The primary goal of OpenTelemetry (OTEL) is to offer vendor-neutral ways of application instrumentation such that customers are able to switch between Telemetry backends. There are three main components of OpenTelemetry: OpenTelemetry SDK, OpenTelemetry API, and OpenTelemetry Collector.

An OTEL collector is one of the core components of OpenTelemetry. OTEL collectors can optionally be deployed between instrumented applications and the Telemetry backends as a Telemetry pipeline component. With an OTEL collector, Telemetry signals can be ingested in multiple formats, translated to OTEL native pdata format, and finally exported out to backend native formats.


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Receivers get the data into the collector. Receivers can be of two types: push-based and pull-based. Generally, a receiver accepts data in a specified format, translates it into the internal format, and passes it to processors and exporters defined in the applicable pipelines. The format of the traces and metrics supported is receiver-specific.

Processors are used for processing the data before sending it out to the exporters. Processors can be used to transform the metrics or modify the name of the span. You can also do things like batching the data before sending it out, retrying in case the exporting fails, adding metadata, and tail-based sampling.

Exporters are used to export data to multiple open-source backends and commercial backends. For example, a logging exporter can be used to export the data to the console output, while a file exporter can be used to dump the data to the file.

Extensions provide capabilities on top of the primary functionality of the collector. They do not require direct access to Telemetry data and are mostly used for managing and monitoring an OTEL collector. Extensions are optional.

The service section is used to enable the components which are configured within receivers, processors, exporters, and extensions sections. The service section consists of two sub-sections: extensions and pipelines.

The extensions consist of a list of all of the extensions to enable. Pipelines can be of traces, metrics, and logs type and consist of a set of receivers, processors, and exporters. Each receiver/processor/exporter must be defined in the configuration outside of the service section to be included in a pipeline.

The OpenTelemetry Collector offers a vendor-agnostic implementation of how toreceive, process and export telemetry data. It removes the need to run, operate,and maintain multiple agents/collectors. This works with improved scalabilityand supports open source observability data formats (e.g. Jaeger, Prometheus,Fluent Bit, etc.) sending to one or more open source or commercial backends. Thelocal Collector agent is the default location to which instrumentation librariesexport their telemetry data.

For trying out and getting started with OpenTelemetry, sending your datadirectly to a backend is a great way to get value quickly. Also, in adevelopment or small-scale environment you can get decent results without acollector.

However, in general we recommend using a collector alongside your service, sinceit allows your service to offload data quickly and the collector can take careof additional handling like retries, batching, encryption or even sensitive datafiltering.

It is also easier to setup a collector than you might think: thedefault OTLP exporters in each language assume a local collector endpoint, so ifyou launch a collector it will automatically start receiving telemetry.

Collector components differ in their maturity levels. Each component has itsstability documented in its README.md. You can find a list of all availablecollector components in the registry.

I am trying to send traces to tempo in multitenant mode by using Zipkin exporter and otel collector.

I am able to do that successfully but the problem is each time I am not able to pass x-scope-orgId dynamically . To make it more clear I am enclosing my configs.

@joeelliott It is just exiting . I am enclosing image for better understanding. tried with the docker image you have suggested.

after adding headers_setter , the docker image just gets exited.

image1360174 53.5 KB

@mariorodriguez @joeelliott

1- Is there any other way to pass X-Scope-OrgId dynamically ?

2- How can we solve that otel collector gets exited while using header_setter extension.

3- Can someone directly send traces to tempo using a grpc request without using otel collector to use in a multitenant setup?

headers_setters extension is perfectly working fine and data is getting pushed in tempo in multitenant mode . but when I am using zipkin exporter for creating traces and zipkin as a reciever then traces are not pushing in tempo in multitenant mode through headers_setter_extension . I guess metadata is not passing.

For zipkin,

receivers:

zipkin:

I am using like this.

I'm developing a POC using otel collector (running on docker) to collect logs, traces, and metrics from a dotnet application and then send the observability data to AppDynamics. I'm following this section of the documentation: -monitoring/appdynamics-for-opentelemetry

Currently I only see traces in the example configuration. Does anyone know if logs and metrics are also supported? And do I need to install dotnet agent to do this (given that I just want to see data successfully received in AppDynamics)?

