Azure Functions

REST API for SaaS application

I'm currently developing an Azure Functions REST API with TypeScript, tsoa, Mongoose, and Typegoose that contains simple CRUD activities. It does the job and has type-safety as well as the ability to generate OpenAPI specs for me.

However, as the app scales up, there are more duplicated codes (for similar operations - like CRUD in each different model). It's also becoming more complex because I need to implement a multi-tenancy SaaS for both the API and the database.

So I chose to implement a repository pattern, and I have a "feeling" that .NET and C# will make development easier because, unlike TypeScript, it includes native support for Dependency Injection and great things like LINQ.

It wouldn't take much effort to migrate because I can easily translate interfaces and basic CRUD operations to C#. So, I'm looking for advice on whether it's worth converting from TypeScript to.NET.

I need to publish an API for recommending items based on preferences passed as criteria. The recommendation engine logic relies on shuffling and sorting the items in the list. The list of items is small (<1000), so I was thinking of bringing them into context at startup (singleton pattern), and letting my Azure Functions (or alternative? must be on Microsoft Azure) do the number crunching with direct access to this dataset. Is this viable, and if so, where should I implement my singleton initial load (from file): directly within the Azure Function? If not, what else? Or if the singleton idea doesn't offer much benefit, what good (and cheap) fast-access data store would be recommended? Azure Cosmos DB? Any other? Each of my items can be represented by a small JSON document, like: { "id" : "...", "name" : "...", "description" : "...", "qualifiers" : [ "qualifier" : "...", "qualifier" : "...", "qualifier" : "..."]} (description < 1000 chars; no more than 10 qualifiers).

AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

• We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
• All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
• The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
• We run separate dev and test versions of each function in each region.

Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

• Why Lambda is an almost perfect match for SaaS. Especially when you're small.
• Why I don't use a "big" framework around it.
• Why distributed background jobs triggered by queues are Lambda's raison d'être.
• Why monitoring & logging is still an issue.

https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

We've found Azure Storage really useful during the last few years. It's really cheap and pretty versatile too. We've used #table storage to help us out with a migration where we needed to store quite a bit of data, use the #queues for integrating with Azure Functions and helping us load balance our SMS sending and the #blob storage helps us host our client installer and keep up our weekly release train!

Visual Studio Azure DevOps Azure Functions Azure Websites #Azure #AzureKeyVault #AzureAD #AzureApps

#Azure Cloud Since Amazon is potentially our competitor then we need a different cloud vendor, also our programmers are microsoft oriented so the choose were obviously #Azure for us.

Azure DevOps Because we need to be able to develop a neww pipeline into Azure environment ina few minutes.

Azure Kubernetes Service We already in #Azure , also need to use K8s , so let's use AKS as it's a manged Kubernetes in the #Azure

In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

• No servers to manage (obviously!)
• Limited fixed costs – you pay only for used time
• Automated scaling and balancing
• Automatic failover (or, at this level of abstraction, no failover problem at all)
• Security easier to provide and audit
• Low overhead at the start (with the certain level of knowledge)
• Short time to market
• Easy handover - deployment coupled with code
• Perfect choice for lean startups with fast-paced iterations
• Augmentation for the classic cloud, server(full) approach

• Not much know-how and best practices available about structuring the code and projects on the market
• Not suitable for complex business logic due to the risk of producing highly coupled code
• Cost difficult to estimate (helpful tools: serverlesscalc.com)
• Difficulty in migration to other platforms (Vendor lock⚠️)
• Little engineers with experience in serverless on the job market
• Steep learning curve for engineers without any cloud experience

More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

I first found .NET in 2003 when I first began learning to create software. Every year since then, I've watched as .NET matured into something great, and now we have .NET Core! At Contessa Health, we use .NET Core for a mixture of things including fine-grained and coarse-grained web services, worker processes for long running tasks, and for our Azure Functions that serve as a replacement for distributing our base class libraries. As a startup, we are constantly evaluating technologies to make sure we stay fresh, and we keep coming back to .NET Core because of its ecosystem, maturity of the tooling, and for its ability to help us iterate and move quickly. Take all of that and combine it with the Kubernetes ecosystem, and we have an easy way to orchestrate and compose power service offerings that meet the needs of our customers. It cannot be said enough that Microsoft’s commitment to open source has yielded incredible benefits for small companies such as ourselves. Our voices are heard, and we get to help make .NET Core better, which in turn helps everyone else.

I used Azure functions as part of an integration service when creating a bulk insert module in azure. Azure Functions