Building highly available architecture on AWS doesn’t have to be overwhelming when you have the right Infrastructure as Code approach. This guide walks you through creating resilient, scalable AWS systems using Terraform infrastructure as code that can handle failures gracefully and scale automatically based on demand.
This comprehensive tutorial is designed for DevOps engineers, cloud architects, and infrastructure teams who want to master AWS infrastructure automation using Terraform. You’ll learn practical techniques for building production-ready systems that stay online even when components fail.
We’ll start by exploring the core principles of highly available architecture and how to translate them into Terraform configurations. You’ll discover how to design AWS network infrastructure Terraform modules that create fault-tolerant foundations for your applications.
Next, we’ll dive into implementing AWS auto scaling with Terraform to build application infrastructure that responds dynamically to traffic changes. You’ll also learn RDS high availability strategies for database resilience and explore infrastructure monitoring AWS solutions to keep your systems healthy and observable.
Understanding High Availability Architecture Principles
Multi-AZ deployment strategies for zero downtime
Multi-Availability Zone deployments form the backbone of AWS high availability architecture. By spreading your infrastructure across multiple physically separated data centers within a region, you create a safety net that protects against hardware failures, power outages, and natural disasters. Each AZ operates on independent infrastructure, including power, networking, and cooling systems, making simultaneous failures extremely unlikely.
When designing multi-AZ deployments with Terraform, you should distribute critical components like EC2 instances, RDS databases, and load balancers across at least two AZs. This approach prevents single points of failure and maintains service availability even when an entire AZ experiences issues. Your Terraform configurations should automatically handle AZ selection and resource distribution, ensuring consistent deployment patterns across environments.
The key advantage lies in the seamless failover capabilities. When properly configured, applications can redirect traffic from unhealthy instances in one AZ to healthy instances in another AZ within seconds. This automatic failover happens without manual intervention, maintaining user experience while your infrastructure recovers from issues.
Auto-scaling mechanisms for dynamic resource allocation
Auto-scaling transforms static infrastructure into responsive systems that adapt to changing demand patterns. AWS auto scaling with Terraform enables you to define precise scaling policies that automatically add or remove resources based on performance metrics, scheduled events, or custom triggers. This dynamic approach eliminates both over-provisioning costs and under-provisioning performance issues.
Application Auto Scaling works across multiple AWS services, including EC2 instances, ECS tasks, DynamoDB tables, and Aurora replicas. Your Terraform configurations should define target tracking policies that maintain optimal performance metrics like CPU utilization, request count per target, or queue depth. These policies continuously monitor your applications and scale resources up or down to meet predefined targets.
Predictive scaling takes this concept further by analyzing historical usage patterns and scaling resources ahead of expected demand spikes. This proactive approach prevents performance degradation during peak periods while optimizing costs during low-traffic times. Your infrastructure as code should incorporate both reactive and predictive scaling strategies for maximum effectiveness.
Load balancing techniques for traffic distribution
Load balancers serve as traffic directors in highly available architecture, intelligently distributing incoming requests across healthy backend instances. Application Load Balancers (ALBs) provide advanced routing capabilities based on request content, headers, and paths, while Network Load Balancers (NLBs) handle high-throughput scenarios with ultra-low latency requirements.
Effective load balancing strategies include health checks that continuously monitor backend instance health and automatically route traffic away from failing instances. Your Terraform infrastructure as code should configure comprehensive health check parameters including check intervals, timeout values, healthy and unhealthy thresholds. These settings determine how quickly the load balancer detects and responds to instance failures.
Cross-zone load balancing ensures even traffic distribution across all AZs, preventing hot spots and maximizing resource utilization. When combined with auto-scaling groups, load balancers create self-healing systems that maintain performance standards regardless of individual instance failures or traffic patterns.
