Trace Sampling at server side

An advantage of a distributed tracing system is that detailed information from the traces can be obtained. However, the downside is that these traces use up a lot of storage. If you enable the trace sampling mechanism at the server side, you will find that the metrics of the service, service instance, endpoint, and topology all have the same accuracy as before. The only difference is that they do not save all traces into storage.

Of course, even if you enable sampling, the traces will be kept as consistent as possible. Being consistent means that once the trace segments have been collected and reported by agents, the backend would do their best not to split the traces. See our recommendation to understand why you should keep the traces as consistent as possible and try not to split them.

Set the sample rate

In the agent-analyzer module, you will find the sampleRate setting by the configuration traceSamplingPolicySettingsFile.

    # The default sampling rate and the default trace latency time configured by the 'traceSamplingPolicySettingsFile' file.
    traceSamplingPolicySettingsFile: ${SW_TRACE_SAMPLING_POLICY_SETTINGS_FILE:trace-sampling-policy-settings.yml}
    forceSampleErrorSegment: ${SW_FORCE_SAMPLE_ERROR_SEGMENT:true} # When sampling mechanism activated, this config would make the error status segment sampled, ignoring the sampling rate.

The default trace-sampling-policy-settings.yml uses the following format. Could use dynamic configuration to update the settings in the runtime.

  # Default sampling rate that replaces the 'agent-analyzer.default.sampleRate'
  # The sample rate precision is 1/10000. 10000 means 100% sample in default.
  rate: 10000
  # Default trace latency time that replaces the 'agent-analyzer.default.slowTraceSegmentThreshold'
  # Setting this threshold about the latency would make the slow trace segments sampled if they cost more time, even the sampling mechanism activated. The default value is `-1`, which means would not sample slow traces. Unit, millisecond.
  duration: -1
#  - name: serverName
#    rate: 1000 # Sampling rate of this specific service
#    duration: 10000 # Trace latency threshold for trace sampling for this specific service

duration.rate allows you to set the sample rate to this backend. The sample rate precision is 1/10000. 10000 means 100% sample by default.

forceSampleErrorSegment allows you to save all error segments when sampling mechanism is activated. When sampling mechanism is activated, this config would cause the error status segment to be sampled, ignoring the sampling rate.

default.duration allows you to save all slow trace segments when sampling mechanism is activated. Setting this threshold on latency (in milliseconds) would cause slow trace segments to be sampled if they use up more time, even if the sampling mechanism is activated. The default value is -1, which means that slow traces would not be sampled.

Note: services.[].rate and services.[].duration has a higher priority than default.rare and default.duration.


You may choose to set different backend instances with different sampleRate values, although we recommend that you set the values to be the same.

When you set the different rates, let’s say:

  • Backend-InstanceA.sampleRate = 35
  • Backend-InstanceB.sampleRate = 55

Assume the agents have reported all trace segments to the backend. 35% of the traces at the global level will be collected and saved in storage consistently/completely together with all spans. 20% of the trace segments which are reported to Backend-Instance B will be saved in storage, whereas some trace segments may be missed, as they are reported to Backend-InstanceA and ignored.


When you enable sampling, the actual sample rate may exceed sampleRate. The reason is that currently all error/slow segments will be saved; meanwhile, the upstream and downstream may not be sampled. This feature ensures that you have the error/slow stacks and segments, although it is not guaranteed that you would have the whole traces.

Note also if most of the access have failed or are slow, the sampling rate would be close to 100%. This may cause the backend or storage clusters to crash.