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Description
hello
Use Case
Predictable retry intervals in microservices can lead to server overload during peak failures. Randomizing these intervals can mitigate such issues.
Current Solution
The existing ExponentialBackOff mechanism predictably increases intervals, which can exacerbate server load during concurrent retries.
Proposed Enhancement
Propose adding a randomizationFactor to ExponentialBackOff to allow interval fluctuations, enhancing load distribution and reducing server stress.
Benefits
- Collision Reduction: Randomizing intervals reduces peak load times by avoiding simultaneous retries.
- Load Smoothing: Helps even out the demands on backend services, enhancing overall system stability.
- Greater Control: Allows for precise adjustments to backoff settings, accommodating diverse application environments.
Implementation
This enhancement is designed to be fully backward compatible.
Here is the proposed change to the ExponentialBackOff class:
package org.springframework.util.backoff;
import org.springframework.util.Assert;
public class ExponentialBackOff implements BackOff {
// Existing fields...
private double randomizationFactor = 0.0; // Default to no randomization
// Existing constructors...
public ExponentialBackOff(long initialInterval, double multiplier, double randomizationFactor) {
checkMultiplier(multiplier);
this.initialInterval = initialInterval;
this.multiplier = multiplier;
this.randomizationFactor = randomizationFactor;
}
// Getter and setter for randomizationFactor...
private class ExponentialBackOffExecution implements BackOffExecution {
// Existing fields and methods...
private long applyRandomization(long interval) {
double random = (1 - randomizationFactor) + Math.random() * 2 * randomizationFactor;
return (long) (interval * random);
}
@Override
public long nextBackOff() {
long nextInterval = computeNextInterval();
return applyRandomization(nextInterval);
}
}
}test code
@Test
void withRandomizationFactor() {
ExponentialBackOff backOff = new ExponentialBackOff(1000L, 2.0, 0.5);
BackOffExecution execution = backOff.start();
long firstBackOff = execution.nextBackOff();
long secondBackOff = execution.nextBackOff();
// Check if the back off is within expected randomization range
assertThat(firstBackOff).isBetween(500L, 1500L);
assertThat(secondBackOff).isBetween(1000L, 3000L);
}
@Test
void randomizationFactorBounds() {
ExponentialBackOff backOff = new ExponentialBackOff();
assertThatIllegalArgumentException().isThrownBy(() ->
backOff.setRandomizationFactor(-0.1));
assertThatIllegalArgumentException().isThrownBy(() ->
backOff.setRandomizationFactor(1.1));
}
@Test
void randomizationEffectiveness() {
ExponentialBackOff backOff = new ExponentialBackOff(1000L, 2.0, 0.5);
BackOffExecution execution = backOff.start();
boolean different = false;
long previous = execution.nextBackOff();
for (int i = 0; i < 10; i++) {
long next = execution.nextBackOff();
if (previous != next) {
different = true;
break;
}
previous = next;
}
assertThat(different).isTrue();
}