├── .github └── FUNDING.yaml ├── .gitignore └── README.md /.github/FUNDING.yaml: -------------------------------------------------------------------------------- 1 | # These are supported funding model platforms 2 | 3 | github: [rohitg00] # Replace with up to 4 GitHub Sponsors-enabled usernames 4 | patreon: # Replace with a single Patreon username 5 | open_collective: # Replace with a single Open Collective username 6 | ko_fi: # Replace with a single Ko-fi username 7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel 8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry 9 | liberapay: # Replace with a single Liberapay username 10 | issuehunt: # Replace with a single IssueHunt username 11 | otechie: # Replace with a single Otechie username 12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry 13 | custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] 14 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Node modules 2 | node_modules/ 3 | 4 | # Build output 5 | dist/ 6 | build/ 7 | 8 | # Environment variables 9 | .env 10 | .env.local 11 | .env.*.local 12 | 13 | # IDE files 14 | .idea/ 15 | .vscode/ 16 | *.swp 17 | *.swo 18 | 19 | # OS files 20 | .DS_Store 21 | Thumbs.db -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DevOps Interview Questions & Answers 2 | 3 | > Click :star: if you like the project. Pull Requests are highly appreciated. 4 | 5 | --- 6 | 7 | **Note:** This repository contains DevOps interview questions and answers. Please check the different sections for specific topics like Docker, Kubernetes, CI/CD, etc. 8 | 9 | ### Table of Contents 10 | 11 |
12 | 13 | Hide/Show table of contents 14 | 15 | 16 | | No. | Questions | 17 | | --- | --------- | 18 | | | **Core DevOps Concepts** | 19 | | 1 | [What is DevOps?](#what-is-devops) | 20 | | 2 | [What are the benefits of DevOps?](#what-are-the-benefits-of-devops) | 21 | | 3 | [What is Continuous Integration?](#what-is-continuous-integration) | 22 | | 4 | [What is Continuous Delivery?](#what-is-continuous-delivery) | 23 | | 5 | [What is Continuous Deployment?](#what-is-continuous-deployment) | 24 | | | **Docker** | 25 | | 6 | [What is Docker?](#what-is-docker) | 26 | | 7 | [What is the difference between Docker Image and Docker Container?](#what-is-the-difference-between-docker-image-and-docker-container) | 27 | | 8 | [What is Dockerfile?](#what-is-dockerfile) | 28 | | 9 | [What is Docker Compose?](#what-is-docker-compose) | 29 | | 10 | [Explain Docker Architecture](#explain-docker-architecture) | 30 | | | **Kubernetes** | 31 | | 11 | [What is Kubernetes?](#what-is-kubernetes) | 32 | | 12 | [What are the main components of Kubernetes architecture?](#what-are-the-main-components-of-kubernetes-architecture) | 33 | | 13 | [What is a Pod in Kubernetes?](#what-is-a-pod-in-kubernetes) | 34 | | 14 | [What is a Service in Kubernetes?](#what-is-a-service-in-kubernetes) | 35 | | 15 | [Explain the difference between Docker Swarm and Kubernetes](#explain-the-difference-between-docker-swarm-and-kubernetes) | 36 | | | **CI/CD** | 37 | | 16 | [What is CI/CD Pipeline?](#what-is-ci-cd-pipeline) | 38 | | 17 | [What is Jenkins?](#what-is-jenkins) | 39 | | 18 | [What are Jenkins Pipelines?](#what-are-jenkins-pipelines) | 40 | | 19 | [What is GitLab CI?](#what-is-gitlab-ci) | 41 | | 20 | [What is the difference between Continuous Delivery and Continuous Deployment?](#what-is-the-difference-between-continuous-delivery-and-continuous-deployment) | 42 | | | **Cloud Platforms** | 43 | | 21 | [What is Cloud Computing?](#what-is-cloud-computing) | 44 | | 22 | [What is AWS (Amazon Web Services)?](#what-is-aws) | 45 | | 23 | [What is Azure?](#what-is-azure) | 46 | | 24 | [What is Google Cloud Platform (GCP)?](#what-is-gcp) | 47 | | 25 | [What are the different types of cloud services?](#what-are-the-different-types-of-cloud-services) | 48 | | | **Infrastructure as Code** | 49 | | 26 | [What is Infrastructure as Code?](#what-is-infrastructure-as-code) | 50 | | 27 | [What is Terraform?](#what-is-terraform) | 51 | | 28 | [What is Ansible?](#what-is-ansible) | 52 | | 29 | [What is the difference between Ansible and Terraform?](#what-is-the-difference-between-ansible-and-terraform) | 53 | | 30 | [What are Terraform providers?](#what-are-terraform-providers) | 54 | | | **Monitoring and Logging** | 55 | | 31 | [What is monitoring in DevOps?](#what-is-monitoring-in-devops) | 56 | | 32 | [What is ELK Stack?](#what-is-elk-stack) | 57 | | 33 | [What is Prometheus?](#what-is-prometheus) | 58 | | 34 | [What is Grafana?](#what-is-grafana) | 59 | | 35 | [Explain the difference between monitoring and logging](#explain-the-difference-between-monitoring-and-logging) | 60 | | | **Security and Compliance** | 61 | | 36 | [What is DevSecOps?](#what-is-devsecops) | 62 | | 37 | [What is Infrastructure Security?](#what-is-infrastructure-security) | 63 | | 38 | [What is Container Security?](#what-is-container-security) | 64 | | 39 | [What is Compliance as Code?](#what-is-compliance-as-code) | 65 | | 40 | [What are Security Best Practices in DevOps?](#what-are-security-best-practices-in-devops) | 66 | | | **Linux Administration** | 67 | | 41 | [What are the basic Linux commands every DevOps engineer should know?](#what-are-the-basic-linux-commands-every-devops-engineer-should-know) | 68 | | 42 | [What is Shell Scripting?](#what-is-shell-scripting) | 69 | | 43 | [What is systemd?](#what-is-systemd) | 70 | | 44 | [How do you manage services in Linux?](#how-do-you-manage-services-in-linux) | 71 | | 45 | [What is Linux File System Hierarchy?](#what-is-linux-file-system-hierarchy) | 72 | | | **Version Control** | 73 | | 46 | [What is Git?](#what-is-git) | 74 | | 47 | [What is Git Branching Strategy?](#what-is-git-branching-strategy) | 75 | | 48 | [What is Git Flow?](#what-is-git-flow) | 76 | | 49 | [What is Trunk Based Development?](#what-is-trunk-based-development) | 77 | | 50 | [How to handle merge conflicts in Git?](#how-to-handle-merge-conflicts-in-git) | 78 | | | **Configuration Management** | 79 | | 51 | [What is Configuration Management?](#what-is-configuration-management) | 80 | | 52 | [What is Puppet?](#what-is-puppet) | 81 | | 53 | [What is Chef?](#what-is-chef) | 82 | | 54 | [What is Salt (SaltStack)?](#what-is-salt) | 83 | | 55 | [Compare different Configuration Management tools](#compare-different-configuration-management-tools) | 84 | | | **Scalability and High Availability** | 85 | | 56 | [What is Scalability in DevOps?](#what-is-scalability-in-devops) | 86 | | 57 | [What is High Availability?](#what-is-high-availability) | 87 | | 58 | [What is Load Balancing?](#what-is-load-balancing) | 88 | | 59 | [What is Auto Scaling?](#what-is-auto-scaling) | 89 | | 60 | [What is Disaster Recovery?](#what-is-disaster-recovery) | 90 | | | **Backup and Disaster Recovery** | 91 | | 61 | [What is Backup and Disaster Recovery?](#what-is-backup-and-disaster-recovery) | 92 | | 62 | [What are different types of backups?](#what-are-different-types-of-backups) | 93 | | 63 | [What is RPO and RTO?](#what-is-rpo-and-rto) | 94 | | 64 | [What is Business Continuity Planning?](#what-is-business-continuity-planning) | 95 | | 65 | [What are backup best practices?](#what-are-backup-best-practices) | 96 | | | **Cloud Native Architecture** | 97 | | 66 | [What is Cloud Native Architecture?](#what-is-cloud-native-architecture) | 98 | | 67 | [What are Microservices?](#what-are-microservices) | 99 | | 68 | [What is Service Mesh?](#what-is-service-mesh) | 100 | | 69 | [What is Event-Driven Architecture?](#what-is-event-driven-architecture) | 101 | | 70 | [What are the 12-Factor App principles?](#what-are-the-12-factor-app-principles) | 102 | | | **Performance Testing** | 103 | | 71 | [What is Performance Testing?](#what-is-performance-testing) | 104 | | 72 | [What are different types of Performance Tests?](#what-are-different-types-of-performance-tests) | 105 | | 73 | [What are Performance Testing Tools?](#what-are-performance-testing-tools) | 106 | | 74 | [What are Performance Testing Best Practices?](#what-are-performance-testing-best-practices) | 107 | | 75 | [How to analyze Performance Test Results?](#how-to-analyze-performance-test-results) | 108 | | | **API Gateway and Service Mesh** | 109 | | 76 | [What is an API Gateway?](#what-is-an-api-gateway) | 110 | | 77 | [What are the benefits of using API Gateway?](#what-are-the-benefits-of-using-api-gateway) | 111 | | 78 | [What is API Security?](#what-is-api-security) | 112 | | 79 | [What is Rate Limiting?](#what-is-rate-limiting) | 113 | | 80 | [What is API Documentation?](#what-is-api-documentation) | 114 | | | **Container Orchestration Advanced** | 115 | | 81 | [What are StatefulSets in Kubernetes?](#what-are-statefulsets-in-kubernetes) | 116 | | 82 | [What are DaemonSets in Kubernetes?](#what-are-daemonsets-in-kubernetes) | 117 | | 83 | [What is Helm?](#what-is-helm) | 118 | | 84 | [What is Istio?](#what-is-istio) | 119 | | 85 | [What is Container Runtime Interface (CRI)?](#what-is-container-runtime-interface) | 120 | | | **DevOps Tools and Automation** | 121 | | 86 | [What is Infrastructure Automation?](#what-is-infrastructure-automation) | 122 | | 87 | [What is GitOps?](#what-is-gitops) | 123 | | 88 | [What is ArgoCD?](#what-is-argocd) | 124 | | 89 | [What is Tekton?](#what-is-tekton) | 125 | | 90 | [What are Deployment Strategies?](#what-are-deployment-strategies) | 126 | | | **Cloud Cost Optimization** | 127 | | 91 | [What is Cloud Cost Optimization?](#what-is-cloud-cost-optimization) | 128 | | 92 | [What are Reserved Instances?](#what-are-reserved-instances) | 129 | | 93 | [What is Spot Instance pricing?](#what-is-spot-instance-pricing) | 130 | | 94 | [How to implement cost tagging strategy?](#how-to-implement-cost-tagging-strategy) | 131 | | 95 | [What are cost allocation reports?](#what-are-cost-allocation-reports) | 132 | | | **Site Reliability Engineering (SRE)** | 133 | | 96 | [What is Site Reliability Engineering?](#what-is-site-reliability-engineering) | 134 | | 97 | [What are Service Level Objectives (SLOs)?](#what-are-service-level-objectives) | 135 | | 98 | [What are Service Level Indicators (SLIs)?](#what-are-service-level-indicators) | 136 | | 99 | [What is Error Budget?](#what-is-error-budget) | 137 | | 100 | [What is Toil in SRE?](#what-is-toil-in-sre) | 138 | | | **DevOps Metrics and KPIs** | 139 | | 101 | [What are DevOps Metrics?](#what-are-devops-metrics) | 140 | | 102 | [What is Mean Time to Recovery (MTTR)?](#what-is-mean-time-to-recovery) | 141 | | 103 | [What is Change Failure Rate?](#what-is-change-failure-rate) | 142 | | 104 | [What is Deployment Frequency?](#what-is-deployment-frequency) | 143 | | 105 | [What is Lead Time for Changes?](#what-is-lead-time-for-changes) | 144 | | | **Serverless Architecture** | 145 | | 106 | [What is Serverless Computing?](#what-is-serverless-computing) | 146 | | 107 | [What is AWS Lambda?](#what-is-aws-lambda) | 147 | | 108 | [What are the benefits of Serverless?](#what-are-the-benefits-of-serverless) | 148 | | 109 | [What are Serverless Best Practices?](#what-are-serverless-best-practices) | 149 | | 110 | [What is Function as a Service (FaaS)?](#what-is-function-as-a-service) | 150 | | | **Database Management in DevOps** | 151 | | 111 | [What is Database DevOps?](#what-is-database-devops) | 152 | | 112 | [What is Database Version Control?](#what-is-database-version-control) | 153 | | 113 | [What are Database Migration Tools?](#what-are-database-migration-tools) | 154 | | 114 | [What is Database Backup Strategy?](#what-is-database-backup-strategy) | 155 | | 115 | [What is Database Performance Tuning?](#what-is-database-performance-tuning) | 156 | | | **Network Security** | 157 | | 116 | [What is Network Security in DevOps?](#what-is-network-security-in-devops) | 158 | | 117 | [What is Zero Trust Security?](#what-is-zero-trust-security) | 159 | | 118 | [What is SSL/TLS?](#what-is-ssl-tls) | 160 | | 119 | [What is a Web Application Firewall (WAF)?](#what-is-a-web-application-firewall) | 161 | | 120 | [What is Network Segmentation?](#what-is-network-segmentation) | 162 | | | **Incident Management** | 163 | | 121 | [What is Incident Management?](#what-is-incident-management) | 164 | | 122 | [What is an Incident Response Plan?](#what-is-an-incident-response-plan) | 165 | | 123 | [What is Post-Mortem Analysis?](#what-is-post-mortem-analysis) | 166 | | 124 | [What are Incident Severity Levels?](#what-are-incident-severity-levels) | 167 | | 125 | [What is On-Call Management?](#what-is-on-call-management) | 168 | | | **DevOps Culture and Practices** | 169 | | 126 | [What is DevOps Culture?](#what-is-devops-culture) | 170 | | 127 | [What are DevOps Best Practices?](#what-are-devops-best-practices) | 171 | | 128 | [What is Blameless Culture?](#what-is-blameless-culture) | 172 | | 129 | [What is Knowledge Sharing in DevOps?](#what-is-knowledge-sharing-in-devops) | 173 | | 130 | [What is Team Collaboration in DevOps?](#what-is-team-collaboration-in-devops) | 174 | | | **Infrastructure Monitoring** | 175 | | 131 | [What is Infrastructure Monitoring?](#what-is-infrastructure-monitoring) | 176 | | 132 | [What are Monitoring Tools?](#what-are-monitoring-tools) | 177 | | 133 | [What are Monitoring Best Practices?](#what-are-monitoring-best-practices) | 178 | | 134 | [What is Application Performance Monitoring?](#what-is-application-performance-monitoring) | 179 | | 135 | [What is Log Management?](#what-is-log-management) | 180 | | | **Cloud Migration** | 181 | | 136 | [What is Cloud Migration?](#what-is-cloud-migration) | 182 | | 137 | [What are Cloud Migration Strategies?](#what-are-cloud-migration-strategies) | 183 | | 138 | [What is Cloud Assessment?](#what-is-cloud-assessment) | 184 | | 139 | [What is Application Modernization?](#what-is-application-modernization) | 185 | | 140 | [What are Cloud Migration Tools?](#what-are-cloud-migration-tools) | 186 | | | **Advanced DevOps & Cloud** | 187 | | 141 | [What is Platform Engineering?](#what-is-platform-engineering) | 188 | | 142 | [What is FinOps?](#what-is-finops) | 189 | | 143 | [What is Policy as Code?](#what-is-policy-as-code) | 190 | | 144 | [What is Chaos Engineering?](#what-is-chaos-engineering) | 191 | | 145 | [What is Blue/Green Deployment?](#what-is-blue-green-deployment) | 192 | | 146 | [What is Feature Flagging?](#what-is-feature-flagging) | 193 | | 147 | [What is a Service Catalog?](#what-is-a-service-catalog) | 194 | | 148 | [What is a Service Level Agreement (SLA)?](#what-is-a-service-level-agreement-sla) | 195 | | 149 | [What is a Service Level Objective (SLO)?](#what-is-a-service-level-objective-slo) | 196 | | 150 | [What is a Service Level Indicator (SLI)?](#what-is-a-service-level-indicator-sli) | 197 | | 151 | [What is a Runbook?](#what-is-a-runbook) | 198 | | 152 | [What is a Playbook in Incident Response?](#what-is-a-playbook-in-incident-response) | 199 | | 153 | [What is Observability?](#what-is-observability) | 200 | | 154 | [What is Tracing in Observability?](#what-is-tracing-in-observability) | 201 | | 155 | [What is a Sidecar Pattern?](#what-is-a-sidecar-pattern) | 202 | | 156 | [What is a Service Mesh Control Plane?](#what-is-a-service-mesh-control-plane) | 203 | | 157 | [What is GitHub Actions?](#what-is-github-actions) | 204 | | 158 | [What is a Self-Healing System?](#what-is-a-self-healing-system) | 205 | | 159 | [What is Canary Analysis?](#what-is-canary-analysis) | 206 | | 160 | [What is Infrastructure Drift?](#what-is-infrastructure-drift) | 207 | 208 | ## Core DevOps Concepts 209 | 210 | 1. ### What is DevOps? 211 | 212 | DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary with Agile software development; several DevOps aspects came from Agile methodology. 213 | 214 | **[⬆ Back to Top](#table-of-contents)** 215 | 216 | 2. ### What are the benefits of DevOps? 