This article explains LeetCode problem 398—Random Pick Index—detailing two solutions: a hash‑map preprocessing method for fixed arrays and a reservoir‑sampling approach for streaming data, complete with Java, C++, and Python implementations and complexity analysis.
This article explores essential sampling strategies for big‑data environments—including simple random, reservoir, stratified, oversampling, undersampling, and weighted sampling—detailing their principles, algorithmic steps, advantages, drawbacks, and suitable application scenarios to help analysts choose the right method.
This article explains how adaptive sampling reduces the performance and storage impact of full‑link tracing by dynamically adjusting the sampling rate based on application QPS, outlines the mathematical model, discusses implementation details such as warm‑up, QPS lag, concurrency, and provides a Java reference sampler.
