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Note: This repository contains DevOps interview questions and answers. Please check the different sections for specific topics like Docker, Kubernetes, CI/CD, etc.
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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.
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The main benefits of DevOps include:
- Faster delivery of features
- More stable operating environments
- Improved communication and collaboration
- More time to innovate (rather than fix/maintain)
- Reduced deployment failures and rollbacks
- Shorter mean time to recovery
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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.
Key aspects of CI include:
- Maintaining a single source repository
- Automating the build
- Making the build self-testing
- Everyone commits to the baseline every day
- Every commit builds on an integration machine
- Keep the build fast
- Test in a clone of the production environment
- Make it easy to get the latest deliverables
- Everyone can see the results of the latest build
- Automate deployment
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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.
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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.
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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.
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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.Example of a simple Dockerfile:
FROM node:14 WORKDIR /app COPY package*.json ./ RUN npm install COPY . . EXPOSE 3000 CMD ["npm", "start"]
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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).
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Kubernetes architecture consists of the following main components:
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Master Node Components:
- API Server
- etcd
- Controller Manager
- Scheduler
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Worker Node Components:
- Kubelet
- Container Runtime
- Kube Proxy
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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.
Example of a simple Pod YAML:
apiVersion: v1 kind: Pod metadata: name: nginx-pod spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80
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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:
- Building the code
- Running automated tests
- Deploying to staging/production environments
Example of a basic Jenkins Pipeline:
pipeline { agent any stages { stage('Build') { steps { sh 'npm install' sh 'npm run build' } } stage('Test') { steps { sh 'npm run test' } } stage('Deploy') { steps { sh './deploy.sh' } } } } -
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).
Key features include:
- Easy installation and configuration
- Hundreds of plugins available
- Built-in GUI tool for easy updates
- Supports distributed builds with master-slave architecture
- Extensible with a huge number of plugins
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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.
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AWS is a comprehensive and widely adopted cloud platform, offering over 200 fully featured services from data centers globally. Key services include:
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Compute:
- EC2 (Elastic Compute Cloud)
- Lambda (Serverless Computing)
- ECS (Elastic Container Service)
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Storage:
- S3 (Simple Storage Service)
- EBS (Elastic Block Store)
- EFS (Elastic File System)
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Database:
- RDS (Relational Database Service)
- DynamoDB (NoSQL Database)
- Redshift (Data Warehouse)
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Azure is Microsoft's cloud computing platform that provides a wide variety of services including:
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Compute Services:
- Virtual Machines
- App Services
- Azure Functions
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Storage Services:
- Blob Storage
- File Storage
- Queue Storage
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Network Services:
- Virtual Network
- Load Balancer
- Application Gateway
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The main types of cloud services are:
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IaaS (Infrastructure as a Service):
- Provides virtualized computing resources
- Examples: AWS EC2, Azure VMs
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PaaS (Platform as a Service):
- Provides platform allowing customers to develop, run, and manage applications
- Examples: Heroku, Google App Engine
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SaaS (Software as a Service):
- Provides software applications over the internet
- Examples: Salesforce, Google Workspace
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FaaS (Function as a Service):
- Provides serverless computing capabilities
- Examples: AWS Lambda, Azure Functions
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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.
Benefits of IaC:
- Version Control
- Reproducibility
- Automation
- Documentation
- Consistency
- Scalability
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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.
Example of a simple Terraform configuration:
provider "aws" { region = "us-west-2" } resource "aws_instance" "example" { ami = "ami-0c55b159cbfafe1f0" instance_type = "t2.micro" tags = { Name = "example-instance" } }
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Ansible is an open-source automation tool that automates software provisioning, configuration management, and application deployment. It uses YAML syntax for expressing automation jobs.
