APIs are used to make these services interact with each other. Each service is assigned to a team of developers, making it easier for them to focus on a particular technology. One big advantage offered by AWS microservices is efficiency while developing new applications or functions, the overall development time is reduced by a huge margin.
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What are Microservices in AWS?
Microservices are components of an application that are interconnected by APIs, Amazon web services use this strategy on a big scale while building applications.
AWS provides several services which can support microservices among the following categories:
- Computing: You can use microservices as containers on Amazon ECS or Kubernetes and with AWS serverless ecosystem.
- Storage: For data storage, microservices implement Amazon S3 and ElasticCache.
- Databases: Amazon RDS, Amazon Aurora, and DynamoDB.
- Messaging: Message queuing can be done via Simple Queue Service(SQS), and for notifications, microservices make use of AWS SNS.
- Monitoring: For monitoring API traffic, Microservices use AWS CloudTrail, and CloudWatch is used for monitoring infrastructure.
- CI/CD: CI/CD workflow in AWS is managed by Amazon ECR (Elastic Container Registry), AWS CloudFormation, and AWS CodeCommit.
- Networking: For communicating and coordinating purposes microservices can make use of Amazon ECS Service Discovery and AWS Mesh.
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Monolith Apps vs Microservices
| Monolith | Microservices |
| Each application component is created independently. | Each application component is created independently. |
| A change to a single component can have an impact on the entire application. | Any changes made to one component have no effect on the others. |
| If a single feature fails, the application as a whole fails. | A single component failure will not bring the entire system down. |
| Centralized data management. | Decentralized data management. |
| Needs large development teams. | Different small teams working independently. |
| Slow service startup. | Quick service startup. |
Benefits of AWS Microservices
Let’s talk about a few benefits that come with using Microservices:
- Flexible Scaling: With microservices, you can easily scale each individual service depending on the need of the application feature it will support.
- Agility: There are multiple small teams to which services are assigned. They work independently on these services which reduces the overall time for app development.
- Easy Deployment: You get the freedom of experimenting with new techniques with microservices, and in case anything goes wrong you can easily roll back. There is also continuous integration and delivery support.
- Resilience: The biggest benefit of microservices is that in case any component fails, the whole application still remains functional while degrading that specific functionality. Whereas in the case of a monolithic application if a feature fails, the whole application goes down.
- Reusable Code: When you write code for defining a function, that specific piece of code can also be used again as a building block for another feature within the same application. This gives developers the freedom of not writing code from scratch every time.
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Microservices Use Cases
We just discussed some benefits of microservices, now let’s discuss a few use cases:
- CPU or RAM Intensive Application Components: A CPU-intensive microservice can be quickly multiplied without affecting the performance of the rest of the application. It can be difficult to restart and redeploy text analysis programs that use a lot of RAM, you can split them in case they change rarely.
- Extensive data Pipeline: A microservice might manage each phase of the data preparation process, including data collection, cleaning, normalization, enrichment, aggregation, reporting, and so forth.
In the event that there are any issues found with the data, it would be simple to identify which microservice needs to be changed because the microservices lifecycle is easily traceable.
- Machine Learning: In order for the machine learning framework to calculate an output, a microservices-based machine learning environment can gather, aggregate, and evaluate data flow.
Before an outcome is reached in such an ML environment, the data must go through numerous phases. Utilizing microservices has the benefit of allowing several machine learning models to be applied to the same data in order to create a more accurate result.
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AWS Microservices Architecture
As we know that the ultimate goal of using microservices is to split an application into several independent services, the diagram below shows how a microservice is implemented on AWS.
User Interface
Most modern online apps are single-page, JavaScript-based, and communicate with one another using RESTful APIs. AWS provides Amazon CloudFront and S3 for hosting static web content.
Microservices
AWS offers building components to facilitate the creation of microservices. Running serverless services with AWS Lambda, running Docker containers in a serverless paradigm through AWS Fargate, and running managed Kubernetes clusters on AWS are three popular methods.
Data Storage
Data persistence for microservices must be done while ensuring scalability and avoiding centralized dependencies. Shared session data can be stored by AWS utilizing in-memory caches like Memcached or Redis. The ElastiCache managed service includes these two technologies.
To offload database reads, a popular technique is to put a cache between the application server and the database. Caching thereby decreases latency.
Also, to know about containerized efficiency try Docker in Redis integration.
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Implementing Microservices on AWS
The following three crucial AWS services can help you lower operational complexity and establish microsystem architecture:
- API management: Without needing to operate a server, you can build and use RESTful APIs with AWS API Gateway. It increases the security of microservice applications and serves as a gateway for web or mobile apps that are hosted on Amazon and other on-premises or cloud infrastructures.
- Lambda functions: The API Gateway and AWS Lambda collaborate as concurrent requests arrive at the application. This makes serverless microservice applications possible.
- Deploy Lambda-based applications: AWS CloudFormation is a tool for defining, deploying, and managing serverless applications. Before publishing the code to the AWS production environment, you can test it on a local PC using the AWS SAM CLI.
Conclusion
Microservices are a fantastic tool for creating, running and updating extremely scalable and resilient applications. AWS provides all the tools needed to replace these parts with open-source equivalents and offers a wide range of managed building blocks for handling every aspect of establishing microservices architecture on AWS.
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