opentelemetry-lambda VS opentelemetry-ext-js

I mean, sure, you can improve performance a bit by increasing the RAM/compute capacity on the Lambda. But it always adds a pretty steep overhead right now, no matter how much capacity you throw at it.

https://github.com/open-telemetry/opentelemetry-lambda/issue...

https://github.com/aws-observability/aws-otel-lambda/issues/...

OpenTelemetry AWS Lambda repository

With the sample apps from the opentelemetry-lambda repository the Lambda part itself was easy to implement. What took me some time was to provide the jaeger Fargate service with IaC ouside of an k8s environment. But with ECS and ServiceDiscovery that was easy in the end. This should be even more simple in an EKS environment with the jaegertracing helm-charts.

OpenTelemetry recently released https://github.com/open-telemetry/opentelemetry-lambda, but they also have this in the official docs https://opentelemetry.io/docs/instrumentation/js/serverless/. What do you consider to be the better option?

opentelemetry-lambda

You don’t have to create an opentelemetry configuration file such as this for each of your lambdas. In fact, you shouldn’t. In AWS, you can use Lambda Layers. You can define the OpenTelemetry tracing piece of code as a Lambda layer and use it in any Lambda you want. Furthermore, OpenTelemetry went ahead and implemented this opentelemetry-lambda layer for us. All we need to do is use it with our config.

Sorry you're having trouble working with the ADOT Lambda Layers :(. Have you had a chance to open an issue on the GitHub repo for OTel Lambda or ADOT Lambda? You should add your expected vs your actual output!

AWS has good tools for tracing, but in this example, I will use another remote and distributed tracing platform – Aspecto.

For this article, I will be using Aspecto to visualize my traces. You can follow along by quickly creating a free account.

If we drill down into one of these traces, we can see in more detail how long each request took and clear visualization of the entire workflow.

For the purposes of this guide, I chose to use Aspecto as my visualization tool. This is because Aspecto provides built-in support for visualizing messaging systems like Kafka (and, of course, any other part of our microservice architectures).

To follow along, you can open a new free-forever Aspecto account or log in to your existing one.

That’s about it for this OpenTelemetry Java guide, folks. If you have any questions or issues with any of these steps, feel free to reach out to us via chat or join our OpenTelemetry Slack channel (part of the CNCF Slack).

At Aspecto, you can sign up for free and use our generous free-forever plan (no limited features).

Aspecto has a free-forever tier and provides everything included in Jaeger and more. Sort of like Jaeger on steroids.

However, you can take your tracing visualization to the next level with Aspecto. Try it yourself with the free-forever plan that has no limited features.

Hi all, we wanted to share some love back to the Kafka community. We hope you'd find this instrumentation helpful, check it out on GitHub 🌟and npm