Example of an Ansible playbook:
--- - name: Install and configure web server hosts: webservers become: yes tasks: - name: Install nginx apt: name: nginx state: present - name: Start nginx service service: name: nginx state: started
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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:
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Infrastructure Monitoring:
- Server health
- Network performance
- Resource utilization
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Application Monitoring:
- Response times
- Error rates
- Request rates
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User Experience Monitoring:
- Page load times
- User interactions
- Conversion rates
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ELK Stack is a collection of three open-source products:
- Elasticsearch: A search and analytics engine
- Logstash: A server‑side data processing pipeline
- Kibana: A visualization tool for Elasticsearch data
Common use cases:
- Log aggregation
- Security analytics
- Application performance monitoring
- Website search
- Business analytics
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Prometheus is an open-source systems monitoring and alerting toolkit. Key features include:
- Time series database
- Flexible query language (PromQL)
- Pull-based metrics collection
- Alert management
- Visualization capabilities
Example of Prometheus configuration:
global: scrape_interval: 15s scrape_configs: - job_name: 'prometheus' static_configs: - targets: ['localhost:9090'] - job_name: 'node' static_configs: - targets: ['localhost:9100']
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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:
- Data source integration
- Dashboard creation
- Alerting
- Visualization
- User interface
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Monitoring and logging are two different practices in DevOps:
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Monitoring:
- Focuses on collecting and analyzing data about the performance and stability of services and infrastructure to improve the system's reliability.
- Key aspects include:
- Infrastructure Monitoring
- Application Monitoring
- User Experience Monitoring
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Logging:
- Focuses on collecting and analyzing log data to help diagnose and troubleshoot issues.
- Key aspects include:
- Log aggregation
- Security analytics
- Application performance monitoring
- Website search
- Business analytics
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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.
Key principles include:
- Security automation
- Early security testing
- Continuous security monitoring
- Security as part of CI/CD pipeline
- Rapid security feedback
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Infrastructure Security involves securing all infrastructure components including:
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Network Security:
- Firewalls
- VPNs
- Network segmentation
- DDoS protection
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Cloud Security:
- Identity and Access Management (IAM)
- Encryption
- Security groups
- Network ACLs
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Host Security:
- OS hardening
- Patch management
- Antivirus
- Host-based firewalls
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Essential Linux commands include:
- File Operations:
ls # List files and directories cd # Change directory pwd # Print working directory cp # Copy files mv # Move/rename files rm # Remove files mkdir # Create directory
- System Information:
top # Show processes df # Show disk usage free # Show memory usage ps # Show process status
- Text Processing:
grep # Search text sed # Stream editor awk # Text processing cat # View file contents
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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.
Key concepts include:
- Repository
- Commit
- Branch
- Merge
- Pull Request
- Clone
- Push/Pull
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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:
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Git Flow:
- Main branches: master, develop
- Supporting branches: feature, release, hotfix
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Trunk-Based Development:
- Single main branch (trunk)
- Short-lived feature branches
- Frequent integration
Example of creating a feature branch:
# Create and switch to a new feature branch git checkout -b feature/new-feature # Make changes and commit git add . git commit -m "Add new feature" # Push to remote git push origin feature/new-feature
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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.
Key aspects include:
- System configuration
- Application configuration
- Dependencies management
- Version control
- Compliance and security
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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.
Example of a Puppet manifest:
class apache { package { 'apache2': ensure => installed, } service { 'apache2': ensure => running, enable => true, require => Package['apache2'], } file { '/var/www/html/index.html': ensure => file, content => 'Hello, World!', require => Package['apache2'], } }
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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:
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Vertical Scaling (Scale Up):
- Adding more power to existing resources
- Example: Upgrading CPU/RAM
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Horizontal Scaling (Scale Out):
- Adding more resources
- Example: Adding more servers
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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.
Key components:
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Redundancy:
- Multiple instances
- No single point of failure
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Monitoring:
- Health checks
- Automated failover
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Load Balancing:
- Traffic distribution
- Resource optimization
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Load Balancing is the process of distributing network traffic across multiple servers to ensure no single server bears too much demand.
Common Load Balancing algorithms:
- Round Robin
- Least Connections
- IP Hash
- Weighted Round Robin
- Resource-Based
Example of Nginx Load Balancer configuration:
http { upstream backend { server backend1.example.com; server backend2.example.com; server backend3.example.com; } server { listen 80; location / { proxy_pass http://backend; } } }
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Auto Scaling is a feature that automatically adjusts the number of compute resources based on the current demand.
Key concepts:
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Scaling Policies:
- Target tracking
- Step scaling
- Simple scaling
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Metrics:
- CPU utilization
- Memory usage
- Request count
- Custom metrics
Example of AWS Auto Scaling configuration:
AutoScalingGroup: MinSize: 1 MaxSize: 10 DesiredCapacity: 2 HealthCheckType: ELB HealthCheckGracePeriod: 300 LaunchTemplate: LaunchTemplateId: !Ref LaunchTemplate Version: !GetAtt LaunchTemplate.LatestVersionNumber
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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.