217 | 218 | The main benefits of DevOps include: 219 | 220 | 1. Faster delivery of features 221 | 2. More stable operating environments 222 | 3. Improved communication and collaboration 223 | 4. More time to innovate (rather than fix/maintain) 224 | 5. Reduced deployment failures and rollbacks 225 | 6. Shorter mean time to recovery 226 | 227 | **[⬆ Back to Top](#table-of-contents)** 228 | 229 | 3. ### What is Continuous Integration? 230 | 231 | Continuous Integration (CI) is a development practice where developers integrate code into a shared repository frequently, preferably several times a day. Each integration can then be verified by an automated build and automated tests. 232 | 233 | Key aspects of CI include: 234 | - Maintaining a single source repository 235 | - Automating the build 236 | - Making the build self-testing 237 | - Everyone commits to the baseline every day 238 | - Every commit builds on an integration machine 239 | - Keep the build fast 240 | - Test in a clone of the production environment 241 | - Make it easy to get the latest deliverables 242 | - Everyone can see the results of the latest build 243 | - Automate deployment 244 | 245 | **[⬆ Back to Top](#table-of-contents)** 246 | 247 | ## Docker 248 | 249 | 6. ### What is Docker? 250 | 251 | Docker is a platform for developing, shipping, and running applications in containers. Containers allow developers to package up an application with all the parts it needs, such as libraries and other dependencies, and ship it all out as one package. 252 | 253 | **[⬆ Back to Top](#table-of-contents)** 254 | 255 | 7. ### What is the difference between Docker Image and Docker Container? 256 | 257 | - **Docker Image:** A Docker image is a read-only template containing a set of instructions for creating a Docker container. It includes the application code, runtime, libraries, dependencies, and system tools. 258 | 259 | - **Docker Container:** A container is a runnable instance of an image. You can create, start, stop, move, or delete a container using the Docker API or CLI. A container is isolated from other containers and the host machine. 260 | 261 | **[⬆ Back to Top](#table-of-contents)** 262 | 263 | 8. ### What is Dockerfile? 264 | 265 | A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image. Using `docker build`, users can create an automated build that executes several command-line instructions in succession. 266 | 267 | Example of a simple Dockerfile: 268 | ```dockerfile 269 | FROM node:14 270 | WORKDIR /app 271 | COPY package*.json ./ 272 | RUN npm install 273 | COPY . . 274 | EXPOSE 3000 275 | CMD ["npm", "start"] 276 | ``` 277 | 278 | **[⬆ Back to Top](#table-of-contents)** 279 | 280 | ## Kubernetes 281 | 282 | 11. ### What is Kubernetes? 283 | 284 | Kubernetes (K8s) is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It was originally developed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). 285 | 286 | **[⬆ Back to Top](#table-of-contents)** 287 | 288 | 12. ### What are the main components of Kubernetes architecture? 289 | 290 | Kubernetes architecture consists of the following main components: 291 | 292 | 1. **Master Node Components:** 293 | - API Server 294 | - etcd 295 | - Controller Manager 296 | - Scheduler 297 | 298 | 2. **Worker Node Components:** 299 | - Kubelet 300 | - Container Runtime 301 | - Kube Proxy 302 | 303 | **[⬆ Back to Top](#table-of-contents)** 304 | 305 | 13. ### What is a Pod in Kubernetes? 306 | 307 | A Pod is the smallest deployable unit in Kubernetes. It represents a single instance of a running process in your cluster. Pods can contain one or more containers, storage resources, a unique network IP, and options that govern how the container(s) should run. 308 | 309 | Example of a simple Pod YAML: 310 | ```yaml 311 | apiVersion: v1 312 | kind: Pod 313 | metadata: 314 | name: nginx-pod 315 | spec: 316 | containers: 317 | - name: nginx 318 | image: nginx:1.14.2 319 | ports: 320 | - containerPort: 80 321 | ``` 322 | 323 | **[⬆ Back to Top](#table-of-contents)** 324 | 325 | ## CI/CD 326 | 327 | 16. ### What is CI/CD Pipeline? 328 | 329 | A CI/CD Pipeline is a series of steps that must be performed in order to deliver a new version of software. A pipeline typically includes stages for: 330 | 331 | 1. Building the code 332 | 2. Running automated tests 333 | 3. Deploying to staging/production environments 334 | 335 | Example of a basic Jenkins Pipeline: 336 | ```groovy 337 | pipeline { 338 | agent any 339 | stages { 340 | stage('Build') { 341 | steps { 342 | sh 'npm install' 343 | sh 'npm run build' 344 | } 345 | } 346 | stage('Test') { 347 | steps { 348 | sh 'npm run test' 349 | } 350 | } 351 | stage('Deploy') { 352 | steps { 353 | sh './deploy.sh' 354 | } 355 | } 356 | } 357 | } 358 | ``` 359 | 360 | **[⬆ Back to Top](#table-of-contents)** 361 | 362 | 17. ### What is Jenkins? 363 | 364 | Jenkins is an open-source automation server that helps automate parts of software development related to building, testing, and deploying, facilitating continuous integration and continuous delivery (CI/CD). 365 | 366 | Key features include: 367 | - Easy installation and configuration 368 | - Hundreds of plugins available 369 | - Built-in GUI tool for easy updates 370 | - Supports distributed builds with master-slave architecture 371 | - Extensible with a huge number of plugins 372 | 373 | **[⬆ Back to Top](#table-of-contents)** 374 | 375 | ## Cloud Platforms 376 | 377 | 21. ### What is Cloud Computing? 378 | 379 | Cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale. 380 | 381 | **[⬆ Back to Top](#table-of-contents)** 382 | 383 | 22. ### What is AWS (Amazon Web Services)? 384 | 385 | AWS is a comprehensive and widely adopted cloud platform, offering over 200 fully featured services from data centers globally. Key services include: 386 | 387 | 1. **Compute:** 388 | - EC2 (Elastic Compute Cloud) 389 | - Lambda (Serverless Computing) 390 | - ECS (Elastic Container Service) 391 | 392 | 2. **Storage:** 393 | - S3 (Simple Storage Service) 394 | - EBS (Elastic Block Store) 395 | - EFS (Elastic File System) 396 | 397 | 3. **Database:** 398 | - RDS (Relational Database Service) 399 | - DynamoDB (NoSQL Database) 400 | - Redshift (Data Warehouse) 401 | 402 | **[⬆ Back to Top](#table-of-contents)** 403 | 404 | 23. ### What is Azure? 405 | 406 | Azure is Microsoft's cloud computing platform that provides a wide variety of services including: 407 | 408 | 1. **Compute Services:** 409 | - Virtual Machines 410 | - App Services 411 | - Azure Functions 412 | 413 | 2. **Storage Services:** 414 | - Blob Storage 415 | - File Storage 416 | - Queue Storage 417 | 418 | 3. **Network Services:** 419 | - Virtual Network 420 | - Load Balancer 421 | - Application Gateway 422 | 423 | **[⬆ Back to Top](#table-of-contents)** 424 | 425 | 25. ### What are the different types of cloud services? 426 | 427 | The main types of cloud services are: 428 | 429 | 1. **IaaS (Infrastructure as a Service):** 430 | - Provides virtualized computing resources 431 | - Examples: AWS EC2, Azure VMs 432 | 433 | 2. **PaaS (Platform as a Service):** 434 | - Provides platform allowing customers to develop, run, and manage applications 435 | - Examples: Heroku, Google App Engine 436 | 437 | 3. **SaaS (Software as a Service):** 438 | - Provides software applications over the internet 439 | - Examples: Salesforce, Google Workspace 440 | 441 | 4. **FaaS (Function as a Service):** 442 | - Provides serverless computing capabilities 443 | - Examples: AWS Lambda, Azure Functions 444 | 445 | **[⬆ Back to Top](#table-of-contents)** 446 | 447 | ## Infrastructure as Code 448 | 449 | 26. ### What is Infrastructure as Code? 450 | 451 | Infrastructure as Code (IaC) is the practice of managing and provisioning infrastructure through machine-readable definition files rather than physical hardware configuration or interactive configuration tools. 452 | 453 | Benefits of IaC: 454 | - Version Control 455 | - Reproducibility 456 | - Automation 457 | - Documentation 458 | - Consistency 459 | - Scalability 460 | 461 | **[⬆ Back to Top](#table-of-contents)** 462 | 463 | 27. ### What is Terraform? 464 | 465 | Terraform is an open-source IaC software tool that enables you to safely and predictably create, change, and improve infrastructure. It codifies cloud APIs into declarative configuration files. 466 | 467 | Example of a simple Terraform configuration: 468 | ```hcl 469 | provider "aws" { 470 | region = "us-west-2" 471 | } 472 | 473 | resource "aws_instance" "example" { 474 | ami = "ami-0c55b159cbfafe1f0" 475 | instance_type = "t2.micro" 476 | 477 | tags = { 478 | Name = "example-instance" 479 | } 480 | } 481 | ``` 482 | 483 | **[⬆ Back to Top](#table-of-contents)** 484 | 485 | 28. ### What is Ansible? 486 | 487 | Ansible is an open-source automation tool that automates software provisioning, configuration management, and application deployment. It uses YAML syntax for expressing automation jobs. 488 | 489 | Example of an Ansible playbook: 490 | ```yaml 491 | --- 492 | - name: Install and configure web server 493 | hosts: webservers 494 | become: yes 495 | 496 | tasks: 497 | - name: Install nginx 498 | apt: 499 | name: nginx 500 | state: present 501 | 502 | - name: Start nginx service 503 | service: 504 | name: nginx 505 | state: started 506 | ``` 507 | 508 | **[⬆ Back to Top](#table-of-contents)** 509 | 510 | ## Monitoring and Logging 511 | 512 | 31. ### What is monitoring in DevOps? 513 | 514 | Monitoring in DevOps is the practice of collecting and analyzing data about the performance and stability of services and infrastructure to improve the system's reliability. Key aspects include: 515 | 516 | 1. **Infrastructure Monitoring:** 517 | - Server health 518 | - Network performance 519 | - Resource utilization 520 | 521 | 2. **Application Monitoring:** 522 | - Response times 523 | - Error rates 524 | - Request rates 525 | 526 | 3. **User Experience Monitoring:** 527 | - Page load times 528 | - User interactions 529 | - Conversion rates 530 | 531 | **[⬆ Back to Top](#table-of-contents)** 532 | 533 | 32. ### What is ELK Stack? 534 | 535 | ELK Stack is a collection of three open-source products: 536 | 537 | 1. **Elasticsearch:** A search and analytics engine 538 | 2. **Logstash:** A server‑side data processing pipeline 539 | 3. **Kibana:** A visualization tool for Elasticsearch data 540 | 541 | Common use cases: 542 | - Log aggregation 543 | - Security analytics 544 | - Application performance monitoring 545 | - Website search 546 | - Business analytics 547 | 548 | **[⬆ Back to Top](#table-of-contents)** 549 | 550 | 33. ### What is Prometheus? 551 | 552 | Prometheus is an open-source systems monitoring and alerting toolkit. Key features include: 553 | 554 | 1. **Time series database** 555 | 2. **Flexible query language (PromQL)** 556 | 3. **Pull-based metrics collection** 557 | 4. **Alert management** 558 | 5. **Visualization capabilities** 559 | 560 | Example of Prometheus configuration: 561 | ```yaml 562 | global: 563 | scrape_interval: 15s 564 | 565 | scrape_configs: 566 | - job_name: 'prometheus' 567 | static_configs: 568 | - targets: ['localhost:9090'] 569 | 570 | - job_name: 'node' 571 | static_configs: 572 | - targets: ['localhost:9100'] 573 | ``` 574 | 575 | **[⬆ Back to Top](#table-of-contents)** 576 | 577 | 34. ### What is Grafana? 578 | 579 | Grafana is an open-source analytics and monitoring solution that allows you to query, visualize, and alert on your metrics no matter where they are stored. Key features include: 580 | 581 | 1. **Data source integration** 582 | 2. **Dashboard creation** 583 | 3. **Alerting** 584 | 4. **Visualization** 585 | 5. **User interface** 586 | 587 | **[⬆ Back to Top](#table-of-contents)** 588 | 589 | 35. ### Explain the difference between monitoring and logging 590 | 591 | Monitoring and logging are two different practices in DevOps: 592 | 593 | 1. **Monitoring:** 594 | - Focuses on collecting and analyzing data about the performance and stability of services and infrastructure to improve the system's reliability. 595 | - Key aspects include: 596 | - Infrastructure Monitoring 597 | - Application Monitoring 598 | - User Experience Monitoring 599 | 600 | 2. **Logging:** 601 | - Focuses on collecting and analyzing log data to help diagnose and troubleshoot issues. 602 | - Key aspects include: 603 | - Log aggregation 604 | - Security analytics 605 | - Application performance monitoring 606 | - Website search 607 | - Business analytics 608 | 609 | **[⬆ Back to Top](#table-of-contents)** 610 | 611 | ## Security and Compliance 612 | 613 | 36. ### What is DevSecOps? 614 | 615 | DevSecOps is the practice of integrating security practices within the DevOps process. It creates a 'security as code' culture with ongoing, flexible collaboration between release engineers and security teams. 616 | 617 | Key principles include: 618 | - Security automation 619 | - Early security testing 620 | - Continuous security monitoring 621 | - Security as part of CI/CD pipeline 622 | - Rapid security feedback 623 | 624 | **[⬆ Back to Top](#table-of-contents)** 625 | 626 | 37. ### What is Infrastructure Security? 627 | 628 | Infrastructure Security involves securing all infrastructure components including: 629 | 630 | 1. **Network Security:** 631 | - Firewalls 632 | - VPNs 633 | - Network segmentation 634 | - DDoS protection 635 | 636 | 2. **Cloud Security:** 637 | - Identity and Access Management (IAM) 638 | - Encryption 639 | - Security groups 640 | - Network ACLs 641 | 642 | 3. **Host Security:** 643 | - OS hardening 644 | - Patch management 645 | - Antivirus 646 | - Host-based firewalls 647 | 648 | **[⬆ Back to Top](#table-of-contents)** 649 | 650 | ## Linux Administration 651 | 652 | 41. ### What are the basic Linux commands every DevOps engineer should know? 653 | 654 | Essential Linux commands include: 655 | 656 | 1. **File Operations:** 657 | ```bash 658 | ls # List files and directories 659 | cd # Change directory 660 | pwd # Print working directory 661 | cp # Copy files 662 | mv # Move/rename files 663 | rm # Remove files 664 | mkdir # Create directory 665 | ``` 666 | 667 | 2. **System Information:** 668 | ```bash 669 | top # Show processes 670 | df # Show disk usage 671 | free # Show memory usage 672 | ps # Show process status 673 | ``` 674 | 675 | 3. **Text Processing:** 676 | ```bash 677 | grep # Search text 678 | sed # Stream editor 679 | awk # Text processing 680 | cat # View file contents 681 | ``` 682 | 683 | **[⬆ Back to Top](#table-of-contents)** 684 | 685 | ## Version Control 686 | 687 | 46. ### What is Git? 688 | 689 | Git is a distributed version control system that tracks changes in source code during software development. It's designed for coordinating work among programmers, but it can be used to track changes in any set of files. 690 | 691 | Key concepts include: 692 | - Repository 693 | - Commit 694 | - Branch 695 | - Merge 696 | - Pull Request 697 | - Clone 698 | - Push/Pull 699 | 700 | **[⬆ Back to Top](#table-of-contents)** 701 | 702 | 47. ### What is Git Branching Strategy? 703 | 704 | A Git branching strategy is a convention or set of rules that specify how and when branches should be created and merged. Common strategies include: 705 | 706 | 1. **Git Flow:** 707 | - Main branches: master, develop 708 | - Supporting branches: feature, release, hotfix 709 | 710 | 2. **Trunk-Based Development:** 711 | - Single main branch (trunk) 712 | - Short-lived feature branches 713 | - Frequent integration 714 | 715 | Example of creating a feature branch: 716 | ```bash 717 | # Create and switch to a new feature branch 718 | git checkout -b feature/new-feature 719 | 720 | # Make changes and commit 721 | git add . 722 | git commit -m "Add new feature" 723 | 724 | # Push to remote 725 | git push origin feature/new-feature 726 | ``` 727 | 728 | **[⬆ Back to Top](#table-of-contents)** 729 | 730 | ## Configuration Management 731 | 732 | 51. ### What is Configuration Management? 