Probabilistic Counting at Scale: Approximate vs Exact

Concepts: pyApproxVsExact, pyHyperLogLog, pySparkApprox

The most important staff-level judgment in frequency counting is knowing when 'good enough' is right. Exact counting requires memory proportional to cardinality. Approximate counting requires constant memory with a tunable error bound. Most business questions about frequency do not require exactness. 'How many distinct users visited today?' does not need to be precise to the person — 99% confidence within 2% is perfectly fine for capacity planning, anomaly detection, and trend analysis. Knowing when to make this call, and how to defend it to stakeholders, is what distinguishes a senior data engineer from a staff data engineer. The Accuracy vs Resource Tradeoff Spark's Approximate Aggregation How to Make the Approximate vs Exact Call