Fewer moving parts and network hops compared to Server-side Discovery

Need to implement client-side service discovery logic for each programming language/framework used by your application, e.g Java/Scala

Compared to client-side discovery, the client code is simpler since it does not have to deal with discovery. Instead, a client simply makes a request to the router

Some cloud environments provide this functionality, e.g. AWS Elastic Load Balancer

Unless it’s part of the cloud environment, the router must is another system component that must be installed and configured. It will also need to be replicated for availability and capacity.

The router must support the necessary communication protocols (e.g HTTP, gRPC, Thrift, etc) unless it is TCP-based router

More network hops are required than when using Client Side Discovery

A service instance knows its own state so can implement a state model that’s more complex than UP/DOWN, e.g. STARTING, AVAILABLE, …

Couples the service to the Service Registry

must implement service registration logic in each programming language/framework that you use to write your services, e.g. NodeJS

A service instance that is running yet unable to handle requests will often lack the self-awareness to unregister itself from the service registry

AWS Cloud Watch

handling a large volume of logs requires substantial infrastructure

Spring Cloud Sleuth

It is easier to view exceptions and track their resolution

The exception tracking service is additional infrastructure

Spring Boot Actuator

The health check endpoint enables the health of a service instance to be periodically tested

The health check might not sufficiently comprehensive or the service instance might fail between health checks and so requests might still be routed to a failed service instance

AWS CloudTrail

Tracking Every Release by Mike Brittain

Enables deployments and changes to be easily correlated with issues leading to faster resolution.

Java Metrics Library

Prometheus client libraries

Prometheus

It provides deep insight into application behavior

Metrics code is intertwined with business logic making it more complicated

Aggregating metrics can require significant infrastructure

This pattern is widely used.

Provides a record of user actions

The auditing code is intertwined with the business logic, which makes the business logic more complicated

Event Sourcing is a reliable way to implement auditing

The identity of the requestor is securely passed around the system

Services can verify that the requestor is authorized to perform an operation

Private-tables-per-service – each service owns a set of tables that must only be accessed by that service

Schema-per-service – each service has a database schema that’s private to that service

Helps ensure that the services are loosely coupled. Changes to one service’s database does not impact any other services.

a service that does text searches could use ElasticSearch

a service that manipulates a social graph could use Neo4j.

Complexity of managing multiple SQL and NoSQL databases

Command Query Responsibility Segregation (CQRS)

https://microservices.io/patterns/data/saga.html