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OpenTelemetry Collector

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Quick Overview

The OpenTelemetry Collector is an open-source project that provides a vendor-agnostic implementation for collecting, processing, and exporting telemetry data. It serves as a central component in the OpenTelemetry ecosystem, allowing users to receive, transform, and transmit observability data from various sources to multiple backends.

Pros

  • Vendor-neutral and highly extensible architecture
  • Supports multiple data formats and protocols for input and output
  • Provides built-in processors for data manipulation and filtering
  • Offers a single agent for metrics, traces, and logs collection

Cons

  • Can be complex to configure for advanced use cases
  • May introduce additional latency in data processing pipeline
  • Requires careful resource management for high-volume deployments
  • Limited built-in visualization capabilities compared to some proprietary solutions

Code Examples

  1. Basic configuration for receiving and exporting traces:
receivers:
  otlp:
    protocols:
      grpc:

processors:
  batch:

exporters:
  otlp:
    endpoint: "otlp.example.com:4317"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp]
  1. Adding a filter processor to exclude certain spans:
processors:
  filter:
    spans:
      exclude:
        match_type: regexp
        services: ["auth.*"]

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [filter, batch]
      exporters: [otlp]
  1. Configuring multiple exporters for data redundancy:
exporters:
  otlp/backend1:
    endpoint: "backend1.example.com:4317"
  otlp/backend2:
    endpoint: "backend2.example.com:4317"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp/backend1, otlp/backend2]

Getting Started

  1. Download the OpenTelemetry Collector binary from the official releases page.
  2. Create a configuration file (e.g., config.yaml) with your desired receivers, processors, and exporters.
  3. Run the collector with the following command:
./otelcol --config config.yaml
  1. Configure your applications to send telemetry data to the collector's receiver endpoint.
  2. Verify that data is being processed and exported by checking the collector's logs and your configured backend systems.

Competitor Comparisons

open-telemetry logo

opentelemetry-java

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OpenTelemetry Java SDK

Pros of opentelemetry-java

  • Native Java implementation, allowing for seamless integration with Java applications
  • Provides a more comprehensive set of Java-specific APIs and instrumentation
  • Better performance for Java-based systems due to direct language support

Cons of opentelemetry-java

  • Limited to Java ecosystem, lacking the language-agnostic approach of the Collector
  • Requires separate implementation and maintenance for non-Java components in a polyglot environment
  • May have a steeper learning curve for developers not familiar with Java-specific concepts

Code Comparison

opentelemetry-java:

Tracer tracer = GlobalOpenTelemetry.getTracer("instrumentation-library-name", "1.0.0");
Span span = tracer.spanBuilder("my span").startSpan();
try (Scope scope = span.makeCurrent()) {
    // Your code here
} finally {
    span.end();
}

opentelemetry-collector:

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:

exporters:
  otlp:
    endpoint: "otelcol:4317"

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp]

The opentelemetry-java example shows how to create and manage spans in Java code, while the opentelemetry-collector example demonstrates configuration for receiving, processing, and exporting telemetry data in a language-agnostic manner.

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Pros of Jaeger

  • More mature project with longer history and established user base
  • Simpler architecture, easier to deploy for basic use cases
  • Built-in UI for trace visualization and analysis

Cons of Jaeger

  • Limited support for non-Jaeger data formats and protocols
  • Less flexible for complex data processing and transformation scenarios
  • Smaller ecosystem of integrations compared to OpenTelemetry Collector

Code Comparison

Jaeger (Go):

import (
    "github.com/uber/jaeger-client-go"
    "github.com/uber/jaeger-client-go/config"
)

cfg := config.Configuration{
    Sampler: &config.SamplerConfig{Type: jaeger.SamplerTypeConst, Param: 1},
    Reporter: &config.ReporterConfig{LogSpans: true},
}

OpenTelemetry Collector (YAML configuration):

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:

exporters:
  jaeger:
    endpoint: jaeger-collector:14250

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [jaeger]

The Jaeger code snippet shows client-side configuration, while the OpenTelemetry Collector example demonstrates server-side setup. Jaeger focuses on its specific protocol, while the Collector supports multiple data formats and provides more extensive processing capabilities.

