BRIEF: How to overcome the limitations AWS Lambda

There are a few ways to overcome some of the limitations of AWS Lambda:

Cold starts: To mitigate the effects of cold starts, you can use provisioned concurrency, which keeps a small number of instances of your function running and ready to handle requests. Additionally, you can use a service such as AWS Elastic Container Service (ECS) or Kubernetes to manage the container instances and keep them running.

Limited Connectivity: To maintain a connection to a database or other resources, you can use VPC (Virtual private cloud) and create ENIs(Elastic network interfaces) to connect with resources within the same VPC.

Memory Constraints: To improve the performance of your function, you can increase the amount of memory allocated to it, which will increase the amount of CPU and network bandwidth it can use.

Limited language support: If your application requires a language that isn't supported by Lambda, you can use other services provided by AWS like Elastic Beanstalk, EC2 or even running a containerized app in ECS.

Time out limit: AWS Lambda currently has a timeout limit of 15 minutes, if you have a long running task like image processing, video conversion etc. you may need to use a different service such as AWS Step Functions or EC2 instances to handle those kind of workloads.

6.Size limit: The deployment package and its layers, including the function code, can't exceed a certain size. So, if your function is too large you can use S3 to store the larger data and access it while your lambda function is running.

It's important to keep in mind that while these solutions can help to overcome some of the limitations of AWS Lambda, they may also introduce additional complexity and cost to your application. Careful consideration of the trade-offs is crucial when determining the best approach for your use case.