Caching is one of the most effective ways to improve application performance while reducing costs. In this guide, I'll show you how to implement a cache-aside pattern using DynamoDB, ElastiCache Redis, AWS Lambda, and API Gateway - all provisioned with Terraform.
📘 Part 1: Infrastructure Setup
1. DynamoDB Table
First, let's create a Products table in DynamoDB:
resource "aws_dynamodb_table" "products" {
name = "Products"
billing_mode = "PAY_PER_REQUEST"
hash_key = "productId"
attribute {
name = "productId"
type = "S"
}
}
2. ElastiCache Redis Cluster
We'll deploy Redis inside a VPC for better security and performance:
resource "aws_elasticache_cluster" "products_cache" {
cluster_id = "products-cache"
engine = "redis"
node_type = "cache.t3.micro"
num_cache_nodes = 1
parameter_group_name = "default.redis6.x"
engine_version = "6.x"
port = 6379
security_group_ids = [aws_security_group.redis.id]
subnet_group_name = aws_elasticache_subnet_group.redis.name
}
resource "aws_elasticache_subnet_group" "redis" {
name = "redis-subnet-group"
subnet_ids = [aws_subnet.private_a.id, aws_subnet.private_b.id]
}
3. Networking Configuration
Proper VPC setup is crucial:
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
}
resource "aws_subnet" "private_a" {
vpc_id = aws_vpc.main.id
cidr_block = "10.0.1.0/24"
availability_zone = "us-east-1a"
}
resource "aws_security_group" "redis" {
name = "redis-sg"
description = "Allow access to Redis"
vpc_id = aws_vpc.main.id
ingress {
from_port = 6379
to_port = 6379
protocol = "tcp"
security_groups = [aws_security_group.lambda.id]
}
}
📘 Part 2: Lambda Function Logic
Here's our Python Lambda function implementing the cache-aside pattern:
import os
import json
import boto3
import redis
from datetime import datetime
# Initialize clients
dynamodb = boto3.resource('dynamodb')
products_table = dynamodb.Table('Products')
# Redis connection
redis_client = redis.Redis(
host=os.environ['REDIS_HOST'],
port=6379,
decode_responses=True
)
def lambda_handler(event, context):
product_id = event['pathParameters']['productId']
cache_key = f"product:{product_id}"
# Try to get from Redis first
cached_product = redis_client.get(cache_key)
if cached_product:
print("Cache hit!")
return {
'statusCode': 200,
'body': cached_product
}
print("Cache miss - fetching from DynamoDB")
# Get from DynamoDB
response = products_table.get_item(Key={'productId': product_id})
if 'Item' not in response:
return {'statusCode': 404, 'body': 'Product not found'}
product = response['Item']
product_json = json.dumps(product)
# Cache with 5 minute TTL
redis_client.setex(cache_key, 300, product_json)
return {
'statusCode': 200,
'body': product_json
}
📘 Part 3: API Gateway Configuration
Let's expose our Lambda through API Gateway:
resource "aws_api_gateway_rest_api" "products_api" {
name = "products-api"
}
resource "aws_api_gateway_resource" "product" {
rest_api_id = aws_api_gateway_rest_api.products_api.id
parent_id = aws_api_gateway_rest_api.products_api.root_resource_id
path_part = "product"
}
resource "aws_api_gateway_resource" "product_id" {
rest_api_id = aws_api_gateway_rest_api.products_api.id
parent_id = aws_api_gateway_resource.product.id
path_part = "{productId}"
}
resource "aws_api_gateway_method" "get_product" {
rest_api_id = aws_api_gateway_rest_api.products_api.id
resource_id = aws_api_gateway_resource.product_id.id
http_method = "GET"
authorization = "NONE"
}
resource "aws_api_gateway_integration" "lambda" {
rest_api_id = aws_api_gateway_rest_api.products_api.id
resource_id = aws_api_gateway_resource.product_id.id
http_method = aws_api_gateway_method.get_product.http_method
integration_http_method = "POST"
type = "AWS_PROXY"
uri = aws_lambda_function.get_product.invoke_arn
}
📘 Part 4: Monitoring and Optimization
1. Adding TTLs
We already implemented TTLs in our Lambda function (setex with 300 seconds), but let's add CloudWatch metrics to track cache performance:
from aws_lambda_powertools import Metrics
metrics = Metrics()
def lambda_handler(event, context):
# ... existing code ...
if cached_product:
metrics.add_metric(name="CacheHits", unit="Count", value=1)
# ... return cached product ...
else:
metrics.add_metric(name="CacheMisses", unit="Count", value=1)
# ... fetch from DynamoDB ...
2. Terraform for Monitoring
Add CloudWatch alarms and dashboards:
resource "aws_cloudwatch_dashboard" "cache" {
dashboard_name = "cache-performance"
dashboard_body = jsonencode({
widgets = [
{
type = "metric"
x = 0
y = 0
width = 12
height = 6
properties = {
metrics = [
["AWS/Lambda", "CacheHits", "FunctionName", aws_lambda_function.get_product.function_name],
["AWS/Lambda", "CacheMisses", "FunctionName", aws_lambda_function.get_product.function_name]
]
period = 300
stat = "Sum"
region = "us-east-1"
title = "Cache Performance"
}
}
]
})
}
3. Complete Terraform Workflow
For a production setup, add a CI/CD pipeline:
resource "aws_codepipeline" "deploy_pipeline" {
name = "products-api-deployment"
role_arn = aws_iam_role.codepipeline.arn
artifact_store {
location = aws_s3_bucket.artifacts.bucket
type = "S3"
}
stage {
name = "Source"
action {
name = "Source"
category = "Source"
owner = "ThirdParty"
provider = "GitHub"
version = "1"
output_artifacts = ["source_output"]
configuration = {
Owner = "your-github-org"
Repo = "products-api"
Branch = "main"
OAuthToken = var.github_token
}
}
}
stage {
name = "Terraform"
action {
name = "Apply"
category = "Build"
owner = "AWS"
provider = "CodeBuild"
input_artifacts = ["source_output"]
version = "1"
configuration = {
ProjectName = aws_codebuild_project.terraform.name
}
}
}
}
Results and Observations
After implementing this architecture, you should see:
- Average latency reduction from ~100ms (DynamoDB) to ~5ms (Redis) for cache hits
- Reduced DynamoDB RCU consumption (and costs) by 80-90% for frequently accessed items
- More consistent performance under load
Final Thoughts
This cache-aside pattern is just one of many ways to optimize DynamoDB performance. For production workloads, consider:
- Adding write-through caching for data modifications
- Implementing cache invalidation strategies
- Monitoring Redis memory usage and eviction policies
- Considering DAX for alternative DynamoDB caching
The complete Terraform code is available in this GitHub repo.
Have you implemented similar caching patterns? Share your experiences in the comments!
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