Serverless vs. Microservices
Microservices and Serverless have definitely become more popular in the past few years. Hence, here is a comparison between both Serverless and Microservices architecture.
Serverless is a different type of cloud computing that relies on the developers’ enterprise and infrastructure capabilities. In this post, we are particularly interested in the serverless computing version “ Feature as a Service” (FaaS) rather than “Backend as a Service” (BaaS). FaaS requires the development of a particular API that encapsulates business logic. It is then incorporated in a cloud service like AWS Lambda, Google Cloud Functions, Azure Functions, or IBM OpenWhisk.
- It's economical- In Lambda, you just pay for the implementation time. A cluster often runs with microservices, for instance, say EC2, so overhead costs are higher.
- No Scaling problems- AWS takes care of the scaling. Lambda permits from 500-3000 functions, depending on the region.
- Provider Lock-in- You will execute functions only on the tools cloud vendors offer you. The backend resources, including CPU or RAM limits, are tailored to restrict the system. This lack of reconfigurability does not exist for Microservices.
- Runtime- Each feature takes about 15 minutes, meaning that intense or time-consuming loading for a serverless device is not appropriate.
Microservices is a wide framework divided into a set of discrete functional modules. Modules or ‘services’ are closely connected. Each microservice focuses on only one part of the application’s features. It can be supported with a basic interface amongst other facilities. For instance, the REST API will have its own data store for every service.
- Faster rollout- A service can be easily upgraded and implemented separately without a complete application being redeployed.
- Simple to detect bugs- Any breakdown in the service ruins the program so that the error can be easily resolved while the remainder of the application is functional.
- Testing challenges- Testing systemic integration can be very tedious and more complex than monolithic architectures.
- Debugging- As it would be helpful to make a server split in only one location. Interpreting logs can be challenging when every server has its collection of logs. Also, there is uncertainty between them.
Which one to use?
Here, there is no suitable answer. It depends on the situation and some individual interests.
Serverless is often preferred for more menial activities such as DB entry. Meaning you can easily start up a job application without much or inclination to handle technology and growth. This is also helpful for designing a lightweight, scalable program and can be easily extended or modified.
However, microservices can be more suitable for intense and time-consuming data processing. Hence, the architecture and application platform can be tailored to the particular requirements. They are best if you need consistent answer time in milliseconds.
Microservices and Serverless have come far from monolithic “old-time” architectures. They meet today’s software criteria, which include simple scalability, versatility, and continuous integration. But attention should be given to choosing between them when planning infrastructure. It all includes choosing each strategy carefully and making the most of the advantages.
Other useful articles:
- What is Serverless Computing And How It Works
- How to Write a Serverless Code
- Serverless Computing Examples
- Serverless vs. Kubernetes
- Serverless vs. Microservices
- Serverless vs. PaaS
- Components of Serverless Computing Model
- Current Trends of Serverless Computing
- Front End and Back End Services
- Pros and Cons of Serverless Computing
- Serverless Computing Problems