opentelemetry-collector-contrib VS nerdctl

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

What about the docker-cli? colima also ships with a docker-compatible cli to interact with containerd called nerdctl. We can execute the same docker cli commands like:

Using nerdctl: https://github.com/containerd/nerdctl

I'd really disagree that compose files are somehow one-shot, or blindly modified. To the contrary, really, we have them checked in with the source code. Upon deployment to the cluster, the (running) services will be intelligently updated or replaced (in a rolling manner, causing zero downtime). LXC might be more elegant, but I have no idea what simple, file-based format I could use to let engineers describe the environment their app should run in without compose.

I need something that even junior devs can start up with a single command, that can be placed in the VCS along with the code, and that will not require deep Linux knowledge to get running. Open for suggestions here, really.

Now since Kubernetes works off of containerd I'll be taking a different approach on handling container builds by using nerdctl and the buildkit that comes bundled with it. I'll do this on the amd64 control plane node since it's beefier than my Raspberry Pi workers for handling builds and build related services. Go ahead and download and unpack the latest nerdctl release as of writing (make sure to check the release page in case there's a new one):

That's why nerdctl, their cli binary, is so well named.

Well, it is indeed a good option. However, containerd is a good alternative that is growing even among developers. Please see: https://github.com/containerd/nerdctl

Nerdctl/containerd has IPFS support :)

https://github.com/containerd/nerdctl/blob/main/docs/ipfs.md

nerdctl supports IPFS for both image pulling and pushing, including encrypted images and eStargz lazy pulling. For building, the current method is a locally hosted translator so that the traditional pulls can be converted to work over IPFS. They even have docs on running it on k8s node, though if my reading is correct this isn't exactly a cloud native approach (running systemd services on each node...).