Architecture

The OpenTelemetry Collector is an executable file that can receive telemetry, process it, and export it to multiple targets, such as observability backends.

The Collector supports several popular open source protocols for receiving and sending telemetry data, and it offers an extensible architecture for adding more protocols.

Data receiving, processing, and exporting are done using pipelines. You can configure the Collector to have one or more pipelines.

Each pipeline includes the following:

A set of receivers that collect the data.
A series of optional processors that get the data from receivers and process it.
A set of exporters which get the data from processors and send it outside the Collector.

The same receiver can be included in multiple pipelines and multiple pipelines can include the same exporter.

Pipelines

A pipeline defines a path that data follows in the Collector: from reception, to processing (or modification), and finally to export.

Pipelines can operate on three telemetry data types: traces, metrics, and logs. The data type is a property of the pipeline defined by its configuration. Receivers, processors, and exporters used in a pipeline must support the particular data type, otherwise the ErrDataTypeIsNotSupported exception is reported when the configuration loads.

The following diagram represents a typical pipeline:

---
title: Pipeline
---
flowchart LR
    R1(Receiver 1) --> P1[Processor 1]
    R2(Receiver 2) --> P1
    RM(...) ~~~ P1
    RN(Receiver N) --> P1
    P1 --> P2[Processor 2]
    P2 --> PM[...]
    PM --> PN[Processor N]
    PN --> FO((fan-out))
    FO --> E1[[Exporter 1]]
    FO --> E2[[Exporter 2]]
    FO ~~~ EM[[...]]
    FO --> EN[[Exporter N]]

    %% The stroke color matches the website header.
    classDef default fill:#e3e8fc,stroke:#4f62ad

Pipelines can have one or more receivers. Data from all receivers is pushed to the first processor, which processes the data and then pushes it to the next processor. A processor might also drop the data if it’s sampling or filtering. This continues until the last processor in the pipeline pushes the data to the exporters. Each exporter gets a copy of each data element. The last processor uses a fanoutconsumer to send the data to multiple exporters.

The pipeline is constructed during Collector startup based on pipeline definition in the configuration.

A pipeline configuration typically looks like this:

service:
  pipelines: # section that can contain multiple subsections, one per pipeline
    traces: # type of the pipeline
      receivers: [otlp, zipkin]
      processors: [memory_limiter, batch]
      exporters: [otlp, zipkin]

The previous example defines a pipeline for the traces type of telemetry data, with three receivers, two processors, and three exporters.

Receivers

Receivers typically listen on a network port and receive telemetry data. They can also actively obtain data, like scrapers. Usually one receiver is configured to send received data to one pipeline. However, it is also possible to configure the same receiver to send the same received data to multiple pipelines. This can be done by listing the same receiver in the receivers key of several pipelines:

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: localhost:4317

service:
  pipelines:
    traces: # a pipeline of “traces” type
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [otlp]
    traces/2: # another pipeline of “traces” type
      receivers: [otlp]
      processors: [transform]
      exporters: [otlp]

In the above example, otlp receiver will send the same data to pipeline traces and to pipeline traces/2.

The configuration uses composite key names in the form of type[/name].

When the Collector loads this config, the result looks like this diagram (part of processors and exporters are omitted for brevity):

flowchart LR
    R1("`#quot;opentelemetry-collector#quot; Receiver`") --> FO((fan-out))
    FO -->|Pipeline 'traces'| P1["`#quot;memory_limiter#quot; Processor`"]
    FO -->|Pipeline 'traces/2'| P2["`#quot;transform#quot; Processor`"]
    P1 ~~~ M1[...]
    P2 ~~~ M2[...]

    classDef default fill:#e3e8fc,stroke:#4f62ad;

Important

When the same receiver is referenced in more than one pipeline, the Collector creates only one receiver instance at runtime that sends the data to a fan-out consumer. The fan-out consumer in turn sends the data to the first processor of each pipeline. The data propagation from receiver to the fan-out consumer and then to processors is completed using a synchronous function call. This means that if one processor blocks the call, the other pipelines attached to this receiver are blocked from receiving the same data, and the receiver itself stops processing and forwarding newly received data.

Exporters

Exporters typically forward the data they get to a destination on a network, but they can also send the data elsewhere. For example, debug exporter writes the telemetry data to the logging destination.

