Microservices – Wikipedia

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Microservices concern Spring Cloud & Netflix OSS Kubernetes Configuration management: configuration for a microservice application needs to be externalized from the code and be retrievable via a simple service call. Spring Config Server, Netflix Archaius both support a Git-repository—based location for configuration. Archaius supports data typing of configuration. Kubernetes ConfigMaps exposes the configuration stored in etcd via services. Kubernetes Secrets supports the service-based secure deployment and usage of sensitive configuration information (such as passwords, certificates, etc.). Service discovery: maintain a list of service instances that are available for work within a microservice domain. Spring Cloud Eureka allows clients to register to it, maintains a heartbeat with registered clients, and maps service names to hostnames for clients that lookup services by service name. Kubernetes Services provide deployment-time registration of instances of services that are internally available within the cluster. Ingress is a mechanism whereby a service can be exposed to clients outside the cluster. Load balancing: The key to scaling a distributed system is being able to run more than one instance of a component. Load has to be then distributed across those instances via a load balancer. Spring Cloud Ribbon provides the ability for service clients to load balance across instances of the service. Kubernetes Service provides the ability for the service to be load-balanced across service instances. This is not the equivalent of what Ribbon provides. API gateway: The granularity of APIs provided by microservices is often different than what a service client needs. API Gateways implement facades and provide additional services like proxying, and protocol translation, and other management functions. Spring Cloud Zuul provides configuration-based API facades Kubernetes Service and Ingress resources, Istio, Ambassador are solutions that provide both north–south (traffic into and out of data center) as well as east–west (traffic across data centers or clouds or regions) API gateway functions. Zuul can also be implemented along with Kubernetes, providing configuration at individual service level. Security concerns: Many security concerns are pushed to the API gateway implementation. With distributed microservice applications, it makes sense to not reinvent the security wheel and allow for policy definition and implementation in components that are shared by all services. Spring Cloud Security addresses many security concerns through Spring Cloud Zuul The Kubernetes ecosystem provides service meshes like Istio, which are capable of providing security through their API gateway mechanisms. Centralized logging: It is important to have a centralized log gathering and analysis infrastructure to manage a plethora of services – many of which are operating in a distributed fashion. ELK Stack (Elasticsearch, LogStash, Kibana) EFK Stack (Elasticsearch, Fluentd, Kibana) Centralized metrics: A centralized area where the health and performance of the individual services and overall system can be monitored is essential to proper operations. Spring Spectator & Atlas Heapster, Prometheus, & Grafana Distributed tracing: Per-process logging and metric monitoring have their place, but neither can reconstruct the complex paths that transactions take as they propagate across a distributed system. Distributed tracing is an essential tool for a microservices platform. Spring Cloud Sleuth Hawkular, Jaeger Resilience and fault tolerance: Distributed systems must be capable of auto-routing around failures, and be capable of routing requests to the service instance that will provide an optimum response. Spring Hystrix, Turbine, & Ribbon Health check, service meshes (example: Istio)[61] Autoscaling and self-healing: Distributed systems respond to higher load by scaling horizontally: the platform must detect and auto-respond to such conditions. Furthermore, the system needs to detect failures and attempt auto-restarts without operator input. – Health check, self-healing, and auto-scaling Packaging, deployment, and scheduling: Large-scale systems require robust package management, and deployment systems to manage rolling or blue-green deployments, and rollbacks if necessary. A scheduler helps determine which particular execution node a new set of services can be deployed to based on current conditions. Spring Boot, Apache Maven. The Spring Cloud system does not have a true scheduler. Docker, Rkt, Kubernetes Scheduler & Deployment, Helm[62] Job management: scheduled computations disconnected from any individual user requests. Spring Batch Kubernetes Jobs and Scheduled Jobs Singleton application: limit a specific service to run as the only instance of that service within the entire system. Spring Cloud Cluster Kubernetes Pods

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