“What? You can’t use MySQL with serverless functions, you’ll just exhaust all the connections as soon as it starts to scale! And what about zombie connections? Lambda doesn’t clean those up for you, meaning you’ll potentially have hundreds of sleeping threads blocking new connections and throwing errors. It can’t be done!” ~ Naysayer
I really like DynamoDB and BigTable (even Cosmos DB is pretty cool), and for most of my serverless applications, they would be my first choice as a datastore. But I still have a love for relational databases, especially MySQL. It had always been my goto choice, perfect for building normalized data structures, enforcing declarative constants, providing referential integrity, and enabling ACID-compliant transactions. Plus the elegance of SQL (structured query language) makes organizing, retrieving and updating your data drop dead simple.
But now we have SERVERLESS. And Serverless functions (like AWS Lambda, Google Cloud Functions, and Azure Functions) scale almost infinitely by creating separate instances for each concurrent user. This is a MAJOR PROBLEM for RDBS solutions like MySQL, because available connections can be quickly maxed out by concurrent functions competing for access. Reusing database connections doesn’t help, and even the release of Aurora Serverless doesn’t solve the
max_connections problem. Sure there are some tricks we can use to mitigate the problem, but ultimately, using MySQL with serverless is a massive headache.
Well, maybe not anymore. 😀 I’ve been dealing with MySQL scaling issues and serverless functions for years now, and I’ve finally incorporated all of my learning into a simple, easy to use NPM module that (I hope) will solve your Serverless MySQL problems.
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. ⚡️
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.
.filter(), and most importantly,
.reduce(). If you are unfamiliar with these concepts, go get a grasp on them first.
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:
let data = await S3.getObject(params).promise()
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.
In my 20+ years of programming, I’ve encountered a near endless amount of opinions on everything from coding styles to programming paradigms to the great whitespace debate. Obviously, I have strong opinions on a number of these. But for me, the one that bothers me the most is this notion that “code is self-documenting.” 😾
I know what you’re probably thinking: “of course not all code is self-documenting, only well-written code is.” I don’t entirely disagree. I can generally look at someone else’s code and understand exactly WHAT it is doing. However, often it’s not obvious WHY they did it that way, or even why they did it in the first place. In my opinion, the programmer’s intent (the WHY) is just as important as the HOW when it comes to properly documenting software.
So whether you agree with me or not, let’s explore how to better document our software by writing cleaner code, following some general commenting etiquette, and commenting more effectively to make you and your team more productive. 👍