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AWS: Debugging Lambda Functions

Our world is imperfect. At any moment, something can go wrong. Not only when we’re talking about humans going about their day to day activities, but even in the IT world. Software and hardware are fallible. So what can we do? Put up with it? No. I have a better thought - automate it and find the best way to debug.


This can be done, for example, with the help of AWS services, which are helping to bring us closer to an ideal tech world. One of the AWS services we will be looking at here in detail is AWS Lambda. In doing so we will try to help you with one of the most common problems regarding it, namely, the inability to debug functions locally. Let’s take Node.js as a basis and see how we can figure it out!


But first...


Why is the AWS Lambda Function so cool?

According to the official information on AWS, Lambda will allow you to forget about the infrastructure of your project and will let you launch applications in the cloud while integrating with other Amazon services. Other benefits include scalability and the low cost of using computing resources.


All you need to get started is to write a function and connect it with events. After that, Amazon will automatically perform the function for each new event. We can stop thinking about scaling and high availability: our function will be able to process thousands of requests per hour without any effort on our part and a backend in the conventional sense.


The Process of Debugging

If we talk about a debugging process for AWS lambda functions, we should look closely at 3 of the most popular methods:


1.AWS service Cloud Watch: It’s a default method. Although this method may be appealing due to its price (its absence), such an approach can be slow. I wasted around 40 minutes for logs output. This happened many times.


2.Logging for deployed Lambda functions: It can be with the help of other AWS services, for example, Kinesis. For developers, this process may seem faster, yet because one needs to create tools manually, developers spend a whole lot more time and, as a result, the company gets burdened with additional costs. And, do not forget about the price of Amazon Infrastructure.


3.Offline debugging: If it is a regular development process, most probably you can resort to offline debugging that provides the possibility to work locally.

AWS Serverless Application Model

For this purpose, such official tools were created:

-AWS Serverless Application Model (SAM)

-SAM CLI.


AWS SAM  (3 506 GitHub stars) extends AWS CloudFormation to provide a simplified way of defining the Gateway APIs, AWS Lambda functions, and Amazon DynamoDB tables needed by your serverless application. Additionally, we can debug locally (or offline) with AWS SAM CLI.


AWS SAM CLI (3 006 GitHub stars) can run code inside a Docker container (emulating a Lambda runtime environment inside Amazon servers), transform your requests data to API Gateway events, and invoke your functions with this event. The really exciting thing is that it’s the only way to run your code inside the environment, which replicates the real Lambda environment. So, you can put breakpoints inside the code and expect that the debug output would be the same as the deployed function. The thing is that if you have a bug in the function deployed to AWS Lambda, you can rely upon local invoking that would give the same bug.


All these things are really great, although there are some serious disadvantages that are worth paying attention to:

-Minimal flexibility and capability. There are no plugins, so you can only send merge requests to main projects.

-AWS SAM is made by Golang, while AWS SAM CLI is made by Python. Thus, any improvements or features need to be developed in these languages as well.

-There are a lot of sources of Lambda function calls. And if your project uses not only Lambda and API Gateway, you need to emulate them as well. But working with all these AWS services offline (not Lambda or API Gateway) is a tricky thing. You need to create an event file for every service and invoke your function with this event by AWS-SAM-CLI module manually.

Serverless and Module for Debugging Lambda

If we talk about Node.js, the most popular module for debugging Lambda locally is  “Serverless Offline”. It’s a plugin for “Serverless Framework”  (25 073 GitHub stars), which is a JS tool and a wrapper on AWS CloudFront.  It can look similar to AWS SAM, but it gives more clear syntax and additional features compared to “SAM”. What is more, it has a lot of plugins that can emulate the logic of different AWS services (S3, SNS, SQS, and others) locally. Serverless has plugins for other programming languages as well (e.g. Python, Swift, PHP, and Golang).


We will show a simple example of debugging Lambda.


To invoke and debug local emulations of AWS Lambda functions, we have to install two NPM packages: serverless and serverless-offline. Yet, remember that these two things are not enough for a real project because there are a lot of triggers of the Lambda function: S3, SQS, API Gateway and other services & connections between these services and your computer by AWS CLI.


Thus, we need to have a connection between AWS and Serverless on our machine and for these purposes, you can install and use other plugins and functionality of Serverless. But for debugging, a simple function is enough, as well as the usage of API Gateway in the role of a trigger (it’s a basic resource where most requests start from).


We will not explain what every line of serverless.yml file means because this is not the purpose of this article. There is a lot of information on the Internet about it. Plus, Serverless Framework and Serverless Offline have awesome documentation.


To exemplify, we will perform the following steps:



1. So far our runtime option of Lambda function is equal to “nodejs8.10”, it will run on your machine. With this, we have to use Node.js version 8.0.0 or higher (8.10 is the best choice). So, check your version by the command:

$ node -v

And if output less than “v8.0.0”, install needed version. You can find installation here, or do it in the way you wish.


2. In it is our simple project and dependencies for it:

$  mkdir debug-lambda-local && cd debug-lambda-local && npm init -f && npm i -D serverless serverless-offline

3. Create files “serverless.yml” and “lambda-foo.js”:

$ touch serverless.yml lambda-foo.js

4. Put to “serverless.yml” file the code below to configure Serverless framework and serverless-offline plugin that emulates API Gateway (proxy server) behavior:


serverless.yml

plugins: serverless-offline
provider:
    name: aws
    runtime: nodejs8.10
    stage: dev
    region: us-east-1
 
functions:
    getresponse:
        handler: lambda-foo.getResponse
        events:
            - http:
                path: getresponse
                method: GET
 
custom:
    serverless-offline:
        port: 1221

5. Put into the “lambda-foo.js” file a simple code with our Lambda function that responds to API Gateway call. We can put “console.log” in any place inside this file:


lambda-foo.js

const TIMEOUT = 1000;
const getResult = () => new Promise((resolve) =>
  setTimeout(() => resolve(`Resolved after ${TIMEOUT} ms`), TIMEOUT));

exports.getResponse = async (event, context) => {
 return {
   statusCode: 200,
   body: JSON.stringify({
     result: await getResult(),
   }),
 }
}

6. Instead of this file, you can put any other handlers for an API Gateway request.


7. Then start your AWS emulation in watch mode:

$ node_modules/.bin/serverless offline start

This will be the emulation process of API Gateway on localhost:1221.


8. And the last step. Call GET localhost:1221/getresponse and see the debug output from the “getResponse” function in the terminal window where we started Serverless.

You can see that working with the Serverless framework is really easy. But we have to keep in mind the main disadvantage. You can’t rely upon breakpoints in your code as they don’t show the same output as a deployed version of the code. The runtime environment is similar but not the same.


Summary

1.Lambda functions work in an isolated environment. The consequence is that to debug becomes even more complicated. As a result, it can be performed locally until deployment, and in the cloud after deployment.

2.It is important to differentiate local tools from communication methods with a cloud after deployment.

3.To monitor and debug your code, when it is in the development phase, use Serverless Offline or Serverless Application Model.

4.Both Serverless and SAM enable testing of functionality manually without wasting time on deployment and further requests to the cloud.

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