We close the testing chapter with a more advanced idea: contract testing between modules. When you build infrastructure by composing modules that connect to each other (remember Chapter 18), you need to ensure that those connections still fit even as the modules evolve. Contract testing protects those "connection points." It's a subtler concept, but understanding it will help you design better modules.
Reminder: modules connect through their interfaces
In Chapter 18 we saw that modules have a contract: some inputs (variables) and some outputs. And we saw that they are composed by connecting the output of one module to the input of another (subchapter 18.2):
That connection is a contract between the two modules: the servers module trusts that the network module will provide it with a valid id_subnet. As long as that contract is respected, everything fits.
The problem: contracts can be broken
Here's the risk. Imagine someone modifies the network module and, without realizing the impact, changes the name or format of its output id_subnet (for example, renames it to subnet_id, or changes what it returns). The network module, by itself, still "works"... but the servers module that depended on that output breaks, because it no longer finds what it expected.
Before: network module → output "id_subnet" → servers module ✓ fits
Change: network module → output "subnet_id" → servers module ✗ broken!
(the contract changed without notice)This type of failure is treacherous: the modified module seems correct in isolation, but it has broken other modules that depended on it. And in a large organization, there may be many modules depending on just one (remember the shared modules from subchapter 18.4).
What contract testing is
Contract testing verifies that the interface between two modules—the "contract" of inputs and outputs—remains stable and compatible. Instead of testing the entire infrastructure, it focuses on the connection points: it checks that a module still offers the outputs others expect, with the correct name and format.
Contract test: "Does the 'network' module still offer an output 'id_subnet'
with the format the 'servers' module expects?"
→ YES → the contract is respected ✓
→ NO → the contract is broken, must warn before merging ✗Analogy: a contract between modules is like a plug and socket. The socket (the output of a module) has a standard shape, and the plug (the input of another) fits into it. Contract testing checks that no one has changed the shape of the socket: if someone modifies it, the devices that depended on it would no longer fit. You verify the "socket," not the whole device.
How it's done in practice
Contract testing between modules is not a single tool with a magic button; it's more of an approach that combines several practices you already know:
- Interface-focused tests
You write tests (for example, with Terratest from subchapter 21.3) that specifically verify that the outputs of a module exist and have the expected format. You don't test the whole infrastructure, just the "contract."
- Strict module versioning
This is where the versioning from subchapter 18.4 shines. If you change a module's interface (its inputs or outputs) in an incompatible way, that's a major change (increase the MAJOR version, e.g., from v1.x to v2.0). That way, modules that depended on it don't update automatically and continue using the compatible version until they consciously adapt.
Compatible change (add a new output) → MINOR version (v1.2 → v1.3) Incompatible change (remove/rename output) → MAJOR version (v1.x → v2.0)
- Integration tests between modules in CI
In CI (subchapter 21.1), when someone changes a shared module, you can run tests that combine that module with those that use it, to confirm they still fit before merging the change.
Why it matters, especially at scale
In a small project with few modules, contracts rarely break and it's easy to detect. But in a large organization (which we'll see in Part VII, with internal platforms and modules shared by many teams), a single base module can be used by dozens of projects. Breaking its contract without warning would cause cascading failures throughout the company.
Real-world example: the platform team maintains a
corporate-networkmodule used by 30 teams. A developer wants to improve it and, in the process, rename an output to make it "clearer." Without contract testing, that change would break all 30 projects. With contract testing and strict versioning, CI detects that the interface is being changed incompatibly and forces her to publish it as a major version (v2.0). The 30 teams stay onv1.xsafely, and migrate tov2.0when they can, adapting their code. The change improves the module without breaking anyone.
The underlying idea: take care of interfaces
The key message of this subchapter, beyond the tools, is a mindset: when you design reusable modules, their interfaces (inputs and outputs) are a sacred contract. Others rely on them. Changing them lightly breaks those who depend on you. Contract testing and versioning are the tools that protect those contracts, allowing modules to evolve without causing chaos.
What you should remember
- Modules connect through their interfaces (outputs of one → inputs of another); that connection is a contract that some modules trust.
- If someone changes a module's interface (renames or alters an output), they can break other modules that depended on it, even if the modified module "works" in isolation. It's a treacherous failure, especially at scale.
- Contract testing verifies that the interface between modules remains stable and compatible, focusing on the connection points (like checking that "the plug hasn't changed shape").
- It's achieved by combining: interface-focused tests (Terratest), strict versioning (an incompatible change is a MAJOR version, subchap. 18.4), and integration tests between modules in CI.
- It matters especially at scale, where a base module is used by many teams. The key mindset: your modules' interfaces are a contract you must take care of.
You've finished Chapter 21! You now know how to ensure the quality and security of your infrastructure at all levels. In Chapter 22 we'll bring all this together in a complete automated flow: Terraform in CI/CD, from linting to automated deployment.
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
