I'm running an ECS service using Fargate tasks to connect to DynamoDB for data queries in a testing environment. Initially, my application had an optimized fetch time of under 100ms when querying DynamoDB. However, I created a new task definition revision (TD2) from the existing one (TD1) using the same Docker image but with minor config changes. TD1 used 0.25 vCPU and 1GiB memory while TD2 was set to 1 vCPU and 2GiB memory. After deploying TD2, I noticed that the fetch time actually increased to 200ms, instead of improving. Even after reverting back to TD1, the performance hasn't returned to its original state and now fetches take around 150ms. I've checked for any changes to DynamoDB tables or configurations and reviewed CloudWatch metrics without finding any issues. I'm looking for insights into what could be causing this drop in performance, especially given that task definitions should be immutable.
2 Answers
Fargate might be using older CPU generations which could lead to inconsistent performance. It could be worth trying to restart your tasks a few times to see if the performance changes. Sometimes you get different results based on which hardware your task is assigned to.
Absolutely! I've experienced similar issues with Fargate where I noticed varying performance.
It's frustrating when scaling vertically leads to worse performance. Sometimes it feels like you're paying more for less compute power. Definitely a tricky situation with resource allocation!
That’s a good point! Graviton instances tend to give more consistent performance since they don't rely on older CPU generations.