Disaster recovery planning with cross-region redundancy
Cross-region disaster recovery planning protects against region-wide outages and catastrophic events that could impact entire geographic areas. This strategy involves replicating critical infrastructure components across multiple AWS regions, creating geographically distributed backup systems that can take over operations when primary regions become unavailable.
Your Terraform AWS deployment should include automated backup and replication mechanisms for databases, file systems, and configuration data. RDS cross-region read replicas provide real-time data replication, while S3 cross-region replication ensures file availability across regions. These automated processes run continuously in the background, maintaining up-to-date copies of your critical data.
Recovery Time Objective (RTO) and Recovery Point Objective (RPO) requirements drive your disaster recovery architecture decisions. Hot standby systems in secondary regions provide near-instantaneous failover but cost more to maintain, while cold standby systems offer cost-effective protection with longer recovery times. Your infrastructure monitoring AWS setup should include cross-region health checks and automated failover procedures that activate secondary regions when primary regions fail health checks for specified durations.
Setting Up Terraform for AWS Infrastructure Management
Installing and configuring Terraform with AWS provider
Setting up Terraform for AWS infrastructure automation starts with downloading the Terraform binary from HashiCorp’s official website. The installation process differs slightly across operating systems, but most developers prefer using package managers like Homebrew on macOS or Chocolatey on Windows for easier updates.
Once installed, create your first AWS provider configuration file. The AWS provider acts as the bridge between Terraform and AWS services, enabling Terraform infrastructure as code capabilities:
terraform {
required_version = ">= 1.0"
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
}
}
provider "aws" {
region = var.aws_region
}
Authentication setup requires either AWS CLI credentials or environment variables. The most secure approach involves using IAM roles with temporary credentials when running from EC2 instances or CI/CD pipelines. For local development, configure AWS CLI with aws configure or export environment variables:
AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_DEFAULT_REGION
Always pin your provider versions to avoid unexpected changes during AWS infrastructure automation deployments. This practice ensures consistent behavior across different environments and team members.
Organizing project structure for scalable code management
A well-structured Terraform project becomes critical as your AWS high availability infrastructure grows. Create a modular approach that separates concerns and promotes reusability across different environments.
The recommended project structure follows this pattern:
terraform/
├── environments/
│ ├── dev/
│ ├── staging/
│ └── prod/
├── modules/
│ ├── vpc/
│ ├── compute/
│ ├── database/
│ └── monitoring/
├── shared/
│ └── variables.tf
└── scripts/
Each environment directory contains environment-specific configurations while referencing shared modules. This approach prevents code duplication and ensures consistency across deployments.
Variables management requires careful consideration. Use terraform.tfvars files for environment-specific values and never commit sensitive data to version control. Instead, leverage AWS Systems Manager Parameter Store or AWS Secrets Manager for sensitive configuration data.
Module design should follow single-responsibility principles. Each module handles one specific infrastructure component, making testing and maintenance easier. For example, a VPC module should only create networking resources, while a compute module handles EC2 instances and Auto Scaling groups.
Implementing remote state management with S3 and DynamoDB
Remote state management is essential for team collaboration and production Terraform AWS deployment. Terraform’s local state files create conflicts when multiple team members work on the same infrastructure.
S3 provides reliable, durable storage for Terraform state files, while DynamoDB handles state locking to prevent concurrent modifications. Here’s the backend configuration:
terraform {
backend "s3" {
bucket = "your-terraform-state-bucket"
key = "environments/prod/terraform.tfstate"
region = "us-west-2"
dynamodb_table = "terraform-state-locks"
encrypt = true
}
}
Create the S3 bucket with versioning enabled and server-side encryption. The DynamoDB table requires a primary key named LockID with string type. These resources should be created separately, often through a bootstrap script or manual setup.
State file organization becomes crucial for large infrastructures. Use descriptive key paths that reflect your project structure. For example:
environments/prod/network/terraform.tfstateenvironments/prod/compute/terraform.tfstateenvironments/dev/terraform.tfstate
Enable S3 bucket policies that restrict access to authorized team members only. Consider implementing MFA requirements for state modifications in production environments. Regular state file backups help recover from accidental deletions or corruptions.
