| 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667 | [discrete][[esql-agg-count-distinct]]=== `COUNT_DISTINCT`*Syntax*[source,esql]----COUNT_DISTINCT(column[, precision])----*Parameters*`column`::Column for which to count the number of distinct values.`precision`::Precision. Refer to <<esql-agg-count-distinct-approximate>>.*Description*Returns the approximate number of distinct values.[discrete][[esql-agg-count-distinct-approximate]]==== Counts are approximateComputing exact counts requires loading values into a set and returning itssize. This doesn't scale when working on high-cardinality sets and/or largevalues as the required memory usage and the need to communicate thoseper-shard sets between nodes would utilize too many resources of the cluster.This `COUNT_DISTINCT` function is based on thehttps://static.googleusercontent.com/media/research.google.com/fr//pubs/archive/40671.pdf[HyperLogLog++]algorithm, which counts based on the hashes of the values with some interestingproperties:include::../../aggregations/metrics/cardinality-aggregation.asciidoc[tag=explanation]The `COUNT_DISTINCT` function takes an optional second parameter to configure theprecision.*Supported types*Can take any field type as input.*Examples*[source.merge.styled,esql]----include::{esql-specs}/stats_count_distinct.csv-spec[tag=count-distinct]----[%header.monospaced.styled,format=dsv,separator=|]|===include::{esql-specs}/stats_count_distinct.csv-spec[tag=count-distinct-result]|===With the optional second parameter to configure the precision:[source.merge.styled,esql]----include::{esql-specs}/stats_count_distinct.csv-spec[tag=count-distinct-precision]----[%header.monospaced.styled,format=dsv,separator=|]|===include::{esql-specs}/stats_count_distinct.csv-spec[tag=count-distinct-precision-result]|===
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