Sampling Strategies Deep Dive

Introduction

In observability, sampling strategies are crucial for efficiently managing the volume of data generated by systems. This lesson explores various sampling strategies, their definitions, and their applications in tracing distributed systems.

Key Concepts

Sampling Techniques

1. Fixed Rate Sampling

In fixed rate sampling, a predetermined percentage of all requests are collected. This approach is simple to implement but may miss important anomalies.

const sampleRate = 0.1; // 10% sampling
if (Math.random() < sampleRate) {
    captureTrace(request);
}

2. Adaptive Sampling

Adaptive sampling adjusts the sampling rate based on the current traffic patterns. This technique is useful in scenarios with fluctuating loads.

let sampleRate = 0.1; // initial rate
function adjustSampleRate(currentLoad) {
    if (currentLoad > threshold) {
        sampleRate = Math.max(0.01, sampleRate - 0.05); // decrease sampling
    } else {
        sampleRate = Math.min(1, sampleRate + 0.05); // increase sampling
    }
}
if (Math.random() < sampleRate) {
    captureTrace(request);
}

Best Practices

• Establish clear objectives for what data you need to collect.
• Choose a sampling strategy that aligns with your system's load and performance characteristics.
• Monitor the effectiveness of your sampling strategy and adjust as necessary.
• Consider combining different sampling techniques for optimal data collection.

FAQ