AWS API Gateway Caching: Boost Your API Speed!

Ready to blast your API to the next dimension? Sick of slow response times and grumpy users? It’s time to explore the electrifying universe of AWS API Gateway caching! 

In this post, we’ll take you on a thrilling journey to optimize your API performance with AWS Gateway caching. We’ve got all the code snippets and emojis you need to make this the ride of a lifetime. So strap in and get ready to launch into API greatness! 

 

 What is AWS API Gateway Caching?

AWS Gateway caching is a feature that allows you to cache the responses of your API Gateway endpoint in the AWS-managed cache. By caching API responses, the API Gateway can serve subsequent requests from the cache instead of invoking the backend Lambda function. This can lead to significantly faster response times and improved API performance. 

Using AWS Gateway caching comes with several benefits, such as improved response times, fewer Lambda invocations, scalability, and customization. By caching responses in the AWS-managed cache, you can handle a greater number of requests without invoking the backend Lambda function, which reduces costs and enhances scalability.

Techniques for Optimizing AWS Gateway Caching

Now that we know what AWS Gateway caching is and why it’s important, let’s dive into some techniques for optimizing its performance.

Use Cache Keys Effectively 

The cache key is used to identify the cached response for a specific request. By choosing cache key parameters that are unique and specific to each request, you can ensure that the cache is used effectively. Here’s an example of how to set the cache key using the AWS SDK for Python:

import boto3

client = boto3.client('apigateway')

response = client.update_integration(
    restApiId='your_rest_api_id',
    resourceId='your_resource_id',
    httpMethod='your_http_method',
    patchOperations=[
        {
            'op': 'replace',
            'path': '/caching/parameters/0/name',
            'value': 'user_id'
        },
        {
            'op': 'replace',
            'path': '/caching/parameters/0/location',
            'value': 'querystring'
        }
    ]
)

Here, we’re setting the cache key to use the `user_id` query string parameter.


Configure the Cache Time-to-Live (TTL) 

The TTL for the cached responses specifies how long the responses should be cached. By setting the TTL to an appropriate value, you can balance the performance benefits of caching with the need to serve fresh data. Here’s an example of how to set the TTL using the AWS SDK for Python:

import boto3

client = boto3.client('apigateway')

response = client.update_integration(
    restApiId='your_rest_api_id',
    resourceId='your_resource_id',
    httpMethod='your_http_method',
    patchOperations=[
        {
            'op': 'replace',
            'path': '/caching/ttlInSeconds',
            'value': '300' # Set TTL to 5 minutes
        }
    ]
)

In this example, we’re setting the TTL to 5 minutes.

Monitor Cache Behavior 

 

It’s important to monitor the cache behavior to ensure that it’s working as expected. By reviewing the cache hit and miss rates, you can identify any issues and adjust the caching settings as necessary. Here’s an example of how to view the cache behavior using the AWS Management Console:

  • Open the AWS Management Console and navigate to the API Gateway service.
  • Select your API from the list of available APIs.
  • Click on the “Stages” tab and select the appropriate stage.
  • Click on the “Logs/Tracing” tab and select “Enable CloudWatch Logs”.
  • Click on the “Metrics” tab and select “CacheHitCount” and “CacheMissCount”.
  • Review the cache hit and miss rates to determine if the caching is working as expected. Adjust the caching settings as necessary.
  • Use Invalidations for Dynamic Data 🔄

If your API serves dynamic data that changes frequently, you can use invalidations to remove stale data from the cache. Here’s an example of how to use invalidations using the AWS SDK for Python:

import boto3

client = boto3.client('apigateway')

response = client.create_invalidation(
    restApiId='your_rest_api_id',
    distributionId='your_distribution_id',
    invalidationBatch={
        'Paths': {
            'Quantity': 1,
            'Items': [
                '/your_path'
            ]
        },
        'CallerReference': 'your_caller_reference'
    }
)

In the above code snippet, we’re invalidating the cache for the `/your_path` resource.

Conclusion

Congratulations, you’ve made it to the end of this adventure! By using AWS Gateway caching, you can significantly improve the performance of your API and provide a better experience for your users. We hope this guide has helped you on your journey to API optimization. 

Remember to use cache keys effectively, configure the TTL appropriately, monitor the cache behavior, and use invalidations for dynamic data. With these techniques in your arsenal, you’ll be unstoppable!

At Craftsmen, we heavily use a vast majority of services provided by AWS, and we believe that sharing our knowledge and experience can help the wider developer community improve their API performance. So go forth and try out AWS Gateway caching on your own APIs – your users will thank you!

Craftsmen Research Team

research

Published: July 17, 2023

UPD: July 20, 2023

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