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🚀 Project Update:

Data API Client: v1.1 Released

Bug fixes and feature updates including support for native JavaScript dates (thanks @cklam2), support for non-specific database queries, and deprecation of the HTTP keepAlive workaround in favor of the native SDK support. Read More...

Announcing the Serverless Reference Architectures Project

Serverless gives us the power to focus on delivering value to our customers without worrying about the maintenance and operations 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.

Almost 2 years ago now, I wrote a post on Serverless Microservice Patterns for AWS that became a popular reference for newbies and serverless veterans alike. The capabilities of serverless have changed dramatically since then, opening up a ton of new patterns and possibilities. Today I’m announcing the Serverless Reference Architectures Project. This project is intended to capture, share, explore, and debate the patterns and practices being used in serverless production applications today.

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The Simple Web Service

A basic of pattern for creating a serverless API or web service. This example uses DynamoDB as the database because it scales nicely with the high concurrency capabilities of AWS Lambda.

Interactive Reference Architecture

Click on the components or numbered steps below to explore how this architecture works.

This is the most basic of patterns you’re likely to see with serverless applications. The Simple Web Service fronts a Lambda function with an API Gateway. I’ve shown DynamoDB as the database here because it scales nicely with the high concurrency capabilities of Lambda.

Deploy this Pattern

Below are the basic configurations for deploying this pattern using different frameworks and platforms. Additional configuration for your environment will be necessary. The source files and additional examples are available in the GitHub repo.

  • Are you a CDK Guru?
    Would you like to contribute patterns to the community?
    Check out the Github repo!

The Scalable Webhook

Simple pattern for handling high-velocity or unpredicatable workloads while mitigating downstream pressure.

Interactive Reference Architecture

Click on the components or numbered steps below to explore how this architecture works.

If you’re building a webhook, the traffic can often be unpredictable. This is fine for Lambda, but if you’re using a “less-scalable” backend like RDS, you might just run into some bottlenecks. There are ways to manage this, but because Lambda supports SQS triggers, we can throttle our workloads by queuing the requests and then using a throttled (low concurrency) Lambda function to work through our queue. Under most circumstances, your throughput should be near real-time. If there is some heavy load for a period of time, you might experience some small delays as the throttled Lambda chews through the messages.

You’ll also want to handle failed messages using a Dead Letter Queues (DLQ). The SQS Poller will adjust its polling frequency based on your Lambda function’s concurrency. You’ll need to configure your redrive policies to appropriately handle failed messages.

Deploy this Pattern

Below are the basic configurations for deploying this pattern using different frameworks and platforms. Additional configuration for your environment will be necessary. The source files and additional examples are available in the GitHub repo.

  • Are you a CDK Guru?
    Would you like to contribute patterns to the community?
    Check out the Github repo!

The Strangler Pattern

This pattern lets you route requests to your legacy APIs, while allowing you to direct specific routes to new serverless services as you add them.

Interactive Reference Architecture

Click on the components or numbered steps below to explore how this architecture works.

The Strangler is another popular pattern that lets you incrementally replace pieces of an application with new or updated services. Typically you would create some sort of a “Strangler Facade” to route your requests, but API Gateway can actually do this for us using “AWS Service Integrations” and “HTTP Integrations”. For example, an existing API (front-ended by an Elastic Load Balancer) can be routed through API Gateway using an “HTTP” integration. You can have all requests default to your legacy API, and then direct specific routes to new serverless service as you add them.

Deploy this Pattern

Below are the basic configurations for deploying this pattern using different frameworks and platforms. Additional configuration for your environment will be necessary. The source files and additional examples are available in the GitHub repo.

  • Are you a CDK Guru?
    Would you like to contribute patterns to the community?
    Check out the Github repo!

Takeaways from Programming AWS Lambda by Mike Roberts and John Chapin

Recently, Symphonia co-founders Mike Roberts and John Chapin wrote a book called Programming AWS Lambda: Build and Deploy Serverless Applications with Java. I personally abandoned Java long ago, but I knew full well that anything written by Mike and John was sure to be great. So despite the title (and my past war stories of working with Java), I picked up the book and gave it a read. I discovered that it’s not really a book about Java, but a book about building serverless applications with the examples in Java. Sure, there are a few very Java specific things (which every Java developer probably needs to read), but overall, this book offers some great insight into serverless from two experts in the field.

I had the chance to catch up with Mike on a recent episode of Serverless Chats. We discussed the book, how John and Mike got started with serverless (by building Java Lambda functions, of course), and what are some of the best practices people need to think about when building serverless applications. It was a great conversation (which you can watch/listen to here), but it was also jam packed with information, so I thought I’d highlight some of the important takeaways.

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Making the Case for Serverless Use Cases

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.

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12 Important Lessons from The DynamoDB Book

Fellow serverless advocate, and AWS Data Hero, Alex DeBrie, recently released The DynamoDB Book, which ventures way beyond the basics of DynamoDB, but still offers an approachable and useful resource for developers of any experience level. I had the opportunity to read the book and then speak with Alex about it on Serverless Chats. We discussed several really important lessons from the book that every DynamoDB practitioner needs to know. Here are twelve of my favorites, in no particular order.

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Takeaways from the State of Serverless Report

On a recent episode of Serverless Chats, I spoke with Stephen Pinkerton and Darcy Rayner of Datadog to dig into The State of Serverless report, which was released at the end of February 2020. After frequently fielding customer questions about the topic, Datadog looked at its data and customer use cases, and examined how they were using serverless. Datadog’s report is a way to break it all down, but it’s also an opportunity for its customers (and serverless users alike) to see how other people are using serverless in a data-driven way. I discussed methodology, findings, and key takeaways with Stephen and Darcy, and thought it’d be worthwhile to consolidate and share that insight.

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🚀 Project Update:

Serverless MySQL: v1.5.4 released

This update includes a minor bug fix that automatically handles reconnections when receiving the occasional EPIPE error. Special thanks to @kernwig for the PR. Read More...