Using Elasticache to Improve DynamoDB Performance

45 minutes
  • 3 Learning Objectives

About this Hands-on Lab

In this lab we are presented with a site that is struggling under load because the backing DynamoDB Table has had its Read and Write provisioning drastically reduced, to avoid incurring significant cost. To correct this we will provision an ElastiCache cluster and modify the Lambda functions in the site architecture, to implement a simple caching scheme that returns the site to a functional state.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Launch an ElastiCache Cluster
  1. Navigate to the ElastiCache web console.
  2. Click Get Started Now
  3. Set the Cluster Engine to Memcached
  4. Provide a name for the cluster (like tatourneycache)
  5. Set the Cluster Node to t2.small
Modify getTaStats Lambda Function
  1. Navigate to the Lambda Web Console.
  2. Select the function with GetTaStats in the name.
  3. Modify the function in editme.py to implement read through data caching (using what’s in the GitHub repo).
Modify the taStreamProcessor Lambda Function
  1. Navigate to the Lambda Web Console.
  2. Select the function with taStreamProcessor in the name.
  3. Modify the function in editme.py to implement cache invalidation when records in the TaTourneyStats table are updated. Use the code in the GitHub repo to do this.

Additional Resources

The Linux Academy AWS Training Architect Tournament site has become very popular within Linux Academy. Unfortunately this means that operating the site has become prohibitively expensive. The backing table has had its read capacity units set to 1, and we've been instructed not to change it. On the other hand, we have gotten permission to create a small ElastiCache cluster, to implement a caching scheme that restores functionality to the site and generally improves performance. This will also reduce the monthly cost of running the site.

The code used for the solution in this lab can be found on GitHub.

Logging In

Use the credentials provided on the hands-on lab overview page, and log into the AWS web console as cloud_user.

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