I’ve written quite extensively about serverless security, and while you don’t need to be an expert on the matter, there are a number of common sense principles that every developer should know. Serverless infrastructures (specifically FaaS and managed services) certainly benefit from an increased security posture given that the cloud provider is handling things like software patching, network security, and to some extent, even DDoS mitigation. But at the end of the day, your application is only as secure as its weakest link, and with serverless, that pretty much always comes down to application layer security.
In this post we’re going to look at ways to mitigate some of these application layer security issues by using some simple strategies as well as a free tool called FunctionShield.
“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.
It’s official! I’m going to AWS re:Invent 2018. 🙌
My goal from this trip is to learn, learn, learn… and then share, share, share. There are over 30 sessions that talk about serverless, plus 40,000 other people there to meet and learn from! I’m so excited. 🙃
I know that many of you will be there, but for those of you who can’t be, I’ll do my best to share insights, tips, how-tos, best practices and more. I’ll even have a drink for you if you’d like 🍺 (no arm twisting necessary)!
I’m a huge fan of building microservices with serverless systems. Serverless gives us the power to focus on just the code and our data without worrying about the maintenance and configuration of the underlying compute resources. Cloud providers (like AWS), also give us a huge number of managed services that we can stitch together to create incredibly powerful, and massively scalable serverless microservices.
I’ve read a lot of posts that mention serverless microservices, but they often don’t go into much detail. I feel like that can leave people confused and make it harder for them to implement their own solutions. Since I work with serverless microservices all the time, I figured I’d compile a list of design patterns and how to implement them in AWS. I came up with 19 of them, though I’m sure there are plenty more.
In this post we’ll look at all 19 in detail so that you can use them as templates to start designing your own serverless microservices.
Amazon announced the General Availability of Aurora Serverless on August 9, 2018. I have been playing around with the preview of Aurora Serverless for a few months, and I must say that overall, I’m very impressed. There are A LOT of limitations with this first release, but I believe that Amazon will do what Amazon does best, and keep iterating until this thing is rock solid.
The announcement gives a great overview and the official User Guide is chock full of interesting and useful information, so I definitely suggest giving those a read. In this post, I want to dive a little bit deeper and discuss the pros and cons of Aurora Serverless. I also want to dig into some of the technical details, pricing comparisons, and look more closely at the limitations.
It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness… ~ A Tale of Two Cities by Charles Dickens
There is a revolution happening in the tech world. An emerging paradigm that’s letting development teams focus on business value instead of technical orchestration. It is helping teams create and iterate faster, without worrying about the limits or configurations of an underlying infrastructure. It is enabling the emergence of new tools and services that foster greater developer freedom. Freedom to experiment. Freedom to do more with less. Freedom to immediately create value by publishing their work without the traditional barriers created by operational limits.
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. ⚡️
In my never ending quest to consume all things serverless, I often find myself scouring the Interwebs for new and interesting serverless articles, blog posts, videos, and podcasts. There are more and more people doing fascinating things with serverless every day, so finding content is becoming easier and easier. However, this increase in content comes with an increase in noise as well. Cutting through that noise isn’t always easy. 🙉
Great content with valuable insights
I personally love reading articles that introduce new use cases or optimizations for serverless. Stories about companies using serverless in production and how their architectures are set up are also extremely interesting. I’ve been working in the serverless space for several years now, and have come across a number of people who produce and/or share really great content. I’ve put together a list of people that I follow and enjoy their content regularly. Hopefully these people will help you learn to love serverless as much as I do. ❤️⚡️
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. 👍