The Dynamic Composer (an AWS serverless pattern)

I’m a big fan of following the Single Responsibility Principle when creating Lambda functions in my serverless applications. The idea of each function doing “one thing well” allows you to easily separate discrete pieces of business logic into reusable components. In addition, the Lambda concurrency model, along with the ability to add fine-grained IAM permissions per function, gives you a tremendous amount of control over the security, scalability, and cost of each part of your application.

However, there are several drawbacks with this approach that often attract criticism. These include things like increased complexity, higher likelihood of cold starts, separation of log files, and the inability to easily compose functions. I think there is merit to these criticisms, but I have personally found the benefits to far outweigh any of the negatives. A little bit of googling should help you find ways to mitigate many of these concerns, but I want to focus on the one that seems to trip most people up: function composition.

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Takeaways from ServerlessNYC 2018

I had the opportunity to attend ServerlessNYC this week (a ServerlessDays community conference) and had an absolutely amazing time. The conference was really well-organized (thanks Iguazio), the speakers were great, and I was able to have some very interesting (and enlightening) conversations with many attendees and presenters. In this post I’ve summarized some of the key takeaways from the event as well as provided some of my own thoughts.

Note: There were several talks that were focused on a specific product or service. While I found these talks to be very interesting, I didn’t include them in this post. I tried to cover the topics and lessons that can be applied to serverless in general.

Update November 16, 2018: Some videos have been posted, so I’ve provided the links to them.

Audio Version:

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What 15 Minute Lambda Functions Tells Us About the Future of Serverless

Amazon Web Services recently announced that they increased the maximum execution time of Lambda functions from 5 to 15 minutes. In addition to this, they also introduced the new “Applications” menu in the Lambda Console, a tool that aggregates functions, resources, event sources and metrics based on services defined by SAM or CloudFormation templates. With AWS re:Invent just around the corner, I’m sure these announcements are just the tip of the iceberg with regards to AWS’s plans for Lambda and its suite of complementary managed services.

While these may seem like incremental improvements to the casual observer, they actually give us an interesting glimpse into the future of serverless computing. Cloud providers, especially AWS, continue to push the limits of what serverless can and should be. In this post, we’ll discuss why these two announcements represent significant progress into serverless becoming the dominant force in cloud computing.

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An Introduction to Serverless Microservices

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.  👊

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Serverless Microservice Patterns for AWS

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.

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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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Mixing VPC and Non-VPC Lambda Functions for Higher Performing Microservices

I came across a post the in the Serverless forums that asked how to disable the VPC for a single function within a Serverless project. This got me thinking about how other people structure their serverless microservices, so I wanted to throw out some ideas. I often mix my Lambda functions between VPC and non-VPC depending on their use and data requirements. In this post, I’ll outline some ways you can structure your Lambda microservices to isolate services, make execution faster, and maybe even save you some money. ⚡️💰

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