io.opencensus.trace.samplers.ProbabilitySampler Maven / Gradle / Ivy
/*
* Copyright 2017, OpenCensus Authors
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package io.opencensus.trace.samplers;
import com.google.auto.value.AutoValue;
import io.opencensus.internal.Utils;
import io.opencensus.trace.Sampler;
import io.opencensus.trace.Span;
import io.opencensus.trace.SpanContext;
import io.opencensus.trace.SpanId;
import io.opencensus.trace.TraceId;
import java.util.List;
import javax.annotation.Nullable;
import javax.annotation.concurrent.Immutable;
/**
* We assume the lower 64 bits of the traceId's are randomly distributed around the whole (long)
* range. We convert an incoming probability into an upper bound on that value, such that we can
* just compare the absolute value of the id and the bound to see if we are within the desired
* probability range. Using the low bits of the traceId also ensures that systems that only use 64
* bit ID's will also work with this sampler.
*/
@AutoValue
@Immutable
abstract class ProbabilitySampler extends Sampler {
ProbabilitySampler() {}
abstract double getProbability();
abstract long getIdUpperBound();
/**
* Returns a new {@link ProbabilitySampler}. The probability of sampling a trace is equal to that
* of the specified probability.
*
* @param probability The desired probability of sampling. Must be within [0.0, 1.0].
* @return a new {@link ProbabilitySampler}.
* @throws IllegalArgumentException if {@code probability} is out of range
*/
static ProbabilitySampler create(double probability) {
Utils.checkArgument(
probability >= 0.0 && probability <= 1.0, "probability must be in range [0.0, 1.0]");
long idUpperBound;
// Special case the limits, to avoid any possible issues with lack of precision across
// double/long boundaries. For probability == 0.0, we use Long.MIN_VALUE as this guarantees
// that we will never sample a trace, even in the case where the id == Long.MIN_VALUE, since
// Math.Abs(Long.MIN_VALUE) == Long.MIN_VALUE.
if (probability == 0.0) {
idUpperBound = Long.MIN_VALUE;
} else if (probability == 1.0) {
idUpperBound = Long.MAX_VALUE;
} else {
idUpperBound = (long) (probability * Long.MAX_VALUE);
}
return new AutoValue_ProbabilitySampler(probability, idUpperBound);
}
@Override
public final boolean shouldSample(
@Nullable SpanContext parentContext,
@Nullable Boolean hasRemoteParent,
TraceId traceId,
SpanId spanId,
String name,
@Nullable List parentLinks) {
// If the parent is sampled keep the sampling decision.
if (parentContext != null && parentContext.getTraceOptions().isSampled()) {
return true;
}
if (parentLinks != null) {
// If any parent link is sampled keep the sampling decision.
for (Span parentLink : parentLinks) {
if (parentLink.getContext().getTraceOptions().isSampled()) {
return true;
}
}
}
// Always sample if we are within probability range. This is true even for child spans (that
// may have had a different sampling decision made) to allow for different sampling policies,
// and dynamic increases to sampling probabilities for debugging purposes.
// Note use of '<' for comparison. This ensures that we never sample for probability == 0.0,
// while allowing for a (very) small chance of *not* sampling if the id == Long.MAX_VALUE.
// This is considered a reasonable tradeoff for the simplicity/performance requirements (this
// code is executed in-line for every Span creation).
return Math.abs(traceId.getLowerLong()) < getIdUpperBound();
}
@Override
public final String getDescription() {
return String.format("ProbabilitySampler{%.6f}", getProbability());
}
}