API Gateway’s caching feature can drastically improve latency and reduce backend load, but it’s not just a simple on/off switch; understanding its nuances is key to leveraging it effectively.
Let’s see it in action. Imagine a GET /products/{id} endpoint that’s frequently hit. Without caching, every request goes to your backend. With caching, the first request fetches from the backend and stores the response. Subsequent requests for the same {id} hit the API Gateway cache, returning the stored response almost instantly.
Here’s a typical configuration snippet in CloudFormation or Terraform:
# Example CloudFormation snippet for API Gateway Cache
Type: AWS::ApiGateway::Stage
Properties:
# ... other stage properties
CacheClusterEnabled: true
CacheDataEncrypted: false # For simplicity in this example, but consider true for production
CacheTtlInSeconds: 300 # Cache entries live for 5 minutes
CacheNodeCount: 1 # Number of cache nodes (scales with traffic)
This configuration enables caching for the entire stage, sets a Time-To-Live (TTL) of 300 seconds (5 minutes), and starts with one cache node.
The core problem API Gateway caching solves is reducing the number of requests that reach your origin services. This leads to:
- Lower Latency: Responses served from the cache are delivered much faster than those requiring a round trip to the backend.
- Reduced Backend Load: Your origin servers handle fewer requests, saving compute resources and potentially costs.
- Increased Availability: Even if your backend experiences temporary issues, cached responses can still be served, providing a degree of resilience.
Internally, API Gateway uses a distributed in-memory cache. When a request comes in, API Gateway checks if a cached response exists for that specific request. The cache key is derived from several factors, including the HTTP method, the resource path, and importantly, any query string parameters or headers you configure to be part of the cache key. If a match is found and the entry hasn’t expired (based on the TTL), the cached response is returned. Otherwise, the request is forwarded to the backend, and the response is cached before being sent back to the client.
You have granular control over what constitutes a cacheable response and what goes into the cache key. You can configure:
- Cacheable Methods: Typically
GETandHEADrequests. - Cache Keys: You define which parts of the request (path parameters, query string parameters, headers) uniquely identify a cache entry. For
GET /products/{id}?color=blue, you might cache based onidandcolor. - TTL: How long responses stay in the cache.
- Invalidation: Mechanisms to explicitly remove entries from the cache before their TTL expires (e.g., when data changes).
The most surprising thing about API Gateway caching is how it interacts with non-GET/HEAD methods and how easily you can accidentally cache identical responses for vastly different inputs. For instance, if you cache based only on the id for a GET /items/{id}/details endpoint, but also have a POST /items/{id}/update that returns the same item details, a POST request could potentially be served a stale GET response if not configured carefully, or vice-versa, due to shared cache keys if not properly differentiated. This is why carefully selecting cache keys that are unique to the specific data being requested is paramount.
The next concept you’ll likely explore is fine-grained cache invalidation and how to integrate it with your deployment pipelines.