ecs

AWS ECS

Amazon Elastic Container Service (ECS) is a fully managed container orchestration service. Run containers on AWS Fargate (serverless) or EC2 instances.

Table of Contents

• Core Concepts
• Common Patterns
• CLI Reference
• Best Practices
• Troubleshooting
• References

Core Concepts

Cluster

Logical grouping of tasks or services. Can contain Fargate tasks, EC2 instances, or both.

Task Definition

Blueprint for your application. Defines containers, resources, networking, and IAM roles.

Task

Running instance of a task definition. Can run standalone or as part of a service.

Service

Maintains desired count of tasks. Handles deployments, load balancing, and auto scaling.

Launch Types

Type	Description	Use Case
Fargate	Serverless, pay per task	Most workloads
EC2	Self-managed instances	GPU, Windows, specific requirements

Common Patterns

Create a Fargate Cluster

AWS CLI:

# Create cluster
aws ecs create-cluster --cluster-name my-cluster

# With capacity providers
aws ecs create-cluster \
  --cluster-name my-cluster \
  --capacity-providers FARGATE FARGATE_SPOT \
  --default-capacity-provider-strategy \
    capacityProvider=FARGATE,weight=1 \
    capacityProvider=FARGATE_SPOT,weight=1

Register Task Definition

cat > task-definition.json << 'EOF'
{
  "family": "web-app",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "256",
  "memory": "512",
  "executionRoleArn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
  "taskRoleArn": "arn:aws:iam::123456789012:role/ecsTaskRole",
  "containerDefinitions": [
    {
      "name": "web",
      "image": "123456789012.dkr.ecr.us-east-1.amazonaws.com/my-app:latest",
      "portMappings": [
        {
          "containerPort": 8080,
          "protocol": "tcp"
        }
      ],
      "environment": [
        {"name": "NODE_ENV", "value": "production"}
      ],
      "secrets": [
        {
          "name": "DB_PASSWORD",
          "valueFrom": "arn:aws:secretsmanager:us-east-1:123456789012:secret:db-password"
        }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/web-app",
          "awslogs-region": "us-east-1",
          "awslogs-stream-prefix": "ecs",
          "mode": "non-blocking",
          "max-buffer-size": "25m"
        }
      },
      "healthCheck": {
        "command": ["CMD-SHELL", "curl -f http://localhost:8080/health || exit 1"],
        "interval": 30,
        "timeout": 5,
        "retries": 3,
        "startPeriod": 60
      }
    }
  ]
}
EOF

aws ecs register-task-definition --cli-input-json file://task-definition.json

Create Service with Load Balancer

aws ecs create-service \
  --cluster my-cluster \
  --service-name web-service \
  --task-definition web-app:1 \
  --desired-count 2 \
  --launch-type FARGATE \
  --network-configuration "awsvpcConfiguration={
    subnets=[subnet-12345678,subnet-87654321],
    securityGroups=[sg-12345678],
    assignPublicIp=DISABLED
  }" \
  --load-balancers "targetGroupArn=arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/web-tg/1234567890123456,containerName=web,containerPort=8080" \
  --health-check-grace-period-seconds 60 \
  --deployment-configuration "deploymentCircuitBreaker={enable=true,rollback=true}"

Run Standalone Task

aws ecs run-task \
  --cluster my-cluster \
  --task-definition my-batch-job:1 \
  --launch-type FARGATE \
  --network-configuration "awsvpcConfiguration={
    subnets=[subnet-12345678],
    securityGroups=[sg-12345678],
    assignPublicIp=ENABLED
  }"

Update Service (Deploy New Image)

# Register new task definition with updated image
aws ecs register-task-definition --cli-input-json file://task-definition.json

# Update service to use new version
aws ecs update-service \
  --cluster my-cluster \
  --service web-service \
  --task-definition web-app:2 \
  --force-new-deployment

Fargate Spot with SQS-Based Scaling

Use FARGATE_SPOT for batch/queue workloads to cut costs ~70%. Always include a fallback to regular FARGATE.

# Create service with Spot + fallback
aws ecs create-service \
  --cluster batch-cluster \
  --service-name queue-processor \
  --task-definition my-processor:1 \
  --desired-count 0 \
  --capacity-provider-strategy \
    capacityProvider=FARGATE_SPOT,weight=4,base=0 \
    capacityProvider=FARGATE,weight=1,base=1 \
  --network-configuration "awsvpcConfiguration={
    subnets=[subnet-12345678],
    securityGroups=[sg-12345678],
    assignPublicIp=DISABLED
  }"

# Register scalable target (scale to zero when queue empty)
aws application-autoscaling register-scalable-target \
  --service-namespace ecs \
  --resource-id service/batch-cluster/queue-processor \
  --scalable-dimension ecs:service:DesiredCount \
  --min-capacity 0 \
  --max-capacity 20

