![]() Where does this piece of code live, if we aren’t deploying a server? Well, that’s where AWS Lambda comes into play.ĪWS Lambda is the glue that binds many AWS services together, including S3, API Gateway, and DynamoDB. You could most definitely handle this task using the traditional model, but since this code runs based on an event (the file upload), Amazon S3 can fire an event and trigger the execution of code to handle the image resize. Now, say you want to resize the image to a max resolution. The application stores these images in Amazon S3. For example, let’s say you have an application that allows users to upload a photo for a profile image. While this is still the right way to handle a lot of scenarios, sometimes you just need to run a bit of code to handle some kind of event. In a traditional cloud usage model, you provision servers, deploy code to the server, and manage resource usage and scaling, along with other traditional server activities. Serverless, or Why you don’t need a server! Note that the usage of the term Lambda here is not related to anonymous functions in Python, which are also known as lambda functions. We’ll test it out, as well as take a look at what Lambda provides for metrics and logging.īy the end of this tutorial, you’ll be ready to start integrating other AWS serverless frameworks using Python Lambda functions as the glue to bind them all together. In order to show how useful Lambda can be, we’ll walk through creating a simple Lambda function using the Python programming language. For a more in-depth introduction to serverless and Lambda, read AWS Lambda: Your Quick Start Guide to Going Serverless. Here is a very contrived example of an AWS Lambda function.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. Conversely, if something blows up, we get all the context we need to debug the execution. With this setup in place, If the execution of an AWS Lambda function was successful, we only pay for the log.info (and other, all depends on your setup) data. Whenever an error happens, in our case followed by a log.error call, the logger releases all log.debug messages, along with the log.error message.The log.info calls will be pushed to STDOUT.The log.debug calls, by default, will not be pushed to STDOUT.Use your logger of choice as you were before.The way we could achieve the logging nirvana I eluded to earlier is surprisingly not that complex (maybe I overlooked something? If so, please let me know!). It turns out the "ideal world" is within reach and, in my personal experience, works great for most AWS Lambda functions, no matter the programming language. In the ideal world, we could log the bare minimum when everything is fine but have very verbose logs when an error occurred. Every error turns into a murder mystery with hours of resolution time. ![]() This time, it is not about the AWS CloudWatch costs but the potential time it takes to debug an issue within the AWS Lambda function. Not having any logs in place or logging very little can also have devastating effects. I've seen serverless applications where Amazon CloudWatch contributed to 80% of the AWS bill. The problem with this approach is that your Amazon CloudWatch bill will most likely be huge (relative to other services you use). Hopefully, this gives you the ability to pinpoint any potential issues fast and resolve them in no time. Logging every action undoubtedly gives you this warm fuzzy feeling of comfort that if something were to happen, you have every piece of context right in front of you. Both scenarios are problematic from an operational perspective. One mistake that, in my experience, developers tend to make is to reach for extremes – either logging everything or not logging at all. Treat the code snippets as pseudo-code rather than actual implementation. This blog post will showcase one technique I'm aware of that, in my personal opinion, works excellent for this particular use case.Īll the code in this blog post is written in TypeScript. So how to eat the cake and have it too? How can we ensure that our logs are sufficient to help us debug potential AWS Lambda issues while still keeping our costs down? The more you log, the more you will pay for Amazon CloudWatch Logs. When implemented with care, it can save you hours of debugging time, but it is a double-edged sword. And when they do, it is crucial to have visibility into what happened.Ī popular choice of gaining visibility into the AWS Lambda execution cycle is logging. The service is not all sunshine and rainbows – things tend to fail. ![]() Powerful and flexible, it allows for arbitrary code execution in various programming languages. AWS Lambda is a go-to compute service for many serverless developers. ![]()
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