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Introduction. Microservice Architecture. From Java EE To Cloud Native. Openshift VS Kubernetes

Introduction

Pets vs Cattle Analogy

  • cloudscaling.com: The History of Pets vs Cattle and How to Use the Analogy Properly
    • In the old way of doing things, we treat our servers like pets, for example Bob the mail server. If Bob goes down, it’s all hands on deck. The CEO can’t get his email and it’s the end of the world. In the new way, servers are numbered, like cattle in a herd. For example, www001 to www100. When one server goes down, it’s taken out back, shot, and replaced on the line.
    • Pets: Servers or server pairs that are treated as indispensable or unique systems that can never be down. Typically they are manually built, managed, and “hand fed”. Examples include mainframes, solitary servers, HA loadbalancers/firewalls (active/active or active/passive), database systems designed as master/slave (active/passive), and so on.
    • Cattle: Arrays of more than two servers, that are built using automated tools, and are designed for failure, where no one, two, or even three servers are irreplaceable. Typically, during failure events no human intervention is required as the array exhibits attributes of “routing around failures” by restarting failed servers or replicating data through strategies like triple replication or erasure coding. Examples include web server arrays, multi-master datastores such as Cassandra clusters, multiple racks of gear put together in clusters, and just about anything that is load-balanced and multi-master.
  • traefik.io: Pets vs. Cattle: The Future of Kubernetes in 2022

Technical Debt

  • medium: Technical debt 101 A primer about technical debt, legacy code, big rewrites and ancient wisdom for non technical managers

Twelve-Factor Apps in Kubernetes

Architecture Decision Records

Self service developer platform

Disaster Recovery

SaaS

Multi Cloud

Cloud Automation

Automation Glossary

Microservices Best Practices

Microservice Patterns

Microservices Anti Patterns

Backends for Frontends

Cloud Migration Checklist

Microservices Failures

Transform Legacy Java Apps to Microservices with automation tools

Namespaces for Data Structuring

From SysAdmin to Architect

Raft Consensus Algorithm

  • The Raft Consensus Algorithm 🌟 etcd is a “distributed reliable key-value store for the most critical data of a distributed system”. It uses the Raft consensus algorithm which was designed to be easy to understand, to scale, and to operate. The protocol and the etcd implementation were very quickly adopted by large distributed systems like Kubernetes, large distributed databases or messaging frameworks, where consensus and strong consistency is a must.

PaaS

Micro Frontend Architecture

Modular Monolith

From Java EE To Cloud Native

Monolith to Microservices Using the Strangler Pattern

Openshift VS Kubernetes

Career Path

Full Stack Developer’s Roadmap

Software Development Models

Software Development Tools

vFunction. A system to transform monolithic Java applications into microservices

Software in Automotive Industry

Bunch of Images

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microservices infographic

you dont need kubenetes

sw consumers

Openshift SaaS VS Kubernetes SaaS

Openshift VS Kubernetes

Kubernetes on its own is not enough

how mature is your microservices architecture

Videos

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Tweets

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