Key components:
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Data Backup:
- Regular data copies
- Multiple backup locations
- Automated backup processes
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Disaster Recovery:
- Recovery procedures
- Failover systems
- Business continuity plans
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Common backup types include:
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Full Backup:
- Complete copy of all data
- Most time and space consuming
- Fastest restore time
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Incremental Backup:
- Only backs up changes since last backup
- Faster and requires less storage
- Longer restore time
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Differential Backup:
- Backs up changes since last full backup
- Balance between full and incremental
- Medium restore time
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Cloud Native Architecture is an approach to designing and building applications that exploits the advantages of the cloud computing delivery model. It emphasizes:
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Characteristics:
- Scalability
- Containerization
- Automation
- Orchestration
- Microservices
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Key Principles:
- Design for automation
- Build for resilience
- Enable scalability
- Embrace containerization
- Practice continuous delivery
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Microservices is an architectural style that structures an application as a collection of small autonomous services, modeled around a business domain.
Key characteristics:
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Independence:
- Separate codebases
- Independent deployment
- Different technology stacks
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Communication:
- API-based interaction
- Event-driven
- Service discovery
Example of a microservice API:
openapi: 3.0.0 info: title: User Service API version: 1.0.0 paths: /users: get: summary: List users responses: '200': description: List of users post: summary: Create user responses: '201': description: User created
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A service mesh is a dedicated infrastructure layer for handling service-to-service communication in microservices architectures.
Key components:
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Data Plane:
- Service proxies (sidecars)
- Traffic handling
- Security enforcement
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Control Plane:
- Configuration management
- Policy enforcement
- Service discovery
Example of Istio configuration:
apiVersion: networking.istio.io/v1alpha3 kind: VirtualService metadata: name: reviews-route spec: hosts: - reviews http: - route: - destination: host: reviews subset: v1 weight: 75 - destination: host: reviews subset: v2 weight: 25
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Performance Testing is a type of testing to determine how a system performs in terms of responsiveness and stability under various workload conditions.
Key aspects include:
-
Performance Metrics:
- Response time
- Throughput
- Resource utilization
- Scalability
- Reliability
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Testing Goals:
- Identify bottlenecks
- Determine system capacity
- Validate performance requirements
- Benchmark performance
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Common types of performance tests include:
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Load Testing:
- Tests system behavior under specific load
- Validates system performance under expected conditions
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Stress Testing:
- Tests system behavior under peak load
- Identifies breaking points
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Endurance Testing:
- Tests system behavior over extended periods
- Identifies memory leaks and resource issues
Example of JMeter test plan:
<?xml version="1.0" encoding="UTF-8"?> <jmeterTestPlan version="1.2"> <hashTree> <TestPlan> <elementProp name="TestPlan.user_defined_variables"> <collectionProp name="Arguments.arguments"/> </elementProp> <stringProp name="TestPlan.comments"></stringProp> <boolProp name="TestPlan.functional_mode">false</boolProp> <boolProp name="TestPlan.serialize_threadgroups">false</boolProp> </TestPlan> </hashTree> </jmeterTestPlan>
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An API Gateway acts as a reverse proxy to accept all API calls, aggregate various services, and return the appropriate result.
Key features:
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Request Handling:
- Authentication
- SSL termination
- Rate limiting
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Integration:
- Service discovery
- Request routing
- Response transformation
Example of Kong API Gateway configuration:
services: - name: user-service url: http://user-service:8000 routes: - name: user-route paths: - /users plugins: - name: rate-limiting config: minute: 5 policy: local
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Key benefits include:
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Security:
- Centralized authentication
- Authorization
- SSL/TLS termination
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Performance:
- Caching
- Request/Response transformation
- Load balancing
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Monitoring:
- Analytics
- Logging
- Rate limiting
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API Security involves protecting APIs from threats and vulnerabilities while ensuring they remain accessible to authorized users.
