A couple of weeks ago I was working on some AWS CDK based code and I was trying to figure out how to configure auto-scaling for the provisioned concurrency configuration of an AWS Lambda function. We wanted to run some performance tests on our service and were wondering how scaling provisioned concurrency would impact our overall latency. We tried with the default configuration but also wanted to experiment with a bit more aggressive scaling policy so we had to provide our own metric configuration. In this post, I will explain what provisioned concurrency is and how to set up an auto-scaling configuration for it using AWS CDK. We’ll be looking at using predefined metric configurations, but also how to do it with a custom metric.
A while ago my team was looking to create a stub for an internal JSON HTTP based API. The to-be stubbed service was quite simple. The service exposed a REST API endpoint for listing resources of a specific type. The API supported paging and some specific request/query parameters.
In a previous post, we looked at AWS App Runner. AWS App Runner is a container service that lets you focus on your application and allows you to deploy your application in minutes without setting up any infrastructure.
About three months ago AWS released a new service named AWS App Runner. After reading the introduction blog post, I got pretty excited to check it out. AWS App Runner is a new service that provides organizations with a fast, simple, and secure way to deploy containerized applications on the AWS platform without managing any infrastructure. AWS already offers a wide range of container based services like AWS Fargate, ECS, Elastic BeanStalk, and AWS EKS, so why did they come up with App Runner?
Over the last 2.5 years, I’ve been working with AWS and a wide range of its services. During this time I noticed that for most projects it’s useful to be able to test your application against AWS services without having to deploy or move your code into the cloud. There are several free solutions available for you to use depending on the services required by your project. In this post, I’ll describe some of the tools that I use. DynamoDB local At one of my previous projects, we made extensive use of the combination of DynamoDB and Elasticsearch for storing and querying data. The fact that DynamoDB is a managed database service with immense scale and performance benefits, makes DynamoDB a great fit for high traffic applications.