From 218274e234f9725d6d971718e04ecc09b44e554c Mon Sep 17 00:00:00 2001 From: Santhi Prakash Date: Thu, 13 Aug 2026 03:11:37 +0000 Subject: [PATCH] docs(practices): fix three typos in histograms and summaries - Problem: three misspelled words on the histograms practices page (accordingy, accurace, clasic), the most-referenced page on histogram usage. - Fix: spell them correctly (accordingly, accurate, classic). - Verification: npm run lint clean (pre-existing PromMarkdown.tsx warning only). Signed-off-by: Santhi Prakash --- docs/practices/histograms.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/practices/histograms.md b/docs/practices/histograms.md index 3e0f6021e..3d6ecb1d2 100644 --- a/docs/practices/histograms.md +++ b/docs/practices/histograms.md @@ -270,7 +270,7 @@ to return an estimated fraction. This estimation is more accurate with higher bucket resolutions. If you already know in advance that, for example, you have an SLO to serve 95% of requests within 300ms, you could use the fixed bucket boundaries of a classic histogram to allow an accurate calculation. However, if -your SLO changes later, changing the fixed bucket layout accordingy will be +your SLO changes later, changing the fixed bucket layout accordingly will be quite tedious. (You have to change the instrumentation of your code. And you will run into the issues reconciling different bucket layouts as described above.) If you pick native histograms with the dynamic exponential buckets, you @@ -318,7 +318,7 @@ target and the tolerated duration: histogram_fraction(0.3, 1.2, sum by (job) (rate(http_request_duration_seconds[5m]))) / 2 In the “pure classic” case, you _must_ have buckets present at the exact -boundaries (giving you an accurace calculation in return). The corresponding +boundaries (giving you an accurate calculation in return). The corresponding PromQL expression looks quite different because the classic buckets are cumulative: @@ -468,7 +468,7 @@ algorithm will yield very accurate results for narrow distributions as in our example). Unfortunately, you cannot use a summary if you need to aggregate the observations from a number of instances. -Luckily, due to your appropriate choice of bucket boundaries for the clasic +Luckily, due to your appropriate choice of bucket boundaries for the classic histogram, in this contrived example of very sharp spikes in the distribution of observed values, the classic histogram was able to identify correctly if you were within or outside of your SLO (although it was bad in telling you how far