733 | 734 | Configuration Management is the process of maintaining systems, such as computer systems and servers, in a desired state. It's a way to make sure that a system performs as it's supposed to as changes are made over time. 735 | 736 | Key aspects include: 737 | - System configuration 738 | - Application configuration 739 | - Dependencies management 740 | - Version control 741 | - Compliance and security 742 | 743 | **[⬆ Back to Top](#table-of-contents)** 744 | 745 | 52. ### What is Puppet? 746 | 747 | Puppet is a configuration management tool that helps you automate the provisioning and management of your infrastructure. It uses a declarative language to describe system configurations. 748 | 749 | Example of a Puppet manifest: 750 | ```puppet 751 | class apache { 752 | package { 'apache2': 753 | ensure => installed, 754 | } 755 | 756 | service { 'apache2': 757 | ensure => running, 758 | enable => true, 759 | require => Package['apache2'], 760 | } 761 | 762 | file { '/var/www/html/index.html': 763 | ensure => file, 764 | content => 'Hello, World!', 765 | require => Package['apache2'], 766 | } 767 | } 768 | ``` 769 | 770 | **[⬆ Back to Top](#table-of-contents)** 771 | 772 | ## Scalability and High Availability 773 | 774 | 56. ### What is Scalability in DevOps? 775 | 776 | Scalability is the capability of a system to handle a growing amount of work by adding resources to the system. There are two types of scaling: 777 | 778 | 1. **Vertical Scaling (Scale Up):** 779 | - Adding more power to existing resources 780 | - Example: Upgrading CPU/RAM 781 | 782 | 2. **Horizontal Scaling (Scale Out):** 783 | - Adding more resources 784 | - Example: Adding more servers 785 | 786 | **[⬆ Back to Top](#table-of-contents)** 787 | 788 | 57. ### What is High Availability? 789 | 790 | High Availability (HA) is a characteristic of a system that aims to ensure an agreed level of operational performance, usually uptime, for a higher than normal period. 791 | 792 | Key components: 793 | 1. **Redundancy:** 794 | - Multiple instances 795 | - No single point of failure 796 | 797 | 2. **Monitoring:** 798 | - Health checks 799 | - Automated failover 800 | 801 | 3. **Load Balancing:** 802 | - Traffic distribution 803 | - Resource optimization 804 | 805 | **[⬆ Back to Top](#table-of-contents)** 806 | 807 | 58. ### What is Load Balancing? 808 | 809 | Load Balancing is the process of distributing network traffic across multiple servers to ensure no single server bears too much demand. 810 | 811 | Common Load Balancing algorithms: 812 | 1. **Round Robin** 813 | 2. **Least Connections** 814 | 3. **IP Hash** 815 | 4. **Weighted Round Robin** 816 | 5. **Resource-Based** 817 | 818 | Example of Nginx Load Balancer configuration: 819 | ```nginx 820 | http { 821 | upstream backend { 822 | server backend1.example.com; 823 | server backend2.example.com; 824 | server backend3.example.com; 825 | } 826 | 827 | server { 828 | listen 80; 829 | location / { 830 | proxy_pass http://backend; 831 | } 832 | } 833 | } 834 | ``` 835 | 836 | **[⬆ Back to Top](#table-of-contents)** 837 | 838 | 59. ### What is Auto Scaling? 839 | 840 | Auto Scaling is a feature that automatically adjusts the number of compute resources based on the current demand. 841 | 842 | Key concepts: 843 | 1. **Scaling Policies:** 844 | - Target tracking 845 | - Step scaling 846 | - Simple scaling 847 | 848 | 2. **Metrics:** 849 | - CPU utilization 850 | - Memory usage 851 | - Request count 852 | - Custom metrics 853 | 854 | Example of AWS Auto Scaling configuration: 855 | ```yaml 856 | AutoScalingGroup: 857 | MinSize: 1 858 | MaxSize: 10 859 | DesiredCapacity: 2 860 | HealthCheckType: ELB 861 | HealthCheckGracePeriod: 300 862 | LaunchTemplate: 863 | LaunchTemplateId: !Ref LaunchTemplate 864 | Version: !GetAtt LaunchTemplate.LatestVersionNumber 865 | ``` 866 | 867 | **[⬆ Back to Top](#table-of-contents)** 868 | 869 | ## Backup and Disaster Recovery 870 | 871 | 61. ### What is Backup and Disaster Recovery? 872 | 873 | Backup and Disaster Recovery (BDR) is a combination of data backup and disaster recovery solutions that work together to ensure an organization's business continuity. 874 | 875 | Key components: 876 | 1. **Data Backup:** 877 | - Regular data copies 878 | - Multiple backup locations 879 | - Automated backup processes 880 | 881 | 2. **Disaster Recovery:** 882 | - Recovery procedures 883 | - Failover systems 884 | - Business continuity plans 885 | 886 | **[⬆ Back to Top](#table-of-contents)** 887 | 888 | 62. ### What are different types of backups? 889 | 890 | Common backup types include: 891 | 892 | 1. **Full Backup:** 893 | - Complete copy of all data 894 | - Most time and space consuming 895 | - Fastest restore time 896 | 897 | 2. **Incremental Backup:** 898 | - Only backs up changes since last backup 899 | - Faster and requires less storage 900 | - Longer restore time 901 | 902 | 3. **Differential Backup:** 903 | - Backs up changes since last full backup 904 | - Balance between full and incremental 905 | - Medium restore time 906 | 907 | **[⬆ Back to Top](#table-of-contents)** 908 | 909 | ## Cloud Native Architecture 910 | 911 | 66. ### What is Cloud Native Architecture? 912 | 913 | Cloud Native Architecture is an approach to designing and building applications that exploits the advantages of the cloud computing delivery model. It emphasizes: 914 | 915 | 1. **Characteristics:** 916 | - Scalability 917 | - Containerization 918 | - Automation 919 | - Orchestration 920 | - Microservices 921 | 922 | 2. **Key Principles:** 923 | - Design for automation 924 | - Build for resilience 925 | - Enable scalability 926 | - Embrace containerization 927 | - Practice continuous delivery 928 | 929 | **[⬆ Back to Top](#table-of-contents)** 930 | 931 | 67. ### What are Microservices? 932 | 933 | Microservices is an architectural style that structures an application as a collection of small autonomous services, modeled around a business domain. 934 | 935 | Key characteristics: 936 | 1. **Independence:** 937 | - Separate codebases 938 | - Independent deployment 939 | - Different technology stacks 940 | 941 | 2. **Communication:** 942 | - API-based interaction 943 | - Event-driven 944 | - Service discovery 945 | 946 | Example of a microservice API: 947 | ```yaml 948 | openapi: 3.0.0 949 | info: 950 | title: User Service API 951 | version: 1.0.0 952 | paths: 953 | /users: 954 | get: 955 | summary: List users 956 | responses: 957 | '200': 958 | description: List of users 959 | post: 960 | summary: Create user 961 | responses: 962 | '201': 963 | description: User created 964 | ``` 965 | 966 | **[⬆ Back to Top](#table-of-contents)** 967 | 968 | 68. ### What is Service Mesh? 969 | 970 | A service mesh is a dedicated infrastructure layer for handling service-to-service communication in microservices architectures. 971 | 972 | Key components: 973 | 1. **Data Plane:** 974 | - Service proxies (sidecars) 975 | - Traffic handling 976 | - Security enforcement 977 | 978 | 2. **Control Plane:** 979 | - Configuration management 980 | - Policy enforcement 981 | - Service discovery 982 | 983 | Example of Istio configuration: 984 | ```yaml 985 | apiVersion: networking.istio.io/v1alpha3 986 | kind: VirtualService 987 | metadata: 988 | name: reviews-route 989 | spec: 990 | hosts: 991 | - reviews 992 | http: 993 | - route: 994 | - destination: 995 | host: reviews 996 | subset: v1 997 | weight: 75 998 | - destination: 999 | host: reviews 1000 | subset: v2 1001 | weight: 25 1002 | ``` 1003 | 1004 | **[⬆ Back to Top](#table-of-contents)** 1005 | 1006 | ## Performance Testing 1007 | 1008 | 71. ### What is Performance Testing? 1009 | 1010 | Performance Testing is a type of testing to determine how a system performs in terms of responsiveness and stability under various workload conditions. 1011 | 1012 | Key aspects include: 1013 | 1. **Performance Metrics:** 1014 | - Response time 1015 | - Throughput 1016 | - Resource utilization 1017 | - Scalability 1018 | - Reliability 1019 | 1020 | 2. **Testing Goals:** 1021 | - Identify bottlenecks 1022 | - Determine system capacity 1023 | - Validate performance requirements 1024 | - Benchmark performance 1025 | 1026 | **[⬆ Back to Top](#table-of-contents)** 1027 | 1028 | 72. ### What are different types of Performance Tests? 1029 | 1030 | Common types of performance tests include: 1031 | 1032 | 1. **Load Testing:** 1033 | - Tests system behavior under specific load 1034 | - Validates system performance under expected conditions 1035 | 1036 | 2. **Stress Testing:** 1037 | - Tests system behavior under peak load 1038 | - Identifies breaking points 1039 | 1040 | 3. **Endurance Testing:** 1041 | - Tests system behavior over extended periods 1042 | - Identifies memory leaks and resource issues 1043 | 1044 | Example of JMeter test plan: 1045 | ```xml 1046 | 1047 | 1048 | 1049 | 1050 | 1051 | 1052 | 1053 | 1054 | false 1055 | false 1056 | 1057 | 1058 | 1059 | ``` 1060 | 1061 | **[⬆ Back to Top](#table-of-contents)** 1062 | 1063 | ## API Gateway and Service Mesh 1064 | 1065 | 76. ### What is an API Gateway? 1066 | 1067 | An API Gateway acts as a reverse proxy to accept all API calls, aggregate various services, and return the appropriate result. 1068 | 1069 | Key features: 1070 | 1. **Request Handling:** 1071 | - Authentication 1072 | - SSL termination 1073 | - Rate limiting 1074 | 1075 | 2. **Integration:** 1076 | - Service discovery 1077 | - Request routing 1078 | - Response transformation 1079 | 1080 | Example of Kong API Gateway configuration: 1081 | ```yaml 1082 | services: 1083 | - name: user-service 1084 | url: http://user-service:8000 1085 | routes: 1086 | - name: user-route 1087 | paths: 1088 | - /users 1089 | plugins: 1090 | - name: rate-limiting 1091 | config: 1092 | minute: 5 1093 | policy: local 1094 | ``` 1095 | 1096 | **[⬆ Back to Top](#table-of-contents)** 1097 | 1098 | 77. ### What are the benefits of using API Gateway? 1099 | 1100 | Key benefits include: 1101 | 1102 | 1. **Security:** 1103 | - Centralized authentication 1104 | - Authorization 1105 | - SSL/TLS termination 1106 | 1107 | 2. **Performance:** 1108 | - Caching 1109 | - Request/Response transformation 1110 | - Load balancing 1111 | 1112 | 3. **Monitoring:** 1113 | - Analytics 1114 | - Logging 1115 | - Rate limiting 1116 | 1117 | **[⬆ Back to Top](#table-of-contents)** 1118 | 1119 | 78. ### What is API Security? 1120 | 1121 | API Security involves protecting APIs from threats and vulnerabilities while ensuring they remain accessible to authorized users. 1122 | 1123 | Key security measures: 1124 | 1. **Authentication:** 1125 | - API keys 1126 | - OAuth 2.0 1127 | - JWT tokens 1128 | 1129 | 2. **Authorization:** 1130 | - Role-based access control 1131 | - Scope-based access 1132 | - Resource-level permissions 1133 | 1134 | Example of OAuth2 configuration: 1135 | ```yaml 1136 | security: 1137 | oauth2: 1138 | client: 1139 | clientId: ${CLIENT_ID} 1140 | clientSecret: ${CLIENT_SECRET} 1141 | resource: 1142 | tokenInfoUri: https://api.auth.com/oauth/check_token 1143 | ``` 1144 | 1145 | **[⬆ Back to Top](#table-of-contents)** 1146 | 1147 | 79. ### What is Rate Limiting? 1148 | 1149 | Rate Limiting is a technique used to control the rate at which requests are processed or transmitted. 1150 | 1151 | Key concepts: 1152 | 1. **Token Bucket Algorithm:** 1153 | - Fixed number of tokens 1154 | - Tokens are replenished at a fixed rate 1155 | - Tokens are consumed at a variable rate 1156 | 1157 | 2. **Leaky Bucket Algorithm:** 1158 | - Fixed size bucket 1159 | - Water leaks out at a fixed rate 1160 | - Water is added at a variable rate 1161 | 1162 | Example of Nginx Rate Limiting configuration: 1163 | ```nginx 1164 | http { 1165 | limit_req_zone $binary_remote_addr zone=one:10m rate=1r/s; 1166 | 1167 | server { 1168 | location / { 1169 | limit_req burst=5 nodelay; 1170 | } 1171 | } 1172 | } 1173 | ``` 1174 | 1175 | **[⬆ Back to Top](#table-of-contents)** 1176 | 1177 | 80. ### What is API Documentation? 1178 | 1179 | API Documentation is a set of documents that describe how to use an API. It includes: 1180 | 1181 | 1. **API Reference:** 1182 | - Detailed description of each API endpoint 1183 | - Request and response formats 1184 | - Example requests and responses 1185 | 1186 | 2. **API Usage Examples:** 1187 | - Code samples 1188 | - API client libraries 1189 | - API testing tools 1190 | 1191 | Example of Swagger API Documentation: 1192 | ```yaml 1193 | swagger: '2.0' 1194 | info: 1195 | title: User Service API 1196 | version: 1.0.0 1197 | paths: 1198 | /users: 1199 | get: 1200 | summary: List users 1201 | responses: 1202 | '200': 1203 | description: List of users 1204 | post: 1205 | summary: Create user 1206 | responses: 1207 | '201': 1208 | description: User created 1209 | ``` 1210 | 1211 | **[⬆ Back to Top](#table-of-contents)** 1212 | 1213 | ## Container Orchestration Advanced 1214 | 1215 | 81. ### What are StatefulSets in Kubernetes? 1216 | 1217 | StatefulSets are used to manage stateful applications, providing guarantees about the ordering and uniqueness of Pods. 1218 | 1219 | Key features: 1220 | 1. **Stable Network Identity:** 1221 | - Predictable Pod names 1222 | - Stable hostnames 1223 | 1224 | 2. **Ordered Deployment:** 1225 | - Sequential creation 1226 | - Sequential scaling 1227 | - Sequential deletion 1228 | 1229 | Example of StatefulSet: 1230 | ```yaml 1231 | apiVersion: apps/v1 1232 | kind: StatefulSet 1233 | metadata: 1234 | name: web 1235 | spec: 1236 | serviceName: "nginx" 1237 | replicas: 3 1238 | selector: 1239 | matchLabels: 1240 | app: nginx 1241 | template: 1242 | metadata: 1243 | labels: 1244 | app: nginx 1245 | spec: 1246 | containers: 1247 | - name: nginx 1248 | image: nginx:1.14.2 1249 | ports: 1250 | - containerPort: 80 1251 | volumeMounts: 1252 | - name: www 1253 | mountPath: /usr/share/nginx/html 1254 | volumeClaimTemplates: 1255 | - metadata: 1256 | name: www 1257 | spec: 1258 | accessModes: [ "ReadWriteOnce" ] 1259 | resources: 1260 | requests: 1261 | storage: 1Gi 1262 | ``` 1263 | 1264 | **[⬆ Back to Top](#table-of-contents)** 1265 | 1266 | 82. ### What are DaemonSets in Kubernetes? 1267 | 1268 | DaemonSets ensure that all (or some) nodes run a copy of a Pod. As nodes are added to the cluster, Pods are added to them. 1269 | 1270 | Use cases: 1271 | 1. **Monitoring Agents** 1272 | 2. **Log Collectors** 1273 | 3. **Node-level Storage** 1274 | 4. **Network Plugins** 1275 | 1276 | Example of DaemonSet: 1277 | ```yaml 1278 | apiVersion: apps/v1 1279 | kind: DaemonSet 1280 | metadata: 1281 | name: fluentd-elasticsearch 1282 | spec: 1283 | selector: 1284 | matchLabels: 1285 | name: fluentd-elasticsearch 1286 | template: 1287 | metadata: 1288 | labels: 1289 | name: fluentd-elasticsearch 1290 | spec: 1291 | containers: 1292 | - name: fluentd-elasticsearch 1293 | image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2 1294 | ``` 1295 | 1296 | **[⬆ Back to Top](#table-of-contents)** 1297 | 1298 | 83. ### What is Helm? 1299 | 1300 | Helm is a package manager for Kubernetes that helps you manage Kubernetes applications through Helm Charts. 1301 | 1302 | Key concepts: 1303 | 1. **Charts:** 1304 | - Package format 1305 | - Collection of files 1306 | - Template mechanism 1307 | 1308 | 2. **Repositories:** 1309 | - Chart storage 1310 | - Version control 1311 | - Distribution 1312 | 1313 | Example of Helm Chart: 1314 | ```yaml 1315 | apiVersion: v2 1316 | name: my-app 1317 | description: A Helm chart for my application 1318 | version: 0.1.0 1319 | dependencies: 1320 | - name: mysql 1321 | version: 8.8.3 1322 | repository: https://charts.bitnami.com/bitnami 1323 | ``` 1324 | 1325 | **[⬆ Back to Top](#table-of-contents)** 1326 | 1327 | 84. ### What is Istio? 