This post takes a look at Kotlin, a relatively new programming language that runs on the JVM, and explores how it can be used to build serverless web apps on AWS using Lambda and API Gateway. In programming language terms Kotlin is a relative newcomer. It was first announced in 2011 by JetBrains, the makers of IntelliJ IDEA, and was designed as a modern successor to Java.
With AWS Lambda, you have basic observability built into the platform with CloudWatch. CloudWatch offers support for both metrics and logging. CloudWatch Metrics gives you basic metrics, visualization and alerting while CloudWatch Logs captures everything that is written to stdout and stderr. In this post, we will take a deep dive into CloudWatch Metrics to see how you can use it to monitor your Lambda functions and its limitations.
We currently have six major cloud platforms offering serverless products, AWS Lambda being the pioneer. Our goal is to provide a quick way to compare and evaluate all. For each service, we will be evaluating: There are smaller service providers on the market that are focused on serverless, but we won’t cover them in the present analysis. For the pricing comparison, we considered regions in the United States east coast. Let the battle begin!
Lambda Layers were introduced by AWS in late 2018 as a way to simplify the developer’s life when managing dependencies and shared resources across a multi-Lambda stack. It’s a versatile feature that brings many benefits, which we will discuss in this article. Using Lambda Layers does increase complexity to monitor and maintain your applications, but there’s no need to fear.
If you’ve dealt with lambda functions you may have run across the RequestEntityTooLargeException - * byte payload is too large for the Event invocation type (limit 131072 bytes) AWS Lambda exception that occurs when a function is invoked with too large of a payload. Current AWS Lambda limits are set at 6 MB for synchronous/RequestResponse invocations, and 128 K for asynchronous/Event invocations.
In the unlikely event that you might not already hear this, AWS Lambda is Amazon’s answer to the serverless computing. The theory behind serverless computer services is, as it sounds, to create a network for a variety of purposes while bypassing the need to have and maintain expensive servers. With AWS Lambda, you are at top of the class in serverless computing. The setup and management is a breeze.
Yesterday our founder and CEO Nate Taggart announced his decision to step down from his post. While his presence will be missed by all of us, he made this choice in order for Stackery to bring in a new CEO with the experience and pedigree to steer the business to the next level.
When I browse Stackery’s documentation and blog, I see some great writing that I know not everyone has read. Check out these great articles essential to growth as a developer.
API Gateway is a powerful tool for creating a coherent API out of a set of multiple disconnected remote function providers, and API Gateway with AWS Lambda, when used properly, can give you the capacity to create truly powerful serverless APIs. However, given that API Gateway is a layer between your requests and the code itself, there are latency concerns that API Gateway Lambdas may face that may not be present when working with other architectures.
In this post, we’ll learn what Amazon Web Services (AWS) Lambda is, and why it might be a good idea to use for your next project. For a more in-depth introduction to serverless and Lambda, read AWS Lambda: Your Quick Start Guide to Going Serverless. In order to show how useful Lambda can be, we’ll walk through creating a simple Lambda function using the Python programming language. We’ll test it out, as well as take a look at what Lambda provides for metrics and logging.