Key security measures:
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Authentication:
- API keys
- OAuth 2.0
- JWT tokens
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Authorization:
- Role-based access control
- Scope-based access
- Resource-level permissions
Example of OAuth2 configuration:
security: oauth2: client: clientId: ${CLIENT_ID} clientSecret: ${CLIENT_SECRET} resource: tokenInfoUri: https://api.auth.com/oauth/check_token
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Rate Limiting is a technique used to control the rate at which requests are processed or transmitted.
Key concepts:
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Token Bucket Algorithm:
- Fixed number of tokens
- Tokens are replenished at a fixed rate
- Tokens are consumed at a variable rate
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Leaky Bucket Algorithm:
- Fixed size bucket
- Water leaks out at a fixed rate
- Water is added at a variable rate
Example of Nginx Rate Limiting configuration:
http { limit_req_zone $binary_remote_addr zone=one:10m rate=1r/s; server { location / { limit_req burst=5 nodelay; } } }
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API Documentation is a set of documents that describe how to use an API. It includes:
-
API Reference:
- Detailed description of each API endpoint
- Request and response formats
- Example requests and responses
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API Usage Examples:
- Code samples
- API client libraries
- API testing tools
Example of Swagger API Documentation:
swagger: '2.0' info: title: User Service API version: 1.0.0 paths: /users: get: summary: List users responses: '200': description: List of users post: summary: Create user responses: '201': description: User created
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StatefulSets are used to manage stateful applications, providing guarantees about the ordering and uniqueness of Pods.
Key features:
-
Stable Network Identity:
- Predictable Pod names
- Stable hostnames
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Ordered Deployment:
- Sequential creation
- Sequential scaling
- Sequential deletion
Example of StatefulSet:
apiVersion: apps/v1 kind: StatefulSet metadata: name: web spec: serviceName: "nginx" replicas: 3 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 volumeMounts: - name: www mountPath: /usr/share/nginx/html volumeClaimTemplates: - metadata: name: www spec: accessModes: [ "ReadWriteOnce" ] resources: requests: storage: 1Gi
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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.
Use cases:
- Monitoring Agents
- Log Collectors
- Node-level Storage
- Network Plugins
Example of DaemonSet:
apiVersion: apps/v1 kind: DaemonSet metadata: name: fluentd-elasticsearch spec: selector: matchLabels: name: fluentd-elasticsearch template: metadata: labels: name: fluentd-elasticsearch spec: containers: - name: fluentd-elasticsearch image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
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Helm is a package manager for Kubernetes that helps you manage Kubernetes applications through Helm Charts.
Key concepts:
-
Charts:
- Package format
- Collection of files
- Template mechanism
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Repositories:
- Chart storage
- Version control
- Distribution
Example of Helm Chart:
apiVersion: v2 name: my-app description: A Helm chart for my application version: 0.1.0 dependencies: - name: mysql version: 8.8.3 repository: https://charts.bitnami.com/bitnami
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Istio is an open-source service mesh that provides a way to control how services communicate with one another. It includes:
-
Traffic Management:
- Load balancing
- Traffic routing
- Fault injection
- Traffic mirroring
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Security:
- Authentication
- Authorization
- Encryption
- Mutual TLS
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Observability:
- Telemetry
- Metrics
- Tracing
- Logging
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Container Runtime Interface (CRI) is an API that allows container runtimes to interact with the container orchestrator. It includes:
-
Image Management:
- Pulling images
- Pushing images
- Listing images
- Deleting images
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Container Management:
- Creating containers
- Starting containers
- Stopping containers
- Killing containers
- Inspecting containers
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Container Runtime:
- Running containers
- Pausing containers
- Resuming containers
- Executing commands in containers
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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.
Key components:
-
Provisioning:
- Resource creation
- Configuration management
- Application deployment
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Orchestration:
- Workflow automation
- Service coordination
- Resource scheduling
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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.
Principles:
-
Declarative:
- Infrastructure as code
- Application configuration as code
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Version Controlled:
- Git as single source of truth
- Audit trail for changes
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Automated:
- Pull-based deployment
- Continuous reconciliation
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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.
Key features:
-
Declarative:
- Infrastructure as code
- Application configuration as code
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Version Controlled:
- Git as single source of truth
- Audit trail for changes
-
Automated:
- Pull-based deployment
- Continuous reconciliation
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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.