1328 | 1329 | Istio is an open-source service mesh that provides a way to control how services communicate with one another. It includes: 1330 | 1331 | 1. **Traffic Management:** 1332 | - Load balancing 1333 | - Traffic routing 1334 | - Fault injection 1335 | - Traffic mirroring 1336 | 1337 | 2. **Security:** 1338 | - Authentication 1339 | - Authorization 1340 | - Encryption 1341 | - Mutual TLS 1342 | 1343 | 3. **Observability:** 1344 | - Telemetry 1345 | - Metrics 1346 | - Tracing 1347 | - Logging 1348 | 1349 | **[⬆ Back to Top](#table-of-contents)** 1350 | 1351 | 85. ### What is Container Runtime Interface (CRI)? 1352 | 1353 | Container Runtime Interface (CRI) is an API that allows container runtimes to interact with the container orchestrator. It includes: 1354 | 1355 | 1. **Image Management:** 1356 | - Pulling images 1357 | - Pushing images 1358 | - Listing images 1359 | - Deleting images 1360 | 1361 | 2. **Container Management:** 1362 | - Creating containers 1363 | - Starting containers 1364 | - Stopping containers 1365 | - Killing containers 1366 | - Inspecting containers 1367 | 1368 | 3. **Container Runtime:** 1369 | - Running containers 1370 | - Pausing containers 1371 | - Resuming containers 1372 | - Executing commands in containers 1373 | 1374 | **[⬆ Back to Top](#table-of-contents)** 1375 | 1376 | ## DevOps Tools and Automation 1377 | 1378 | 86. ### What is Infrastructure Automation? 1379 | 1380 | Infrastructure Automation is the process of scripting environments - from installing an operating system, to installing and configuring servers on instances, to configuring how the instances and software communicate with one another. 1381 | 1382 | Key components: 1383 | 1. **Provisioning:** 1384 | - Resource creation 1385 | - Configuration management 1386 | - Application deployment 1387 | 1388 | 2. **Orchestration:** 1389 | - Workflow automation 1390 | - Service coordination 1391 | - Resource scheduling 1392 | 1393 | **[⬆ Back to Top](#table-of-contents)** 1394 | 1395 | 87. ### What is GitOps? 1396 | 1397 | GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools. 1398 | 1399 | Principles: 1400 | 1. **Declarative:** 1401 | - Infrastructure as code 1402 | - Application configuration as code 1403 | 1404 | 2. **Version Controlled:** 1405 | - Git as single source of truth 1406 | - Audit trail for changes 1407 | 1408 | 3. **Automated:** 1409 | - Pull-based deployment 1410 | - Continuous reconciliation 1411 | 1412 | **[⬆ Back to Top](#table-of-contents)** 1413 | 1414 | 88. ### What is ArgoCD? 1415 | 1416 | ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes. It allows you to declaratively manage your Kubernetes applications by using Git repositories as the source of truth. 1417 | 1418 | Key features: 1419 | 1. **Declarative:** 1420 | - Infrastructure as code 1421 | - Application configuration as code 1422 | 1423 | 2. **Version Controlled:** 1424 | - Git as single source of truth 1425 | - Audit trail for changes 1426 | 1427 | 3. **Automated:** 1428 | - Pull-based deployment 1429 | - Continuous reconciliation 1430 | 1431 | **[⬆ Back to Top](#table-of-contents)** 1432 | 1433 | 89. ### What is Tekton? 1434 | 1435 | Tekton is an open-source, cloud-native CI/CD framework that allows you to define, run, and observe CI/CD pipelines. It's designed to be extensible and can be used with any container runtime. 1436 | 1437 | Key features: 1438 | 1. **Extensible:** 1439 | - Custom tasks 1440 | - Custom resources 1441 | - Custom pipelines 1442 | 1443 | 2. **Cloud-native:** 1444 | - Container-based 1445 | - Kubernetes-native 1446 | - Serverless-friendly 1447 | 1448 | **[⬆ Back to Top](#table-of-contents)** 1449 | 1450 | 90. ### What are Deployment Strategies? 1451 | 1452 | Deployment Strategies are methods used to deploy applications to Kubernetes clusters. Common strategies include: 1453 | 1454 | 1. **Blue-Green Deployment:** 1455 | - Deploy a new version of the application 1456 | - Traffic is routed to the new version 1457 | - Old version is kept running 1458 | 1459 | 2. **Canary Deployment:** 1460 | - Deploy a new version of the application 1461 | - Traffic is routed to the new version 1462 | - Old version is kept running 1463 | 1464 | 3. **Rolling Update:** 1465 | - Deploy a new version of the application 1466 | - Old version is gradually replaced 1467 | - Traffic is routed to the new version 1468 | 1469 | 4. **Blue-Green with Rolling Update:** 1470 | - Deploy a new version of the application 1471 | - Traffic is routed to the new version 1472 | - Old version is gradually replaced 1473 | 1474 | **[⬆ Back to Top](#table-of-contents)** 1475 | 1476 | ## Cloud Cost Optimization 1477 | 1478 | 91. ### What is Cloud Cost Optimization? 1479 | 1480 | Cloud Cost Optimization is the process of reducing your overall cloud spend by identifying mismanaged resources, eliminating waste, reserving capacity for higher discounts, and right-sizing computing services to scale. 1481 | 1482 | Key strategies include: 1483 | 1. **Resource Optimization:** 1484 | - Right-sizing instances 1485 | - Shutting down unused resources 1486 | - Using auto-scaling effectively 1487 | 1488 | 2. **Pricing Optimization:** 1489 | - Reserved Instances 1490 | - Spot Instances 1491 | - Savings Plans 1492 | 1493 | **[⬆ Back to Top](#table-of-contents)** 1494 | 1495 | 92. ### What are Reserved Instances? 1496 | 1497 | Reserved Instances (RIs) provide a significant discount compared to On-Demand pricing in exchange for a commitment to use a specific instance configuration for a one or three-year term. 1498 | 1499 | Types of RIs: 1500 | ```yaml 1501 | Standard RIs: 1502 | - Highest discount (up to 75%) 1503 | - Least flexibility 1504 | - Best for steady-state workloads 1505 | 1506 | Convertible RIs: 1507 | - Lower discount (up to 54%) 1508 | - More flexibility 1509 | - Can change instance family, OS, tenancy 1510 | 1511 | Scheduled RIs: 1512 | - For predictable recurring schedules 1513 | - Match capacity reservation to usage pattern 1514 | ``` 1515 | 1516 | **[⬆ Back to Top](#table-of-contents)** 1517 | 1518 | ## Site Reliability Engineering (SRE) 1519 | 1520 | 96. ### What is Site Reliability Engineering? 1521 | 1522 | Site Reliability Engineering (SRE) is a discipline that incorporates aspects of software engineering and applies them to infrastructure and operations problems to create scalable and highly reliable software systems. 1523 | 1524 | Key principles: 1525 | 1. **Embrace Risk:** 1526 | - Define acceptable risk levels 1527 | - Use error budgets 1528 | - Balance reliability and innovation 1529 | 1530 | 2. **Eliminate Toil:** 1531 | - Automate manual tasks 1532 | - Reduce operational overhead 1533 | - Focus on engineering work 1534 | 1535 | **[⬆ Back to Top](#table-of-contents)** 1536 | 1537 | 97. ### What are Service Level Objectives (SLOs)? 1538 | 1539 | Service Level Objectives (SLOs) are specific, measurable targets for service performance that you set and agree to meet. 1540 | 1541 | Example SLO definition: 1542 | ```yaml 1543 | Service: User Authentication 1544 | SLO: 1545 | Metric: Availability 1546 | Target: 99.9% 1547 | Window: 30 days 1548 | Measurement: 1549 | - Success rate of authentication requests 1550 | - Latency under 300ms for 99% of requests 1551 | ``` 1552 | 1553 | **[⬆ Back to Top](#table-of-contents)** 1554 | 1555 | 98. ### What are Service Level Indicators (SLIs)? 1556 | 1557 | Service Level Indicators (SLIs) are quantitative measures of service level aspects such as latency, throughput, availability, and error rate. 1558 | 1559 | Common SLIs: 1560 | 1. **Request Latency:** 1561 | - Time to handle a request 1562 | - Distribution of response times 1563 | 1564 | 2. **Error Rate:** 1565 | - Failed requests/total requests 1566 | - Error budget consumption 1567 | 1568 | 3. **System Throughput:** 1569 | - Requests per second 1570 | - Transactions per second 1571 | 1572 | **[⬆ Back to Top](#table-of-contents)** 1573 | 1574 | 99. ### What is Error Budget? 1575 | 1576 | An Error Budget is the maximum amount of time that a technical system can fail without contractual consequences. It's the difference between the SLO target and 100% reliability. 1577 | 1578 | Example calculation: 1579 | ``` 1580 | SLO Target: 99.9% uptime 1581 | Error Budget: 100% - 99.9% = 0.1% 1582 | Monthly Error Budget: 43.2 minutes (0.1% of 30 days) 1583 | ``` 1584 | 1585 | Key concepts: 1586 | 1. **Budget Calculation:** 1587 | - Based on SLO targets 1588 | - Measured over time windows 1589 | - Reset periodically 1590 | 1591 | 2. **Budget Usage:** 1592 | - Track incidents 1593 | - Monitor consumption 1594 | - Alert on budget burn 1595 | 1596 | **[⬆ Back to Top](#table-of-contents)** 1597 | 1598 | 100. ### What is Toil in SRE? 1599 | 1600 | Toil is the kind of work tied to running a production service that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and that scales linearly as a service grows. 1601 | 1602 | Characteristics of toil: 1603 | 1. **Manual work:** 1604 | - No automation 1605 | - Human intervention required 1606 | - Repetitive tasks 1607 | 1608 | 2. **Impact:** 1609 | - Reduces time for project work 1610 | - Increases operational overhead 1611 | - Affects team morale 1612 | 1613 | 3. **Solutions:** 1614 | 1615 | Automation: 1616 | - Script repetitive tasks 1617 | - Implement self-service tools 1618 | - Create automated workflows 1619 | 1620 | Process Improvement: 1621 | - Identify toil sources 1622 | - Set toil budgets 1623 | - Track toil metrics 1624 | 1625 | Engineering Solutions: 1626 | - Design for automation 1627 | - Build self-healing systems 1628 | - Implement proper monitoring 1629 | 1630 | 1631 | **[⬆ Back to Top](#table-of-contents)** 1632 | 1633 | ## DevOps Metrics and KPIs 1634 | 1635 | 101. ### What are DevOps Metrics? 1636 | 1637 | DevOps metrics are measurements used to evaluate the performance and efficiency of DevOps practices and processes. 1638 | 1639 | Key categories: 1640 | 1. **Velocity Metrics:** 1641 | - Deployment frequency 1642 | - Lead time for changes 1643 | - Time to market 1644 | 1645 | 2. **Quality Metrics:** 1646 | - Change failure rate 1647 | - Bug detection rate 1648 | - Test coverage 1649 | 1650 | 3. **Operational Metrics:** 1651 | ```yaml 1652 | Performance: 1653 | - Application response time 1654 | - Error rates 1655 | - Resource utilization 1656 | 1657 | Reliability: 1658 | - System uptime 1659 | - MTTR 1660 | - MTBF 1661 | ``` 1662 | 1663 | **[⬆ Back to Top](#table-of-contents)** 1664 | 1665 | 102. ### What is Mean Time to Recovery (MTTR)? 1666 | 1667 | MTTR is the average time it takes to recover from a system failure or incident. 1668 | 1669 | Calculation: 1670 | ``` 1671 | MTTR = Total Recovery Time / Number of Incidents 1672 | ``` 1673 | 1674 | Components of MTTR: 1675 | 1. **Detection Time:** 1676 | - Time to identify the issue 1677 | - Monitoring alerts 1678 | 1679 | 2. **Response Time:** 1680 | - Time to begin addressing the issue 1681 | - Team mobilization 1682 | 1683 | 3. **Resolution Time:** 1684 | - Time to fix the issue 1685 | - System restoration 1686 | 1687 | **[⬆ Back to Top](#table-of-contents)** 1688 | 1689 | ## Serverless Architecture 1690 | 1691 | 106. ### What is Serverless Computing? 1692 | 1693 | Serverless computing is a cloud computing execution model where the cloud provider manages the infrastructure and automatically allocates resources based on demand. 1694 | 1695 | Key characteristics: 1696 | 1. **No Server Management:** 1697 | - Zero infrastructure maintenance 1698 | - Automatic scaling 1699 | - Pay-per-use billing 1700 | 1701 | 2. **Event-Driven:** 1702 | - Function triggers 1703 | - Automatic execution 1704 | - Stateless operations 1705 | 1706 | Example AWS Lambda function: 1707 | ```javascript 1708 | exports.handler = async (event) => { 1709 | try { 1710 | const result = await processEvent(event); 1711 | return { 1712 | statusCode: 200, 1713 | body: JSON.stringify(result) 1714 | }; 1715 | } catch (error) { 1716 | return { 1717 | statusCode: 500, 1718 | body: JSON.stringify({ error: error.message }) 1719 | }; 1720 | } 1721 | }; 1722 | ``` 1723 | 1724 | **[⬆ Back to Top](#table-of-contents)** 1725 | 1726 | ## Database Management in DevOps 1727 | 1728 | 111. ### What is Database DevOps? 1729 | 1730 | Database DevOps is the practice of applying DevOps principles to database development and management. 1731 | 1732 | Key practices: 1733 | 1. **Version Control:** 1734 | - Schema versioning 1735 | - Code-first approach 1736 | - Migration scripts 1737 | 1738 | 2. **Automation:** 1739 | ```yaml 1740 | Continuous Integration: 1741 | - Automated testing 1742 | - Schema validation 1743 | - Data consistency checks 1744 | 1745 | Continuous Delivery: 1746 | - Automated deployments 1747 | - Rollback procedures 1748 | - Data synchronization 1749 | ``` 1750 | 1751 | **[⬆ Back to Top](#table-of-contents)** 1752 | 1753 | ## Network Security 1754 | 1755 | 116. ### What is Network Security in DevOps? 1756 | 1757 | Network Security in DevOps involves implementing security measures throughout the development and deployment pipeline to protect applications and infrastructure. 1758 | 1759 | Key components: 1760 | 1. **Infrastructure Security:** 1761 | - Firewalls 1762 | - VPNs 1763 | - Network segmentation 1764 | 1765 | 2. **Application Security:** 1766 | - TLS encryption 1767 | - API security 1768 | - Authentication/Authorization 1769 | 1770 | Example of security group configuration: 1771 | ```yaml 1772 | SecurityGroup: 1773 | Type: AWS::EC2::SecurityGroup 1774 | Properties: 1775 | GroupDescription: Web tier security group 1776 | SecurityGroupIngress: 1777 | - IpProtocol: tcp 1778 | FromPort: 443 1779 | ToPort: 443 1780 | CidrIp: 0.0.0.0/0 1781 | - IpProtocol: tcp 1782 | FromPort: 80 1783 | ToPort: 80 1784 | CidrIp: 0.0.0.0/0 1785 | ``` 1786 | 1787 | **[⬆ Back to Top](#table-of-contents)** 1788 | 1789 | 117. ### What is Zero Trust Security? 1790 | 1791 | Zero Trust Security is a security model that requires strict identity verification for every person and device trying to access resources in a private network. 1792 | 1793 | Principles: 1794 | 1. **Never Trust, Always Verify:** 1795 | - Identity-based access 1796 | - Continuous verification 1797 | - Least privilege access 1798 | 1799 | 2. **Implementation:** 1800 | ```yaml 1801 | Access Control: 1802 | - Multi-factor authentication 1803 | - Identity and access management 1804 | - Device verification 1805 | 1806 | Network Security: 1807 | - Micro-segmentation 1808 | - Network isolation 1809 | - Encrypted communications 1810 | ``` 1811 | 1812 | **[⬆ Back to Top](#table-of-contents)** 1813 | 1814 | 118. ### What is SSL/TLS? 1815 | 1816 | SSL/TLS is a cryptographic protocol used to secure communications between a client and a server. 1817 | 1818 | Key concepts: 1819 | 1. **Encryption:** 1820 | - Data is encrypted before transmission 1821 | - Data is decrypted after transmission 1822 | 1823 | 2. **Authentication:** 1824 | - Verifies the identity of the communicating parties 1825 | 1826 | Example of SSL/TLS configuration: 1827 | ```yaml 1828 | security: 1829 | ssl: 1830 | enabled: true 1831 | protocol: TLSv1.2 1832 | ciphers: 1833 | - ECDHE-RSA-AES256-GCM-SHA384 1834 | - ECDHE-RSA-AES128-GCM-SHA256 1835 | ``` 1836 | 1837 | **[⬆ Back to Top](#table-of-contents)** 1838 | 1839 | 119. ### What is a Web Application Firewall (WAF)? 1840 | 1841 | A Web Application Firewall (WAF) is a security device that monitors incoming traffic to a web application and blocks malicious traffic. 1842 | 1843 | Key features: 1844 | 1. **Filtering:** 1845 | - Filters out malicious traffic 1846 | - Allows legitimate traffic 1847 | 1848 | 2. **Authentication:** 1849 | - Verifies the identity of the communicating parties 1850 | 1851 | Example of WAF configuration: 1852 | ```yaml 1853 | security: 1854 | waf: 1855 | enabled: true 1856 | rules: 1857 | - rule1 1858 | - rule2 1859 | ``` 1860 | 1861 | **[⬆ Back to Top](#table-of-contents)** 1862 | 1863 | 120. ### What is Network Segmentation? 