Thank you for the response! Just so I understand clearly, does that mean that we cannot use AppDynamics .NET agent to report both OpenTelemetry span data and Application Performance Monitoring (APM) data from the trial version I'm using?

I m not expert in .Net agent but to get #1 flow to work, you need to set the Application name as "opentelemetry" rather than "opentelemetry_otel". Also, note your trial account should have .net or any APM license to get #1 work flow.

For otel flow, the service.namespace name you use "opentelemetry" is translated to "opentelemetry_otel" Application name in the controller to distinguish between otel data and appd data. Ideally you should see 2 applications

Hi I have been trying to deploy opentelemetry collector in my aws EKS cluster to send logs to Splunk enterprise, I have deployed this using SignalFx open telemetry collector helm chart: -otel-collector


But it seems to be an issue, what it is doing is not sending logs that have older timestamps than Otel pods it starts sending logs once new logs start ingesting any log files, old logs it is not ingesting, seems to be an issue with some configuration, want help in this

Hi! Can you clarify what the actual source of these logs are?


Are these logs in the pod's stdout/stderr logs from /var/log/pods, or are they in some other location? What is the path to them?


Nix nodes or windows nodes?


As mentioned already, OTel does fingerprint logs and filelog settings expect to be tailing live logs, so if this is a bit different use case we can add a custom receiver in the "extraFilelogs" section of the helm chart. 



Since your otel collector is already installed without start_at=beginning parameter, you should remove otel , delete checkpoint files on your nodes under "/var/addon/splunk/otel_pos/" folder, and install otel again.. This should make otel to re-read all files.

Hi I have done all the things that you have tell in this communication , still it is not sending logs , can it be an issue of time or cluster, or any other configuration we have to do in helm chart 


Your help will be appreciated

Thanks

@pranay03 Are you able to resolve this issue.


I am as well facing same issue. In the console log it shows it started watching, however i could not see the logs in Web portal.


Please let me know your thoughts and suggestions.

otel-collector | Error: failed to get config: cannot unmarshal the configuration: 1 error(s) decoding:error decoding 'exporters': unknown type: "jaeger" for id: "jaeger" (valid values: [logging otlp otlphttp file kafka prometheus debug opencensus prometheusremotewrite zipkin])

The OpenTelemetry Collector is a new, vendor-agnostic agent that can receive and send metrics and traces of many formats. It is a powerful tool in a cloud-native observability stack, especially when you have apps using multiple distributed tracing formats, like Zipkin and Jaeger; or, you want to send data to multiple backends like an in-house solution and a vendor. This article will walk you through configuring and deploying the OpenTelemetry Collector for such scenarios.

The OpenTelemetry project is building tools to wrangle all these different observability producers and receivers, and make them usable for organizations. The Collector tool specifically acts as a universal middleware application for receiving traces and metrics data of various standards, and forwarding them to various backends in the correct standard that backend expects.

The easiest way to get started with the collector is to get the example deployment file from Github. It will create a Deployment for the Collector, and a DaemonSet of agents that will forward to the collector from Kubernetes node, along with ConfigMaps for both to provide configuration. With the Kubernetes CLI installed and your cluster setup (which will vary by provider), and the file downloaded, run:

Here is where the magic happens, and the flexibility of the collector shines. We have three export pipelines, one for sending traces to the vendor, one for sending them to our in-house Jaeger, and one for sending metrics to our in-house Prometheus. Note that in these pipelines the order of the processors does matter.

The possibilities of the OpenTelemetry architecture in an observability pipeline are nearly endless. The configuration above is centralized, but the collector itself is lightweight. Teams could deploy their own collectors to avoid having to manage many hands working on one configuration.

The OpenTelemetry project is also currently exploring adding logging to its domain, meaning soon creating flexible, extensible pipelines for the entirety of the logs/metrics/traces observability trio could be this easy. Read our guide to OpenTelemetry to learn more. 152ee80cbc

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