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Pros of Prometheus

  • Mature and battle-tested monitoring system with a large ecosystem
  • Powerful PromQL query language for data analysis
  • Built-in alerting capabilities

Cons of Prometheus

  • Limited support for distributed tracing and logs
  • Less flexible data model compared to OpenTelemetry
  • Primarily focused on metrics, requiring additional tools for complete observability

Code Comparison

Prometheus configuration (prometheus.yml):

scrape_configs:
  - job_name: 'example'
    static_configs:
      - targets: ['localhost:8080']

OpenTelemetry Collector configuration (config.yaml):

receivers:
  otlp:
    protocols:
      grpc:
exporters:
  prometheus:
    endpoint: "0.0.0.0:8889"

Summary

Prometheus is a robust monitoring system focused on metrics, with a powerful query language and built-in alerting. OpenTelemetry Collector offers a more flexible and comprehensive approach to observability, supporting metrics, traces, and logs in a vendor-neutral format. While Prometheus excels in metric collection and analysis, OpenTelemetry Collector provides a more adaptable solution for modern, distributed systems requiring full observability capabilities.

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Pros of APM Server

  • Tightly integrated with Elastic Stack ecosystem
  • Built-in support for Elastic APM agents
  • Optimized for Elasticsearch data storage

Cons of APM Server

  • Limited to Elastic ecosystem, less flexible for other backends
  • Fewer supported data formats compared to OpenTelemetry Collector
  • Smaller community and fewer third-party integrations

Code Comparison

APM Server (Go):

func (p *processor) processTransformationResult(ctx context.Context, result *transform.Transformable) error {
    if result == nil {
        return nil
    }
    return p.pubHandler.Publish(ctx, *result)
}

OpenTelemetry Collector (Go):

func (r *traceReceiver) Start(_ context.Context, host component.Host) error {
    r.host = host
    r.server = r.config.ServerConfig.ToServer(r.nextConsumer)
    err := r.server.Start(host)
    if err != nil {
        return err
    }
    return nil
}

Both repositories use Go and focus on processing and handling telemetry data. APM Server is more specialized for Elastic Stack integration, while OpenTelemetry Collector offers broader compatibility and extensibility for various observability backends and formats.

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Pros of datadog-agent

  • Comprehensive monitoring solution with built-in integrations for various services and platforms
  • Advanced analytics and visualization capabilities out-of-the-box
  • Robust alerting and anomaly detection features

Cons of datadog-agent

  • Proprietary solution with associated costs, unlike the open-source opentelemetry-collector
  • Less flexibility in terms of customization and extensibility compared to opentelemetry-collector
  • Potential vendor lock-in due to proprietary data formats and APIs

Code Comparison

datadog-agent:

func (a *Agent) run() {
    a.startPool()
    a.startForwarders()
    a.startMetadataProviders()
    a.startAutoConfig()
}

opentelemetry-collector:

func (app *Application) Start() error {
    app.logger.Info("Starting "+app.info.Name+"...")
    app.builtExporters = app.factories.Exporters.BuildExporters(app.config.Exporters, app)
    app.builtReceivers = app.factories.Receivers.BuildReceivers(app.config.Receivers, app)
    return nil
}

The code snippets show the initialization process for both agents. datadog-agent focuses on starting specific components like pools, forwarders, and metadata providers. opentelemetry-collector, on the other hand, builds exporters and receivers based on the configuration, allowing for more flexibility in setup.

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tempo

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Grafana Tempo is a high volume, minimal dependency distributed tracing backend.

