Monolithic architecture
Monolithic architecture is considered a traditional way and it’s easy to work with. At the startup’s beginning, monolithic architecture comes as a default because you want a simple start and have your business live as soon as possible. Monolithic architecture is great for proving an idea and looking for product-market fit.
A database, a user interface, and a server-side application are the three components of a typical monolith. Also, the term monolith is frequently used to refer to a server-side application that is constructed as a single unit, whereas monolithic architecture refers to a software architecture that is built as a single unit. With monoliths usually, one small team works on a whole software. Consider all of the MVPs and proofs-of-concept that small teams have created over time. Some of them grew into popular applications, and monolithic architecture was no longer an option.
Monolithic architecture becomes hard to maintain, the software is harder to scale compared to other architectures and it’s hard for multiple teams to work on one code base.
Microservices
Microservices are most often the outcome of an upgrade or the need to develop new features faster. Switching from a monolithic to a microservices architecture is not easy, but it may be extremely advantageous to your company. Microservices, in a nutshell, are small autonomous services that work together. Most developers are already accustomed to distributed systems, thus the general concept of microservices isn’t unfamiliar to them. The time for microservices is ideal, given the requirement for continuous integration, infrastructure automation, domain-driven design, and cross-functional teams, among other things. Although the preceding line may appear to be a jumble of buzzwords, those concepts do have an impact on software architecture. Although there are numerous rules for creating microservices, the industry is currently working on best practices and how to operate with them.
As the company grows, it can have multiple teams, where each team owns and develops a few microservice. One microservice is one codebase and is easier to scale than a monolith because you can scale just 1, 2, 3, …, N microservice depending on the needs.
As in every architecture, proper testing is important, e.g. integration tests look at how distinct modules (or classes) interact with one another, usually inside the same subsystem, to ensure that they work together as expected when delivering a high-level functionality. Integration tests also ensure that all communication pathways through the subsystem are proper, as well as detect any erroneous assumptions that each module may have about how its peers should behave.
Sometimes developers include lambda functions in this architecture because they’re easier to develop than a whole microservice. Lambda manages your computational resources and runs your code on high-availability infrastructure. Maintenance of the server and operating system, capacity provisioning and automatic scaling, code and security patch release, and code monitoring and logging are all part of this. All you have to do now is provide the code.
They might be an excellent deployment strategy for microservices since they provide the quickest time to market, the lowest entry cost, and high elasticity.
Event-driven architecture
An event-driven architecture, which is popular in current applications created using microservices, uses events to trigger and communicate across disconnected services. It is usually an update from microservice architecture. Teams must be comfortable working with microservices and have sufficient knowledge and experience to develop and deploy event-driven architecture, which firms typically employ with extensive technical expertise.
An event is a state change or update, such as when an item is added to a shopping cart on an e-commerce website. Events can have information on the item that is purchased, e.g. name, price, delivery address etc.
The three main components of event-driven architectures are event producers, event routers, and event consumers. The router filters and pushes events to consumers once a producer publishes an event to it. Producer and consumer services are decoupled, allowing for autonomous scaling, updating, and deployment.
Event-driven architecture can be used to coordinate systems between teams operating in and deploying across different regions. Using an event router to transfer data between systems, you may design, scale, and deploy services independently of other teams.
This architecture usually relies on eventual consistency. When an update is made to a distributed database, eventual consistency ensures that the change is reflected in all nodes that store the data, resulting in the same response every time the data is queried. When a database query returns the same data every time the same request is made, it is said to be consistent. Strong consistency indicates that the most recent data is returned, however, it may result in higher latency or delay owing to internal consistency techniques. Early on, results are less reliable than later on, but they arrive much faster with minimal latency. Because it takes time for updates to reach replicas across a database cluster, early results of eventual consistency data queries may not have the most current updates.
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Bruno Ziterbart
