opentelemetry-helm-charts VS opentelemetry-collector-contrib

Refer to the official documentation for the Helm chart for comprehensive instructions and configuration options: OpenTelemetry Helm Charts Documentation.

In my latest Nomadification Project (TM), I got the OpenTelemetry Demo App to run on Nomad (with HashiQube, of course). To do this, I used the OpenTelemetry Demo App Helm Chart as my guide. In doing this, and other Nomadifications, I realized that I’ve never gone through the process of explaining the conversion process from Kubernetes manifests to Nomad jobspecs.

Y’all...I’m so excited, because I finally got to work on an item on my tech bucket list. Last week, I began the process of translating OpenTelemetry (OTel) Demo App’s Helm Charts to HashiCorp Nomad job specs.

After creating a Kubernetes cluster, we needed to create a Kubernetes resource before we could apply the Helm chart to install the OpenTelemetry demo app. The Demo App’s Helm Chart deploys an OpenTelemetry Collector. We wanted to configure the Collector to send OTel data to Lightstep. To do so, you need to add a Lightstep Access Token, which is stored as a Kubernetes secret.

Deploy the OpenTelemetry Demo App using the OpenTelemetry Demo Helm Chart

Now let's dive right in and figure out how to use the Helm chart provided by OpenTelemetry.

you can add https://github.com/open-telemetry/opentelemetry-collector-co... at signoz's otel-collector which will scrape your service's endpoint periodically. If your service is down, this will give 5xx error and you can set an alert on that.

Another alternative is to use an alert to notify on a metric being absent for sometime. Both of these should work

The open telemetry collector does just that. https://github.com/open-telemetry/opentelemetry-collector-co...

There are two official distributions of the OpenTelemetry Collector: Core, and Contrib.

Maybe, you are asking yourself: "But I already had instrumented my applications with vendor-specific libraries and I'm using their agents and monitoring tools, why should I change to OpenTelemetry?". The answer is: maybe you're right and I don't want to encourage you to update the way how you are doing observability in your applications, that's a hard and complex task. But, if you are starting from scratch or you are not happy with your current observability infrastructure, OpenTelemetry is the best choice, independently of the backend telemetry tool that you are using. I would like to invite you to take a look at the number of exporters available in the collector contrib section, if your backend tracing tool is not there, probably it's already using the Open Telemetry Protocol (OTLP) and you will be able to use the core collector. Otherwise, you should consider changing your backend telemetry tool or contributing to the project creating a new exporter.

To receive OTLP data, you set up the standard otlp receiver to receive data in HTTP or gRPC format. To forward traces and metrics, a batch processor was defined to accumulate data and send it every 100 milliseconds. Then set up a connection to Tempo (in otlp/tempo exporter, with a standard top exporter) and to Prometheus (in prometheus exporter, with a control exporter). A debug exporter also was added to log info on container standard I/O and see how the collector is working.

Thanks for the reply. Would the Spotlight sidecar possibly be able to run independently and consume spans emitted by the Sentry exporter[0] or some other similar flow beyond strictly exporting directly from the Sentry SDK provided by Spotlight?

This tooling looks really cool and I'd love to play around with it, but am already pretty entrenched into OTel and funneling data through the collector and don't want to introduce too much additional overhead for devs.

[0] https://github.com/open-telemetry/opentelemetry-collector-co...

A list of all metric definitions can be found here.

receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 hostmetrics: collection_interval: 60s scrapers: cpu: {} disk: {} load: {} filesystem: {} memory: {} network: {} paging: {} process: mute_process_name_error: true mute_process_exe_error: true mute_process_io_error: true processes: {} prometheus: config: global: scrape_interval: 60s scrape_configs: - job_name: otel-collector-binary scrape_interval: 60s static_configs: - targets: ["localhost:8889>"] - job_name: "jvm-metrics" scrape_interval: 10s metrics_path: "/actuator/prometheus" static_configs: - targets: ["localhost:8090>"] processors: batch: send_batch_size: 1000 timeout: 10s # Ref: https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/processor/resourcedetectionprocessor/README.md resourcedetection: detectors: [env, system] # Before system detector, include ec2 for AWS, gcp for GCP and azure for Azure. # Using OTEL_RESOURCE_ATTRIBUTES envvar, env detector adds custom labels. timeout: 2s system: hostname_sources: [os] # alternatively, use [dns,os] for setting FQDN as host.name and os as fallback extensions: health_check: {} zpages: {} exporters: otlp: endpoint: "ingest.{region}.signoz.cloud:443" tls: insecure: false headers: "signoz-access-token": logging: verbosity: normal service: telemetry: metrics: address: 0.0.0.0:8888 extensions: [health_check, zpages] pipelines: metrics: receivers: [otlp] processors: [batch] exporters: [otlp] metrics/internal: receivers: [prometheus, hostmetrics] processors: [resourcedetection, batch] exporters: [otlp] traces: receivers: [otlp] processors: [batch] exporters: [otlp] logs: receivers: [otlp] processors: [batch] exporters: [otlp]

If you are using the prometheus exporter, you can use the transform processor to get specific resource attributes into metric labels.

With the advantage that you get only the specific attributes you want, thus avoiding a cardinality explosion.

https://github.com/open-telemetry/opentelemetry-collector-co...