Here's how you can optimize your microservices-based system architecture.

I will go to define clear API contracts for communication and use API contract first approach and then implement microservice. Defining clear contracts and version api that ensures our microservice boundaries clear and consistent

Always remember physical boundaries ARE NOT the same as logical boundaries. - try to finish a full workflow on one physical device as much as possible - do not take network latency for granted, once you have a high enough traffic it will drastically add up

A rule of thumb I like to use is that only one service should access an underlying data source (probably the database, but maybe files or something else). If two or more microservices are directly connecting to the same database, their boundaries overlap at the database and neither truly "owns" that database. If two microservices need to get data from the same database, consider creating a microservice for interacting with the database. The database microservice "owns" the database, and the other two microservices become clients of the database microservice. Something like PostgREST is a simple option for a MVP.

Optimizing a microservice-based system architecture involves defining clear boundaries between services. Here's how: 1. **Identify Service Responsibilities**: Clearly define each service's role and responsibilities based on business functions or domains. 2. **Decompose Monolithic Components**: Break down monolithic components into smaller, more manageable services, each focused on a single task or business capability. 3. **Establish Communication Protocols**: Use lightweight protocols like RESTful APIs or message queues to facilitate communication between services 4. **Set Service Boundaries**: Define clear boundaries between services to minimize dependencies and enable independent deployment, scaling, and maintenance.

Here are some key points to consider in order to optimize the Microservice-based system architecture of your planning process. 1.Domain-Driven Design (DDD): Divide your system into finite content based on business domains. Each microservice must represent an overlapping area in your system. 2.Service Interface: Define explicit interfaces for communication between microservices. Avoid direct database access between services, use APIs for communication. 3.Data structure: Each microservice should have its own database, allowing independent use of data. 4.Fault isolation and resilience: Boundaries must be defined in such a way that failures in one microservice do not trickle down to other services.

Docker container help a lot in scalability like we have docker swarm group we have manger leader and worker node and when application start will start on the bases of resources availability Docker's Swarm, allows to turn a group of Docker hosts into a single, virtual Docker host. It's known for its simplicity and ease of use, making it a good choice for smaller deployments or those deeply integrated with Docker tools

Runtime options The best choice for running a microservices application architecture is application containers. Containers encapsulate a lightweight runtime environment for your application, presenting a consistent software environment that can follow the application from the developer's desktop to testing to final production deployment, and you can run containers on physical or virtual machines.

To optimize your microservices-based system architecture, implement service discovery. Service discovery automates the process of finding network locations of services, enabling seamless communication between microservices. This enhances scalability, fault tolerance, and dynamic configuration, ensuring efficient and reliable service interactions.

Service Discovery is prime concept for micro service architecture. Choose a Service Discovery Mechanism is prime while designing Microservice architecture Client-side Discovery: The client service determines the location of available service instances and makes requests directly to them. Common tools: Netflix Eureka, Consul. Server-side Discovery: The client makes a request to a load balancer, which queries the service registry and forwards the request to the appropriate service instance. Common tools: AWS Elastic Load Balancing, Consul with Nginx

Polyglot Persistence: Use different databases suited to specific needs of each microservice. For example, use NoSQL databases for unstructured data and relational databases for transactional data. Event-Driven Architecture: Implement an event-driven approach to maintain data consistency across services. This ensures that updates and synchronization are handled efficiently.

Distributed Tracing: Use tools like Jaeger or Zipkin to trace requests as they move through different services. This helps identify performance issues and understand service dependencies. Blue-Green Deployments: Use blue-green deployments to switch traffic between two identical environments during updates, minimizing downtime and risk.

To optimize your microservices-based system architecture, implement robust monitoring. Track key metrics like response times, error rates, and resource usage. Utilize tools like Prometheus, Grafana, and ELK stack for comprehensive insights. Ensure real-time alerts for anomalies to maintain system health and performance.

Service registry health checks and monitors are critical component of a microservices architecture. They ensure that only healthy instances of a service are available to handle requests. Here’s an in-depth guide on implementing service registry health checks & monitors: Key Concepts Service Registry: A database that keeps track of all service instances. Examples include Consul, Eureka, and Zookeeper. Health Check Endpoint: A specific URL exposed by each service instance, used by the service registry to verify the instance's health. Health Check Configuration: The settings and parameters that define how health checks are performed, including the frequency and criteria for determining health.

Optimizing and securing a microservices-based architecture involves several key practices. One approach is to use container orchestration platforms like Kubernetes, which streamline the deployment, scaling, and management of microservices. For instance, Spotify migrated to a microservices architecture using Docker and Kubernetes, allowing them to scale services independently and reduce downtime. For security, implement API gateways to manage authentication and authorization, ensuring only valid requests reach your services. Netflix uses API gateways to handle millions of requests securely, incorporating OAuth for authentication and SSL/TLS for encrypted communications. and regularly update and patch your servicces

Infrastructure monitor takes big role for microservices as its distributed systems. So New Relic is good help to monitor infrastructure to identify the issue early.

Securing microservices isn't just a checkbox task—it's an ongoing challenge I've tackled head-on. Dapr’s mTLS is great for encrypting and authenticating service connections, ensuring your data is locked up tight. But don’t stop there. Dive into Istio for mutual TLS, adding another encryption layer. Libraries like OAuth2 offer robust token-based authentication, and Open Policy Agent manages authorization policies. Service meshes like Linkerd enforce encryption and access control across your network. Regularly updating your services and dependencies is like brushing your teeth—simple but crucial. By weaving these practices into your workflow, you stay ahead of threats.

It's also important to avoid pitfalls such as over engineering and complicated deployment strategies to make the architecture sustainable.

Canary Releases: Introduce new features to a small number of users at first, monitor their impact and ensure stability before full release. This technique assists in early defect identification. Chaos Engineering: Make it a habit in regularly causing managed failures to test for robustness of your architectures. This will help you identify and solve potential vulnerabilities. Serverless Functions: Employ serverless functions for tasks that have short lifetime or that are triggered by events. It minimizes operational costs and achieves scalable performance. Backward Compatibility: Ensure backward compatibility of your APIs. This ensures smooth transition to new versions by clients without breaking existing integrations.