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+---
+title: "SQS Queue-Depth Autoscaling"
+description: "Scale your workers based on Amazon SQS queue depth using the SQS Exporter addon"
+---
+
+# SQS Queue-Depth Autoscaling
+
+If your application processes messages from Amazon SQS, Porter can automatically scale your worker services based on queue depth. When messages pile up, Porter scales up your workers. When the queue drains, it scales back down.
+
+This guide uses the [SQS Exporter](https://github.com/porter-dev/sqs-exporter) Porter addon — a lightweight Prometheus exporter that polls SQS queue depth and feeds it to Porter's built-in [metric-based autoscaler](/applications/observability/custom-metrics-and-autoscaling) via KEDA.
+
+This guide assumes you're running on EKS and uses [IRSA](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html) (IAM Roles for Service Accounts) so the exporter and worker pick up short-lived, auto-rotated credentials from a pod-scoped IAM role — no long-lived keys to store or rotate.
+
+## Prerequisites
+
+- An AWS account with an SQS queue
+- A worker service on Porter that processes messages from the queue
+- Permissions to create IAM roles in your AWS account
+
+## Step 1: Create an IAM Role for the Exporter
+
+Porter deploys Helm chart addons with the release name `helmchart`, so the exporter's Kubernetes service account will always be named `helmchart-sqs-exporter`. You'll reference this in the trust policy below.
+
+**1. Find your OIDC provider URL.**
+
+In the AWS Console, go to **EKS → your cluster → Overview tab → OpenID Connect provider URL**. Copy the URL and strip the `https://` prefix — this is your `OIDC_PROVIDER`.
+
+**2. Create an IAM role with a custom trust policy.**
+
+Go to **IAM → Roles → Create role → Custom trust policy** and paste the following, substituting your values:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [{
+    "Effect": "Allow",
+    "Principal": {
+      "Federated": "arn:aws:iam::{ACCOUNT_ID}:oidc-provider/{OIDC_PROVIDER}"
+    },
+    "Action": "sts:AssumeRoleWithWebIdentity",
+    "Condition": {
+      "StringEquals": {
+        "{OIDC_PROVIDER}:sub": "system:serviceaccount:{NAMESPACE}:helmchart-sqs-exporter",
+        "{OIDC_PROVIDER}:aud": "sts.amazonaws.com"
+      }
+    }
+  }]
+}
+```
+
+- `ACCOUNT_ID`: your AWS account ID
+- `OIDC_PROVIDER`: the value from step 1 (without `https://`)
+- `NAMESPACE`: the namespace of your Porter deployment target (typically `default`)
+
+**3. Attach a permissions policy** to the role. Create an inline policy:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [{
+    "Effect": "Allow",
+    "Action": "sqs:GetQueueAttributes",
+    "Resource": "arn:aws:sqs:{REGION}:{ACCOUNT_ID}:{QUEUE_NAME}"
+  }]
+}
+```
+
+Keep the role ARN handy — you'll use it in the next step.
+
+## Step 2: Deploy the SQS Exporter
+
+1. Navigate to your application in the Porter dashboard
+2. Go to the **Add-ons** tab
+3. Click **Add New** and select **Helm Chart**
+4. Configure the Helm chart:
+   - **Repository URL**: `oci://ghcr.io/porter-dev`
+   - **Chart Name**: `sqs-exporter`
+   - **Chart Version**: `0.1.1`
+5. In the **Helm Values** section, paste:
+
+```yaml
+sqsQueueUrls: "https://sqs.us-east-1.amazonaws.com/123456789/your-queue-name"
+
+serviceAccount:
+  roleArn: "arn:aws:iam::{ACCOUNT_ID}:role/{ROLE_NAME}"
+
+aws:
+  region: "us-east-1"
+
+pollIntervalSeconds: 10
+```
+
+<Warning>
+Replace `sqsQueueUrls` with your full SQS queue URL. To monitor multiple queues, provide a comma-separated list of URLs.
+</Warning>
+
+6. Click **Deploy**
+
+Verify the exporter is running by checking the add-on logs — you should see `using ambient AWS credential chain`, followed by the exporter polling your queue with no `AccessDenied` errors.
