Developing and testing serverless applications locally can be a challenge. Even with tools like SAM and the Serverless Framework, you often end up mocking your cloud resources, or resorting to tricks (like using pseudo-variables) to build ARNs and service endpoint URLs manually. While these workarounds may have the desired result, they also complicate our configuration files with (potentially brittle) user-constructed strings, which duplicates information already available to CloudFormation.
This is a common problem for me and other serverless developers I know. So I decided to come up with a solution.
In the serverless world, we often get the impression that our applications can scale without limits. With the right design (and enough money), this is theoretically possible. But in reality, many components of our serverless applications DO have limits. Whether these are physical limits, like network throughput or CPU capacity, or soft limits, like AWS Account Limits or third-party API quotas, our serverless applications still need to be able to handle periods of high load. And more importantly, our end users should experience minimal, if any, negative effects when we reach these thresholds.
There are many ways to add resiliency to our serverless applications, but this post is going to focus on dealing specifically with quotas in third-party APIs. We’ll look at how we can use a combination of SQS, CloudWatch Events, and Lambda functions to implement a precisely controlled throttling system. We’ll also discuss how you can implement (almost) guaranteed ordering, state management (for multi-tiered quotas), and how to plan for failure. Let’s get started!
An extremely useful AWS serverless microservice pattern is to distribute an event to one or more SQS queues using SNS. This gives us the ability to use multiple SQS queues to “buffer” events so that we can throttle queue processing to alleviate pressure on downstream resources. For example, if we have an event that needs to write information to a relational database AND trigger another process that needs to call a third-party API, this pattern would be a great fit.
This is a variation of the Distributed Trigger Pattern, but in this example, the SNS topic AND the SQS queues are contained within a single microservice. It is certainly possible to subscribe other microservices to this SNS topic as well, but we’ll stick with intra-service subscriptions for now. The diagram below represents a high-level view of how we might trigger an SNS topic (API Gateway → Lambda → SNS), with SNS then distributing the message to the SQS queues. Let’s call it the Distributed Queue Pattern.
This post assumes you know the basics of setting up a serverless application, and will focus on just the SNS topic subscriptions, permissions, and implementation best practices. Let’s get started!
Posted in Serverless on December 21, 2018 Last Updated: March 30, 2020
I’ve been building serverless applications since AWS Lambda went GA in early 2015. I’m not saying that makes me an expert on the subject, but as I’ve watched the ecosystem mature and the community expand, I have formed some opinions around what it means exactly to be “serverless.” I often see tweets or articles that talk about serverless in a way that’s, let’s say, incompatible with my interpretation. This sometimes makes my blood boil, because I believe that “serverless” isn’t a buzzword, and that it actually stands for something important.
I’m sure that many people believe that this is just a semantic argument, but I disagree. When we refer to something as being “serverless”, there should be an agreed upon understanding of not only what that means, but also what it empowers you to do. If we continue to let marketers hijack the term, then it will become a buzzword with absolutely no discernible meaning whatsoever. In this post, we’ll look at how some leaders in the serverless space have defined it, I’ll add some of my thoughts, and then offer my own definition at the end.
Our serverless applications become a lot more interesting when they interact with third-party APIs like Twilio, SendGrid, Twitter, MailChimp, Stripe, IBM Watson and others. Most of these APIs respond relatively quickly (within a few hundred milliseconds or so), allowing us to include them in the execution of synchronous workflows (like our own API calls). Sometimes we run these calls asynchronously as background tasks completely disconnected from any type of front end user experience.
Regardless how they’re executed, the Lambda functions calling them need to stay running while they wait for a response. Unfortunately, Step Functions don’t have a way to create HTTP requests and wait for a response. And even if they did, you’d at least have to pay for the cost of the transition, which can get a bit expensive at scale. This may not seem like a big deal on the surface, but depending on your memory configuration, the cost can really start to add up.
In this post we’ll look at the impact of memory configuration on the performance of remote API calls, run a cost analysis, and explore ways to optimize our Lambda functions to minimize cost and execution time when dealing with third-party APIs.
Posted in Serverless on December 3, 2018 Last Updated: March 30, 2020
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.
Posted in Serverless on November 21, 2018 Last Updated: April 22, 2020
Update June 5, 2019: The Data API team has released another update that adds improvements to the JSON serialization of the responses. Any unused type fields will be removed, which makes the response size 80+% smaller.
Update June 4, 2019: After playing around with the updated Data API, I found myself writing a few wrappers to handle parameter formation, transaction management, and response formatting. I ended up writing a full-blown client library for it. I call it the “Data API Client“, and it’s available now on GitHub and NPM.
Update May 31, 2019: AWS has released an updated version of the Data API (see here). There have been a number of improvements (especially to the speed, security, and transaction handling). I’ve updated this post to reflect the new changes/improvements.
On Tuesday, November 20, 2018, AWS announced the release of the new Aurora Serverless Data API. This has been a long awaited feature and has been at the top of many a person’s #awswishlist. As you can imagine, there was quite a bit of fanfare over this on Twitter.
Ya'll been waiting eagerly for this one! Access an Aurora Serverless database via HTTPS API: https://t.co/OUkqA8U3Y1 still in beta, but will change the way you think about #awsLambda and relational databases! #serverless
Obviously, I too was excited. The prospect of not needing to use VPCs with Lambda functions to access an RDS database is pretty compelling. Think about all those cold start savings. Plus, connection management with serverless and RDBMS has been quite tricky. I even wrote an NPM package to help deal with the max_connections issue and the inevitable zombies 🧟♂️ roaming around your RDS cluster. So AWS’s RDS via HTTP seems like the perfect solution, right? Well, not so fast. 😞 (Update May 31, 2019: There have been a ton of improvements, so read the full post.)
Posted in Serverless on November 4, 2018 Last Updated: March 30, 2020
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.
Posted in Serverless on October 15, 2018 Last Updated: March 30, 2020
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.