This repository presents three different ways to use caching in AWS Lambda functions. You can use this to start with your own caching setup by using one of the following approaches:
- Simple Caching: you only cache data within a Lambda function. This makes sense if you your cached data will not change frequently. The disadvantage is that no cache synchronization happens between the instances of your Lambda functions and their local caches.
- Custom Caching: you are deploying your own caching service. Since you don't want to put your cached data to a public EC2 instance, you'll keep your cache instances in a VPC. Unfortunately this requires you to also put your Lambda functions into the same VPC in order to let them access the cache. In this example I'm using a Java Lambda function which is connecting to Hazelcast on an EC2 instance.
- Managed Caching: you are using ElastiCache which is a managed caching service by AWS. The setup is the same as in the Custom Caching example. You can choose between Redis and Memcached here. The example is based on a NodeJS Lambda connecting to a Redis cluster.
Each folder contains a deploy.sh file which lets you easily deploy the related CloudFormation template.
You only need to provide a LAMBDA_BUCKET environment variable pointing to an S3 bucket where the Lambda function artifacts are uploaded to (necessary for deployment).
For further information you can also read my blog post about Caching in AWS Lambda or take a look at the slides of my talk.
MIT License
Copyright (c) 2018 Sebastian Hesse
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