How To: Add Test Coverage to your Serverless Applications

Writing serverless functions brings developers closer and closer to the stack that runs their code. While this gives them a tremendous amount of freedom, it also adds additional responsibility. Serverless applications require developers to think more about security and optimizations, as well as perform other tasks that were traditionally assigned to operations teams. And of course, code quality and proper testing continue to be at the top of the list for production-level applications. In this post, we’ll look at how to add test coverage to our Node.js applications and how we can apply it to our Serverless framework projects. ⚡️

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How To: Tag Your Lambda Functions for Smarter Serverless Applications

As our serverless applications start to grow in complexity and scope, we often find ourselves publishing dozens if not hundreds of functions to handle our expanding workloads. It’s no secret that serverless development workflows have been a challenge for a lot of organizations. Some best practices are starting to emerge, but many development teams are simply mixing their existing workflows with frameworks like Serverless and AWS SAM to build, test and deploy their serverless applications.

Beyond workflows, another challenge serverless developers encounter as their applications expand, is simply trying to keep all of their functions organized. You may have several functions and resources as part of a microservice contained in their own git repo. Or you might simply put all your functions in a single repository for better common library sharing. Regardless of how code is organized locally, much of that is lost when all your functions end up in a big long list in the AWS Lambda console. In this post we’ll look at how we can use AWS’s resource tagging as a way to apply structure to our deployed functions. This not only give us more insight into our applications, but can be used to apply Cost-Allocation Tags to our billing reports as well. 👍

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How To: Optimize the Serverless Optimizer Plugin

I’m sure you’re already well aware of how awesome the ⚡ Serverless Framework is for managing and deploying your serverless applications. And you’re probably aware that there are several great plugins available that make Serverless even better. But did you know that there was a plugin to optimize your functions and reduce the size of your deployment packages? Or are you already using this plugin to optimize your functions, but hate how it takes too long to optimize locally run functions? In this post I’ll share some quick tips to help you optimize your Serverless Optimizer experience.

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How To: Manage Serverless Environment Variables Per Stage

I often find myself creating four separate stages for each ⚡ Serverless Framework project I work on: dev, staging, prod, and local. Obviously the first three are meant to be deployed to the cloud, but the last one, local, is meant to run and test interactions with local resources. It’s also great to have an offline version (like when you’re on a plane ✈ or have terrible wifi somewhere). Plus, development is much faster because you’re not waiting for round trips to the server. 😉

A really great feature of Serverless is the ability to configure ENVIRONMENT variables in the serverless.yml file. This lets us store important global information like database names, service endpoints and more. We can even reference passwords securely using AWS’s Service Manager Parameter Store and decode encrypted secrets on deployment, keeping them safe from developers and source repositories alike. 😬 Just reference the variable with ${ssm:/myapp/my-secure-value~true} in your configuration file.

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How To: Stub “.promise()” in AWS-SDK Node.js

Since AWS released support for Node v8.10 in Lambda, I was able to refactor Lambda API to use async/await instead of Bluebird promises. The code is not only much cleaner now, but I was able to remove a lot of unnecessary overhead as well. As part of the refactoring, I decided to use AWS-SDK’s native promise implementation by appending .promise() to the end of an S3 getObject call. This works perfectly in production and the code is super compact and simple:

The issue came with stubbing the call using Sinon.js. With the old promise method, I was using promisifyAll() to wrap new AWS.S3() and then stubbing the getObjectAsync method. If you’re not familiar with stubbing AWS services, read my post: How To: Stub AWS Services in Lambda Functions using Serverless, Sinon.JS and Promises.

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How To: Manage RDS Connections from AWS Lambda Serverless Functions

Someone asked a great question on my How To: Reuse Database Connections in AWS Lambda post about how to end the unused connections left over by expired Lambda functions:

I’m playing around with AWS lambda and connections to an RDS database and am finding that for the containers that are not reused the connection remains. I found before that sometimes the connections would just die eventually. I was wondering, is there some way to manage and/or end the connections without needing to wait for them to end on their own? The main issue I’m worried about is that these unused connections would remain for an excessive amount of time and prevent new connections that will actually be used from being made due to the limit on the number of connections.

🧟‍♂️ Zombie RDS connections leftover on container expiration can become a problem when you start to reach a high number of concurrent Lambda executions. My guess is that this is why AWS is launching Aurora Serverless, to deal with relational databases at scale. At the time of this writing it is still in preview mode.

