fix: prevent Summary quantiles from collapsing to the minimum observation#2316
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fix: prevent Summary quantiles from collapsing to the minimum observation#2316manduinca wants to merge 1 commit into
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…tion CKMSQuantiles returned the minimum observation for every targeted quantile whenever 2*epsilon >= 1-quantile (e.g. quantile(0.9, 0.05) or quantile(0.99, 0.005)). At the boundary the error function permits a sample's uncertainty (delta) to reach the order of n at low ranks, which broke both the query and compression: - get() stopped at the first sample whose maximum rank exceeded desiredRank + f(desiredRank)/2 and returned the preceding sample. A freshly inserted low-rank sample (delta = f(r) - 1) then made the scan stop almost immediately, returning a value near the minimum. get() now returns the sample whose possible-rank interval is centered closest to the desired rank. - compress() bounded merges by the error function at the left edge only, where f is huge for low ranks, so it merged away all resolution between the median and the maximum. Merges are now bounded by the error function over the whole rank interval the merged sample would span, via a new f(lo, hi) overload (f(r) delegates to f(r, r), so single-rank behavior is unchanged). Adds regression tests for the boundary configuration. Fixes prometheus#2292 Signed-off-by: Jean Pierre Mandujano G. <jeanpierre.mandujano@gmail.com>
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July 19, 2026 23:51
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Fixes #2292
When a targeted quantile has
2*epsilon >= 1 - quantile, all reported quantiles could collapse to the minimum observation. The query scan stopped too early: a freshly inserted low-rank sample can carry adeltaon the order ofn, so the runningrank + g + deltaexceeded the target far sooner than it should and returned a value near the minimum.The fix has two parts, matching the diagnosis in the issue.
compress()now keeps a merged sample from spanning a quantile's target rank, and the query uses the minimum of the error function over a sample's rank interval[lo, hi]rather than at a single point. To support that I split the single-argumentf(r)intof(lo, hi)(the plain error function isf(r, r)), which is numerically identical for the existing call sites.Added a regression test that reproduces the collapse and asserts distinct quantiles.
CKMSQuantilesTestpasses (18 tests).