OpenTelemetry

Sourcegraph 4.0+

Sourcegraph exports OpenTelemetry data to a bundled OpenTelemetry Collector instance. This service can be configured to ingest, process, and then export observability data to an observability backend of choice. This approach offers a great deal of flexibility.

Configuration

Sourcegraph’s OpenTelemetry Collector is deployed with a custom image, sourcegraph/opentelemetry-collector, and is configured with a configuration YAML file. By default, sourcegraph/opentelemetry-collector is configured to not do anything with the data it receives, but exporters to various backends can be configured for each signal we currently support—currently, only traces data is supported.

Refer to the documentation for an in-depth explanation of the parts that compose a full collector pipeline.

For more details on configuring the OpenTelemetry collector for your deployment method, refer to the deployment-specific guidance:

Tracing

Sourcegraph traces are exported in OpenTelemetry format to the bundled OpenTelemetry collector. To learn more about Sourcegraph traces in general, refer to our tracing documentation.

sourcegraph/opentelemetry-collector includes the following exporters that support traces:

Basic configuration for each tracing backend type is described below. Note that just adding a backend to the exporters block does not enable it—it must also be added to the service block. Refer to the next snippet for a basic but complete example, which is the default out-of-the-box configuration:

receivers:
  otlp:
    protocols:
      grpc:
      http:

exporters:
  logging:
    loglevel: warn
    sampling_initial: 5
    sampling_thereafter: 200

service:
  pipelines:
    traces:
      receivers:
        - otlp
      exporters:
        - logging # The exporter name must be added here to enable it

Sampling traces

To reduce the volume of traces being exported, the collector can be configured to apply sampling to the exported traces. Sourcegraph bundles the probabilistic sampler and the tail sampler as part of it’s default collector container image.

If enabled, this sampling mechanism will be applied to all traces, regardless if a request was explictly marked as to be traced.

Probabilistic sampler

The probabilistic sampler hashes Trace IDs and determines whether a trace should be sampled based on this hash. Note that semantic convention of tags on a trace take precedence over Trace ID hashing when deciding whether a trace should be sampled or not.

Refer to the next snippet for an example on how to update the configuration to enable sampling using the probabilistic sampler.

exporters:
  # ...

processors:
  probabilistic_sampler:
    hash_seed: 22 # An integer used to compute the hash algorithm. Note that all collectors for a given tier (e.g. behind the same load balancer) should have the same hash_seed.
    sampling_percentage: 10.0 # (default = 0): Percentage at which traces are sampled; >= 100 samples all traces

service:
  pipelines:
    # ...
    traces:
      #...
      processors: [probabilistic_sampler] # Plug the probabilistic sampler to the traces.

Tail sampling

The tail sampler samples traces according to policies and the sampling decision of whether a trace should be sampled is determined at the tail end of a pipeline. For more information on the supported policies and other configuration options of the sampler see tail sampler configuration.

The sampler waits for a certain amount of spans before making applying the configured policy. Due to it keeping a certain amount of spans in memory the sampler incurs as slight performance cost compared to the Probabilistic sampler. For a better comparison on probabilistic vs tail sampling processors see here

Refer to the next snippet for an example on how to update the configuration to enable tail sampling with a particular policy.

receivers:
    # ...
exporters:
    # ...

processors:
  tail_sampling:
    # Wait time since the first span of a trace before making a sampling decision
    decision_wait: 30s # default value = 30s
    # Number of traces kept in memory
    num_traces: 50000 # default value = 50000
    # Expected number of new traces (helps in allocating data structures)
    expected_new_traces_per_sec: 10 # default value = 0
    # Recommended reading to understand how the policies are applied:
    # https://sourcegraph.com/github.com/open-telemetry/[email protected]7efaa0ca/-/blob/processor/tailsamplingprocessor/processor.go?L214
    policies:
      [
          {
            # If a span contains `sampling_retain: true`, it will always be sampled (not dropped),
            # regardless of the probabilistic sampling.
            name: policy-retain,
            type: string_attribute,
            string_attribute: {key: sampling.retain, values: ['true']},
          },
          {
            # Only keep 10% of the traces.
            name: policy-probalistic,
            type: probabilistic,
            probabilistic: {sampling_percentage: 10}
          }
        ]

service:
  pipelines:
    traces:
      # ...
      processors: [tail_sampling]

Filtering traces

As part of the default container image Sourcegraph bundles the filter processor. By configuring a pipeline to have a filter processor one is able to include or exclude (depending on configuration!) on whether a trace should be allowed through the pipeline and be exported.

Refer to the following snippet where a filter processor is configured to only allow traces with the service name “foobar” to continue through the pipeline. All other traces that do not have this service name will be dropped.

exporters:
    # ...

receivers:
    # ...

processors:
   filter/foobar: # the format is <processor type>/<name> - note a name is NOT required
      spans:
          include:
             match_type: strict # regexp is also a supported type
             services:
             - "foobar"

service:
    pipelines:
        traces: # pipeline accepts all traces
        traces/foobar: # pipeline that only export foobar traces
            # ...
            processors: [filter/foobar]

Exporters

Exporters send observability data from OpenTelemetry collector to desired backends. Each exporter can support one, or several, OpenTelemetry signals.

This section outlines some common configurations for exporters—for more details, refer to the official OpenTelemetry exporters documentation.

OTLP-compatible backends

Backends compatible with the OpenTelemetry protocol (OTLP) include services like Honeycomb and Grafana Tempo. OTLP-compatible backends typically accept the OTLP gRPC protocol, but they can also implement the OTLP HTTP protocol.

OTLP gRPC backends

Refer to the otlp exporter documentation for all available options.

exporters:
  otlp:
    endpoint: otelcol2:4317
    tls:
      cert_file: file.cert
      key_file: file.key
  otlp/2:
    endpoint: otelcol2:4317
    tls:
      insecure: true

OTLP HTTP backends

Refer to the otlphttp exporter documentation for all available options.

exporters:
  otlphttp:
    endpoint: https://example.com:4318/v1/traces

Jaeger

Refer to the jaeger exporter documentation for all options.

Most Sourcegraph deployment methods still ship with an opt-in Jaeger instance—to set this up, follow the relevant deployment guides, which will also set up the appropriate configuration for you:

If you wish to do additional configuration or connect to your own Jaeger instance, the deployed Collector image is bundled with a basic configuration with Jaeger exporting. If this configuration serves your needs, you do not have to provide a separate config—the Collector startup command can be set to /bin/otelcol-sourcegraph --config=/etc/otel-collector/configs/jaeger.yaml. Note that this requires the environment variable $JAEGER_HOST to be set on the Collector instance (i.e. the container in Kubernetes or Docker Compose):

exporters:
  jaeger:
    # Default Jaeger gRPC server
    endpoint: "$JAEGER_HOST:14250"
    tls:
      insecure: true

Google Cloud

Refer to the googlecloud exporter documentation for all available options.

If you run Sourcegraph on a GCP workload, all requests will be authenticated automatically. The documentation describes other authentication methods.

exporters:
  googlecloud:
    # See docs
    project: project-name # or fetched from credentials
    retry_on_failure:
      enabled: false