Cross-account deployments require careful IAM role configuration. Use assume role patterns when deploying infrastructure across multiple AWS accounts, ensuring proper permissions without sharing long-term credentials.
Building Resilient Network Infrastructure
Creating VPCs with Multiple Availability Zones
Building a highly available AWS infrastructure starts with designing your Virtual Private Cloud (VPC) to span multiple availability zones. When you create a VPC with Terraform, you’re establishing the foundation for AWS network infrastructure Terraform that can withstand individual zone failures.
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
enable_dns_support = true
tags = {
Name = "main-vpc"
}
}
data "aws_availability_zones" "available" {
state = "available"
}
Your VPC should leverage at least three availability zones for optimal redundancy. This approach ensures that if one zone experiences issues, your applications continue running in the remaining zones. The key principle here involves distributing resources across zones while maintaining consistent networking configurations.
Each availability zone operates independently with its own power, cooling, and networking infrastructure. By spreading your resources across multiple zones, you create natural boundaries that prevent single points of failure from taking down your entire system.
Designing Subnet Architecture for Public and Private Resources
Effective subnet design separates your infrastructure into logical tiers that enhance both security and availability. Your highly available architecture should include both public and private subnets in each availability zone.
resource "aws_subnet" "public" {
count = length(data.aws_availability_zones.available.names)
vpc_id = aws_vpc.main.id
cidr_block = "10.0.${count.index + 1}.0/24"
availability_zone = data.aws_availability_zones.available.names[count.index]
map_public_ip_on_launch = true
tags = {
Name = "public-subnet-${count.index + 1}"
Type = "Public"
}
}
resource "aws_subnet" "private" {
count = length(data.aws_availability_zones.available.names)
vpc_id = aws_vpc.main.id
cidr_block = "10.0.${count.index + 10}.0/24"
availability_zone = data.aws_availability_zones.available.names[count.index]
tags = {
Name = "private-subnet-${count.index + 1}"
Type = "Private"
}
}
Public subnets house resources that need direct internet access, like load balancers and bastion hosts. Private subnets contain your application servers, databases, and other backend components that shouldn’t be directly accessible from the internet. This layered approach creates multiple security boundaries while enabling proper traffic flow.
Your subnet CIDR blocks should provide enough IP addresses for future growth while avoiding conflicts with other networks. Planning your address space carefully prevents headaches when you need to peer VPCs or establish VPN connections later.
Configuring NAT Gateways for Secure Outbound Connectivity
NAT gateways provide secure internet access for resources in private subnets without exposing them to inbound traffic from the internet. For true high availability, deploy NAT gateways in each availability zone rather than sharing a single gateway across zones.
resource "aws_eip" "nat" {
count = length(aws_subnet.public)
domain = "vpc"
tags = {
Name = "nat-eip-${count.index + 1}"
}
}
resource "aws_nat_gateway" "main" {
count = length(aws_subnet.public)
allocation_id = aws_eip.nat[count.index].id
subnet_id = aws_subnet.public[count.index].id
tags = {
Name = "nat-gateway-${count.index + 1}"
}
depends_on = [aws_internet_gateway.main]
}
Each private subnet should route its outbound traffic through the NAT gateway in the same availability zone. This configuration prevents cross-zone dependencies that could create failure scenarios. If one availability zone goes down, the other zones continue operating independently with their own NAT gateways.
The cost of multiple NAT gateways might seem significant, but this investment pays dividends when you avoid outages caused by single NAT gateway failures. Your AWS infrastructure automation should prioritize availability over cost optimization in critical networking components.
