for Azure Event Hubs have always had two levels of abstraction. There is the low-level client, which includes event sender and receiver classes which allow for maximum control by the application, but also force the application to understand the configuration of the Event Hub and maintain an event receiver connected to each partition. Built on top of that low-level client is a higher-level library, Event Processor Host, which hides most of those details for the receiving side. Event Processor Host automatically distributes ownership of Event Hub partitions across multiple host instances and delivers events to a processing method provided by the application.
Service Fabric is another Microsoft-provided library, which is a generalized framework for dividing an application into shards and distributing those shards across multiple compute nodes. Many customers are using Service Fabric for their applications, and some of those applications need to receive events from an Event Hub. It is possible to use Event Processor Host within a Service Fabric application, but it is also inelegant and redundant. The combination means that there are two separate layers attempting to distribute load across nodes, and neither one is aware of the other. It also introduces a dependency on
With this set of changes to the Azure Functions Core Tools and the Azure Functions Extension for Visual Studio Code, Azure Functions now supports TypeScript out of the box! Included with these changes are a set of templates for TypeScript, type definitions, and npm scripts. Read on to learn more details about the new experience.
Templates for TypeScript
In the latest version of the Azure Functions Core Tools and the
Azure Functions provides a productive programming model based on triggers and bindings for accelerated development and serverless hosting of event-driven applications. It enables developers to build apps using the programming languages and tools of their choice, with an end-to-end developer experience that spans from building and debugging locally, to deploying and monitoring in the cloud. Today, we’re pleased to announce the general availability of Java support in Azure Functions 2.0!
Ever since we first released the preview of Java in Functions, an increasing number of users and organizations have leveraged the capability to build and host their serverless applications in Azure. With the help of input from a great community of preview users, we’ve steadily improved the feature by adding support for easier authoring experiences and a more robust hosting platform.
What’s in the release?
With this release, Functions is now ready to support Java workloads in production, backed by our 99.95 percent SLA for both the Consumption Plan and the App Service Plan. You can build your functions based on Java SE 8 LTS and the Functions 2.0 runtime, while being able to use the platform (Windows, Mac, or Linux) and tools of your choice. This enables a wide
We have been incredibly excited to be a part of the rise of event-driven programming as a core building block for cloud application architecture. By making the following features generally available, we want to enable you to build more sophisticated, performant, and stable event-driven applications in Azure. We are proud to announce the general availability of the following set of features, previously in preview:
Dead lettering Retry policies Storage Queues as a destination Hybrid Connections as a destination Manual Validation Handshake
To take advantage of the GA status of the features, make sure you are using our 2019-01-01 API and SDKs. If you are using the Azure portal or CloudShell, you’re already good to go. If you are using CLI or PowerShell, make sure you have versions 2.0.56 or later for CLI and 1.1.0 for PowerShell.
Dead lettering gives you an at-least-once guarantee that you will receive your events in mission critical systems. With a dead letter destination set, you will never lose a message even if your event handler is down, your authorization fails, or a bug in your endpoint is overwhelmed with volume.
Dead lettering allows you to connect each event subscription to a storage account,
Built-in machine learning (ML) models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide.
What is Azure Stream Analytics?
Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data, and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language (extensible to include custom code) to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies.
Traditional way to incorporate anomaly detection capabilities in stream processing
Many customers use Azure Stream Analytics to continuously monitor massive amounts of fast-moving streams of data in order to detect issues that do not conform to expected patterns and prevent catastrophic losses. This in essence is anomaly detection.
For anomaly detection, customers traditionally relied on either sub-optimal methods of hard coding control limits in their queries, or used custom machine learning models. Development of custom learning models not only requires time, but also high levels of data science expertise along with nuanced data pipeline engineering skills. Such
The IT industry is experiencing a shift from monolithic applications to microservices-based architectures. The benefits of this new approach include:
Independent development and freedom to choose technology – Developers can work on different microservices at the same time and choose the best technologies for the problem they are solving. Independent deployment and release cycle – Microservices can be updated individually on their own schedule. Granular scaling – Individual microservices can scale independently, reducing the overall cost and increasing reliability. Simplicity – Smaller services are easier to understand which expedites development, testing, debugging, and launching a product. Fault isolation – Failure of a microservice does not have to translate into failure of other services.
In this blog post we will explore:
How to design a simplified online store system to realize the above benefits. Why and how to manage public facing APIs in microservice-based architectures. How to get started with Azure API Management and microservices. Example: Online store implemented with microservices
Let’s consider a simplified online store system. A visitor of the website needs to be able to see product’s details, place an order, review a placed order.
Whenever an order is placed, the system needs to process the order details
With Azure DevOps Projects we want to make it is easy for you to set up a fully functional DevOps pipeline tailored to the development language and application platform you want to leverage.
We have been making continuous enhancements to Azure DevOps Projects and in the latest deployment now available to all customers, we have added support for Azure Cosmos DB and Azure Functions as target destinations for your application. This builds on the existing Azure App Service, Azure SQL Database, and Azure Kubernetes Service (AKS) support.
The support of Azure Cosmos DB in Azure DevOps Projects means that you will now be able to create a skeleton two tier Node.js application backed by Azure Cosmos DB in just a few clicks. Azure DevOps Projects creates all the scaffolding for your pipeline to give you everything you need to develop, deploy, and monitor your application including:
A Git code repository hosted in Azure Repos with a skeleton Node.js application A CI/CD pipeline in Azure Pipelines for deploying the database tier to Azure Cosmos DB and the web-tier on Azure Web Apps for containers, Azure Kubernetes Service, or as a Windows Web App in Azure Provisioning all the Azure resources in
Every platform has limits, workstations and physical servers have resource boundaries, APIs may be rate-limited, and even the perceived endlessness of the virtual public cloud enforces limitations that protect the platform from overuse or misuse. You can learn more about these limitations by visiting our documentation, “Azure subscription and service limits, quotas, and constraints.” When working on scenarios that take platforms to their extreme, those limits become real and therefore thought should be put into overcoming them.
The following post includes essential notes taken from my work with Mike Kiernan, Mayur Dhondekar, and Idan Shahar. It also covers some iterations where we try to reach a limit of 10K virtual machines running on Microsoft Azure and explores the pros/cons of the different implementations.
Load tests at cloud scale
Load and stress tests before moving a new version to production are critical on the one hand, but pose a real challenge for IT on the other. This is because they require a considerable amount of resources to be available for only a short amount of time, every release-cycle. When purchased the infrastructure doesn’t justify its cost over extended periods, making this a perfect use-case for a public cloud platform where payment
This blog was co-authored by Sumeet Mittal, Senior Program Manager, Azure Networking.
Earlier this year in July, we announced the public preview for Virtual Network Service Endpoints and Firewall rules for both Azure Event Hubs and Azure Service Bus. Today, we’re excited to announce that we are making these capabilities generally available to our customers.
This feature adds to the security and control Azure customers have over their cloud environments. Now, traffic from your virtual network to your Azure Service Bus Premium namespaces and Standard and Dedicated Azure Event Hubs namespaces can be kept secure from public Internet access and completely private on the Azure backbone network.
Virtual Network Service Endpoints do this by extending your virtual network private address space and the identity of your virtual network to your virtual networks. Customers dealing with PII (Financial Services, Insurance, etc.) or looking to further secure access to their cloud visible resources will benefit the most from this feature. For more details on the finer workings of Virtual Network service endpoints, refer to the documentation.
Firewall rules further allow a specific IP address or a specified range of IP addresses to access the resources.
Virtual Network Service Endpoints and Firewall rules