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CRI-O Tracing

Configuration

To enable OpenTelemetry tracing support in CRI-O, either start crio with --enable-tracing or add the corresponding option to a config overwrite, for example /etc/crio/crio.conf.d/01-tracing.conf:

[crio.tracing]
enable_tracing = true

Traces in CRI-O get exported via the OpenTelemetry Protocol by using an gRPC endpoint. This endpoint defaults to 0.0.0.0:4317, but can be configured by using the --tracing-endpoint flag or the corresponding TOML configuration:

[crio.tracing]
tracing_endpoint = "0.0.0.0:4317"

The final configuration aspect of OpenTelemetry tracing in CRI-O is the --tracing-sampling-rate-per-million / tracing_sampling_rate_per_million configuration, which refers to the amount of samples collected per million spans. This means if it being set to 0 (the default), then CRI-O will not collect any traces at all. If set to 1000000 (one million), then CRI-O will create traces for all created spans. If the value is below one million, then there is no way right now to select a subset of spans other than the modulo of the set value.

conmon-rs has the capability to add additional tracing past the scope of CRI-O. This is automatically enabled when the pod runtime type is chosen, like so:

[crio.runtime]
default_runtime = "runc"

[crio.runtime.runtimes.runc]
runtime_type = "pod"

Then conmon-rs will export traces and spans in the same way CRI-O does automatically. Both CRI-O and conmon-rs will correlate their logs to the traces and spans. If the connection to the OTLP instance gets lost, then CRI-O will not block, and all the traces during that time will be lost.

Usage example

The OpenTelemetry Collector alone cannot be used to visualize traces and spans. For that a frontend like Jaeger can be used to connect to it. To achieve that, a configuration file for OTLP needs to be created, like this otel-collector-config.yaml:

receivers:
  otlp:
    protocols:
      http:
      grpc:

exporters:
  logging:
    loglevel: debug

  jaeger:
    endpoint: localhost:14250
    tls:
      insecure: true

processors:
  batch:

extensions:
  health_check:
  pprof:
    endpoint: localhost:1888
  zpages:
    endpoint: localhost:55679

service:
  extensions: [pprof, zpages, health_check]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [logging, jaeger]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [logging]

The jaeger endpoint has been set to localhost:14250, means before starting the collector we have to start the jaeger instance:

podman run -it --rm --network host jaegertracing/all-in-one:1.41.0

After jaeger is up and running we can start the OpenTelemetry collector:

podman run -it --rm --network host \
    -v ./otel-collector-config.yaml:/etc/otel-collector-config.yaml \
    otel/opentelemetry-collector:0.70.0 --config=/etc/otel-collector-config.yaml

The collector logs will indicate that the connection to Jaeger was successful:

2023-01-26T13:26:22.015Z        info    jaegerexporter@v0.70.0/exporter.go:184  \
    State of the connection with the Jaeger Collector backend \
    {"kind": "exporter", "data_type": "traces", "name": "jaeger", "state": "READY"}

The Jaeger UI should be now available on http://localhost:16686.

It's now possible to start CRI-O with enabled tracing:

sudo crio --enable-tracing --tracing-sampling-rate-per-million 1000000

And when now running a CRI API call, for example by usingcrictl:

sudo crictl ps

Then the OpenTelemetry collector will indicate that it has received new traces and spans, where the trace with the ID 1987d3baa753087d60dd1a566c14da31 contains the invocation for listing the containers via crictl ps:

Span #2
    Trace ID       : 1987d3baa753087d60dd1a566c14da31
    Parent ID      :
    ID             : 3b91638c1aa3cf30
    Name           : /runtime.v1.RuntimeService/ListContainers
    Kind           : Internal
    Start time     : 2023-01-26 13:29:44.409289041 +0000 UTC
    End time       : 2023-01-26 13:29:44.409831126 +0000 UTC
    Status code    : Unset
    Status message :
Events:
SpanEvent #0
     -> Name: log
     -> Timestamp: 2023-01-26 13:29:44.409324579 +0000 UTC
     -> DroppedAttributesCount: 0
     -> Attributes::
          -> log.severity: Str(DEBUG)
          -> log.message: Str(Request: &ListContainersRequest{Filter:&ContainerFilter{Id:,State:&ContainerStateValue{State:CONTAINER_RUNNING,},PodSandboxId:,LabelSelector:map[string]string{},},})
          -> id: Str(4e395179-b3c6-4f87-ac77-a70361dd4ebd)
          -> name: Str(/runtime.v1.RuntimeService/ListContainers)
SpanEvent #1
     -> Name: log
     -> Timestamp: 2023-01-26 13:29:44.409813328 +0000 UTC
     -> DroppedAttributesCount: 0
     -> Attributes::
          -> log.severity: Str(DEBUG)
          -> log.message: Str(Response: &ListContainersResponse{Containers:[]*Container{},})
          -> name: Str(/runtime.v1.RuntimeService/ListContainers)
          -> id: Str(4e395179-b3c6-4f87-ac77-a70361dd4ebd)

We can see that there are SpanEvents attached to the Span, carrying the log messages from CRI-O. The visualization of the trace, its spans and log messages can be found in Jaeger as well, under http://localhost:16686/trace/1987d3baa753087d60dd1a566c14da31:

trace

If kubelet tracing is enabled, then the spans are nested under the kubelet traces. This is caused by the CRI calls from the kubelet, which propagates the trace ID through the gRPC API.