Serverless computing, Function as a Service (FaaS) application model, offers a number of advantages over traditional cloud-based or server-centric infrastructure. For many developers, serverless architectures offer greater scalability, more flexibility, and quicker time to release, all at a reduced cost. With serverless architectures, developers do not need to worry about purchasing, provisioning, and managing backend servers.
Although 'serverless' computing does actually take place on servers, developers never have to deal with the servers. They are managed by the vendor. This can reduce the investment necessary in DevOps, which lowers expenses, and it also frees up developers to create and expand their applications without being constrained by server capacity.
As in a 'pay-as-you-go' phone plan, developers are only charged for what they use. Code only runs when backend functions are needed by the serverless application, and the code automatically scales up as needed. In contrast, in a traditional 'server-full' architecture, developers have to project in advance how much server capacity they will need and then purchase that capacity, whether they end up using it or not.
Quick deployments (update, patch, fix) and updates are possible. Developers can very quickly upload bits of code and release a new product. They can upload code all at once or one function at a time, since the application is not a single monolithic stack but rather a collection of functions provisioned by the vendor. This also makes it possible to quickly update, patch, fix, or add new features to an application. It is not necessary to make changes to the whole application; instead, developers can update the application one function at a time.
Serverless computing's concerns include Debugging, Security, Versioning, Multi-tenancy, Not for long-running processes (cost), 'boot up'/'cold start' time, and vendor lock-in (AWS Lamda, Azure Function, Google Cloud Functions, or Openshift Function).
AWS Lambda natively supports JavaScript, Java, Python, Go, C#, F#, PowerShell, and Ruby code.
Azure Functions has runtimes for JavaScript, Java, Python, C#, F#, and PowerShell (preview). Azure lacks Go and Ruby—otherwise, the language options are very similar.
AWS Lambda has a straightforward programming model. A function receives a JSON object as input and may return another JSON as output. The event type defines the schema of those objects, which are documented and defined in language SDKs.
Azure Functions has a more sophisticated model based on triggers and bindings. A trigger is an event that the function listens to. The function may have any number of input and output bindings to pull and/or push extra data at the time of processing.
AWS Lambda always reserves a separate instance for a single execution. Each execution has its exclusive pool of memory and CPU cycles. Therefore, the performance is entirely predictable and stable.
Azure Functions allocates multiple concurrent executions to the same virtual node. If one execution is idle waiting for a response from the network, other executions may use resources which would otherwise be wasted.
Advantages of containers
• Control and Flexibility
• Vendor-agnostic
• Easier migration path
• Portability
Advantages of serverless
• Zero administration
• Pay-per-execution
• Zero cost for idle time
• Auto-scaling
• Faster time-to-market
• Microservice nature: Clear codebase separation
• Significantly reduced administration and maintenance burden
Containers are great if you need the flexibility to install and use software with specific version requirements. With containers, you can choose the underlying operating system and have full control of the installed programming language and runtime version.