Update September 2, 2018: I wrote an NPM module that manages MySQL connections for you in serverless environments. Check it out here.

Update August 9, 2018: Aurora Serverless is now Generally Available!

Overall, I’ve found that Lambda is pretty good about closing database connections when the container expires, but even if it does it reliably, it still doesn’t solve the MAX CONNECTIONS problem. Here are several strategies that I’ve used to deal with this issue.

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How To: Normalize URLs Stored in MySQL

I came across an interesting problem the other day. As part of our URL normalization strategy at AlertMe, we have been adding a trailing slash to URLs without file extensions. We did a lot of research when deciding on this tactic and the general consensus around the web was to use trailing slashes for directories and (obviously) no slashes on filenames. See this article from the official Google Webmasters blog: https://webmasters.googleblog.com/2010/04/to-slash-or-not-to-slash.html (I know it’s old, but the concept is still relevant).

We even tested a number of publisher URLs to see what their redirection strategies were. Every one we tested responded correctly to both the slash and no-slash versions of the URL. Some redirected to a trailing slash, some redirected to no trailing slash, but they all returned (or redirected to) the intended page.

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How To: Stub AWS Services in Lambda Functions using Serverless, Sinon.JS and Promises

I know the title is a quite a mouthful, but if you are trying to run tests on your Lambda functions that interact with AWS Services using the aws-sdk node module, then you’ve probably run into an issue stubbing or mocking the requests. In this post we’ll learn how to stub different AWS Services with Sinon.JS so that you can properly test your scripts.

UPDATE: AWS Lambda now supports Node v8.10, so we can use async/await instead of promises. The examples below still work with either v6.10 or v8.10, however, I recommend switching to async/await as they are more compact than promises. Read my post How To: Stub “.promise()” in AWS-SDK Node.js to learn how to deal with the .promise() method on aws-sdk services.

Let’s say you have a Lambda function that interacts with AWS’s SQS (Simple Queue Service). v6.10 of Node doesn’t support async/await, so you will most likely use promises if you don’t want to transpile your code or deal with callback hell. This means you need to Promisify an instance of the AWS SQS service. This is easy enough with:

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How To: Access Your AWS VPC-based Elasticsearch Cluster Locally

AWS recently announced that their Elasticsearch Service now supports VPC, which is awesome, for a number of reasons:

1. No more signing every request

Remember this?

Every request had to be signed with AWS’s SigV4 so that the Elasticsearch endpoint could be properly authorized. That meant additional code to sign all your requests, and additional time for the endpoint to decode it. It might only be a few milliseconds of extra processing time, but those can add up. Now we can call our VPC Elasticsearch endpoint with a simple HTTP request.

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How To: Reuse Database Connections in AWS Lambda

Update 9/2/2018: I wrote an NPM module that manages MySQL connections for you in serverless environments. Check it out here.

I work with AWS Lambda quite a bit. The ability to use this Functions-as-a-Service (FaaS) has dramatically reduced the complexity and hardware needs of the apps I work on. This is what’s known as a “Serverless” architecture since we do not need to provision any servers in order to run these functions. FaaS is great for a number of use cases (like processing images) because it will scale immediately and near infinitely when there are spikes in traffic. There’s no longer the need to run several underutilized processing servers just waiting for someone to request a large job.

AWS Lambda is event-driven, so it’s also possible to have it respond to API requests through AWS’s API Gateway. However, since Lambda is stateless, you’ll most likely need to query a persistent datastore in order for it to do anything exciting. Setting up a new database connection is relatively expensive. In my experience it typically takes more than 200ms. If we have to reconnect to the database every time we run our Lambda functions (especially if we’re responding to an API request) then we are already adding over 200ms to the total response time. Add that to your queries and whatever additional processing you need to perform and it becomes unusable under normal circumstance. Luckily, Lambda lets us “freeze” and then “thaw” these types of connections.

Update 4/5/2018: After running some new tests, it appears that “warm” functions now average anywhere between 4 and 20ms to connect to RDS instances in the same VPC. Cold starts still average greater than 100ms. Lambda does handle setting up DB connections really well under heavy load, but I still favor connection reuse as it cuts several milliseconds off your execution time.

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