Microservices are independently deployable services that work together to accomplish a larger goal. In a microservices architecture, each distinct business capability is represented as an independent service. These services communicate with each other through well-defined interfaces and APIs. There are several techniques that allow microservices to effectively communicate and interact with each other:
Service Discovery: For a microservice to interact with another, it first needs to find or discover where that service is located. This is done through a service discovery mechanism. Common service discovery tools include Consul, Etcd, Eureka, and Zookeeper. These centralized registries allow services to dynamically register themselves and discover the locations of other services. When a microservice needs to call another, it queries the discovery registry to get the IP address and port of the destination service instance.
Inter-Service Communication: Once a microservice locates another through discovery, it needs a protocol to communicate and make requests. The most common protocols for microservice communication are RESTful HTTP APIs and messaging queues. REST APIs allow services to make synchronous requests to each other using HTTP methods like GET, PUT, POST, DELETE. Messaging queues like RabbitMQ or Apache Kafka provide an asynchronous communication channel where services produce and consume messages.
Service Versioning: As microservices evolve independently, their contract or API definition may change over time which can break consumers. Semantic versioning is used to manage backwards compatibility of APIs and allow services to gracefully handle changes. Major versions indicate incompatible changes, minor versions add backwards compatible functionality, and patch versions are for backwards compatible bug fixes.
Circuit Breakers: Reliability patterns like circuit breakers protect microservices from cascading failures. A circuit breaker monitors for failures or slow responses when calling external services. After a configured threshold, it trips open and stops sending requests, instead immediately returning errors until it resets after a timeout. This prevents overloading other services during outages.
Client-Side Load Balancing: Since there may be multiple instances of a service running for scalability and high availability, clients need to distribute requests among them. Load balancers such as Ribbon from Netflix OSS or Spring Cloud LoadBalancer provide client-side service discovery and load balancing capabilities to ensure requests are evenly distributed. Service calls are weighted, throttled, and retried automatically in case of failures.
Data Management: Microservices may need to share data which raises challenges around data consistency, availability, and partitioning. Distributed data solutions like Event-Driven Architecture using streams process (Apache Kafka), Event Sourcing, CQRS patterns, and data grid caches (Hazelcast) help microservices share data while maintaining autonomy. Database per service and polyglot persistence is also common where each service uses the database best suited for its needs.
Security: As microservices communicate over distributed systems, security is paramount. Authentication ensures clients are authorized, typically using standards like JSON Web Tokens (JWTs). Transport Layer Security (TLS) encrypts the network traffic. Fine-grained authorization restricts access at the resource and method level. Other concerns like auditing, non-repudiation, and encryption at rest are addressed with tools like Spring Security, OAuth 2.0, Keycloak, Vault, and data encryption.
Monitoring and Logging: Observability is critical for microservices but difficult due to their distributed nature. Centralized logging, metrics, and monitoring services like Elasticsearch, Logstash, Kibana, Prometheus and Grafana provide insights into microservice performance, errors and account for traceability. Distributed tracing tools like Zipkin and Jaeger allow correlation of requests as they flow through multiple services. Alerting notifies operators about failures or performance degradation.
Deployment Pipelines: Continuous delivery is essential to deploy microservice changes rapidly and reliably. Automated workflows defined in pipelines using tools like Jenkins, GitLab CI/CD, Azure DevOps streamline building, testing, and deploying to ephemeral containers or production environments. Canary releasing, feature toggles, and rollback capabilities allow safe, controlled rollouts. Centralized configuration ensures parameter consistency.
This covers some of the major techniques and patterns for how microservices effectively communicate with each other at scale in a distributed systems context. Of course, there are many other considerations around operational aspects like high availability, disaster recovery, updating, and rolling back changes as well. Microservices leverage these interaction mechanisms while maintaining separation of concerns to be developed and deployed independently yet work together as a cohesive application.