Key features:
-
Extensible:
- Custom tasks
- Custom resources
- Custom pipelines
-
Cloud-native:
- Container-based
- Kubernetes-native
- Serverless-friendly
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Deployment Strategies are methods used to deploy applications to Kubernetes clusters. Common strategies include:
-
Blue-Green Deployment:
- Deploy a new version of the application
- Traffic is routed to the new version
- Old version is kept running
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Canary Deployment:
- Deploy a new version of the application
- Traffic is routed to the new version
- Old version is kept running
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Rolling Update:
- Deploy a new version of the application
- Old version is gradually replaced
- Traffic is routed to the new version
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Blue-Green with Rolling Update:
- Deploy a new version of the application
- Traffic is routed to the new version
- Old version is gradually replaced
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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.
Key strategies include:
-
Resource Optimization:
- Right-sizing instances
- Shutting down unused resources
- Using auto-scaling effectively
-
Pricing Optimization:
- Reserved Instances
- Spot Instances
- Savings Plans
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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.
Types of RIs:
Standard RIs: - Highest discount (up to 75%) - Least flexibility - Best for steady-state workloads Convertible RIs: - Lower discount (up to 54%) - More flexibility - Can change instance family, OS, tenancy Scheduled RIs: - For predictable recurring schedules - Match capacity reservation to usage pattern
-
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.
Key principles:
-
Embrace Risk:
- Define acceptable risk levels
- Use error budgets
- Balance reliability and innovation
-
Eliminate Toil:
- Automate manual tasks
- Reduce operational overhead
- Focus on engineering work
-
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Service Level Objectives (SLOs) are specific, measurable targets for service performance that you set and agree to meet.
Example SLO definition:
Service: User Authentication SLO: Metric: Availability Target: 99.9% Window: 30 days Measurement: - Success rate of authentication requests - Latency under 300ms for 99% of requests
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Service Level Indicators (SLIs) are quantitative measures of service level aspects such as latency, throughput, availability, and error rate.
Common SLIs:
-
Request Latency:
- Time to handle a request
- Distribution of response times
-
Error Rate:
- Failed requests/total requests
- Error budget consumption
-
System Throughput:
- Requests per second
- Transactions per second
-
-
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.
Example calculation:
SLO Target: 99.9% uptime Error Budget: 100% - 99.9% = 0.1% Monthly Error Budget: 43.2 minutes (0.1% of 30 days)Key concepts:
-
Budget Calculation:
- Based on SLO targets
- Measured over time windows
- Reset periodically
-
Budget Usage:
- Track incidents
- Monitor consumption
- Alert on budget burn
-
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.
Characteristics of toil:
1. **Manual work:**
- No automation
- Human intervention required
- Repetitive tasks
2. **Impact:**
- Reduces time for project work
- Increases operational overhead
- Affects team morale
3. **Solutions:**
Automation:
- Script repetitive tasks
- Implement self-service tools
- Create automated workflows
Process Improvement:
- Identify toil sources
- Set toil budgets
- Track toil metrics
Engineering Solutions:
- Design for automation
- Build self-healing systems
- Implement proper monitoring
**[⬆ Back to Top](#table-of-contents)**
DevOps metrics are measurements used to evaluate the performance and efficiency of DevOps practices and processes.
Key categories:
1. **Velocity Metrics:**
- Deployment frequency
- Lead time for changes
- Time to market
2. **Quality Metrics:**
- Change failure rate
- Bug detection rate
- Test coverage
3. **Operational Metrics:**
```yaml
Performance:
- Application response time
- Error rates
- Resource utilization
Reliability:
- System uptime
- MTTR
- MTBF
```
**[⬆ Back to Top](#table-of-contents)**
MTTR is the average time it takes to recover from a system failure or incident.
Calculation:
```
MTTR = Total Recovery Time / Number of Incidents
```
Components of MTTR:
1. **Detection Time:**
- Time to identify the issue
- Monitoring alerts
2. **Response Time:**
- Time to begin addressing the issue
- Team mobilization
3. **Resolution Time:**
- Time to fix the issue
- System restoration
**[⬆ Back to Top](#table-of-contents)**
Serverless computing is a cloud computing execution model where the cloud provider manages the infrastructure and automatically allocates resources based on demand.
