You’ve probably seen Rush Hour, a logic puzzle where you have to slide cars and trucks out of the way to steer the red car towards the exit. In real life, when your customers are responsible for tracking hundreds or thousands of data points from dozens of valuable, mission-critical sensors, you’re tracking engine speed, network signal level, distance from the RF, and more—and not just through traffic but across continents.
When building systems that need to scale above a certain number of users, we usually can’t stay on one machine. This is where cloud providers like AWS usually come into play. They allow us to rent VMs or containers for small intervals. This way, we can start a few different machines when more traffic hits, and when it goes down later, we can simply turn off our extra capacity and save money. The question is, how does all this traffic get to our new machines? AWS Elastic Load Balancing!
The combination of SNS to SQS to Lambda is a common sight in serverless applications on AWS. Perhaps triggered by messages from an API function. This architecture is great for improving UX by offloading slow, asynchronous tasks so the API can stay responsive. It presents an interesting challenge for observability, however. Because observability tools are not able to trace invocations through this combination end-to-end. In X-Ray, for example, the trace would stop at SNS.
There are numerous types of services and processes in place to make software development more accessible and efficient for programmers. One ready-for-launch service developers can access remotely is Function as a Service, abbreviated as (FaaS).
Serverless backends have different monitoring challenges when compared with traditional applications, mostly due to the distributed and proprietary nature of serverless. Making monitoring and debugging efficient for serverless requires a unique set of tools and techniques. In this article, we’ll discuss the challenges of debugging serverless backends and how to utilize third party tools to improve the monitoring process.
When you build a cloud-based application, you can choose to deploy the resources using the GUI (Graphical User Interface) or CLI (Command Line Interface) provided by the cloud provider. This approach can work well with just a handful of resources, but as the complexity of your application increases, it can become difficult to manage the infrastructure manually.
Serverless technologies let us do more with less effort, time and energy. They let us focus on creating user value and let the cloud handle undifferentiated heavy-lifting like scaling and securing the underlying infrastructure that runs our code. Serverless technologies have allowed me to accomplish tasks as a solo engineer that used to take a whole team of engineers to accomplish, and I’m able to complete these tasks in a fraction of the time and cost to my customers.
This is the second post in a 2 part blog series on debugging, monitoring and tracing NodeJS Lambda applications. If you haven’t yet seen part 1, check it out here (it’s a great read!) Now let’s get back into our post with one of the most commonly experienced issues when it comes to Lambda functions, Cold Starts.
TL;DR: Dashbird launched observability for AWS AppSync. Additionally to AWS Lambda, SQS, DynamoDB, API Gateway, ECS, Kinesis, Step Functions, ELB, SNS, RDS, OpenSearch, and HTTP API Gateway you can now get detailed insights and metrics in the Dashbird app for AWS AppSync. Since Facebook released its previously internally used query language GraphQL in 2016, it has seen an outstanding increase in adoptions for all kinds of applications.
In this two post series, we are going to explore some ways to trace and debug NodeJS Lambda applications. Delving into some methods to look further into resources utilized to and some methods to optimize code. AWS Lambda, an event-driven compute service first introduced roughly eight years ago, changed how we build out cloud applications as an industry.
