Tag
histograms
- Mar 6, 2026 Histogram Buckets, the +Inf Bucket, and How Quantiles Are Interpolated Why histograms store counts per bucket rather than values, how cumulative bucket counts make merging trivial, and what linear interpolation inside a bucket assumes about the distribution. observability
- Nov 18, 2025 Selectivity Estimation from Histograms and Most-Common-Value Lists How a single-column statistic turns a predicate into a row count, why equidepth buckets are chosen over equiwidth, and where the estimate fails for correlated columns. databases
- Jun 27, 2024 Deriving Latency Targets from Percentile Distributions Why a percentile objective is really a tail-count budget, how bucket layout limits the achievable precision, and what a latency target costs in headroom. observability
- May 4, 2024 Exponential Histograms and the Mergeable Bucket Layout How boundaries derived from a growth factor give uniform relative error, why an offset and a scale factor are enough to merge two histograms, and where the resolution limits sit. observability
- Jul 6, 2023 Two-Sample Tests for Distribution Drift in a Column A mean and a count can hold steady while a column reshapes itself; comparing histograms detects that, but the test, the bins, and the sample size all change the answer. data engineering
- Aug 17, 2022 Aggregating Percentiles Across a Fleet Without Losing the Tail Why per-host percentiles cannot be combined directly, how cumulative bucketed histograms make merging exact, and what temporal aggregation does to the tail that spatial aggregation does not. performance engineering