Key characteristics:
1. **No Server Management:**
- Zero infrastructure maintenance
- Automatic scaling
- Pay-per-use billing
2. **Event-Driven:**
- Function triggers
- Automatic execution
- Stateless operations
Example AWS Lambda function:
```javascript
exports.handler = async (event) => {
try {
const result = await processEvent(event);
return {
statusCode: 200,
body: JSON.stringify(result)
};
} catch (error) {
return {
statusCode: 500,
body: JSON.stringify({ error: error.message })
};
}
};
```
**[⬆ Back to Top](#table-of-contents)**
Database DevOps is the practice of applying DevOps principles to database development and management.
Key practices:
1. **Version Control:**
- Schema versioning
- Code-first approach
- Migration scripts
2. **Automation:**
```yaml
Continuous Integration:
- Automated testing
- Schema validation
- Data consistency checks
Continuous Delivery:
- Automated deployments
- Rollback procedures
- Data synchronization
```
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Network Security in DevOps involves implementing security measures throughout the development and deployment pipeline to protect applications and infrastructure.
Key components:
1. **Infrastructure Security:**
- Firewalls
- VPNs
- Network segmentation
2. **Application Security:**
- TLS encryption
- API security
- Authentication/Authorization
Example of security group configuration:
```yaml
SecurityGroup:
Type: AWS::EC2::SecurityGroup
Properties:
GroupDescription: Web tier security group
SecurityGroupIngress:
- IpProtocol: tcp
FromPort: 443
ToPort: 443
CidrIp: 0.0.0.0/0
- IpProtocol: tcp
FromPort: 80
ToPort: 80
CidrIp: 0.0.0.0/0
```
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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.
Principles:
1. **Never Trust, Always Verify:**
- Identity-based access
- Continuous verification
- Least privilege access
2. **Implementation:**
```yaml
Access Control:
- Multi-factor authentication
- Identity and access management
- Device verification
Network Security:
- Micro-segmentation
- Network isolation
- Encrypted communications
```
**[⬆ Back to Top](#table-of-contents)**
SSL/TLS is a cryptographic protocol used to secure communications between a client and a server.
Key concepts:
1. **Encryption:**
- Data is encrypted before transmission
- Data is decrypted after transmission
2. **Authentication:**
- Verifies the identity of the communicating parties
Example of SSL/TLS configuration:
```yaml
security:
ssl:
enabled: true
protocol: TLSv1.2
ciphers:
- ECDHE-RSA-AES256-GCM-SHA384
- ECDHE-RSA-AES128-GCM-SHA256
```
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A Web Application Firewall (WAF) is a security device that monitors incoming traffic to a web application and blocks malicious traffic.
Key features:
1. **Filtering:**
- Filters out malicious traffic
- Allows legitimate traffic
2. **Authentication:**
- Verifies the identity of the communicating parties
Example of WAF configuration:
```yaml
security:
waf:
enabled: true
rules:
- rule1
- rule2
```
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Network Segmentation is the practice of dividing a network into smaller, more manageable segments to improve security and performance.
Key concepts:
1. **Segmentation:**
- Divides the network into smaller segments
- Each segment is isolated from other segments
2. **Security:**
- Prevents unauthorized access to sensitive data
- Improves network performance
Example of network segmentation configuration:
```yaml
security:
network:
segmentation:
enabled: true
rules:
- rule1
- rule2
```
**[⬆ Back to Top](#table-of-contents)**
-
Incident Management is the process of responding to and resolving IT service disruptions.
Key components:
-
Detection:
- Monitoring alerts
- User reports
- Automated detection
-
Response:
Initial Response: - Acknowledge incident - Assess severity - Notify stakeholders Resolution: - Investigate root cause - Apply fix - Verify solution
-
-
DevOps Culture is a set of practices and values that promotes collaboration between Development and Operations teams.
Key principles:
-
Collaboration:
- Shared responsibility
- Cross-functional teams
- Open communication
-
Continuous Improvement:
- Learning from failures
- Experimentation
- Feedback loops
-
Automation:
- Automate repetitive tasks
- Infrastructure as Code
- Continuous Integration/Delivery
-
-
DevOps best practices are proven methods that enhance software development and delivery.