Implementing Security Groups and NACLs for Network Protection
Security groups and Network Access Control Lists (NACLs) work together to create defense-in-depth networking protection. Security groups operate at the instance level, while NACLs provide subnet-level filtering, giving you multiple layers of control.
resource "aws_security_group" "web_tier" {
name_prefix = "web-tier-"
vpc_id = aws_vpc.main.id
ingress {
from_port = 80
to_port = 80
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
ingress {
from_port = 443
to_port = 443
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
egress {
from_port = 0
to_port = 0
protocol = "-1"
cidr_blocks = ["0.0.0.0/0"]
}
tags = {
Name = "web-tier-sg"
}
}
resource "aws_security_group" "app_tier" {
name_prefix = "app-tier-"
vpc_id = aws_vpc.main.id
ingress {
from_port = 8080
to_port = 8080
protocol = "tcp"
security_groups = [aws_security_group.web_tier.id]
}
tags = {
Name = "app-tier-sg"
}
}
Design your security groups with the principle of least privilege. Each tier should only accept traffic from the appropriate sources and only on necessary ports. Your web tier accepts HTTP and HTTPS traffic from anywhere, while your application tier only accepts traffic from the web tier security group.
NACLs provide an additional filtering layer that can block traffic before it reaches your instances. While security groups are stateful (return traffic is automatically allowed), NACLs are stateless and require explicit rules for both inbound and outbound traffic. This dual-layer approach creates robust network security that protects your infrastructure as code best practices.
Deploying Auto-Scaling Application Infrastructure
Launching EC2 instances with auto-scaling groups
Auto Scaling Groups (ASGs) form the backbone of highly available architecture on AWS, automatically managing EC2 instances based on demand and health status. When building AWS auto scaling with Terraform, you create a self-healing infrastructure that adapts to traffic patterns without manual intervention.
Start by defining a launch template that serves as the blueprint for your instances. This template specifies the AMI, instance type, security groups, and user data scripts. The beauty of using Terraform lies in its declarative approach – you describe what you want, and Terraform handles the implementation details.
resource "aws_launch_template" "app_template" {
name_prefix = "app-server-"
image_id = data.aws_ami.amazon_linux.id
instance_type = "t3.medium"
vpc_security_group_ids = [aws_security_group.app_sg.id]
user_data = base64encode(templatefile("${path.module}/user_data.sh", {
app_version = var.app_version
}))
}
The Auto Scaling Group configuration defines minimum, maximum, and desired capacity values. These parameters control how your infrastructure scales. Setting the minimum ensures you always have instances running, while the maximum prevents runaway scaling costs. The desired capacity represents your typical operational baseline.
Multi-AZ deployment across different availability zones protects against zone-level failures. Configure your ASG to span multiple subnets in different AZs, ensuring that if one zone experiences issues, your application continues running in others.
Setting up Application Load Balancers for high availability
Application Load Balancers (ALBs) distribute incoming traffic across multiple EC2 instances, creating a single point of entry while eliminating single points of failure. Unlike Classic Load Balancers, ALBs operate at the application layer and provide advanced routing capabilities.
The ALB configuration in Terraform requires careful attention to security groups, subnet placement, and target group definitions. Place your load balancer in public subnets while keeping your application instances in private subnets for enhanced security.
resource "aws_lb" "app_alb" {
name = "app-load-balancer"
internal = false
load_balancer_type = "application"
security_groups = [aws_security_group.alb_sg.id]
subnets = aws_subnet.public[*].id
enable_deletion_protection = true
}
Target groups define how the ALB routes traffic to your instances. Configure multiple target groups for different application components or versions, enabling blue-green deployments and canary releases. The target group health check settings determine when instances are considered healthy and ready to receive traffic.
Cross-zone load balancing ensures even distribution of requests across all healthy instances, regardless of their availability zone. This feature prevents hot spots and maximizes resource utilization across your highly available architecture.
Configuring health checks for automatic failover
Health checks act as the nervous system of your auto-scaling infrastructure, continuously monitoring instance health and triggering replacement actions when problems arise. Proper health check configuration prevents traffic from reaching unhealthy instances and ensures rapid recovery from failures.