+
+## Step 3: Grant the Worker AWS Access
+
+The worker pod needs AWS credentials to consume from SQS. First, create an IAM role with these permissions on your queue:
+
+- `sqs:ReceiveMessage`
+- `sqs:DeleteMessage`
+- `sqs:GetQueueAttributes`
+
+The role's trust policy must name the worker's service account so IRSA can assume it:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [{
+    "Effect": "Allow",
+    "Principal": {
+      "Federated": "arn:aws:iam::{ACCOUNT_ID}:oidc-provider/{OIDC_PROVIDER}"
+    },
+    "Action": "sts:AssumeRoleWithWebIdentity",
+    "Condition": {
+      "StringEquals": {
+        "{OIDC_PROVIDER}:sub": "system:serviceaccount:{NAMESPACE}:{APP_NAME}-{SERVICE_NAME}",
+        "{OIDC_PROVIDER}:aud": "sts.amazonaws.com"
+      }
+    }
+  }]
+}
+```
+
+Porter names the worker's service account `{APP_NAME}-{SERVICE_NAME}` (e.g. `sqs-poller-poller` for app `sqs-poller` with service `poller`).
+
+Then attach the role to the worker using one of the following.
+
+### Option A: `porter.yaml`
+
+Declare the [`awsRole` connection](/applications/configuration-as-code/services/connections#aws-role-connection) under your worker service. If your app doesn't have a `porter.yaml`, you can create one at the root of your repo with just the worker service declared — Porter merges it with your dashboard config.
+
+```yaml
+services:
+  - name: worker
+    # ...
+    connections:
+      - type: awsRole
+        role: my-worker-sqs-access
+```
+
+### Option B: Annotate the service account manually
+
+If you'd rather not introduce a `porter.yaml`, annotate the worker's service account directly:
+
+```bash
+kubectl annotate sa {APP_NAME}-{SERVICE_NAME} -n {NAMESPACE} \
+  eks.amazonaws.com/role-arn=arn:aws:iam::{ACCOUNT_ID}:role/{ROLE_NAME}
+```
+
+Either option results in the service account carrying the `eks.amazonaws.com/role-arn` annotation, which EKS uses to inject IRSA credentials into the pod.
+
+### Set the worker's environment variables
+
+Under the worker service's **Environment** tab, set:
+
+```
+SQS_QUEUE_URL={QUEUE_URL}
+AWS_REGION={AWS_REGION}
+```
+
+## Step 4: Configure Metric-Based Autoscaling
+
+1. Navigate to your application in the Porter dashboard
+2. Go to the **Services** tab and click on your worker service
+3. Under **Autoscaling**, select the **Metric-based** tab
+4. Fill in the following fields:
+   - **Min replicas**: `0` (scale to zero when queue is empty) or `1` (always keep one worker warm)
+   - **Max replicas**: `10` (adjust based on your workload)
+   - **Metric name**: `sqs_messages_visible`
+   - **Query**: `avg(sqs_messages_visible{queue_name="your-queue-name"})`
+   - **Threshold**: `10` (target number of messages per worker instance)
+
+<Warning>
+Replace `your-queue-name` with the name portion of your SQS queue URL (e.g., for `https://sqs.us-east-1.amazonaws.com/123456789/order-processing`, use `order-processing`). The `queue_name` label must match exactly or autoscaling won't trigger.
+</Warning>
+
+## Scaling Latency
+
+Scale-up isn't instant — expect on the order of a minute or two between messages arriving in your queue and new worker pods becoming ready. This latency comes from a combination of SQS polling, metrics scraping, and the stabilization period before the autoscaler commits to a scale-up.
+
+For queue-based workloads where faster scale-up matters, you can shorten the stabilization window via your worker service's **Settings** tab → **Helm overrides**:
+
+```yaml
+keda:
+  hpa:
+    scaleUp:
+      stabilizationWindowSeconds: 0
+      policy:
+        type: Percent
+        value: 100
+        periodSeconds: 15
+```
+
+Node provisioning is often the remaining bottleneck once the autoscaling pipeline is tuned.
diff --git a/mint.json b/mint.json
index 835f3d0..0dfcacb 100644
--- a/mint.json
+++ b/mint.json
@@ -102,6 +102,7 @@
                 "applications/configure/basic-configuration",
                 "applications/configure/autoscaling",
                 "applications/configure/temporal-autoscaling",
+                "applications/configure/sqs-autoscaling",
                 "applications/configure/custom-domains",
                 "applications/configure/zero-downtime-deployments",
                 "applications/configure/advanced-networking"