Key practices:
Technical Practices: - Infrastructure as Code - Continuous Integration - Automated Testing - Continuous Deployment - Monitoring and Logging Cultural Practices: - Shared Responsibility - Blameless Post-mortems - Knowledge Sharing - Continuous Learning - Cross-functional Teams Process Practices: - Agile Methodology - Version Control - Configuration Management - Release Management - Incident Management
-
Infrastructure Monitoring is the process of collecting and analyzing data from IT infrastructure components to ensure optimal performance and availability.
Key components:
-
Metrics Collection:
- System metrics
- Network metrics
- Application metrics
-
Analysis:
Monitoring Areas: - Resource utilization - Performance metrics - Availability - Error rates - Response times
-
-
Common monitoring tools used in DevOps:
-
Infrastructure Monitoring:
- Prometheus
- Nagios
- Zabbix
- Datadog
-
Application Monitoring:
Tools: - New Relic - AppDynamics - Dynatrace Features: - Transaction tracing - Error tracking - Performance analytics
-
-
Monitoring Best Practices are proven methods that enhance the effectiveness of monitoring tools and processes.
Key practices:
Technical Practices: - Infrastructure as Code - Continuous Integration - Automated Testing - Continuous Deployment - Monitoring and Logging Cultural Practices: - Shared Responsibility - Blameless Post-mortems - Knowledge Sharing - Continuous Learning - Cross-functional Teams Process Practices: - Agile Methodology - Version Control - Configuration Management - Release Management - Incident Management
-
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.
Key components:
-
Metrics Collection:
- Application metrics
- Transaction tracing
- Error tracking
- Performance analytics
-
Analysis:
Monitoring Areas: - Application response times - Error rates - Resource utilization - Scalability - Reliability
-
-
Log Management is the practice of collecting, analyzing, and managing log data to help diagnose and troubleshoot issues.
Key components:
-
Log Collection:
- Collecting log data from various sources
- Centralized logging infrastructure
-
Log Analysis:
- Log aggregation
- Security analytics
- Application performance monitoring
- Website search
- Business analytics
-
Log Visualization:
- Dashboard creation
- Alerting
- Visualization
-
Cloud Migration is the process of moving digital assets — applications, data, IT resources — from on-premises infrastructure to cloud infrastructure.
Key aspects:
1. **Planning:**
- Assessment
- Strategy development
- Resource planning
2. **Execution:**
```yaml
Migration Steps:
- Data migration
- Application migration
- Testing
- Validation
- Cutover
```
**[⬆ Back to Top](#table-of-contents)**
Common cloud migration strategies (6 R's):
1. **Rehosting (Lift and Shift):**
- Moving applications without changes
- Quickest migration method
- Minimal optimization
2. **Replatforming (Lift, Tinker and Shift):**
- Minor optimizations
- Cloud-specific improvements
- Maintaining core architecture
3. **Refactoring/Re-architecting:**
```yaml
Benefits:
- Better cloud-native features
- Improved scalability
- Enhanced performance
Challenges:
- More time-consuming
- Higher initial costs
- Required expertise
```
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Cloud Assessment is the process of evaluating the suitability of cloud services for a specific use case or workload.
Key components:
1. **Assessment Criteria:**
- Cloud service capabilities
- Cost and pricing
- Security and compliance
- Performance and scalability
- Disaster recovery and high availability
2. **Assessment Methodology:**
- Cloud service comparison
- Risk assessment
- Cost-benefit analysis
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Application Modernization is the process of transforming existing applications to leverage cloud-native features and capabilities.
Key components:
1. **Application Analysis:**
- Current application state
- Application architecture
- Technology stack
2. **Modernization Strategy:**
- Cloud-native architecture
- Microservices
- Containerization
- Serverless computing
3. **Migration:**
- Data migration
- Application migration
- Testing
- Validation
- Cutover
**[⬆ Back to Top](#table-of-contents)**
Cloud Migration Tools are software tools that help automate the migration of applications and data to cloud platforms.
Key components:
1. **Data Migration Tools:**
- Database migration tools
- Application migration tools
- Data synchronization tools
2. **Application Migration Tools:**
- Application packaging tools
- Application containerization tools
- Application serverless tools
3. **Migration Orchestration Tools:**
- Workflow automation tools
- Service coordination tools
- Resource scheduling tools
**[⬆ Back to Top](#table-of-contents)**