Pros of Tempo

  • Specialized for distributed tracing, offering a more focused solution
  • Integrates seamlessly with other Grafana observability tools
  • Supports high-cardinality data and provides efficient querying capabilities

Cons of Tempo

  • Limited to tracing data, while OpenTelemetry Collector handles metrics and logs as well
  • Less flexibility in data processing and transformation compared to OpenTelemetry Collector
  • Smaller ecosystem and community support

Code Comparison

Tempo (Go):

func (r *Reader) FindTraceByID(ctx context.Context, id common.ID) (*tempopb.Trace, error) {
    // Implementation for finding a trace by ID
}

OpenTelemetry Collector (Go):

func (r *traceReceiver) Start(ctx context.Context, host component.Host) error {
    // Implementation for starting the trace receiver
}

Both projects use Go and have similar code structures for their core functionalities. However, Tempo focuses on trace-specific operations, while OpenTelemetry Collector has a broader scope, handling various telemetry data types.

OpenTelemetry Collector offers more extensive data processing capabilities and supports a wider range of observability signals. It's designed as a vendor-agnostic solution with greater flexibility and extensibility. Tempo, on the other hand, provides a more streamlined experience for distributed tracing within the Grafana ecosystem, with optimizations for high-cardinality data and integration with other Grafana tools.

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README

Getting Started   •   Getting Involved   •   Getting In Touch

Build Status Go Report Card Codecov Status GitHub release (latest by date including pre-releases) Fuzzing Status

Vision   •   Configuration   •   Security   •   Package

OpenTelemetry Icon OpenTelemetry Collector

The OpenTelemetry Collector offers a vendor-agnostic implementation on how to receive, process and export telemetry data. In addition, it removes the need to run, operate and maintain multiple agents/collectors in order to support open-source telemetry data formats (e.g. Jaeger, Prometheus, etc.) to multiple open-source or commercial back-ends.

Objectives:

  • Usable: Reasonable default configuration, supports popular protocols, runs and collects out of the box.
  • Performant: Highly stable and performant under varying loads and configurations.
  • Observable: An exemplar of an observable service.
  • Extensible: Customizable without touching the core code.
  • Unified: Single codebase, deployable as an agent or collector with support for traces, metrics and logs.

Community

The OpenTelemetry Collector SIG is present at the #otel-collector channel on the CNCF Slack and meets once a week via video calls. Everyone is invited to join those calls, which typically serves the following purposes:

  • meet the humans behind the project
  • get an opinion about specific proposals
  • look for a sponsor for a proposed component after trying already via GitHub and Slack
  • get attention to a specific pull-request that got stuck and is difficult to discuss asynchronously

Between 11 July 2024 and 09 January 2025, we'll have our video calls rotating between three time slots, in order to allow everyone to join at least once every three meetings:

Contributors to the project are also welcome to have ad-hoc meetings for synchronous discussions about specific points. Post a note in #otel-collector on Slack inviting others, specifying the topic to be discussed. Unless there are strong reasons to keep the meeting private, please make it an open invitation for other contributors to join. Try also to identify who would be the other contributors interested on that topic and in which timezones they are.

Remember that our source of truth is GitHub: every decision made via Slack or video calls has to be recorded in the relevant GitHub issue. Ideally, the agenda items from the meeting notes would include a link to the issue or pull request where a discussion is happening already. We acknowledge that not everyone can join Slack or the synchronous calls and don't want them to feel excluded.

Supported OTLP version

This code base is currently built against using OTLP protocol v1.3.1, considered Stable. See the OpenTelemetry Protocol Stability definition here.

Stability levels

The collector components and implementation are in different stages of stability, and usually split between functionality and configuration. The status for each component is available in the README file for the component. While we intend to provide high-quality components as part of this repository, we acknowledge that not all of them are ready for prime time. As such, each component should list its current stability level for each telemetry signal, according to the following definitions:

Development

Not all pieces of the component are in place yet and it might not be available as part of any distributions yet. Bugs and performance issues should be reported, but it is likely that the component owners might not give them much attention. Your feedback is still desired, especially when it comes to the user-experience (configuration options, component observability, technical implementation details, ...). Configuration options might break often depending on how things evolve. The component should not be used in production.

