In this subchapter, we look at two EC2 features that solve specific problems: Elastic IPs (to have a fixed IP address) and Placement Groups (to control where your instances are physically placed). You’ll use the first one very soon; the second is for more specialized cases, but it’s good to know about it.
The Problem of Changing IPs
Each EC2 instance has IP addresses to communicate over the network:
- Private IP: to talk to other resources within your network (VPC). It remains as long as the instance exists.
- Public IP: so the internet can access it. Here’s the problem: by default, this public IP changes every time you stop and start the instance.
Why this is a problem: Imagine you point your website’s name (
myweb.com) to the public IP54.12.34.56. You stop the instance at night to save money (subchapter 4.3) and, when you start it in the morning, AWS gives it another IP, for example54.99.88.77. Now your website “doesn’t work” because the name still points to the old IP.
The Solution: Elastic IP
An Elastic IP (EIP) is a fixed public IP address that you reserve and can associate with an instance. Unlike a normal public IP, it doesn’t change, even if you stop and start the instance.
Analogy: A normal public IP is like a phone number that gets reassigned to you each time. An Elastic IP is like buying your own fixed number: it’s yours and you’re always reachable at it.
Advantages of an Elastic IP:
- It’s constant: ideal for pointing domain names.
- You can move it from one instance to another. If an instance fails, you associate the EIP to a backup and traffic keeps arriving at the same place. This is useful for failover.
⚠️ Cost detail (important): AWS charges you for Elastic IPs that you reserve but DO NOT use (those not associated with a running instance). The logic: IPv4 addresses are a scarce resource, so AWS penalizes hoarding them without use. Release Elastic IPs you don’t need to avoid unnecessary charges. Since 2024, AWS also charges for public IPv4s in general, so use them wisely.
Modern note: In current architectures, Elastic IPs are often not used directly. Instead, a load balancer (Chapter 13) or CloudFront (Chapter 16) is placed in front, providing a stable entry point without tying an IP to a specific instance. Still, you should know about EIPs because they’re useful in many cases.
Placement Groups: Controlling Physical Placement
By default, AWS decides where to physically place your instances within a datacenter. Normally that’s fine. But sometimes you want to influence that placement for performance or resilience reasons. That’s what Placement Groups are for.
There are three strategies, each solving a different need:
- Cluster — Maximum Performance
Places the instances very close together, in the same physical location, so they communicate with lowest latency and highest bandwidth possible.
- Use case: applications requiring ultra-fast communication between instances (high-performance computing, big data, scientific calculations).
- Risk: since they’re all together, a physical failure could affect them all at once. Less resilience in exchange for more speed.
- Spread — Maximum Resilience
Places each instance on different physical hardware, as far apart as possible.
- Use case: critical applications where you don’t want two instances sharing the same point of failure. If a physical server dies, only one instance goes down.
- Example: the few servers supporting a critical system, where losing two at once would be unacceptable.
- Partition — Large-Scale Balance
Divides instances into groups (partitions), each on separate hardware. Instances within a partition share infrastructure, but partitions themselves are isolated from each other.
- Use case: large distributed systems (like Hadoop, Cassandra, Kafka databases) that already manage replicas and want to control which groups can fail together.
Quick Comparison
| Strategy | Places instances… | Prioritizes | Use case |
|---|---|---|---|
| Cluster | Very close together | Performance/latency | HPC, big data |
| Spread | Very far apart | Resilience | Few critical instances |
| Partition | In isolated groups | Large-scale balance | Large distributed systems |
Don’t worry if Placement Groups seem advanced: they are. Most projects don’t need them at first. What’s important now is to know that they exist and what each strategy is for.
What You Should Remember
- The normal public IP of an instance changes when stopping/starting; this breaks domains pointing to it.
- An Elastic IP is a fixed public IP that you reserve and can move between instances (useful for failover).
- Watch out for costs: AWS charges for reserved and unused Elastic IPs. Release those you don’t need.
- In modern architectures, a load balancer or CloudFront usually replaces the Elastic IP as a stable entry point.
- Placement Groups control physical placement: Cluster (performance), Spread (resilience), and Partition (large-scale balance). They’re for advanced cases.
In the last EC2 subchapter, we’ll look at something that directly affects your bill: purchase models (On-Demand, Reserved, Savings Plans, and Spot) and when to use each one.
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