Configure both ALB target group health checks and ASG health checks for comprehensive monitoring. The ALB health check determines if an instance can receive traffic, while the ASG health check decides if the instance should be replaced.
resource "aws_lb_target_group" "app_tg" {
name = "app-targets"
port = 80
protocol = "HTTP"
vpc_id = aws_vpc.main.id
health_check {
enabled = true
healthy_threshold = 2
interval = 30
matcher = "200"
path = "/health"
port = "traffic-port"
protocol = "HTTP"
timeout = 5
unhealthy_threshold = 3
}
}
The health check path should point to a lightweight endpoint that validates critical application components. Avoid complex health checks that might fail during normal operation spikes. The endpoint should verify database connectivity, external service availability, and core application functionality.
Tune the threshold values based on your application’s characteristics. Lower healthy thresholds reduce failover time but might cause premature instance replacement during temporary issues. Higher unhealthy thresholds provide stability but increase recovery time during actual failures.
Implementing CloudWatch monitoring for proactive scaling
CloudWatch metrics drive intelligent scaling decisions, moving beyond simple CPU utilization to comprehensive application performance monitoring. Custom metrics provide deeper insights into application behavior and enable more precise scaling policies.
Configure CloudWatch alarms that trigger scaling actions based on multiple metrics. CPU utilization remains important, but consider request count per target, response time, and queue depth for more accurate scaling triggers. These metrics provide better signals about actual application load versus resource consumption.
resource "aws_autoscaling_policy" "scale_up" {
name = "scale-up"
scaling_adjustment = 2
adjustment_type = "ChangeInCapacity"
cooldown = 300
autoscaling_group_name = aws_autoscaling_group.app_asg.name
}
resource "aws_cloudwatch_metric_alarm" "high_cpu" {
alarm_name = "high-cpu-utilization"
comparison_operator = "GreaterThanThreshold"
evaluation_periods = "2"
metric_name = "CPUUtilization"
namespace = "AWS/EC2"
period = "60"
statistic = "Average"
threshold = "75"
alarm_description = "This metric monitors ec2 cpu utilization"
alarm_actions = [aws_autoscaling_policy.scale_up.arn]
}
Target tracking scaling policies offer a simpler alternative to step scaling. Define a target value for a specific metric, and the ASG automatically adjusts capacity to maintain that target. This approach works well for metrics like request count per instance or average CPU utilization.
Predictive scaling analyzes historical patterns to scale proactively rather than reactively. Enable this feature for workloads with predictable patterns, such as business applications with daily or weekly cycles. The system learns from past behavior and provisions capacity ahead of expected demand spikes.
Database High Availability with RDS
Creating Multi-AZ RDS deployments for automatic failover
Multi-AZ RDS deployments provide the backbone for RDS high availability in your AWS infrastructure. When you configure a Multi-AZ deployment through Terraform, AWS automatically maintains a synchronous standby replica in a different Availability Zone. This setup gives you automatic failover capabilities without any manual intervention when your primary database instance fails.
resource "aws_db_instance" "main" {
identifier = "production-database"
engine = "mysql"
engine_version = "8.0"
instance_class = "db.t3.medium"
allocated_storage = 20
max_allocated_storage = 100
storage_type = "gp2"
storage_encrypted = true
db_name = "myapp"
username = "admin"
password = var.db_password
multi_az = true
vpc_security_group_ids = [aws_security_group.rds.id]
db_subnet_group_name = aws_db_subnet_group.main.name
backup_retention_period = 7
backup_window = "03:00-04:00"
maintenance_window = "sun:04:00-sun:05:00"
skip_final_snapshot = false
final_snapshot_identifier = "production-database-final-snapshot"
tags = {
Name = "Production Database"
Environment = "production"
}
}
The failover process typically completes within 60-120 seconds, making it transparent to your application with proper connection handling. Your Terraform configuration should include appropriate subnet groups spanning multiple AZs to support this architecture.