Alpha

The component is ready to be used for limited non-critical workloads and the authors of this component would welcome your feedback. Bugs and performance problems should be reported, but component owners might not work on them right away. The configuration options might change often without backwards compatibility guarantees.

Beta

Same as Alpha, but the configuration options are deemed stable. While there might be breaking changes between releases, component owners should try to minimize them. A component at this stage is expected to have had exposure to non-critical production workloads already during its Alpha phase, making it suitable for broader usage.

Stable

The component is ready for general availability. Bugs and performance problems should be reported and there's an expectation that the component owners will work on them. Breaking changes, including configuration options and the component's output are not expected to happen without prior notice, unless under special circumstances.

Deprecated

The component is planned to be removed in a future version and no further support will be provided. Note that new issues will likely not be worked on. When a component enters "deprecated" mode, it is expected to exist for at least two minor releases. See the component's readme file for more details on when a component will cease to exist.

Unmaintained

A component identified as unmaintained does not have an active code owner. Such component may have never been assigned a code owner or a previously active code owner has not responded to requests for feedback within 6 weeks of being contacted. Issues and pull requests for unmaintained components will be labelled as such. After 6 months of being unmaintained, these components will be removed from official distribution. Components that are unmaintained are actively seeking contributors to become code owners.

Compatibility

When used as a library, the OpenTelemetry Collector attempts to track the currently supported versions of Go, as defined by the Go team. Removing support for an unsupported Go version is not considered a breaking change.

Support for Go versions on the OpenTelemetry Collector is updated as follows:

  1. The first release after the release of a new Go minor version N will add build and tests steps for the new Go minor version.
  2. The first release after the release of a new Go minor version N will remove support for Go version N-2.

Official OpenTelemetry Collector distro binaries will be built with a release in the latest Go minor version series.

Verifying the images signatures

[!NOTE] To verify a signed artifact or blob, first install Cosign, then follow the instructions below.

We are signing the images otel/opentelemetry-collector and otel/opentelemetry-collector-contrib using sigstore cosign tool and to verify the signatures you can run the following command:

$ cosign verify \
  --certificate-identity=https://github.com/open-telemetry/opentelemetry-collector-releases/.github/workflows/base-release.yaml@refs/tags/<RELEASE_TAG> \
  --certificate-oidc-issuer=https://token.actions.githubusercontent.com \
  <OTEL_COLLECTOR_IMAGE>

where:

  • <RELEASE_TAG>: is the release that you want to validate
  • <OTEL_COLLECTOR_IMAGE>: is the image that you want to check

Example:

$ cosign verify --certificate-identity=https://github.com/open-telemetry/opentelemetry-collector-releases/.github/workflows/base-release.yaml@refs/tags/v0.98.0 --certificate-oidc-issuer=https://token.actions.githubusercontent.com ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-contrib:0.98.0

Verification for ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-contrib:0.98.0 --
The following checks were performed on each of these signatures:
  - The cosign claims were validated
  - Existence of the claims in the transparency log was verified offline
  - The code-signing certificate was verified using trusted certificate authority certificates

[{"critical":{"identity":{"docker-reference":"ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-contrib"},"image":{"docker-manifest-digest":"sha256:5cea85bcbc734a3c0a641368e5a4ea9d31b472997e9f2feca57eeb4a147fcf1a"},"type":"cosign container image signature"},"optional":{"1.3.6.1.4.1.57264.1.1":"https://token.actions.githubusercontent.com","1.3.6.1.4.1.57264.1.2":"push","1.3.6.1.4.1.57264.1.3":"9e20bf5c142e53070ccb8320a20315fffb41469e","1.3.6.1.4.1.57264.1.4":"Release 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[!NOTE] We started signing the images with release v0.95.0

Contributing

See the Contributing Guide for details.

Here is a list of community roles with current and previous members:

Learn more about roles in Community membership. In addition to what is described at the organization-level, the SIG Collector requires all core approvers to take part in rotating the role of the release manager.

Thanks to all the people who already contributed!