For quite some time, there was a running joke that “serverless” was just for converting images to thumbnails. That’s still a great use case for serverless, of course, but since AWS released Lambda in 2014, serverless has definitely come a long way. Even still, newcomers to the space often don’t realize just how many use cases there are for serverless. I spoke with Gareth McCumskey, a Solutions Architect at Serverless Inc, on a recent two part episode (part 1 and part 2) of Serverless Chats, and we discussed nine very applicable use cases that I thought I’d share with you here.
“Trust no one.” Or at least that’s what Fox Mulder told me back in the 90s.
With the recent GA of HTTP APIs for API Gateway, I decided to start evaluating my existing API Gateway REST APIs to see if I could migrate them over to take advantage of the decreased latency and reduced cost of the new HTTP APIs. Several of them were disqualified because they utilize service integrations (a feature that AWS is working to add), but for one of my largest applications, the lack of Custom Authorizers is what brought me to a dead end. Or so I initially thought. 😉
After a bit of research (okay, it was actually several hours because I decided to read through a bunch of specs and blog posts and then run a ton of experiments), it turns out that hosting your own OIDC Conformant “server” to verify self-signed JSON Web Tokens with HTTP APIs is actually quite simple. So as long as you can use JWT for your bearer tokens, you can utilize your existing authentication service (and probably dramatically reduce your latency and cost).
In this post, I’ll show you everything you need to know to set this up yourself. We’ll generate certificates, create our OIDC discovery service, set up our HTTP API authorizers, generate and sign our JWTs, and protect routes with scopes.
Last week I spent six incredibly exhausting days in Las Vegas at the AWS re:Invent conference. More than 50,000 developers, partners, customers, and cloud enthusiasts came together to experience this annual event that continues to grow year after year. This was my first time attending, and while I wasn’t quite sure what to expect, I left with not just the feeling that I got my money’s worth, but that AWS is doing everything in their power to help customers like me succeed.
There have already been some really good wrap-up posts about the event. Take a look at James Beswick’s What I learned from AWS re:Invent 2018, Paul Swail’s What new use cases do the re:Invent 2018 serverless announcements open up?, and All the Serverless announcements at re:Invent 2018 from the Serverless, Inc. blog. There’s a lot of good analysis in these posts, so rather than simply rehash everything, I figured I touch on a few of the announcements that I think really matter. We’ll get to that in a minute, but first I want to point out a few things about Amazon Web Services that I learned this past week.
Thinking about microservices, especially their communication patterns, can be a bit of a mind-bending experience for developers. The idea of splitting an application into several (if not hundreds of) independent services, can leave even the most experienced developer scratching their head and questioning their choices. Add serverless event-driven architecture into the mix, eliminating the idea of state between invocations, and introducing a new per function concurrency model that supports near limitless scaling, it’s not surprising that many developers find this confusing. 😕 But it doesn’t have to be. 😀
In this post, we’ll outline a few principles of microservices and then discuss how we might implement them using serverless. If you are familiar with microservices and how they communicate, this post should highlight how these patterns are adapted to fit a serverless model. If you’re new to microservices, hopefully you’ll get enough of the basics to start you on your serverless microservices journey. We’ll also touch on the idea of orchestration versus choreography and when one might be a better choice than the other with serverless architectures. I hope you’ll walk away from this realizing both the power of the serverless microservices approach and that the basic fundamentals are actually quite simple. 👊
UPDATE: I’ve started the Serverless Reference Architectures Project that provides additional context and interactive architectures for some of theses patterns along with code examples to deploy them to AWS. Check it out.
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.
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. 👍