Setting up read replicas for improved performance
Read replicas distribute your database read traffic across multiple instances, reducing the load on your primary database while improving application performance. AWS infrastructure automation through Terraform makes managing these replicas straightforward and consistent.
resource "aws_db_instance" "read_replica" {
count = var.read_replica_count
identifier = "production-database-replica-${count.index + 1}"
replicate_source_db = aws_db_instance.main.id
instance_class = "db.t3.medium"
publicly_accessible = false
vpc_security_group_ids = [aws_security_group.rds.id]
auto_minor_version_upgrade = true
tags = {
Name = "Production Database Replica ${count.index + 1}"
Environment = "production"
}
}
Cross-region read replicas provide additional disaster recovery capabilities. You can create these by specifying a different region and ensuring your Terraform provider configuration supports multi-region deployments. Read replicas use asynchronous replication, so expect some lag between your primary instance and replicas.
Your application architecture should implement read/write splitting to take full advantage of read replicas. Direct read queries to replica endpoints while keeping write operations on the primary instance.
Implementing automated backup and point-in-time recovery
Automated backups form a critical component of your highly available architecture strategy. RDS automatically creates daily snapshots and maintains transaction logs for point-in-time recovery within your specified retention period.
resource "aws_db_instance" "main" {
# ... other configuration ...
backup_retention_period = 30
backup_window = "03:00-04:00"
delete_automated_backups = false
# Point-in-time recovery enabled automatically
# when backup_retention_period > 0
}
# Manual snapshot for major changes
resource "aws_db_snapshot" "before_migration" {
db_instance_identifier = aws_db_instance.main.id
db_snapshot_identifier = "pre-migration-snapshot-${formatdate("YYYY-MM-DD", timestamp())}"
tags = {
Purpose = "Pre-migration backup"
Created = timestamp()
}
}
Point-in-time recovery lets you restore your database to any specific second within your backup retention period. This capability proves invaluable when dealing with data corruption or accidental deletions. Your backup window should align with low-traffic periods to minimize performance impact.
Consider implementing a backup verification process using AWS Lambda functions triggered by CloudWatch Events. This ensures your backups remain restorable and complete. Terraform AWS deployment patterns should include monitoring for backup failures and automated alerts when backup operations don’t complete successfully.
Cross-region backup copying provides additional protection against regional outages. Use lifecycle policies to manage backup costs while maintaining compliance with your recovery requirements.
Implementing Infrastructure Monitoring and Alerting
Deploying CloudWatch dashboards for real-time visibility
Building effective dashboards starts with understanding what matters most to your AWS infrastructure. CloudWatch dashboards provide the visual foundation for monitoring your Terraform-deployed resources across regions and availability zones. Start by creating dashboards that focus on key infrastructure components like EC2 instances, load balancers, and RDS databases.
Create separate dashboards for different layers of your infrastructure monitoring AWS setup. A network dashboard should track VPC flow logs, NAT gateway metrics, and load balancer performance. An application dashboard focuses on EC2 CPU utilization, memory usage, and custom application metrics. Database dashboards monitor RDS performance metrics, connection counts, and query execution times.
Use Terraform to deploy these dashboards as code, ensuring consistency across environments:
resource "aws_cloudwatch_dashboard" "infrastructure_overview" {
dashboard_name = "Infrastructure-Overview-${var.environment}"
dashboard_body = jsonencode({
widgets = [
"
properties = {
metrics = [
["AWS/EC2", "CPUUtilization", "AutoScalingGroupName", aws_autoscaling_group.web.name],
["AWS/ApplicationELB", "RequestCount", "LoadBalancer", aws_lb.main.arn_suffix]
]
period = 300
stat = "Average"
region = var.aws_region
title = "Application Performance"
}
}
]
})
}
Creating custom metrics for application-specific monitoring
Custom metrics bridge the gap between AWS infrastructure monitoring and your application’s unique requirements. These metrics provide insights that standard CloudWatch metrics can’t capture, like business-specific KPIs, custom error rates, or application workflow states.
