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.