Jaeger libraries implement consistent upfront (or head-based) sampling. For example, assume we have a simple call graph where service A calls service B, and B calls service C: A -> B -> C. When service A receives a request that contains no tracing information, Jaeger tracer will start a new trace, assign it a random trace ID, and make a sampling decision based on the currently installed sampling strategy. The sampling decision will be propagated with the requests to B and to C, so those services will not be making the sampling decision again but instead will respect the decision made by the top service A. This approach guarantees that if a trace is sampled, all its spans will be recorded in the backend. If each service was making its own sampling decision we would rarely get complete traces in the backend.

Client Sampling Configuration

When using configuration object to instantiate the tracer, the type of sampling can be selected via sampler.type and sampler.param properties. Jaeger libraries support the following samplers:

• Constant (sampler.type=const) sampler always makes the same decision for all traces. It either samples all traces (sampler.param=1) or none of them (sampler.param=0).
• Probabilistic (sampler.type=probabilistic) sampler makes a random sampling decision with the probability of sampling equal to the value of sampler.param property. For example, with sampler.param=0.1 approximately 1 in 10 traces will be sampled.
• Rate Limiting (sampler.type=ratelimiting) sampler uses a leaky bucket rate limiter to ensure that traces are sampled with a certain constant rate. For example, when sampler.param=2.0 it will sample requests with the rate of 2 traces per second.
• Remote (sampler.type=remote, which is also the default) sampler consults Jaeger agent for the appropriate sampling strategy to use in the current service. This allows controlling the sampling strategies in the services from a central configuration in Jaeger backend, or even dynamically (see Adaptive Sampling ).

Adaptive Sampler

Adaptive sampler is a composite sampler that combines two functions:

• It makes sampling decisions on a per-operation basis, i.e. based on span operation name. This is especially useful in the API services whose endpoints may have very different traffic volumes and using a single probabilistic sampler for the whole service might starve (never sample) some of the low QPS endpoints.
• It supports a minimum guaranteed rate of sampling, such as always allowing up to N traces per seconds and then sampling anything above that with a certain probability (everything is per-operation, not per-service).

Per-operation parameters can be configured statically or pulled periodically from Jaeger backend with the help of Remote sampler. Adaptive sampler is designed to work with the upcoming Adaptive Sampling  feature of the Jaeger backend.

Collector Sampling Configuration

Collectors can be instantiated with static sampling strategies (which are propagated to the respective service if configured with Remote sampler) via the --sampling.strategies-file option. This option requires a path to a json file which have the sampling strategies defined.

Example strategies.json

{
  "service_strategies": [
    {
      "service": "foo",
      "type": "probabilistic",
      "param": 0.8,
      "operation_strategies": [
        {
          "operation": "op1",
          "type": "probabilistic",
          "param": 0.2
        },
        {
          "operation": "op2",
          "type": "probabilistic",
          "param": 0.4
        }
      ]
    },
    {
      "service": "bar",
      "type": "ratelimiting",
      "param": 5
    }
  ],
  "default_strategy": {
    "type": "probabilistic",
    "param": 0.5
  }
}

service_strategies defines service specific sampling strategies and operation_strategies defines operation specific sampling strategies. There are 2 types of strategies possible: probabilistic and ratelimiting which are described above (NOTE: ratelimiting is not supported for operation_strategies). default_strategy defines the catch-all sampling strategy that is propagated if the service is not included as part of service_strategies.

In the above example, all service foo operations are sampled probabilistically with a probability of 0.8 except op1 and op2 which are probabilistically sampled with a probability of 0.2 and 0.4 respectively. All operations for service bar are ratelimited at 5 traces per second. Any other service is probabilistically sampled with a probability of 0.5.