Design your custom metrics around your application’s critical success factors. E-commerce applications might track cart abandonment rates, while SaaS platforms focus on user session duration and feature adoption rates. Create metrics that directly correlate with business outcomes and user experience.
Implement custom metrics through CloudWatch Agent or direct API calls from your application code. The CloudWatch Agent can collect system-level custom metrics, while application code can push business metrics through the AWS SDK:
import boto3
cloudwatch = boto3.client('cloudwatch')
def publish_custom_metric(metric_name, value, unit='Count'):
cloudwatch.put_metric_data(
Namespace='CustomApp/Business',
MetricData=[
{
'MetricName': metric_name,
'Value': value,
'Unit': unit,
'Dimensions': [
{
'Name': 'Environment',
'Value': 'production'
}
]
}
]
)
Structure your custom metrics with meaningful namespaces and dimensions. Use consistent naming conventions across your infrastructure as code deployment to make metrics easily discoverable and actionable.
Setting up SNS notifications for critical system events
SNS notifications transform monitoring data into actionable alerts that reach the right people at the right time. Design notification strategies that balance comprehensive coverage with alert fatigue prevention. Critical alerts should go to on-call engineers immediately, while warning-level alerts can route to team channels or ticketing systems.
Create topic hierarchies that match your organization structure and escalation procedures. Separate topics for different severity levels allow fine-grained control over who receives what alerts and when:
resource "aws_sns_topic" "critical_alerts" {
name = "infrastructure-critical-${var.environment}"
tags = {
Environment = var.environment
AlertLevel = "critical"
}
}
resource "aws_sns_topic_subscription" "email_critical" {
topic_arn = aws_sns_topic.critical_alerts.arn
protocol = "email"
endpoint = var.oncall_email
}
resource "aws_sns_topic_subscription" "sms_critical" {
topic_arn = aws_sns_topic.critical_alerts.arn
protocol = "sms"
endpoint = var.oncall_phone
}
Configure CloudWatch alarms to trigger SNS notifications based on threshold breaches, anomaly detection, or composite alarm conditions. Multi-dimensional alarms provide more context and reduce false positives compared to simple threshold-based alerts.
Configuring automated remediation with Lambda functions
Automated remediation transforms your infrastructure from reactive to self-healing. Lambda functions can respond to CloudWatch alarms and SNS notifications by taking corrective actions without human intervention. This approach reduces mean time to recovery and handles routine operational issues automatically.
Start with simple remediation patterns like restarting unhealthy instances, scaling up resources under high load, or cleaning up disk space. More advanced patterns include failover procedures, security incident response, and capacity optimization:
resource "aws_lambda_function" "auto_remediation" {
filename = "remediation.zip"
function_name = "infrastructure-auto-remediation"
role = aws_iam_role.lambda_remediation.arn
handler = "index.handler"
runtime = "python3.9"
timeout = 300
environment {
variables = {
ENVIRONMENT = var.environment
SNS_TOPIC = aws_sns_topic.remediation_results.arn
}
}
}
resource "aws_cloudwatch_event_rule" "instance_state_change" {
name = "instance-state-change-${var.environment}"
event_pattern = jsonencode({
source = ["aws.ec2"]
detail-type = ["EC2 Instance State-change Notification"]
detail = {
state = ["terminated", "stopped"]
}
})
}
Build remediation functions with proper error handling, logging, and notification capabilities. Each automated action should report its success or failure back through SNS topics, creating an audit trail for all remediation activities. This transparency helps teams understand what happened and refine automated responses over time.
Managing Code Versioning and Deployment Pipeline
Implementing Git workflows for infrastructure changes
Managing infrastructure as code requires a structured approach to version control that prevents costly mistakes and ensures smooth collaboration across teams. The foundation starts with establishing clear branching strategies that separate production-ready code from experimental changes.