1864 | 1865 | Network Segmentation is the practice of dividing a network into smaller, more manageable segments to improve security and performance. 1866 | 1867 | Key concepts: 1868 | 1. **Segmentation:** 1869 | - Divides the network into smaller segments 1870 | - Each segment is isolated from other segments 1871 | 1872 | 2. **Security:** 1873 | - Prevents unauthorized access to sensitive data 1874 | - Improves network performance 1875 | 1876 | Example of network segmentation configuration: 1877 | ```yaml 1878 | security: 1879 | network: 1880 | segmentation: 1881 | enabled: true 1882 | rules: 1883 | - rule1 1884 | - rule2 1885 | ``` 1886 | 1887 | **[⬆ Back to Top](#table-of-contents)** 1888 | 1889 | ## Incident Management 1890 | 1891 | 121. ### What is Incident Management? 1892 | 1893 | Incident Management is the process of responding to and resolving IT service disruptions. 1894 | 1895 | Key components: 1896 | 1. **Detection:** 1897 | - Monitoring alerts 1898 | - User reports 1899 | - Automated detection 1900 | 1901 | 2. **Response:** 1902 | ```yaml 1903 | Initial Response: 1904 | - Acknowledge incident 1905 | - Assess severity 1906 | - Notify stakeholders 1907 | 1908 | Resolution: 1909 | - Investigate root cause 1910 | - Apply fix 1911 | - Verify solution 1912 | ``` 1913 | 1914 | **[⬆ Back to Top](#table-of-contents)** 1915 | 1916 | ## DevOps Culture and Practices 1917 | 1918 | 126. ### What is DevOps Culture? 1919 | 1920 | DevOps Culture is a set of practices and values that promotes collaboration between Development and Operations teams. 1921 | 1922 | Key principles: 1923 | 1. **Collaboration:** 1924 | - Shared responsibility 1925 | - Cross-functional teams 1926 | - Open communication 1927 | 1928 | 2. **Continuous Improvement:** 1929 | - Learning from failures 1930 | - Experimentation 1931 | - Feedback loops 1932 | 1933 | 3. **Automation:** 1934 | - Automate repetitive tasks 1935 | - Infrastructure as Code 1936 | - Continuous Integration/Delivery 1937 | 1938 | **[⬆ Back to Top](#table-of-contents)** 1939 | 1940 | 127. ### What are DevOps Best Practices? 1941 | 1942 | DevOps best practices are proven methods that enhance software development and delivery. 1943 | 1944 | Key practices: 1945 | ```yaml 1946 | Technical Practices: 1947 | - Infrastructure as Code 1948 | - Continuous Integration 1949 | - Automated Testing 1950 | - Continuous Deployment 1951 | - Monitoring and Logging 1952 | 1953 | Cultural Practices: 1954 | - Shared Responsibility 1955 | - Blameless Post-mortems 1956 | - Knowledge Sharing 1957 | - Continuous Learning 1958 | - Cross-functional Teams 1959 | 1960 | Process Practices: 1961 | - Agile Methodology 1962 | - Version Control 1963 | - Configuration Management 1964 | - Release Management 1965 | - Incident Management 1966 | ``` 1967 | 1968 | **[⬆ Back to Top](#table-of-contents)** 1969 | 1970 | ## Infrastructure Monitoring 1971 | 1972 | 131. ### What is Infrastructure Monitoring? 1973 | 1974 | Infrastructure Monitoring is the process of collecting and analyzing data from IT infrastructure components to ensure optimal performance and availability. 1975 | 1976 | Key components: 1977 | 1. **Metrics Collection:** 1978 | - System metrics 1979 | - Network metrics 1980 | - Application metrics 1981 | 1982 | 2. **Analysis:** 1983 | ```yaml 1984 | Monitoring Areas: 1985 | - Resource utilization 1986 | - Performance metrics 1987 | - Availability 1988 | - Error rates 1989 | - Response times 1990 | ``` 1991 | 1992 | **[⬆ Back to Top](#table-of-contents)** 1993 | 1994 | 132. ### What are Monitoring Tools? 1995 | 1996 | Common monitoring tools used in DevOps: 1997 | 1998 | 1. **Infrastructure Monitoring:** 1999 | - Prometheus 2000 | - Nagios 2001 | - Zabbix 2002 | - Datadog 2003 | 2004 | 2. **Application Monitoring:** 2005 | ```yaml 2006 | Tools: 2007 | - New Relic 2008 | - AppDynamics 2009 | - Dynatrace 2010 | Features: 2011 | - Transaction tracing 2012 | - Error tracking 2013 | - Performance analytics 2014 | ``` 2015 | 2016 | **[⬆ Back to Top](#table-of-contents)** 2017 | 2018 | 133. ### What are Monitoring Best Practices? 2019 | 2020 | Monitoring Best Practices are proven methods that enhance the effectiveness of monitoring tools and processes. 2021 | 2022 | Key practices: 2023 | ```yaml 2024 | Technical Practices: 2025 | - Infrastructure as Code 2026 | - Continuous Integration 2027 | - Automated Testing 2028 | - Continuous Deployment 2029 | - Monitoring and Logging 2030 | 2031 | Cultural Practices: 2032 | - Shared Responsibility 2033 | - Blameless Post-mortems 2034 | - Knowledge Sharing 2035 | - Continuous Learning 2036 | - Cross-functional Teams 2037 | 2038 | Process Practices: 2039 | - Agile Methodology 2040 | - Version Control 2041 | - Configuration Management 2042 | - Release Management 2043 | - Incident Management 2044 | ``` 2045 | 2046 | **[⬆ Back to Top](#table-of-contents)** 2047 | 2048 | 134. ### What is Application Performance Monitoring? 2049 | 2050 | Application Performance Monitoring (APM) is the practice of collecting and analyzing data about the performance and stability of applications to improve their reliability and responsiveness. 2051 | 2052 | Key components: 2053 | 1. **Metrics Collection:** 2054 | - Application metrics 2055 | - Transaction tracing 2056 | - Error tracking 2057 | - Performance analytics 2058 | 2059 | 2. **Analysis:** 2060 | ```yaml 2061 | Monitoring Areas: 2062 | - Application response times 2063 | - Error rates 2064 | - Resource utilization 2065 | - Scalability 2066 | - Reliability 2067 | ``` 2068 | 2069 | **[⬆ Back to Top](#table-of-contents)** 2070 | 2071 | 135. ### What is Log Management? 2072 | 2073 | Log Management is the practice of collecting, analyzing, and managing log data to help diagnose and troubleshoot issues. 2074 | 2075 | Key components: 2076 | 1. **Log Collection:** 2077 | - Collecting log data from various sources 2078 | - Centralized logging infrastructure 2079 | 2080 | 2. **Log Analysis:** 2081 | - Log aggregation 2082 | - Security analytics 2083 | - Application performance monitoring 2084 | - Website search 2085 | - Business analytics 2086 | 2087 | 3. **Log Visualization:** 2088 | - Dashboard creation 2089 | - Alerting 2090 | - Visualization 2091 | 2092 | **[⬆ Back to Top](#table-of-contents)** 2093 | 2094 | ## Cloud Migration 2095 | 2096 | 136. ### What is Cloud Migration? 2097 | 2098 | Cloud Migration is the process of moving digital assets — applications, data, IT resources — from on-premises infrastructure to cloud infrastructure. 2099 | 2100 | Key aspects: 2101 | 1. **Planning:** 2102 | - Assessment 2103 | - Strategy development 2104 | - Resource planning 2105 | 2106 | 2. **Execution:** 2107 | ```yaml 2108 | Migration Steps: 2109 | - Data migration 2110 | - Application migration 2111 | - Testing 2112 | - Validation 2113 | - Cutover 2114 | ``` 2115 | 2116 | **[⬆ Back to Top](#table-of-contents)** 2117 | 2118 | 137. ### What are Cloud Migration Strategies? 2119 | 2120 | Common cloud migration strategies (6 R's): 2121 | 2122 | 1. **Rehosting (Lift and Shift):** 2123 | - Moving applications without changes 2124 | - Quickest migration method 2125 | - Minimal optimization 2126 | 2127 | 2. **Replatforming (Lift, Tinker and Shift):** 2128 | - Minor optimizations 2129 | - Cloud-specific improvements 2130 | - Maintaining core architecture 2131 | 2132 | 3. **Refactoring/Re-architecting:** 2133 | ```yaml 2134 | Benefits: 2135 | - Better cloud-native features 2136 | - Improved scalability 2137 | - Enhanced performance 2138 | Challenges: 2139 | - More time-consuming 2140 | - Higher initial costs 2141 | - Required expertise 2142 | ``` 2143 | 2144 | **[⬆ Back to Top](#table-of-contents)** 2145 | 2146 | 138. ### What is Cloud Assessment? 2147 | 2148 | Cloud Assessment is the process of evaluating the suitability of cloud services for a specific use case or workload. 2149 | 2150 | Key components: 2151 | 1. **Assessment Criteria:** 2152 | - Cloud service capabilities 2153 | - Cost and pricing 2154 | - Security and compliance 2155 | - Performance and scalability 2156 | - Disaster recovery and high availability 2157 | 2158 | 2. **Assessment Methodology:** 2159 | - Cloud service comparison 2160 | - Risk assessment 2161 | - Cost-benefit analysis 2162 | 2163 | **[⬆ Back to Top](#table-of-contents)** 2164 | 2165 | 139. ### What is Application Modernization? 2166 | 2167 | Application Modernization is the process of transforming existing applications to leverage cloud-native features and capabilities. 2168 | 2169 | Key components: 2170 | 1. **Application Analysis:** 2171 | - Current application state 2172 | - Application architecture 2173 | - Technology stack 2174 | 2175 | 2. **Modernization Strategy:** 2176 | - Cloud-native architecture 2177 | - Microservices 2178 | - Containerization 2179 | - Serverless computing 2180 | 2181 | 3. **Migration:** 2182 | - Data migration 2183 | - Application migration 2184 | - Testing 2185 | - Validation 2186 | - Cutover 2187 | 2188 | **[⬆ Back to Top](#table-of-contents)** 2189 | 2190 | 140. ### What are Cloud Migration Tools? 2191 | 2192 | Cloud Migration Tools are software tools that help automate the migration of applications and data to cloud platforms. 2193 | 2194 | Key components: 2195 | 1. **Data Migration Tools:** 2196 | - Database migration tools 2197 | - Application migration tools 2198 | - Data synchronization tools 2199 | 2200 | 2. **Application Migration Tools:** 2201 | - Application packaging tools 2202 | - Application containerization tools 2203 | - Application serverless tools 2204 | 2205 | 3. **Migration Orchestration Tools:** 2206 | - Workflow automation tools 2207 | - Service coordination tools 2208 | - Resource scheduling tools 2209 | 2210 | **[⬆ Back to Top](#table-of-contents)** 2211 | 2212 | ## Advanced DevOps & Cloud 2213 | 2214 | 141. ### What is Platform Engineering? 2215 | 2216 | Platform Engineering is the discipline of designing, building, and maintaining an Internal Developer Platform (IDP). An IDP provides a self-service layer that enables development teams to autonomously manage the lifecycle of their applications without needing deep expertise in underlying infrastructure, CI/CD, or operational tooling. The goal is to enhance developer experience, productivity, and velocity while ensuring standardization, compliance, and operational excellence. 2217 | 2218 | **Key Aspects of Platform Engineering:** 2219 | 1. **Internal Developer Platform (IDP):** The core product created by a platform engineering team. It typically includes: 2220 | * **Self-Service Capabilities:** Developers can provision infrastructure, set up CI/CD pipelines, deploy applications, and access monitoring/logging tools through a user-friendly interface or API. 2221 | * **Golden Paths:** Pre-configured, validated workflows and toolchains for common tasks (e.g., creating a new microservice, deploying to Kubernetes). 2222 | * **Abstraction:** Hides the complexity of underlying tools and infrastructure. 2223 | * **Standardization:** Enforces best practices, security policies, and compliance across teams. 2224 | 2. **Developer Experience (DevEx):** A primary focus is to reduce cognitive load on developers and streamline their workflows. 2225 | 3. **Automation:** Automating as much of the application lifecycle as possible. 2226 | 4. **Collaboration:** Platform teams work closely with development teams to understand their needs and gather feedback. 2227 | 5. **Product Mindset:** Treating the IDP as a product with users (developers), requiring continuous iteration and improvement. 2228 | 2229 | **Benefits:** 2230 | * **Increased Developer Velocity & Productivity:** Developers spend less time on infrastructure and operational tasks. 2231 | * **Improved Reliability & Stability:** Standardized and automated processes reduce human error. 2232 | * **Enhanced Security & Compliance:** Policies are embedded into the platform. 2233 | * **Faster Time to Market:** Streamlined workflows accelerate the delivery of new features. 2234 | * **Scalability:** Enables organizations to scale their development efforts more effectively. 2235 | 2236 | **Example IDP Components:** 2237 | ```mermaid 2238 | graph TD 2239 | subgraph IDP [Internal Developer Platform] 2240 | A[Developer Portal/CLI] --> B{Self-Service APIs} 2241 | B --> C[Service Catalog] 2242 | B --> D[CI/CD Automation] 2243 | B --> E[Infrastructure Provisioning] 2244 | B --> F[Monitoring & Observability Tools] 2245 | B --> G[Security & Compliance Policies] 2246 | end 2247 | Dev[Developer] --> A 2248 | D --> H[Deployment Targets e.g., Kubernetes] 2249 | E --> I[Cloud Providers/On-prem Infra] 2250 | F --> J[Logging & Metrics Systems] 2251 | G --> D 2252 | G --> E 2253 | ``` 2254 | 2255 | **[⬆ Back to Top](#table-of-contents)** 2256 | 2257 | 142. ### What is FinOps? 2258 | 2259 | FinOps (Cloud Financial Operations) is an evolving cloud financial management discipline and cultural practice that enables organizations to get maximum business value by helping engineering, finance, technology, and business teams to collaborate on data-driven spending decisions. It focuses on understanding cloud costs, optimizing spending, and implementing governance. 2260 | 2261 | **Core Principles of FinOps:** 2262 | 1. **Collaboration:** Teams need to collaborate. Engineering, finance, product, and leadership must work together. 2263 | 2. **Ownership:** Decisions are driven by the business value of cloud. Teams take ownership of their cloud usage, cost, and efficiency. 2264 | 3. **Centralized Team:** A centralized FinOps team (often a CCoE - Cloud Center of Excellence subset) drives governance and best practices. 2265 | 4. **Reporting & Visibility:** Timely, accessible, and accurate reports are crucial for understanding cloud spend. 2266 | 5. **Cost Optimization:** Teams are empowered to optimize for cost, balancing performance, quality, and speed. 2267 | 6. **Predictable Economics:** Strive for predictable cloud economics through forecasting, budgeting, and managing variances. 2268 | 2269 | **Phases of FinOps Lifecycle:** 2270 | 1. **Inform:** Provide visibility into cloud spending through allocation, tagging, showback, and chargeback. 2271 | * Tools: Cloud provider cost management tools (AWS Cost Explorer, Azure Cost Management, GCP Billing), third-party tools (Cloudability, Apptio Cloudability, Flexera One). 2272 | 2. **Optimize:** Implement cost-saving measures. 2273 | * Examples: Right-sizing instances, using reserved instances/savings plans, identifying and terminating idle resources, implementing auto-scaling, choosing appropriate storage tiers. 2274 | 3. **Operate:** Define and enforce policies, establish budgets, and continuously monitor and improve. 2275 | * Examples: Setting budget alerts, automating cost control measures, performing regular cost reviews. 2276 | 2277 | **Benefits of FinOps:** 2278 | * Improved financial control and predictability of cloud costs. 2279 | * Increased ROI from cloud investments. 2280 | * Better alignment between cloud spending and business objectives. 2281 | * Enhanced collaboration between finance and engineering teams. 2282 | * Data-driven decision-making for cloud resource utilization. 2283 | 2284 | **[⬆ Back to Top](#table-of-contents)** 2285 | 2286 | 143. ### What is Policy as Code? 2287 | 2288 | Policy as Code (PaC) is the practice of defining, managing, and automating policies using code and version control systems, similar to Infrastructure as Code (IaC). Instead of manually configuring policies through UIs or disparate systems, PaC allows organizations to express policies in a high-level, human-readable language, store them in a Git repository, and apply them automatically throughout the development lifecycle and in production environments. 2289 | 2290 | **Key Concepts:** 2291 | 1. **Policy Definition:** Policies are written in a declarative language (e.g., Rego for Open Policy Agent, Sentinel for HashiCorp tools). 