The configuration allows for multiple exporters of the same type, even in the same pipeline. For example, you can have two otlp exporters defined, each one sending to a different OTLP endpoint:

exporters:
  otlp/1:
    endpoint: example.com:4317
  otlp/2:
    endpoint: localhost:14317

An exporter usually gets the data from one pipeline. However, you can configure multiple pipelines to send data to the same exporter:

exporters:
  otlp:
    protocols:
      grpc:
        endpoint: localhost:14250

service:
  pipelines:
    traces: # a pipeline of “traces” type
      receivers: [zipkin]
      processors: [memory_limiter]
      exporters: [otlp]
    traces/2: # another pipeline of “traces” type
      receivers: [otlp]
      processors: [transform]
      exporters: [otlp]

In the above example, otlp exporter gets data from pipeline traces and from pipeline traces/2. When the Collector loads this config, the result looks like this diagram (part of processors and receivers are omitted for brevity):

flowchart LR
    M1[...] ~~~ P1["`#quot;memory_limiter#quot; Processor`"]
    M2[...] ~~~ P2["`#quot;transform#quot; Processor`"]
    P1 -->|Pipeline 'traces'|E1[["`#quot;otlp#quot; Exporter`"]]
    P2 -->|Pipeline 'traces/2'|E1

    classDef default fill:#e3e8fc,stroke:#4f62ad;

Processors

A pipeline can contain sequentially connected processors. The first processor gets the data from one or more receivers that are configured for the pipeline, and the last processor sends the data to one or more exporters that are configured for the pipeline. All processors between the first and last receive the data from only one preceding processor and send data to only one succeeding processor.

Processors can transform the data before forwarding it, such as adding or removing attributes from spans. They can also drop the data by deciding not to forward it (for example, the probabilisticsampler processor). Or they can generate new data.

The same name of the processor can be referenced in the processors key of multiple pipelines. In this case, the same configuration is used for each of these processors, but each pipeline always gets its own instance of the processor. Each of these processors has its own state, and the processors are never shared between pipelines. For example, if batch processor is used in several pipelines, each pipeline has its own batch processor, but each batch processor is configured exactly the same way if they reference the same key in the configuration. See the following configuration:

processors:
  batch:
    send_batch_size: 10000
    timeout: 10s

service:
  pipelines:
    traces: # a pipeline of “traces” type
      receivers: [zipkin]
      processors: [batch]
      exporters: [otlp]
    traces/2: # another pipeline of “traces” type
      receivers: [otlp]
      processors: [batch]
      exporters: [otlp]

When the Collector loads this config, the result looks like this diagram:

---
title: Pipeline "traces"
---
flowchart LR
    R1("`zipkin Receiver`") --> P1["`#quot;batch#quot; Processor`"]
    P1 --> E1[["`#quot;otlp#quot; Exporter`"]]

    classDef default fill:#e3e8fc,stroke:#4f62ad;

---
title: Pipeline "traces/2"
---
flowchart LR
    R1("`otlp Receiver`") --> P1["`#quot;batch#quot; Processor`"]
    P1 --> E1[["`#quot;otlp#quot; Exporter`"]]

    classDef default fill:#e3e8fc,stroke:#4f62ad;

Note that each batch processor is an independent instance, although they are configured the same way with a send_batch_size of 10000.

The same name of the processor must not be referenced multiple times in the processors key of a single pipeline.

Running as an agent

On a typical VM/container, user applications are running in some processes/pods with an OpenTelemetry library. Previously, the library did all the recording, collecting, sampling, and aggregation of traces, metrics, and logs, and then either exported the data to other persistent storage backends through the library exporters, or displayed it on local zpages. This pattern has several drawbacks, for example:

For each OpenTelemetry library, exporters and zpages must be re-implemented in native languages.
In some programming languages (for example, Ruby or PHP), it is difficult to do the stats aggregation in process.
To enable exporting of OpenTelemetry spans, stats, or metrics, application users need to manually add library exporters and redeploy their binaries. This is especially difficult when an incident has occurred, and users want to use OpenTelemetry to investigate the issue right away.
Application users need to take the responsibility for configuring and initializing exporters. These tasks are error-prone (for example, setting up incorrect credentials or monitored resources), and users may be reluctant to “pollute” their code with OpenTelemetry.

