In the previous subchapter, we added a DynamoDB table to our backend "for locking." Now let's really understand what state locking is and why it is essential when several people work on the same infrastructure. It is the piece that prevents one of the worst possible disasters with Terraform: state corruption.
The problem: two people at the same time
Remember that the state (Chapter 11) is the file that records which resources exist and how they relate. It is Terraform's "source of truth." Now imagine this situation in a team:
Ana runs "terraform apply" ┐
├─► AT THE SAME TIME! on the SAME state
Carlos runs "terraform apply" ┘If both modify the state at the same time, their changes mix and get corrupted. The state file can become inconsistent: half-written records, duplicated resources, or a state that no longer reflects reality. A corrupt state is a nightmare: Terraform loses track of what exists, and fixing it by hand is slow and dangerous.
Analogy: it's like two people editing the same document at the same time without coordination, each saving over the other's changes. The result is a ruined document where changes are lost and nothing makes sense. You need that, while one edits, the other waits their turn.
The solution: state locking
State locking solves this with a simple rule: while someone is modifying the state, no one else can do it at the same time. Terraform puts a "lock" before starting to work and removes it when finished.
Ana runs apply → Terraform puts the LOCK 🔒
(Ana works calmly)
Carlos runs apply → "The state is locked, please wait..."
(Carlos waits)
Ana finishes → Terraform removes the lock 🔓
Carlos → now he can put his lock and workThis way, operations are serialized: they happen one after another, never at the same time. The state is never corrupted by simultaneous access.
How it works with DynamoDB
This is where the DynamoDB table we set up in subchapter 20.1 comes in. It acts as the "lock doorman":
- When someone runs
apply(orplan), Terraform writes a lock record in the DynamoDB table (in theLockIDkey). - If someone else tries to run Terraform, it checks the table, sees that there is already an active lock and waits (or notifies that it is locked).
- When the first person finishes, Terraform deletes the lock record, releasing the lock.
DynamoDB (locks table)
LockID: "my-state" → occupied by Ana since 10:32 🔒
(Carlos sees this and waits)DynamoDB is ideal for this because it guarantees atomic and consistent operations: two people cannot "grab the lock" at the same time; only one wins.
What you see when the state is locked
If you try to run Terraform while someone else has the lock, you'll see a message like this:
Error: Error acquiring the state lock Lock Info: ID: a1b2c3d4-... Who: ana@company.com Created: 2024-...
This tells you who has the lock and since when. It's not a "bad" error: it's Terraform protecting you. You simply wait for the other person to finish and try again.
Locking happens automatically
The good news: once the backend is configured with DynamoDB (subchapter 20.1), locking works automatically, without you having to do anything. Terraform puts on and removes the lock automatically in each operation. You simply work as usual, and the system protects you in the background.
When a lock gets "stuck"
Occasionally, a lock can get "stuck": for example, if someone's connection is cut off in the middle of an apply, the lock might not be released. In those rare cases, there is the terraform force-unlock command to manually remove the lock:
⚠️ Use it with great care. Before forcing the unlock, make sure that no one is actually working on the state. If you force the unlock while someone is really applying changes, you risk the same corruption that locking was meant to prevent. Always confirm with your team first.
Why this is so important
State locking, together with remote state (subchapter 20.1), is what makes Terraform safe for teams. Without it, collaboration would be a ticking time bomb: sooner or later two people would overlap and corrupt the state. With it, dozens of people can work on the same infrastructure without fear. It is the foundation, along with the PR flow (subchapter 12.5), of professional work with Terraform.
What you should remember
- If two people modify the state at the same time, it gets corrupted (becomes inconsistent), which is a disaster that's hard to fix. Like two people editing the same document without coordination.
- State locking (locking) prevents this: while someone is working, they put a lock that prevents others from modifying the state at the same time. Operations are serialized (one after another).
- With the S3 backend + DynamoDB, locking works automatically: Terraform writes a lock record in DynamoDB when starting and deletes it when finished.
- If the state is locked, you'll see a message indicating who has it; it's not a bad error, it's protection. You just wait.
- In rare cases of a "stuck" lock, there is
terraform force-unlock, but use it with extreme care and only after confirming that no one is working.
In the next subchapter, we'll see how to move the state between backends safely, something necessary when reorganizing or migrating your infrastructure.
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