A proven Git workflow for Terraform AWS deployment involves creating feature branches for each infrastructure modification. When updating auto-scaling configurations or modifying AWS network infrastructure Terraform modules, developers work in isolated branches that can be thoroughly tested before merging. The main branch should always represent production-ready infrastructure state.
Pull request workflows become critical when multiple team members contribute to infrastructure changes. Each PR should include detailed descriptions of what resources will be modified, created, or destroyed. Code reviews help catch potential issues before they impact production systems, especially when dealing with highly available architecture components.
Branch protection rules prevent direct commits to main branches and require status checks to pass before merging. This approach ensures that all infrastructure as code best practices are followed consistently across the team.
Creating automated testing for infrastructure configurations
Testing Terraform configurations before deployment prevents outages and reduces the risk of misconfigurations in production environments. Static analysis tools like terraform validate and terraform plan provide the first line of defense against syntax errors and resource conflicts.
Automated testing goes beyond basic validation to include security scanning, cost estimation, and compliance checks. Tools like Checkov scan Terraform code for security vulnerabilities and compliance violations specific to AWS services. TFSec performs additional security analysis, identifying potential misconfigurations in RDS high availability setups or auto-scaling groups.
Unit testing for Terraform involves creating test scenarios that verify expected resource configurations. Terratest, written in Go, enables comprehensive testing by actually deploying infrastructure to isolated environments and validating the results. This approach works particularly well for testing complex AWS infrastructure automation scenarios.
Integration tests validate that different Terraform modules work together correctly. When building infrastructure monitoring AWS systems alongside application infrastructure, integration tests ensure all components communicate properly and maintain the desired highly available architecture.
Setting up CI/CD pipelines for safe infrastructure updates
Continuous integration and deployment pipelines transform infrastructure changes from manual, error-prone processes into reliable, repeatable operations. GitHub Actions, GitLab CI, or Jenkins can orchestrate the entire workflow from code commit to production deployment.
A typical CI/CD pipeline for Terraform begins with automated testing on every pull request. The pipeline runs terraform validate, security scans, and cost analysis before allowing human review. This early feedback prevents problematic changes from reaching production environments.
The deployment pipeline includes multiple stages with approval gates. After successful testing, changes deploy to development environments first, then staging, and finally production. Each stage runs terraform plan to show exactly what changes will occur, giving operators visibility into the impact.
State file management becomes crucial in CI/CD environments. Remote backends stored in S3 with DynamoDB locking prevent concurrent modifications that could corrupt infrastructure state. The pipeline should handle state file conflicts gracefully and provide rollback capabilities when deployments fail.
Deployment strategies like blue-green deployments work well for infrastructure updates that affect running applications. The pipeline can create parallel infrastructure, validate its functionality, then switch traffic over seamlessly. This approach maintains high availability during infrastructure updates.
Pipeline notifications keep teams informed about deployment status and any issues that require attention. Slack integrations, email alerts, or webhook notifications ensure the right people know when infrastructure changes complete successfully or encounter problems.
Building highly available AWS infrastructure with Terraform isn’t just about writing code – it’s about creating systems that keep running when things go wrong. We’ve covered the essential building blocks: designing resilient network architectures, setting up auto-scaling for your applications, and making sure your databases can handle failures gracefully. The monitoring and alerting pieces we discussed will help you catch issues before your users do, while proper code versioning keeps your infrastructure changes safe and trackable.
The real power of this approach shows up when you combine all these elements together. Your Terraform code becomes a blueprint for reliability, letting you rebuild entire environments quickly and consistently. Start small with one component – maybe begin with auto-scaling groups or RDS multi-AZ deployments – then gradually add more high availability features as you get comfortable. Remember, the best highly available system is one that fails gracefully and recovers automatically, giving you peace of mind and your users a smooth experience.