To resolve the issues above, you can run OpenTelemetry Collector as an agent. The agent runs as a daemon in the VM/container and can be deployed independent of the library. Once the agent is deployed and running, it should be able to retrieve traces, metrics, and logs from the library, and export them to other backends. We may also give the agent the ability to push configurations (such as sampling probability) to the library. For those languages that cannot do stats aggregation in process, they can send raw measurements and have the agent do the aggregation.

flowchart LR
    subgraph S1 ["#nbsp;"]
        subgraph S2 ["#nbsp;"]
        end
        subgraph S3 ["#nbsp;"]
        subgraph VM [VM]
            PR["Process [Library]"] -->|Push sample spans, metrics| AB[Agent Binary]
            AB -->|Push configs| PR
        end
        subgraph K8s-pod [K8s Pod]
            AC["`App Container [Library]`"] --> AS[Agent Sidecar]
            AS --> AC
        end
        subgraph K8s-node [K8s Node]
            subgraph Pod1 [Pod]
                APP1[App] ~~~ APP2[App]
            end
            subgraph Pod2 [Pod]
                APP3[App] ~~~ APP4[App]
            end
            subgraph Pod3 [Pod]
                APP5[App] ~~~ APP6[App]
            end
            subgraph AD [Agent Daemonset]
            end
            APP1 --> AD
            APP2 --> AD
            APP4 --> AD
            APP6 --> AD
        end
        end
        subgraph Backends ["#nbsp;"]
            AB --> BE[Backend]
            AS --> PRM[Prometheus Backend]
            AS --> JA[Jaeger Backend]
            AD --> JA
        end
    end

class S1,S2,S3 noLines;
class VM,K8s-pod,K8s-node,Pod1,Pod2,Pod3,Backends withLines;
class PR,AB,AC,AS,APP1,APP2,APP3,APP4,APP5,APP6,AD,BE,PRM,JA nodeStyle
classDef noLines fill:#fff,stroke:#fff,stroke-width:4px;
classDef withLines fill:#fff,stroke:#4f62ad
classDef nodeStyle fill:#e3e8fc,stroke:#4f62ad;

For developers and maintainers of other libraries: By adding specific receivers, you can configure an agent to accept traces, metrics, and logs from other tracing/monitoring libraries, such as Zipkin, Prometheus, etc. See Receivers for details.

Running as a gateway

The OpenTelemetry Collector can run as a gateway instance and receive spans and metrics exported by one or more agents or libraries or by tasks/agents that emit in one of the supported protocols. The Collector is configured to send data to the configured exporter(s). The following figure summarizes the deployment architecture:

flowchart LR
    subgraph S1 ["#nbsp;"]
        subgraph S2 ["#nbsp;"]
        subgraph S3 ["#nbsp;"]
        subgraph VM [VM]
            PR["Process [Library]"]
        end
        subgraph K8s-pod [K8s Pod]
            AC["`App Container [Library]`"]
        end
        subgraph K8s-node [K8s Node]
            subgraph Pod1 [Pod]
                APP1[App] ~~~ APP2[App]
            end
            subgraph Pod2 [Pod]
                APP3[App] ~~~ APP4[App]
            end
            subgraph Pod3 [Pod]
                APP5[App] ~~~ APP6[App]
            end
            subgraph AD [Agent Daemonset]
            end
            APP1 --> AD
            APP2 --> AD
            APP4 --> AD
            APP6 --> AD
        end
        end
        subgraph S4 ["#nbsp;"]
            PR --> OTEL["`OpenTelemetry Collector Service`"]
            AC --> OTEL
            AD --> OTEL
            OTEL ---> BE[Backend X]
        end
        end
        subgraph S5 ["#nbsp;"]
        subgraph S6 ["#nbsp;"]
            JA[Jaeger Backend]
        end
        subgraph S7 ["#nbsp;"]
            PRM[Prometheus Backend]
        end
        end
        JA ~~~ PRM
        OTEL --> JA
        OTEL --> PRM
    end

class S1,S3,S4,S5,S6,S7,S8 noLines;
class VM,K8s-pod,K8s-node,Pod1,Pod2,Pod3 withLines;
class S2 lightLines
class PR,AC,APP1,APP2,APP3,APP4,APP5,APP6,AD,OTEL,BE,JA,PRM nodeStyle
classDef noLines fill:#fff,stroke:#fff,stroke-width:4px;
classDef withLines fill:#fff,stroke:#4f62ad
classDef lightLines fill:#fff,stroke:#acaeb0
classDef nodeStyle fill:#e3e8fc,stroke:#4f62ad;

The OpenTelemetry Collector can also be deployed in other configurations, such as receiving data from other agents or clients in one of the formats supported by its receivers.

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最終更新 March 18, 2024: Remove mention of spanmetrics processor (#4178) (666f5835)