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Ashok Nagaraj
Ashok Nagaraj

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Open-telemetry collector: the powerful recipe for observability pipelines

OTel collector is a component used to receive, process and export telemetry data (signals) from sources to observability backends like elasticsearch, cassandra, Datadog, NewRelic ...

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  1. Avoids resource contentions due to scalable nature and variety of deployment modes
  2. Customizable at multiple levels, no need for constant reboot/reload of the pipeline
  3. Tolerant to network partitions to the most part

Use a collector when you

  • need a common ingestion point for variety of signals like metrics and traces
  • need to collect signals from multiple sources like application, infra, cluster, framework, databases
  • apply transformation to the signal data before storing it in the backend
  • enrich signals with additional meta-data
  • filter-out signals based on various predefined criteria
  • send signal data to multiple observability backends
  • build a loosely coupled, scalable pipeline for signal data flow


  1. Create a configuration file, say config.yaml; details here
  2. Run the collector
$ docker pull otel/opentelemetry-collector:latest
$ docker run [-d] -v $(pwd)/config.yaml:/etc/otelcol/config.yaml [port-config] otel/opentelemetry-collector:latest
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# port configuration
      - "1888:1888"   # pprof extension
      - "8888:8888"   # Prometheus metrics exposed by the collector
      - "8889:8889"   # Prometheus exporter metrics
      - "13133:13133" # health_check extension
      - "4317:4317"   # OTLP gRPC receiver
      - "4318:4318"   # OTLP http receiver
      - "55679:55679" # zpages extension
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Has 3 (or 4) components all of which needs to be enabled in service section
Note: Atleast one service > pipeline is mandatory

  1. Receivers - describe modes of how collector gets the data IN and can be PUSH or PULL based (eg: host metrics, application metrics, zipkin traces).
  2. Processors - run on the data being transported and optionally massage, transform and filter-out data (eg: filter, batch, samplers)
  3. Exporters - specify how data is sent out to one/more configured backends, can be PUSH or PULL based (eg: file, jaeger, prometheus). They generally involve details of authentication in production environments.
  4. Extensions(optional) - provide additional capabilities to the collector, but not requiring direct access to signal data (eg: health_check, pprof)

More info: configuration wiki

Deployment modes
  1. Agent: A collector instance running on the same node as the application (binary, side-car or daemonset)
  2. Gateway: One or more instances collectively running centrally as a standalone service. It can often offer advanced capabilities like simple load-balancing, tail based sampling, independent scaling .. generally acting as a receiver for the agents.


Gist to working demo
Note: metrics exporter does not seem to work based on official documentation

OTel Deployment patterns

Source of all the below information CNCF presentation

Basic - instrument and send to a collector
Used when application is instrumented with OTel SDK and signals are sent to a predefined collector

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Basic - fanout
Used when signals are (processed and) sent to multiple destinations. Useful in situations where multiple views/perspectives of same data is to be generated (eg: one from Jaeger one from Datadog)

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Collector works as an intermediate proxy and massages the data before passing on to destination; used when common processors are to be applied on the incoming signals

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Kubernetes sidecar
Workloads send signals to a OTel collector sidecar which is sent over to a collector residing in a central namespace (which processes and sends it to destination). Advantages of this pattern is decoupled central collector, easily customizable side-car and implicit load balancing.

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Kubernetes daemonset
Collector is deployed as a daemonset; while it eases management, multi-tenancy and scaling requirements are hard to customize.

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Loadbalanced collector
A central load-balancing collector is used to aggregate all signals from a given source to a given backend collector (like how session affinity is handled). The idea behind the implementation is that any given collector should provide full picture of the source application independently.

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A common Otel collector is deployed on a central cluster which acts as the final stop before writing to destinations. It is useful in regulatory scenarios where common point of control need be established

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Multiple destinations generally are involved and Otel collector processes and sends to multiple destinations based on filtering tags

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Per signal
An otel collector per signal type (eg: one for metrics, one for traces ..). Useful to establish saperate observability pipelines per signal. Note: A PUSH based collector can be scaled easily while PULL based (prometheus) is not straight-forward given the idempotency semantics.

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