# Scale-out alarm: messages > 100
aws cloudwatch put-metric-alarm \
  --alarm-name queue-scale-out \
  --metric-name ApproximateNumberOfMessagesVisible \
  --namespace AWS/SQS \
  --dimensions Name=QueueName,Value=my-queue \
  --statistic Average \
  --period 60 \
  --evaluation-periods 1 \
  --threshold 100 \
  --comparison-operator GreaterThanThreshold \
  --alarm-actions <scale-out-policy-arn>

# Scale-in alarm: queue empty for 3 periods (conservative to avoid flapping)
aws cloudwatch put-metric-alarm \
  --alarm-name queue-scale-in \
  --metric-name ApproximateNumberOfMessagesVisible \
  --namespace AWS/SQS \
  --dimensions Name=QueueName,Value=my-queue \
  --statistic Average \
  --period 60 \
  --evaluation-periods 3 \
  --threshold 0 \
  --comparison-operator LessThanOrEqualToThreshold \
  --alarm-actions <scale-in-policy-arn>

Backlog per task (AWS-recommended): Raw queue depth over-scales. Target-track queue depth / RunningTaskCount via metric math instead; Application Auto Scaling manages the alarms. Requires Container Insights on the cluster (RunningTaskCount in ECS/ContainerInsights). With 0 running tasks the divisor has no data, so this cannot scale from zero; keep the alarm pattern above when min-capacity is 0.

# TargetValue = acceptable latency / avg processing time per message (e.g. 10s / 0.1s = 100)
aws application-autoscaling put-scaling-policy \
  --service-namespace ecs \
  --resource-id service/batch-cluster/queue-processor \
  --scalable-dimension ecs:service:DesiredCount \
  --policy-name sqs-backlog-per-task \
  --policy-type TargetTrackingScaling \
  --target-tracking-scaling-policy-configuration '{
    "TargetValue": 100,
    "CustomizedMetricSpecification": {
      "Metrics": [
        {"Id": "m1", "ReturnData": false, "MetricStat": {"Stat": "Sum",
          "Metric": {"Namespace": "AWS/SQS", "MetricName": "ApproximateNumberOfMessagesVisible",
            "Dimensions": [{"Name": "QueueName", "Value": "my-queue"}]}}},
        {"Id": "m2", "ReturnData": false, "MetricStat": {"Stat": "Average",
          "Metric": {"Namespace": "ECS/ContainerInsights", "MetricName": "RunningTaskCount",
            "Dimensions": [{"Name": "ClusterName", "Value": "batch-cluster"},
                           {"Name": "ServiceName", "Value": "queue-processor"}]}}},
        {"Id": "e1", "Expression": "m1 / m2", "ReturnData": true}
      ]
    }
  }'

Protect in-flight work from scale-in: the worker sets ProtectionEnabled=true while processing (container agent endpoint $ECS_AGENT_URI/task-protection/v1/state, or aws ecs update-task-protection --cluster <c> --tasks <id> --protection-enabled --expires-in-minutes 60) and clears it when done. Task role needs ecs:GetTaskProtection and ecs:UpdateTaskProtection. Service tasks only.

Fargate Spot interruption handling: Spot tasks receive a SIGTERM 2 minutes before termination. Catch it in your application for graceful shutdown. For SQS consumers, call ChangeMessageVisibility on in-flight messages so they return to the queue rather than timing out.

Auto Scaling

# Register scalable target
aws application-autoscaling register-scalable-target \
  --service-namespace ecs \
  --resource-id service/my-cluster/web-service \
  --scalable-dimension ecs:service:DesiredCount \
  --min-capacity 2 \
  --max-capacity 10

# Target tracking policy
aws application-autoscaling put-scaling-policy \
  --service-namespace ecs \
  --resource-id service/my-cluster/web-service \
  --scalable-dimension ecs:service:DesiredCount \
  --policy-name cpu-target-tracking \
  --policy-type TargetTrackingScaling \
  --target-tracking-scaling-policy-configuration '{
    "TargetValue": 70.0,
    "PredefinedMetricSpecification": {
      "PredefinedMetricType": "ECSServiceAverageCPUUtilization"
    },
    "ScaleOutCooldown": 60,
    "ScaleInCooldown": 120
  }'

Faster scaling with 20-second metrics: enable high-resolution service metrics, then use ECSServiceAverageCPUUtilizationHighResolution or ECSServiceAverageMemoryUtilizationHighResolution as the PredefinedMetricType. On an existing service the --monitoring change triggers a deployment; create the high-res policy only after it completes. Not supported with CODE_DEPLOY/EXTERNAL deployment controllers. Extra CloudWatch charges apply.

aws ecs update-service \
  --cluster my-cluster \
  --service web-service \
  --monitoring "metricConfigurations=[{metricNames=[CPUUtilization,MemoryUtilization],resolutionSeconds=20}]"

CLI Reference

Cluster Management

Command	Description
aws ecs create-cluster	Create cluster
aws ecs describe-clusters	Get cluster details
aws ecs list-clusters	List clusters
aws ecs delete-cluster	Delete cluster

Task Definitions

| Command | Descript

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