2292 | 2. **Version Control:** Policies are stored in Git, enabling versioning, auditing, and collaboration. 2293 | 3. **Automation:** Policies are automatically enforced at various stages (e.g., CI/CD pipeline, infrastructure provisioning, Kubernetes admission control). 2294 | 4. **Shift Left:** Enables early detection and prevention of policy violations during development. 2295 | 5. **Auditability:** Provides a clear audit trail of policy changes and enforcement. 2296 | 2297 | **Use Cases:** 2298 | * **Security:** Enforcing security best practices, such as disallowing public S3 buckets or ensuring encryption. 2299 | * **Compliance:** Meeting regulatory requirements (e.g., GDPR, HIPAA) by codifying compliance rules. 2300 | * **Cost Management:** Preventing the creation of overly expensive resources. 2301 | * **Operational Consistency:** Ensuring standardized configurations across environments. 2302 | * **Kubernetes Governance:** Controlling what can be deployed to a Kubernetes cluster (e.g., required labels, resource limits, image sources). 2303 | 2304 | **Popular Tools:** 2305 | * **Open Policy Agent (OPA):** An open-source, general-purpose policy engine. 2306 | * **HashiCorp Sentinel:** A policy as code framework embedded in HashiCorp enterprise products (Terraform, Vault, Nomad, Consul). 2307 | * **Kyverno:** A policy engine designed specifically for Kubernetes. 2308 | * Cloud provider specific tools (e.g., AWS Config Rules, Azure Policy). 2309 | 2310 | **Example (Conceptual OPA/Rego):** 2311 | ```rego 2312 | package main 2313 | 2314 | # Deny deployments if an image is not from a trusted registry 2315 | deny[msg] { 2316 | input.kind == "Deployment" 2317 | image_name := input.spec.template.spec.containers[_].image 2318 | not startswith(image_name, "trusted.registry.io/") 2319 | msg := sprintf("Image '%v' is not from a trusted registry", [image_name]) 2320 | } 2321 | ``` 2322 | 2323 | **[⬆ Back to Top](#table-of-contents)** 2324 | 2325 | 144. ### What is Chaos Engineering? 2326 | 2327 | Chaos Engineering is the discipline of experimenting on a distributed system in production in order to build confidence in the system's capability to withstand turbulent and unexpected conditions. It's a proactive approach to identifying weaknesses by intentionally injecting failures and observing the system's response. 2328 | 2329 | **Principles of Chaos Engineering:** 2330 | 1. **Build a Hypothesis around Steady State Behavior:** Define what normal system behavior looks like (e.g., key performance indicators, SLIs). 2331 | 2. **Vary Real-world Events:** Simulate failures that can occur in production (e.g., server crashes, network latency, disk failures, dependency unavailability). 2332 | 3. **Run Experiments in Production (or a Production-like Environment):** Testing in production is crucial as it's the only way to understand how the system behaves under real-world load and conditions. Start with staging environments if needed. 2333 | 4. **Automate Experiments to Run Continuously:** Integrate chaos experiments into CI/CD pipelines or run them regularly to ensure ongoing resilience. 2334 | 5. **Minimize Blast Radius:** Start with small, controlled experiments and gradually increase the scope to limit potential negative impact. 2335 | 2336 | **Process of a Chaos Experiment:** 2337 | 1. **Define Steady State:** Identify measurable metrics that indicate normal system behavior. 2338 | 2. **Hypothesize:** Formulate a hypothesis about how the system will respond to a specific failure. (e.g., "If we introduce 100ms latency to the database, the API response time will increase by no more than 150ms, and there will be no errors.") 2339 | 3. **Design Experiment:** Determine the type of failure to inject, the scope, and the duration. 2340 | 4. **Execute Experiment:** Inject the failure. 2341 | 5. **Measure and Analyze:** Observe the system's behavior and compare it to the hypothesis. 2342 | 6. **Learn and Improve:** If the system didn't behave as expected, identify the weakness and implement fixes. If it did, increase confidence or expand the experiment. 2343 | 2344 | **Benefits:** 2345 | * Uncovers hidden issues and weaknesses before they cause major outages. 2346 | * Improves system resilience and fault tolerance. 2347 | * Increases confidence in the system's ability to handle failures. 2348 | * Reduces incident response time and mean time to recovery (MTTR). 2349 | * Validates monitoring, alerting, and auto-remediation mechanisms. 2350 | 2351 | **Common Tools:** 2352 | * **Chaos Monkey (Netflix):** Randomly terminates virtual machine instances. 2353 | * **Gremlin:** A "Failure-as-a-Service" platform offering various chaos experiments. 2354 | * **Chaos Mesh:** A cloud-native chaos engineering platform for Kubernetes. 2355 | * **AWS Fault Injection Simulator (FIS):** A managed service for running fault injection experiments on AWS. 2356 | * **LitmusChaos:** An open-source chaos engineering framework for Kubernetes. 2357 | 2358 | **[⬆ Back to Top](#table-of-contents)** 2359 | 2360 | 145. ### What is Blue/Green Deployment? 2361 | 2362 | Blue/Green Deployment is a continuous deployment strategy that aims to minimize downtime and risk by maintaining two identical production environments, referred to as "Blue" and "Green." Only one environment serves live production traffic at any given time. 2363 | 2364 | **How it Works:** 2365 | 1. **Live Environment (Blue):** The current production environment handling all user traffic. 2366 | 2. **Staging/New Environment (Green):** An identical environment where the new version of the application is deployed and thoroughly tested. 2367 | 3. **Traffic Switch:** Once the Green environment is verified, a router or load balancer redirects all incoming traffic from Blue to Green. The Green environment now becomes the live production environment. 2368 | 4. **Rollback:** If issues are detected in the Green environment after the switch, traffic can be quickly routed back to the Blue environment (which still runs the old, stable version). 2369 | 5. **Promotion:** After a period of monitoring the new Green environment, the Blue environment can be updated to the new version to become the staging environment for the next release, or it can be decommissioned. 2370 | 2371 | **Diagram:** 2372 | ```mermaid 2373 | graph LR 2374 | subgraph Initial State 2375 | LB1[Load Balancer] --> Blue1[Blue Environment (v1 - Live)] 2376 | Green1[Green Environment (v1 - Idle)] 2377 | end 2378 | 2379 | subgraph Deployment & Testing 2380 | LB2[Load Balancer] --> Blue2[Blue Environment (v1 - Live)] 2381 | Deploy --> Green2[Green Environment (v2 - Staging/Testing)] 2382 | end 2383 | 2384 | subgraph Traffic Switch 2385 | LB3[Load Balancer] --> Green3[Green Environment (v2 - Live)] 2386 | Blue3[Blue Environment (v1 - Idle/Hot Standby)] 2387 | end 2388 | 2389 | subgraph Optional Rollback 2390 | LB4[Load Balancer] --> Blue4[Blue Environment (v1 - Live again)] 2391 | Green4[Green Environment (v2 - Problematic)] 2392 | end 2393 | ``` 2394 | 2395 | **Benefits:** 2396 | * **Near-Zero Downtime:** Traffic is switched instantaneously. 2397 | * **Reduced Risk:** The new version is fully tested in an identical production environment before going live. 2398 | * **Rapid Rollback:** Reverting to the previous version is as simple as switching traffic back. 2399 | * **Simplified Release Process:** The process is straightforward and well-understood. 2400 | 2401 | **Considerations:** 2402 | * **Resource Costs:** Requires maintaining two full production environments, which can be expensive. 2403 | * **Database Compatibility:** Managing database schema changes and data synchronization between Blue and Green environments can be complex. Strategies like using backward-compatible changes or separate database instances are often employed. 2404 | * **Stateful Applications:** Handling user sessions and other stateful components requires careful planning during the switch. 2405 | * **Long-running Transactions:** Can be affected during the switchover. 2406 | 2407 | **[⬆ Back to Top](#table-of-contents)** 2408 | 2409 | 146. ### What is Feature Flagging? 2410 | 2411 | Feature Flagging (also known as Feature Toggles or Feature Switches) is a software development technique that allows teams to modify system behavior without changing code and redeploying. It involves wrapping new features in conditional logic (the "flag") that can be toggled on or off in a running application, often via a configuration service. 2412 | 2413 | **Core Concepts:** 2414 | 1. **Decoupling Deployment from Release:** Code can be deployed to production environments with new features "turned off" (hidden behind a flag). The feature is then "released" (turned on) for users at a later time, independently of the deployment. 2415 | 2. **Conditional Logic:** Code paths for the new feature are executed only if the corresponding flag is enabled. 2416 | 3. **Configuration Service:** A central service or configuration file is often used to manage the state of feature flags, allowing dynamic updates without code changes. 2417 | 2418 | **Types of Feature Flags:** 2419 | * **Release Toggles:** Used to enable or disable features for all users, often for canary releases or to quickly disable a problematic feature. 2420 | * **Experiment Toggles (A/B Testing):** Used to show different versions of a feature to different segments of users to measure impact. 2421 | * **Ops Toggles:** Used to control operational aspects of the system, like enabling detailed logging or switching to a backup system during an incident. 2422 | * **Permission Toggles:** Used to control access to features for specific user groups (e.g., beta testers, premium users). 2423 | 2424 | **Benefits:** 2425 | * **Reduced Risk:** New features can be tested in production with a limited audience (canary release) or turned off quickly if issues arise ("kill switch"). 2426 | * **Continuous Delivery/Trunk-Based Development:** Allows developers to merge code to the main branch more frequently, even if features are incomplete, by keeping them hidden behind flags. 2427 | * **A/B Testing and Experimentation:** Facilitates testing different feature variations with real users. 2428 | * **Gradual Rollouts:** Features can be rolled out to progressively larger groups of users. 2429 | * **Operational Control:** Provides levers to manage system behavior in production. 2430 | * **Faster Feedback Loops:** Get feedback on features from a subset of users before a full release. 2431 | 2432 | **Considerations:** 2433 | * **Flag Management Complexity:** A large number of flags can become difficult to manage. Requires a clear strategy for naming, organizing, and retiring flags. 2434 | * **Testing Overhead:** Need to test code paths with flags both on and off. 2435 | * **Technical Debt:** Old flags that are no longer needed should be removed to avoid cluttering the codebase. 2436 | * **Performance:** Checking flag states might add a small overhead, though usually negligible. 2437 | 2438 | **[⬆ Back to Top](#table-of-contents)** 2439 | 2440 | 147. ### What is a Service Catalog? 2441 | 2442 | A Service Catalog is a centralized, curated list of IT services that an organization offers to its employees or customers. In the context of DevOps and Platform Engineering, it's a key component of an Internal Developer Platform (IDP), providing developers with a self-service portal to discover, request, and provision standardized resources, tools, and environments. 2443 | 2444 | **Key Characteristics & Purpose:** 2445 | 1. **Discoverability:** Provides a single place for users (typically developers) to find available services (e.g., databases, CI/CD pipeline templates, Kubernetes clusters, monitoring dashboards). 2446 | 2. **Standardization:** Offers pre-configured, vetted, and compliant versions of services, ensuring consistency and adherence to organizational best practices. 2447 | 3. **Self-Service:** Enables users to request and provision services on-demand without manual intervention from IT operations or platform teams. 2448 | 4. **Automation:** Behind the scenes, service requests from the catalog trigger automated provisioning workflows. 2449 | 5. **Lifecycle Management:** Can include information about service versions, support, and decommissioning. 2450 | 6. **Transparency:** Often includes details about service SLAs, costs, and usage guidelines. 2451 | 2452 | **Benefits:** 2453 | * **Increased Developer Productivity:** Developers can quickly access the resources they need without waiting for manual fulfillment. 2454 | * **Improved Governance & Compliance:** Ensures that only approved and compliant services are used. 2455 | * **Reduced Operational Overhead:** Automates service provisioning, freeing up operations teams. 2456 | * **Enhanced Consistency:** Standardized services reduce configuration drift and compatibility issues. 2457 | * **Cost Control:** Can provide visibility into service costs and help manage cloud spend by offering optimized options. 2458 | * **Better User Experience:** Simplifies the process of obtaining IT resources. 2459 | 2460 | **Examples of Services in a Developer-Focused Service Catalog:** 2461 | * New Microservice Template (with CI/CD pipeline) 2462 | * Managed PostgreSQL Database (various sizes) 2463 | * Kubernetes Namespace with pre-defined quotas 2464 | * On-demand Test Environment 2465 | * Access to a specific logging or monitoring tool 2466 | * Vulnerability Scanning Service 2467 | 2468 | **Tools:** 2469 | * **Backstage (CNCF):** An open platform for building developer portals, often used to create service catalogs. 2470 | * **Port:** A developer portal platform. 2471 | * IT Service Management (ITSM) tools (e.g., ServiceNow, Jira Service Management) can also be adapted. 2472 | * Custom-built portals. 2473 | 2474 | **[⬆ Back to Top](#table-of-contents)** 2475 | 2476 | 148. ### What is a Service Level Agreement (SLA)? 2477 | 2478 | A Service Level Agreement (SLA) is a formal, externally-facing contract or commitment between a service provider and its customers (or users). It defines the specific level of service that will be provided, including metrics, responsibilities, and remedies or penalties if the agreed-upon service levels are not met. 2479 | 2480 | **Key Components of an SLA:** 2481 | 1. **Service Description:** Clearly defines the service being provided. 2482 | 2. **Parties Involved:** Identifies the service provider and the customer. 2483 | 3. **Agreement Period:** Specifies the duration for which the SLA is valid. 2484 | 4. **Service Availability:** Defines the expected uptime or availability of the service (e.g., 99.9% uptime per month). 2485 | 5. **Performance Metrics:** Specifies key performance indicators (KPIs) and their targets (e.g., API response time, data processing throughput). 2486 | 6. **Responsibilities:** Outlines the duties of both the service provider and the customer. 2487 | 7. **Support and Escalation Procedures:** Details how support will be provided, response times for issues, and how problems will be escalated. 2488 | 8. **Exclusions:** Lists conditions or events that are not covered by the SLA (e.g., scheduled maintenance, force majeure). 2489 | 9. **Remedies or Penalties (Service Credits):** Describes the compensation or actions (e.g., service credits, discounts) if the provider fails to meet the SLA terms. 2490 | 10. **Reporting and Monitoring:** Specifies how service performance will be tracked and reported to the customer. 2491 | 2492 | **Purpose in DevOps/SRE:** 2493 | * **Sets Expectations:** Clearly communicates to users what level of service they can expect. 2494 | * **Drives Reliability Efforts:** While SLAs are external, they often drive internal targets (SLOs) to ensure commitments are met. 2495 | * **Accountability:** Provides a basis for holding the service provider accountable for performance. 