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docs/reference/aggregations/_snippets/search-aggregations-metrics-percentile-aggregation-approximate.md
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There are many different algorithms to calculate percentiles. The naive implementation simply stores all the values in a sorted array. To find the 50th percentile, you simply find the value that is at `my_array[count(my_array) * 0.5]`.
Clearly, the naive implementation does not scale — the sorted array grows linearly with the number of values in your dataset. To calculate percentiles across potentially billions of values in an Elasticsearch cluster, *approximate* percentiles are calculated.
Clearly, the naive implementation does not scale — the sorted array grows linearly with the number of values in your dataset. To calculate percentiles across potentially billions of values in an Elasticsearch cluster, *approximate* percentiles are calculated.
The algorithm used by the `percentile` metric is called TDigest (introduced by Ted Dunning in [Computing Accurate Quantiles using T-Digests](https://github.com/tdunning/t-digest/blob/master/docs/t-digest-paper/histo.pdf)).