AWS Announces Big Updates To Lambda Functions

AWS announces 10 GB memory, container image support, and millisecond billing to its Lambda functions.

Recently, AWS announced some big updates to its Lambda serverless function service. 

You can now allocate up to 10 GB of memory to a Lambda function - a 3x increase compared to previous limits. As Lambda allocates CPU and other resources linearly in proportion to the amount of memory configured, you can now have access to up to 6 vCPUs in each execution environment. So, your multithreaded and multiprocess applications run faster. And as Lambda charges are proportional to memory configured and function duration, the costs for using more memory can be offset by lower duration. 

Source: AWS

AWS said that now with more memory and CPU power, and support for the AVX2 instruction set, new use cases like ML applications; batch and extract, transform, load jobs; modelling; high-performance computing; and media processing become easier to implement and scale with Lambda functions.

AWS also said that it is rounding up duration to the nearest millisecond with no minimum execution time. This new pricing will make you pay less most of the time, but it’s going to be more noticeable when you have functions whose execution time is much lower than 100ms, for example low latency APIs.

Another big update is the container image support for Lambda functions. Users can now package and deploy Lambda functions as container images of up to 10 GB in size. Similer to functions packaged as ZIP archives, functions deployed as container images benefit from the same operational simplicity, automatic scaling, high availability, and native integrations with many services.

Source: AWS

The company is providing base images for all the supported Lambda runtimes i.e Python, Node.js, Java, .NET, Go, and Ruby;  to help you easily add your code and dependencies. AWS also have base images for custom runtimes based on Amazon Linux.