2496 | * **Business Alignment:** Helps align IT services with business needs and user expectations. 2497 | 2498 | **Distinction from SLOs and SLIs:** 2499 | * **SLA (Agreement):** The formal contract with consequences. 2500 | * **SLO (Objective):** Internal targets set by the service provider to meet or exceed the SLA. SLOs are typically stricter than SLAs to provide a buffer. 2501 | * **SLI (Indicator):** The actual measurements of service performance (e.g., measured uptime, actual response time). SLIs are used to track performance against SLOs. 2502 | 2503 | **Example SLA Clause for Availability:** 2504 | "The Service Provider guarantees 99.9% Uptime for the Service during any calendar month. Uptime is defined as the percentage of time the Service is accessible and functioning correctly. If Uptime falls below 99.9% in a given month, the Customer will be eligible for a Service Credit of 5% of their monthly service fee for that month." 2505 | 2506 | **[⬆ Back to Top](#table-of-contents)** 2507 | 2508 | 149. ### What is a Service Level Objective (SLO)? 2509 | 2510 | A Service Level Objective (SLO) is a specific, measurable, and achievable internal target for a particular aspect of service performance or reliability. SLOs are a key component of Site Reliability Engineering (SRE) practices and are used to guide engineering decisions and balance reliability work with feature development. 2511 | 2512 | **Key Characteristics of an SLO:** 2513 | 1. **Service-Specific:** Defined for a particular user-facing service or critical internal system. 2514 | 2. **User-Focused:** Based on what matters to users (e.g., availability, latency, correctness). 2515 | 3. **Measurable:** Quantifiable using specific metrics (SLIs). 2516 | 4. **Target Value:** A specific numerical goal (e.g., 99.9% availability, 99th percentile latency < 200ms). 2517 | 5. **Measurement Window:** The period over which the SLO is evaluated (e.g., rolling 28 days, calendar month). 2518 | 6. **Internal Target:** Used by the team providing the service to manage and improve reliability. SLOs are typically stricter than any corresponding SLAs to provide a safety margin. 2519 | 2520 | **Purpose of SLOs:** 2521 | * **Data-Driven Decisions:** Provide a quantitative basis for making decisions about reliability, such as when to invest in more resilient infrastructure or when to prioritize bug fixes over new features. 2522 | * **Error Budgets:** SLOs directly define error budgets. An error budget is the amount of time or number of events a service can fail to meet its SLO without breaching it. For example, an SLO of 99.9% availability over 30 days allows for approximately 43 minutes of downtime (the error budget). 2523 | * **Balancing Reliability and Innovation:** If the service is consistently meeting its SLOs (i.e., not consuming its error budget), the team can focus more on feature development. If the error budget is being consumed rapidly, the team must prioritize reliability work. 2524 | * **Shared Understanding:** Creates a common language and understanding of reliability goals across development, operations, and product teams. 2525 | * **Alerting:** SLO burn rates (how quickly the error budget is being consumed) are often used to trigger alerts, prompting action before the SLO is breached. 2526 | 2527 | **How to Define Good SLOs:** 2528 | 1. **Identify Critical User Journeys (CUJs):** What are the most important things users do with the service? 2529 | 2. **Choose Appropriate SLIs:** Select metrics that accurately reflect the user experience for those CUJs (e.g., request success rate, latency at a specific percentile). 2530 | 3. **Set Achievable Targets:** Consider historical performance, user expectations, and business requirements. Don't aim for 100% if it's not necessary or feasible, as it can be prohibitively expensive and stifle innovation. 2531 | 4. **Document and Communicate:** Ensure SLOs are well-documented and understood by all stakeholders. 2532 | 5. **Iterate:** Regularly review and refine SLOs based on new data and changing requirements. 2533 | 2534 | **Example SLO:** 2535 | * **Service:** User Login API 2536 | * **SLI:** Percentage of successful login requests (HTTP 200 responses) over all valid login attempts. 2537 | * **Target:** 99.95% 2538 | * **Period:** Measured over a rolling 28-day window. 2539 | * **Consequence (Internal):** If the error budget (0.05%) is exceeded, new feature development for the login service is paused, and all engineering effort is directed towards reliability improvements until the service is back within SLO. 2540 | 2541 | **[⬆ Back to Top](#table-of-contents)** 2542 | 2543 | 150. ### What is a Service Level Indicator (SLI)? 2544 | 2545 | A Service Level Indicator (SLI) is a quantitative measure of some aspect of the level of service provided to users. SLIs are the raw data points or metrics used to assess performance against Service Level Objectives (SLOs). They are crucial for objectively understanding how a service is performing from a user's perspective. 2546 | 2547 | **Key Characteristics of an SLI:** 2548 | 1. **Quantitative Measure:** A specific, numerical value derived from system telemetry. 2549 | 2. **User-Centric:** Reflects an aspect of service performance that directly impacts user experience. 2550 | 3. **Directly Measurable:** Can be obtained from monitoring systems, logs, or other data sources. 2551 | 4. **Good Proxy for User Happiness:** A change in the SLI should correlate with a change in user satisfaction. 2552 | 5. **Reliably Measured:** The measurement itself should be accurate and dependable. 2553 | 2554 | **Common Types of SLIs:** 2555 | * **Availability:** Measures the proportion of time the service is usable or the percentage of successful requests. 2556 | * *Example:* (Number of successful HTTP requests / Total valid HTTP requests) * 100%. 2557 | * **Latency:** Measures the time taken to serve a request. Often measured at specific percentiles (e.g., 95th, 99th percentile) to understand typical and worst-case performance. 2558 | * *Example:* The 99th percentile of API response times for the `/users` endpoint over the last 5 minutes. 2559 | * **Error Rate:** Measures the proportion of requests that result in errors. 2560 | * *Example:* (Number of HTTP 5xx responses / Total valid HTTP requests) * 100%. 2561 | * **Throughput:** Measures the rate at which the system processes requests or data. 2562 | * *Example:* Requests per second (RPS) handled by the shopping cart service. 2563 | * **Durability:** Measures the likelihood that data stored in the system will be retained over a long period without corruption. 2564 | * *Example:* Probability of a stored object remaining intact and accessible after one year. 2565 | * **Correctness/Quality:** Measures if the service provides the right answer or performs the right action. 2566 | * *Example:* Percentage of search queries that return relevant results, or proportion of financial transactions processed without data errors. 2567 | 2568 | **How to Choose Good SLIs:** 2569 | 1. **Focus on User Experience:** What aspects of performance or reliability are most important to your users? 2570 | 2. **Keep it Simple:** Choose a small number of meaningful SLIs rather than trying to track everything. 2571 | 3. **Ensure it's Actionable:** The SLI should provide data that can lead to improvements or inform decisions. 2572 | 4. **Distinguish from Raw Metrics:** While SLIs are derived from metrics, they are specifically chosen and often processed (e.g., aggregated, percentiled) to represent service level. 2573 | 2574 | **Relationship with SLOs and SLAs:** 2575 | * SLIs are the **measurements**. 2576 | * SLOs are the **targets** for those measurements (e.g., SLI for availability >= 99.9%). 2577 | * SLAs are the **agreements** with users, often based on achieving certain SLOs, and typically include consequences if not met. 2578 | 2579 | **Example:** 2580 | * **User Journey:** User uploads a photo. 2581 | * **Possible SLIs:** 2582 | * `upload_success_rate`: (Number of successful photo uploads / Total photo upload attempts) * 100% 2583 | * `upload_latency_p95`: 95th percentile of time taken from initiating upload to confirmation. 2584 | * **Corresponding SLO for `upload_success_rate` might be:** 99.9% over a 7-day window. 2585 | 2586 | **[⬆ Back to Top](#table-of-contents)** 2587 | 2588 | 151. ### What is a Runbook? 2589 | 2590 | A Runbook is a detailed document or a collection of procedures that outlines the steps required to perform a specific operational task or to respond to a particular situation or alert. Traditionally, runbooks were manual guides for system administrators and operators. In modern DevOps and SRE practices, there's a strong emphasis on automating runbooks wherever possible (Runbook Automation). 2591 | 2592 | **Key Characteristics and Purpose of Runbooks:** 2593 | 1. **Standardization:** Provides a consistent and repeatable way to perform routine tasks or respond to incidents, reducing human error. 2594 | 2. **Documentation:** Serves as a knowledge base for operational procedures, especially for less common tasks or for new team members. 2595 | 3. **Efficiency:** Streamlines operations by providing clear, step-by-step instructions, reducing the time taken to resolve issues or complete tasks. 2596 | 4. **Incident Response:** Crucial for quickly addressing known issues, system failures, or alerts by providing pre-defined diagnostic and remediation steps. 2597 | 5. **Training:** Useful for training new operations staff or for cross-training team members. 2598 | 6. **Automation Target:** Well-defined manual runbooks are excellent candidates for automation. Each step in a runbook can potentially be scripted. 2599 | 2600 | **Common Contents of a Runbook:** 2601 | * **Title/Purpose:** Clear description of the task or situation the runbook addresses. 2602 | * **Triggers/Symptoms:** When to use this runbook (e.g., specific alert, error message, user report). 2603 | * **Prerequisites:** Any conditions that must be met or tools/access required before starting. 2604 | * **Step-by-Step Procedures:** Detailed instructions for diagnosis, remediation, or task execution. 2605 | * **Verification Steps:** How to confirm the task was successful or the issue is resolved. 2606 | * **Rollback Procedures:** Steps to revert any changes if the procedure fails or causes unintended consequences. 2607 | * **Escalation Points:** Who to contact if the runbook doesn't resolve the issue or if further assistance is needed. 2608 | * **Expected Outcomes:** What the system state should be after successful execution. 2609 | * **Associated Logs/Metrics:** Pointers to relevant logs or dashboards for investigation. 2610 | 2611 | **Evolution to Runbook Automation:** 2612 | The goal is to automate as many runbook procedures as possible to reduce manual toil, improve response times, and ensure consistency. This involves using scripting languages (Python, Bash), configuration management tools (Ansible), orchestration tools (Kubernetes operators), or specialized runbook automation platforms. 2613 | 2614 | **Example Scenario for a Runbook: High CPU Utilization on a Web Server** 2615 | 1. **Trigger:** Alert: "CPU utilization on webserver-01 > 90% for 5 minutes." 2616 | 2. **Diagnosis Steps:** 2617 | * SSH into `webserver-01`. 2618 | * Run `top` or `htop` to identify high-CPU processes. 2619 | * Check application logs for errors related to the identified process (`/var/log/app/error.log`). 2620 | * Check web server access logs for unusual traffic patterns (`/var/log/nginx/access.log`). 2621 | 3. **Possible Remediation Steps (based on diagnosis):** 2622 | * If it's a known memory leak in the application: Restart the application service (`sudo systemctl restart myapp`). 2623 | * If it's a sudden traffic spike: Consider temporarily scaling out if auto-scaling hasn't kicked in. 2624 | * If it's a rogue process: Identify and kill the process (use with caution). 2625 | 4. **Verification:** Monitor CPU utilization for the next 15 minutes to ensure it returns to normal levels. 2626 | 5. **Escalation:** If the issue persists, escalate to the on-call SRE for the web application. 2627 | 2628 | **Benefits of Well-Maintained Runbooks:** 2629 | * Faster Mean Time To Resolution (MTTR). 2630 | * Reduced operator errors. 2631 | * Improved operational consistency. 2632 | * Better knowledge sharing within the team. 2633 | * Facilitates automation efforts. 2634 | 2635 | **[⬆ Back to Top](#table-of-contents)** 2636 | 2637 | 152. ### What is a Playbook in Incident Response? 2638 | 2639 | An Incident Response Playbook is a specialized type of runbook focused specifically on guiding the actions of a response team during and after a security incident or significant operational outage. It provides a predefined and structured set of steps to detect, analyze, contain, eradicate, and recover from specific types of incidents. 2640 | 2641 | **Key Differences from General Runbooks:** 2642 | * **Focus:** Primarily on security incidents (e.g., data breach, malware infection, DDoS attack) or major service outages, whereas runbooks can cover routine operational tasks as well. 2643 | * **Goal:** To minimize the impact of an incident, restore service quickly and securely, and gather information for post-incident analysis and learning. 2644 | * **Audience:** Often used by security teams (CSIRT - Computer Security Incident Response Team), SREs, and operations staff involved in incident handling. 2645 | 2646 | **Core Components of an Incident Response Playbook:** 2647 | 1. **Incident Type:** Clearly defines the specific incident the playbook addresses (e.g., "Phishing Attack Leading to Credential Compromise," "Ransomware Outbreak," "Database Unavailability"). 2648 | 2. **Roles and Responsibilities:** Identifies who is responsible for each action (e.g., Incident Commander, Communications Lead, Technical Lead). 2649 | 3. **Preparation/Prerequisites:** Steps taken before an incident occurs (e.g., ensuring logging is enabled, access to necessary tools). 2650 | 4. **Detection and Identification:** How to recognize that this specific type of incident is occurring (e.g., specific alerts, user reports, anomalous behavior). 2651 | 5. **Containment Strategy:** Steps to limit the scope and impact of the incident (e.g., isolating affected systems, blocking malicious IPs, disabling compromised accounts). 2652 | 6. **Eradication:** How to remove the cause of the incident (e.g., removing malware, patching vulnerabilities). 2653 | 7. **Recovery:** Steps to restore affected systems and services to normal operation safely. 2654 | 8. **Post-Incident Activities (Postmortem):** Procedures for analyzing the incident, documenting lessons learned, and improving defenses and response capabilities. This includes evidence preservation. 2655 | 9. **Communication Plan:** Guidelines for internal and external communication (e.g., notifying stakeholders, legal, PR, customers if necessary). 2656 | 10. **Checklists and Decision Trees:** To guide responders through complex scenarios. 2657 | 11. **Tools and Resources:** List of necessary tools, contact information, and knowledge base articles. 2658 | 2659 | **Benefits of Incident Response Playbooks:** 2660 | * **Faster Response Times:** Enables quicker, more decisive action during high-stress situations. 2661 | * **Consistency:** Ensures a standardized approach to incident handling, regardless of who is responding. 2662 | * **Reduced Human Error:** Minimizes mistakes made under pressure. 2663 | * **Improved Decision Making:** Provides a framework for making critical decisions. 2664 | * **Compliance and Legal Adherence:** Helps meet regulatory requirements for incident response. 2665 | * **Effective Training Tool:** Can be used for drills and exercises to prepare teams. 