Serverless Application Architecture: Know the pros, cons and best practices

Serverless architecture is quickly becoming the most popular way to develop and deploy applications in the cloud. The serverless model has resulted in faster time to market and lower costs, but it also has its share of drawbacks.

While it’s tempting to just jump right in to the development process, serverless requires careful planning, especially when it comes to architecting microservices-based applications. This article will explore some of these challenges as well as best practices for overcoming them so that you can get started out on the right foot with your own serverless app architecture projects!

Good Read: Build Well-architected Serverless Applications with AWS Lambda

Serverless is quickly becoming the most popular way to develop and deploy applications in the cloud.

Serverless is a new way of thinking about how to build, deploy and run applications.

Serverless architectures are more cost effective than traditional architectures because they eliminate the need for servers, which can be expensive if you pay for them on an hourly basis.

Additionally, serverless architectures allow developers to focus on building smaller components that do one thing well instead of large applications with many moving parts.

Serverless architecture is definitely not serverless, but it still offers lots of advantages.

It’s a design pattern that uses services from the cloud and third-party providers to run applications without managing servers or provisioning infrastructure. Here are some examples:

• You pay only for what you use — no need to reserve or buy software licenses in advance and no recurring costs after deployment.
• You can scale up or down quickly based on current needs. You can also change your services gradually during deployments without downtime (or with limited downtime). This makes serverless an ideal choice for startups with unpredictable growth patterns as well as larger organizations that want to reduce their operational expenses by transitioning away from traditional infrastructures like data centers and virtual machines (VMs).

Serverless app architectures employ functions as a service (FaaS) platforms like AWS Lambda and Azure Functions.

FaaS platforms allow you to run code in response to events — for example, when a file is uploaded to your cloud storage bucket or a user signs up for your service.

In this way, the underlying infrastructure runs code that’s invocations are triggered by events. For example, you might have an Amazon S3 event trigger the execution of some JavaScript code that reads data from an S3 object and sends it over HTTP(s) to another application or device on demand.

Serverless can be more than just a technology option. It’s a design pattern that requires careful planning and thinking about how systems will interact with each other.

It’s easy to think of serverless as just another way to deploy applications on the cloud, but it is much more than that. Serverless isn’t just about deploying applications in containers or functions — it’s about designing your application architecture differently so it benefits from stateless computing and event-driven architectures.

Serverless allows you to focus on what you want your applications to do rather than how they should be built, which gives you freedom from having to worry about managing servers, monitoring them or scaling them up or down when needed. But it also imposes some constraints on how you build these applications:

While it’s tempting to just jump right in to the development process, serverless requires careful planning, especially when it comes to architecting microservices-based applications.

While it’s tempting to just jump right in to the development process, serverless requires careful planning, especially when it comes to architecting microservices-based applications. Unlike traditional applications that run on virtual machines or containers, serverless apps have a different architecture and set of best practices. This is because they are designed around event-driven functions rather than long running processes.

While this may seem overly simplistic at first glance, it reveals some very important differences between serverless and traditional architectures:

• Serverless applications are stateless (or nearly so).
• There are no servers for you to manage manually (and therefore no need for infrastructure management tools such as Chef or Puppet).

The serverless model has resulted in faster time to market and lower costs, but it also has its share of drawbacks.

The main downside is a greater reliance on third-party services. This means that there are fewer systems you control, which means less flexibility over how your applications behave and function. Another drawback is that if an application fails, it can be harder to diagnose problems because you’re not generally aware of what was happening in the serverless layer at the time of failure (unless you were monitoring your functions).

As with any new technology paradigm shift, organizations must understand all the pros and cons before making decisions around how they will adopt this new way of working with applications.

Serverless functions must be ephemeral, stateless and immutable.

In a serverless architecture, functions are the building blocks of your application. Each function represents a single operation that can be executed in response to an event such as an HTTP request or a message from another system. Functions do not have access to any data other than what is passed as input parameters, that is: they are stateless and ephemeral. This means you cannot use them for things like storing user information or keeping track of user sessions.

The concept of immutability refers to the fact that once something has been created it cannot be changed; only new copies can be made with new values (e.g., when creating duplicates of files or objects).

One big challenge with stateless functions is how to handle long-running tasks or jobs.

A common challenge with stateless functions is how to handle long-running tasks or jobs. Stateless functions are not designed to handle long-running tasks, but you can use a stateless function to call another stateful Lambda function (the one that can persist information in an S3 bucket) and retrieve its response. You could also use it to invoke another Lambda function that calls another Lambda function, and so on until the desired result is obtained.

If you want your application’s logic to be accessible by other parts of the system or client applications, then this will not work because they cannot access data stored in an Amazon DynamoDB table directly through a web API alone. You need some sort of middleware layer between your API and these other applications which allows them access via REST/JSON or WebSocket protocols.

The vendor lock-in problem is mitigated by using open source tools that rely on industry standards like SAML and OpenID Connect for security or taking advantage of application virtualization tools like Docker containers that are portable between ecosystems.

If you’re going to use AWS for your serverless solution, you’re likely going to use Amazon API Gateway to manage your APIs, as well as Amazon Cognito for user authentication. But if another vendor comes along who offers better pricing or additional features, or if an outage causes downtime, then it would be helpful to have an option that doesn’t require switching vendors.

How to test the performance of your serverless application before putting it into production? How do you know how much memory or how many CPUs your application will need?

Testing is an important aspect of serverless application architecture. When you’re building an application, it’s important to test the performance on a small scale before scaling up your application. This will help you identify bottlenecks and memory leaks in your system, as well as find out how much memory or CPU power your application needs.

There are several ways you can test the performance of your serverless applications. Use performance testing tools such as Cloud Functions Performance Lab (CFPL) or BlazeMeter OpsDash to simulate traffic with synthetic clients that generate load on an existing production environment. You can use this tool to simulate real-world traffic patterns and measure response times for specific endpoints in real time. You can also use these services for identifying bottlenecks in a specific part of your codebase, which will help reduce response times during peak hours when more users are accessing it at once.

Conclusion

If you’re considering moving to a serverless application architecture, it’s important to understand the pros and cons of this approach. Serverless apps have their own set of challenges that you need to be aware of before you start building out your application. This article has covered some potential pitfalls and best practices for developing serverless applications, but there are many more aspects we didn’t cover as well!