OpenSearch/docs/reference/search/aggregations/metrics/cardinality-aggregation.asciidoc

[[search-aggregations-metrics-cardinality-aggregation]]
=== Cardinality Aggregation

A `single-value` metrics aggregation that calculates an approximate count of
distinct values. Values can be extracted either from specific fields in the
document or generated by a script.

Assume you are indexing books and would like to count the unique authors that
match a query:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "author_count" : {
            "cardinality" : {
                "field" : "author"
            }
        }
    }
}
--------------------------------------------------

==== Precision control

experimental[]

This aggregation also supports the `precision_threshold` and `rehash` options:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "author_count" : {
            "cardinality" : {
                "field" : "author_hash",
                "precision_threshold": 100, <1>
                "rehash": false <2>
            }
        }
    }
}
--------------------------------------------------

<1> experimental[] The `precision_threshold` options allows to trade memory for accuracy, and
defines a unique count below which counts are expected to be close to
accurate. Above this value, counts might become a bit more fuzzy. The maximum
supported value is 40000, thresholds above this number will have the same
effect as a threshold of 40000.
Default value depends on the number of parent aggregations that multiple
create buckets (such as terms or histograms).
<2> experimental[] If you computed a hash on client-side, stored it into your documents and want
Elasticsearch to use them to compute counts using this hash function without
rehashing values, it is possible to specify `rehash: false`. Default value is
`true`. Please note that the hash must be indexed as a long when `rehash` is
false.

==== Counts are approximate

Computing exact counts requires loading values into a hash set and returning its
size. This doesn't scale when working on high-cardinality sets and/or large
values as the required memory usage and the need to communicate those
per-shard sets between nodes would utilize too many resources of the cluster.

This `cardinality` aggregation is based on the
http://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 interesting
properties:

 * configurable precision, which decides on how to trade memory for accuracy,
 * excellent accuracy on low-cardinality sets,
 * fixed memory usage: no matter if there are tens or billions of unique values,
   memory usage only depends on the configured precision.

For a precision threshold of `c`, the implementation that we are using requires
about `c * 8` bytes.

The following chart shows how the error varies before and after the threshold:

image:images/cardinality_error.png[]

For all 3 thresholds, counts have been accurate up to the configured threshold
(although not guaranteed, this is likely to be the case). Please also note that
even with a threshold as low as 100, the error remains under 5%, even when
counting millions of items.

==== Pre-computed hashes

If you don't want Elasticsearch to re-compute hashes on every run of this
aggregation, it is possible to use pre-computed hashes, either by computing a
hash on client-side, indexing it and specifying `rehash: false`, or by using
the special `murmur3` field mapper, typically in the context of a `multi-field`
in the mapping:

[source,js]
--------------------------------------------------
{
    "author": {
        "type": "string",
        "fields": {
            "hash": {
                "type": "murmur3"
            }
        }
    }
}
--------------------------------------------------

With such a mapping, Elasticsearch is going to compute hashes of the `author`
field at indexing time and store them in the `author.hash` field. This
way, unique counts can be computed using the cardinality aggregation by only
loading the hashes into memory, not the values of the `author` field, and
without computing hashes on the fly:

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "author_count" : {
            "cardinality" : {
                "field" : "author.hash"
            }
        }
    }
}
--------------------------------------------------

NOTE: `rehash` is automatically set to `false` when computing unique counts on
a `murmur3` field.

NOTE: Pre-computing hashes is usually only useful on very large and/or
high-cardinality fields as it saves CPU and memory. However, on numeric
fields, hashing is very fast and storing the original values requires as much
or less memory than storing the hashes. This is also true on low-cardinality
string fields, especially given that those have an optimization in order to
make sure that hashes are computed at most once per unique value per segment.

==== Script

The `cardinality` metric supports scripting, with a noticeable performance hit
however since hashes need to be computed on the fly.

[source,js]
--------------------------------------------------
{
    "aggs" : {
        "author_count" : {
            "cardinality" : {
                "script": "doc['author.first_name'].value + ' ' + doc['author.last_name'].value"
            }
        }
    }
}
--------------------------------------------------