2666 | * **Continuous Improvement:** Forms the basis for learning from incidents and refining response strategies. 2667 | 2668 | **Example Playbook Scenario: DDoS Attack Mitigation** 2669 | * **Detection:** Monitoring alerts for unusually high traffic volumes, high server load, and service unavailability. 2670 | * **Initial Triage:** Confirm it's a DDoS attack and not a legitimate traffic spike. Identify attack vectors (e.g., volumetric, protocol, application layer). 2671 | * **Containment/Mitigation:** 2672 | * Engage DDoS mitigation service (e.g., Cloudflare, AWS Shield). 2673 | * Implement rate limiting and IP blocking at edge firewalls/load balancers. 2674 | * Scale out backend resources if applicable. 2675 | * **Recovery:** Monitor traffic and service health. Gradually remove mitigation measures once the attack subsides. 2676 | * **Post-Incident:** Analyze attack patterns, identify vulnerabilities, update mitigation strategies, and document the incident. 2677 | 2678 | **[⬆ Back to Top](#table-of-contents)** 2679 | 2680 | 153. ### What is Observability? 2681 | 2682 | Observability is a measure of how well you can understand the internal state or condition of a complex system based only on knowledge of its external outputs (logs, metrics, traces). It's about being able to ask arbitrary questions about your system's behavior without having to pre-define all possible failure modes or dashboards in advance. While monitoring tells you *whether* a system is working, observability helps you understand *why* it isn't (or is) working. 2683 | 2684 | **Three Pillars of Observability:** 2685 | 1. **Logs:** 2686 | * **What:** Immutable, timestamped records of discrete events that happened over time. Logs provide detailed, context-rich information about specific occurrences. 2687 | * **Use Cases:** Debugging specific errors, auditing, understanding event sequences. 2688 | * **Examples:** Application logs (e.g., stack traces), system logs, audit logs, web server access logs. 2689 | 2. **Metrics:** 2690 | * **What:** Aggregated numerical representations of data about your system measured over intervals of time. Metrics are good for understanding trends, patterns, and overall system health. 2691 | * **Use Cases:** Dashboarding, alerting on thresholds, capacity planning, trend analysis. 2692 | * **Examples:** CPU utilization, memory usage, request counts, error rates, queue lengths, latency percentiles. 2693 | 3. **Traces (Distributed Tracing):** 2694 | * **What:** Show the lifecycle of a request as it flows through a distributed system. A single trace is composed of multiple "spans," where each span represents a unit of work (e.g., an API call, a database query) within a service. 2695 | * **Use Cases:** Understanding request paths, identifying bottlenecks in distributed systems, debugging latency issues, visualizing service dependencies. 2696 | * **Examples:** A trace showing a user request hitting an API gateway, then an authentication service, then a product service, and finally a database. 2697 | 2698 | **Diagram: The Three Pillars** 2699 | ```mermaid 2700 | graph TD 2701 | O[Observability] --> L[Logs] 2702 | O --> M[Metrics] 2703 | O --> T[Traces] 2704 | 2705 | L --Provides--> LD[Detailed Event Context] 2706 | M --Provides--> MA[Aggregated System Health & Trends] 2707 | T --Provides--> TP[Request Flow & Bottleneck Analysis] 2708 | 2709 | subgraph System 2710 | App1[Application/Service 1] 2711 | App2[Application/Service 2] 2712 | App3[Infrastructure] 2713 | end 2714 | 2715 | App1 --> L 2716 | App1 --> M 2717 | App1 -- Generates Spans For --> T 2718 | App2 --> L 2719 | App2 --> M 2720 | App2 -- Generates Spans For --> T 2721 | App3 --> L 2722 | App3 --> M 2723 | ``` 2724 | 2725 | **Why is Observability Important?** 2726 | * **Complex Systems:** Modern applications are often distributed, microservice-based, and run on dynamic infrastructure, making them harder to understand and debug. 2727 | * **Unknown Unknowns:** Observability helps investigate issues you didn't anticipate or for which you don't have pre-built dashboards. 2728 | * **Faster Debugging & MTTR:** Enables quicker root cause analysis when incidents occur. 2729 | * **Better Performance Understanding:** Provides deep insights into how different parts of the system interact and perform. 2730 | * **Proactive Issue Detection:** While often used reactively, rich observability data can help identify anomalies before they become major problems. 2731 | 2732 | **Monitoring vs. Observability:** 2733 | * **Monitoring:** Typically involves collecting predefined sets of metrics and alerting when these metrics cross certain thresholds. It answers known questions (e.g., "Is the CPU over 80%?"). 2734 | * **Observability:** Provides the tools and data to explore and understand system behavior, enabling you to answer new questions about states you didn't predict. It helps explore the unknown unknowns. 2735 | Monitoring is a part of observability, but observability encompasses a broader capability to interrogate your system. 2736 | 2737 | **Key Enablers for Observability:** 2738 | * **Rich Instrumentation:** Applications and infrastructure must be thoroughly instrumented to emit quality logs, metrics, and traces. 2739 | * **Correlation:** The ability to correlate data across logs, metrics, and traces is crucial (e.g., linking a specific log entry to a trace ID and relevant metrics). 2740 | * **High Cardinality Data:** Ability to analyze data with many unique attribute values (e.g., user IDs, request IDs). 2741 | * **Querying & Analytics:** Powerful tools to query, visualize, and analyze the collected telemetry data. 2742 | 2743 | **[⬆ Back to Top](#table-of-contents)** 2744 | 2745 | 154. ### What is Tracing in Observability? 2746 | 2747 | Tracing is the process of tracking the flow of requests through a distributed system, helping to identify bottlenecks and performance issues. Tools like Jaeger and Zipkin are commonly used. 2748 | 2749 | **[⬆ Back to Top](#table-of-contents)** 2750 | 2751 | 155. ### What is a Sidecar Pattern? 2752 | 2753 | The Sidecar Pattern is a container-based design pattern where an auxiliary container (the "sidecar") is deployed alongside the main application container within the same deployment unit (e.g., a Kubernetes Pod). The sidecar container enhances or extends the functionality of the main application container by providing supporting features, and they share resources like networking and storage. 2754 | 2755 | **Key Characteristics:** 2756 | 1. **Co-location:** The main application container and the sidecar container(s) run together in the same Pod (in Kubernetes) or task definition (in ECS). 2757 | 2. **Shared Lifecycle:** Sidecars are typically started and stopped with the main application container. 2758 | 3. **Shared Resources:** They share the same network namespace (can communicate via `localhost`) and can share volumes for data exchange. 2759 | 4. **Encapsulation & Separation of Concerns:** The sidecar encapsulates common functionalities (like logging, monitoring, proxying) that would otherwise need to be built into each application or run as separate agents on the host. 2760 | 5. **Language Agnostic:** Sidecars can be written in different languages than the main application, allowing teams to use the best tool for the job for auxiliary tasks. 2761 | 2762 | **Diagram: Sidecar Pattern in a Kubernetes Pod** 2763 | ```mermaid 2764 | graph TD 2765 | subgraph Kubernetes Pod 2766 | direction LR 2767 | AppContainer[Main Application Container] 2768 | SidecarContainer[Sidecar Container] 2769 | AppContainer -- localhost --> SidecarContainer 2770 | SidecarContainer -- localhost --> AppContainer 2771 | subgraph Shared Resources 2772 | Network[Shared Network Namespace] 2773 | Volumes[Shared Volumes] 2774 | end 2775 | AppContainer --> Network 2776 | SidecarContainer --> Network 2777 | AppContainer --> Volumes 2778 | SidecarContainer --> Volumes 2779 | end 2780 | ExternalTraffic --> Network 2781 | Network --> ExternalServices 2782 | ``` 2783 | 2784 | **Common Use Cases for Sidecars:** 2785 | * **Log Aggregation:** A sidecar (e.g., Fluentd, Fluent Bit) collects logs from the main application container (e.g., from stdout/stderr or a shared volume) and forwards them to a centralized logging system. 2786 | * **Metrics Collection:** A sidecar exports metrics from the application (e.g., Prometheus exporter) or provides a metrics endpoint. 2787 | * **Service Mesh Proxy:** In a service mesh (e.g., Istio, Linkerd), a sidecar proxy (e.g., Envoy) runs alongside each application instance to manage network traffic, enforce policies, provide security (mTLS), and collect telemetry. 2788 | * **Configuration Management:** A sidecar can fetch configuration updates from a central store and make them available to the main application, or reload the application when configuration changes. 2789 | * **Secrets Management:** A sidecar can fetch secrets from a vault and inject them into the application environment or a shared volume. 2790 | * **Network Utilities:** Providing network-related functions like SSL/TLS termination, circuit breaking, or acting as a reverse proxy. 2791 | * **File Synchronization:** Syncing files from a remote source (like Git or S3) to a shared volume for the application to use. 2792 | 2793 | **Benefits:** 2794 | * **Modularity and Reusability:** Common functionalities can be developed and deployed as separate sidecar containers, reusable across multiple applications. 2795 | * **Reduced Application Complexity:** Keeps the main application focused on its core business logic. 2796 | * **Independent Upgrades:** Sidecar functionalities can be updated independently of the main application. 2797 | * **Polyglot Environments:** Allows auxiliary functions to be written in different languages/technologies. 2798 | * **Encapsulation:** Isolates auxiliary tasks from the main application. 2799 | 2800 | **Considerations:** 2801 | * **Resource Overhead:** Each sidecar consumes additional resources (CPU, memory). 2802 | * **Increased Complexity (Deployment Unit):** While simplifying the application, it makes the deployment unit (Pod) more complex with multiple containers. 2803 | * **Inter-Process Communication:** Communication between the app and sidecar (though often via localhost or shared volumes) needs to be efficient. 2804 | 2805 | **[⬆ Back to Top](#table-of-contents)** 2806 | 2807 | 156. ### What is a Service Mesh Control Plane? 2808 | 2809 | In a service mesh architecture, the **Control Plane** is the centralized component responsible for configuring, managing, and monitoring the behavior of the data plane proxies (typically sidecar proxies like Envoy) that run alongside each service instance. It does not handle any of the actual request traffic between services; that is the role of the data plane. 2810 | 2811 | **Key Responsibilities of a Service Mesh Control Plane:** 2812 | 1. **Configuration Distribution:** 2813 | * It pushes configuration updates (e.g., routing rules, traffic policies, security policies, telemetry configurations) to all the sidecar proxies in the mesh. 2814 | * This allows dynamic changes to traffic flow and policies without restarting services or proxies. 2815 | 2. **Service Discovery:** 2816 | * Provides an up-to-date registry of all services and their instances within the mesh, enabling proxies to know where to route traffic. 2817 | * Often integrates with the underlying platform's service discovery (e.g., Kubernetes DNS, Consul). 2818 | 3. **Policy Enforcement Configuration:** 2819 | * Defines and distributes policies related to security (e.g., mTLS requirements, authorization rules), traffic management (e.g., retries, timeouts, circuit breakers), and rate limiting. 2820 | * The control plane tells the proxies *what* policies to enforce; the proxies do the actual enforcement. 2821 | 4. **Certificate Management:** 2822 | * Manages the lifecycle of TLS certificates used for mutual TLS (mTLS) authentication between services, ensuring secure communication. 2823 | * Distributes certificates and keys to the proxies. 2824 | 5. **Telemetry Aggregation (or Configuration for it):** 2825 | * While proxies collect raw telemetry data (metrics, logs, traces), the control plane often provides a central point to configure what telemetry is collected and where it should be sent. Some control planes may also aggregate certain metrics. 2826 | 6. **API for Operators:** 2827 | * Exposes APIs and CLIs for operators to interact with the service mesh, define configurations, and observe its state. 2828 | 2829 | **Interaction with Data Plane:** 2830 | ```mermaid 2831 | graph TD 2832 | CP[Control Plane] -- Config & Policy Updates --> DP1[Data Plane Proxy 1 (Sidecar)] 2833 | CP -- Config & Policy Updates --> DP2[Data Plane Proxy 2 (Sidecar)] 2834 | CP -- Config & Policy Updates --> DPN[Data Plane Proxy N (Sidecar)] 2835 | 2836 | S1[Service A] <--> DP1 2837 | S2[Service B] <--> DP2 2838 | SN[Service N] <--> DPN 2839 | 2840 | DP1 -- Actual Traffic --> DP2 2841 | DP2 -- Actual Traffic --> DPN 2842 | 2843 | DP1 -- Telemetry --> O[Observability Backend] 2844 | DP2 -- Telemetry --> O 2845 | DPN -- Telemetry --> O 2846 | 2847 | Operator -->|Manages via API/CLI| CP 2848 | ``` 2849 | * The Control Plane configures the Data Plane proxies. 2850 | * The Data Plane proxies handle all request traffic between services based on the configuration received from the Control Plane. 2851 | * The Data Plane proxies send telemetry data back to monitoring/observability systems (often configured via the Control Plane). 2852 | 2853 | **Popular Service Mesh Control Planes:** 2854 | * **Istio:** `istiod` is the control plane daemon. 2855 | * **Linkerd:** The control plane is composed of several components (e.g., `controller`, `destination`). 2856 | * **Consul Connect:** Consul servers act as the control plane. 2857 | * **Kuma/Kong Mesh:** `kuma-cp` is the control plane. 2858 | 2859 | **Benefits of a Separate Control Plane:** 2860 | * **Centralized Management:** Provides a single point of control and visibility over the entire service mesh. 2861 | * **Decoupling:** Separates the management logic from the request processing logic, making the system more modular and resilient. 2862 | * **Scalability:** The control plane can be scaled independently of the data plane. 2863 | * **Dynamic Configuration:** Enables runtime changes to traffic management and policies without service restarts. 2864 | 2865 | **[⬆ Back to Top](#table-of-contents)** 2866 | 2867 | 157. ### What is GitHub Actions? 2868 | 2869 | GitHub Actions is a CI/CD and automation platform built into GitHub that allows you to automate workflows for building, testing, and deploying code directly from your repository. 2870 | 2871 | **[⬆ Back to Top](#table-of-contents)** 2872 | 2873 | 158. ### What is a Self-Healing System? 2874 | 2875 | A Self-Healing System is an architecture that can automatically detect and recover from failures, often using automation, monitoring, and orchestration tools to maintain availability. 2876 | 2877 | **[⬆ Back to Top](#table-of-contents)** 2878 | 2879 | 159. ### What is Canary Analysis? 2880 | 2881 | Canary Analysis is a deployment strategy that releases changes to a small subset of users or servers before rolling out to the entire infrastructure, allowing for early detection of issues. 2882 | 2883 | **[⬆ Back to Top](#table-of-contents)** 2884 | 2885 | 160. ### What is Infrastructure Drift? 2886 | 2887 | Infrastructure Drift occurs when the actual state of infrastructure diverges from the desired state defined in code, often due to manual changes or configuration errors. Tools like Terraform and Ansible can help detect and correct drift. 2888 | 2889 | **[⬆ Back to Top](#table-of-contents)** 2890 | --------------------------------------------------------------------------------