opentelemetry-collector-contrib VS Scientist

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...

OpenTelemetry Operators

For some google-fu, the ruby / .NET equivalent of this is https://github.com/github/scientist / https://github.com/scientistproject/Scientist.net

The readme (here https://github.com/github/scientist#alternatives) doesn't mention, but here is one for Rust: https://crates.io/crates/scientisto

Something I've learned in Ruby land (prob standard in other places, forgive my ignorance) that seems a bit different than what the article advocates for (fake services):

- Write your service wrapper (eg your logic to interact with Twilio)

- Call the service and record API outputs, save those as fixtures that will be returned as responses in your tests without hitting the real thing (eg VCR, WebMock)

- You can now run your tests against old responses (this runs your logic except for getting a real response from the 3rd party; this approach leaves you exposed to API changes or you have edge cases not handled)

For the last part, two approaches to overcome this:

- Wrap any new logic in try/catch and report to Sentry: you avoid breaking prod and get info on new edge cases you didn't cover (this may not be feasible if the path where you're inserting new logic into does not work at all without the new feature; address this with thoughtful design/rollout of new features)

- Run new logic side by side to see what happens to the new logic when running in production (https://github.com/github/scientist)

I use the first approach bc small startup.

Check out GitHub scientist if you are doing a migration with a ruby based system: https://github.com/github/scientist

Great support and functionality for testing differences between two systems of record.

As far as this idea, I have seen this before in a few different forms. The closest thing that I've personally witnessed being used is the scientist gem for Ruby applications. You have to do it manually, but you can instrument your code to compare old and new versions of some code. It also does some fancy stuff like randomly choosing which version gets run, almost like an A/B test. I wonder if there's a similar library for Python?

Not sure what OP had in mind, but for my dream strangler (that's a phrase I never expected to use), I'd love functionality like github's scientist library; basically, the ability to implement a route, continue to serve most requests through the original service, but duplicate a small percentage to the new implementation, compare the outputs of the two services, and log wherever the responses differ, so you get live production tests to exercise the new service without impacting users.

However, the good news is that it’s easy and safe to do so in Ruby and Rails using the Scientist gem. Scientist's name is based on the scientific method of conducting experiments to verify a given hypothesis. In this case, our hypothesis is that the new code does the job.

Github scientist.