opentelemetry-collector-contrib VS hyperdx

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

Hi HN, Mike and Warren here! We’ve been building an open source local-dev-friendly mode for HyperDX (hyperdx.io). It's a single Docker container that lets you view logs, metrics, and traces for local development in a similar way you can use them for production (live tail, filter/correlate logs & spans, build charts, flamegraph, etc.) Basically, imagine you can run Datadog in a single container for local development. We does this by spinning up an OpenTelemetry collector, Clickhouse DB and HyperDX UI all in a single container - it only takes a few seconds to start and lets you start live tailing your local logs and traces immediately (and graph metrics of course).

README (w/ demo gif): https://github.com/hyperdxio/hyperdx/blob/main/LOCAL.md

We started building local mode as it became one of our most loved [1] community issue after sharing HyperDX. It made sense - as we constantly use HyperDX to debug our own issues locally, and have found it to be a huge productivity boost for things like…

1. Tailing multiple container logs (and grepping/isolating them) without a ton of different terminal splits open at the same time.

2. Be able to sensibly view structured logs and correlated traces to debug local issues (because you’re using structured logging and tracing right?) instead of adding ad-hoc console statements that print out exactly what I could’ve gotten from tracing.

3. Testing telemetry are actually emitting/correlating as expected, before shipping it all the way out to prod and realizing I accidentally created a very expensive high-cardinality metric or a span that has the wrong properties.

We spent some time packaging & tuning our existing OSS stack to run in a single container with less memory/space requirements by staring at `dive` to slim down the image and applied incantations from documentation until memory usage improved. Additionally we removed a few non-local-friendly things like authentication requirements and extraneous services.

It still has all the goodness you’d want - so you can full text search your logs/traces, live tail all your events, view spans correlated with logs (and vice versa), create dashboards based on logs, metrics, traces, and is fully OpenTelemetry compatible - just point your Otel SDK/collector to http://localhost:4318 (or 4317 for the grpc folks) and you’re already good to go.

I’m excited to share what we’ve been working on and would love to hear your feedback and opinions!

Spin up the container yourself to try it out:

docker run -p 8000:8000 -p 4318:4318 -p 4317:4317 -p 8080:8080 -p 8002:8002 hyperdx/hyperdx-local

Main Open Source Repo: https://github.com/hyperdxio/hyperdx

Hosted Demo (in case you want to play around in a cloud sandbox instead): https://api.hyperdx.io/login/demo

HyperDX Landing Page: https://hyperdx.io

[1]: https://github.com/hyperdxio/hyperdx/issues/7

ClickHouse also excels at storing and querying semi-structured data, like event logs. Previously, many engineering teams used Elasticsearch in a similar niche to ClickHouse, building applications like Kibana. Increasingly, developers are choosing ClickHouse over Elasticsearch for its unparalleled performance characteristics. For example, our friends at hyperdx.io are using ClickHouse to build an open-source OpenTelemetry provider!

HyperDX

Biased as a founder in the space [1] but I think with OpenTelemetry + OSS extensible observability tooling, the holy grail of one tool is more realizable than ever.

Vendor lock in with Otel now is hopefully a thing of the past - but now that more obs solutions are going open source, hopefully it's not necessarily true that one tool would be mediocre over all use cases (since DD and the likes are inherently limited by their own engineering teams, vs OSS products can have community/customer contributions to improve the surface area over time on top of the core maintainer's work).

[1] https://github.com/hyperdxio/hyperdx

HyperDX (YC S22) | Founding Engineer | SF Bay Area HQ | REMOTE

We're building an open source, dev friendly observability tool (think Datadog, but something developers actually love to use and companies can actually afford).

We're in the intersection of needing to build rock solid infrastructure ingesting TBs of data, searching it incredibly quickly and scalably, and layering on top a buttery smooth DX from our language-specific SDKs, APIs and web app.

We're super early and hiring our first founding engineer. We already have a cloud product customers pay for and love, loads of runway regardless of the wider economy, 5k+ Github stars weeks after our OSS launch, and tons of hard technical problems.

The vast majority of our work is open source, so you can get a sense of what you'd be working with here: https://github.com/hyperdxio/hyperdx

Our job listing is here as well: https://www.ycombinator.com/companies/hyperdx/jobs/zFXTbzl-f...

Come by our discord as well if you just want to talk shop: https://discord.gg/FErRRKU78j

I'm Mike, one of the cofounders. If you love shipping quickly and want to help us build from the ground up an open source developer tool (that devs won't hate when they're on-call), let me know! mike [at] hyperdx.io

Oh huh! What would you do with span start independent of stops?

A tangent on logcat - local observability to me is a really intriguing area, I think there's a story of Otel for local as well if someone can build a good enough local DX for consuming them (we've been told a number of times about this https://github.com/hyperdxio/hyperdx/issues/7 as an example)

Hi HN, Mike and Warren here! We’ve been building HyperDX (hyperdx.io). HyperDX allows you to easily search and correlate logs, traces, metrics (alpha), and session replays all in one place. For example, if a user reports a bug “this button doesn’t work," an engineer can play back what the user was doing in their browser and trace API calls back to the backend logs for that specific request, all from a single view. Github Repo: https://github.com/hyperdxio/hyperdx

We're building the OSS equivalent when it comes to the observability side of Splunk/DD, on Clickhouse naturally of course but believe in the same end goal of lowering cost via separation of compute and storage.

https://github.com/hyperdxio/hyperdx