How to Track Performance and Errors of a Serverless Project

Since the launch of Dashbird 6 months ago, we’ve offered overview dashboards on account and function level. Now, we launched project views.

Grouping projects into a single dashboard gives you an overview of your Serverless service in one screen (or any other grouping of functions, like production, staging etc.). For ourselves and beta testers, this feature has proven valuable in detecting errors and optimising services towards cost or speed.

The service dashboard lets you keep an eye on your service performance metrics.

Breakdown of data in the project views

Time-series graphs of:

  • Invocations
  • Errors
  • Durations
  • Memory utilisation

Service level statistics:

  • Invocation count
  • Error count
  • Health score
  • Cost

Function statistics:

  • Cost
  • Average memory utilisation
  • Total invocation count
  • Total error count

Detecting optimization opportunities

Functions table allows you to pinpoint expensive lambdas.

You can also optimise function memory usage by noticing the outliers in the graph. For instance, if a function is using around 10-20% of memory, it’s a pretty good candidate for optimization, which in large scales can help save money.

Now let’s go and set up your first project…

Setting up

_If you haven’t already, sign up for [Dashbird to monitor your Lambda functions](/register)._

From the main dashboard, go to Projects -> Create a new project view

Add a short title and description for your service. To select the Lambda functions, you need to specify a glob pattern filter. Let’s say your service name is alpha and you want to monitor all production lambdas. Then you would insert the following filter: alpha-prod-*

Click Create project view and you’re done.

Congratulations, you’ve just gotten visibility into your Serverless project.

Read our blog

Introducing easy custom event monitoring for serverless applications.

Today we are excited to announce scheduled searches – a new feature on Dashbird that allows you to track any log event across your stack, turn it into time-series metric and also configure alert notifications based on it.

Why and How to Monitor Amazon OpenSearch Service

One of the most vital aspects to monitor is the metrics. You should know how your cluster performs and if it can keep up with the traffic. Learn more about monitoring Amazon OpenSearch Service.

Why and How to Monitor AWS Elastic Load Balancing

Dashbird recently added support for ELB, so now you can keep track of your load balancers in one central place. It comes with all the information you expect from AWS monitoring services and more!

More articles

Made by developers for developers

Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.

What our customers say

Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.

Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.

Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.

I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.

Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.

Great UI. Easy to navigate through CloudWatch logs. Simple setup.

Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.