The Circllhist algorithm is interesting, but I get an uneasy feeling from the use of a _relative error_ measure to evaluate the performance of quantile estimati

The Circllhist algorithm is interesting, but I get an uneasy feeling from the use of a _relative error_ measure to evaluate the performance of quantile estimation. Note that other authors don't use this measure. Dunning uses Mean Absolute Error in his latest T-digest paper: https://arxiv.org/pdf/1902.04023.pdf https://arxiv.org/pdf/1902.04023.pdf Cohen uses Normalized Root-Mean-Squared Error to evaluate sampling schemes, which are equally capable of estimating latency quantiles: https://dl.acm.org/doi/abs/10.1145/3234338 https://dl.acm.org/doi/abs/10.1145/3234338 The problem with Relative Error as a measure of accuracy is that it depends on the location of the distribution. The same size absolute error becomes a large relative error near zero and becomes a small relative error farther up the number line. Another thing about this study is that only one value for the T-digest quality is tested. Of course, the T-digest quality parameter equates directly with compressed size, so it's unsurprising that T-digest's size is fixed throughout the experiment. I also suspect that the choice of data set matters quite a lot in this study. If your latency values were clustered around a small range, then the algorithms like DDSketch and Circllhist will indeed have relative error less than 5% (as they prove) but T-digest will be significantly more accurate.