179 lines
6.2 KiB
Plaintext
179 lines
6.2 KiB
Plaintext
[[search-aggregations-metrics-cardinality-aggregation]]
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=== Cardinality Aggregation
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A `single-value` metrics aggregation that calculates an approximate count of
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distinct values. Values can be extracted either from specific fields in the
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document or generated by a script.
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Assume you are indexing books and would like to count the unique authors that
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match a query:
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[source,js]
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--------------------------------------------------
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{
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"aggs" : {
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"author_count" : {
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"cardinality" : {
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"field" : "author"
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}
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}
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}
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}
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--------------------------------------------------
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==== Precision control
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This aggregation also supports the `precision_threshold` and `rehash` options:
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experimental[The `precision_threshold` and `rehash` options are specific to the current internal implementation of the `cardinality` agg, which may change in the future]
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[source,js]
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--------------------------------------------------
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{
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"aggs" : {
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"author_count" : {
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"cardinality" : {
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"field" : "author_hash",
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"precision_threshold": 100, <1>
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"rehash": false <2>
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}
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}
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}
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}
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--------------------------------------------------
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<1> The `precision_threshold` options allows to trade memory for accuracy, and
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defines a unique count below which counts are expected to be close to
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accurate. Above this value, counts might become a bit more fuzzy. The maximum
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supported value is 40000, thresholds above this number will have the same
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effect as a threshold of 40000.
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Default value depends on the number of parent aggregations that multiple
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create buckets (such as terms or histograms).
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<2> If you computed a hash on client-side, stored it into your documents and want
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Elasticsearch to use them to compute counts using this hash function without
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rehashing values, it is possible to specify `rehash: false`. Default value is
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`true`. Please note that the hash must be indexed as a long when `rehash` is
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false.
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==== Counts are approximate
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Computing exact counts requires loading values into a hash set and returning its
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size. This doesn't scale when working on high-cardinality sets and/or large
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values as the required memory usage and the need to communicate those
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per-shard sets between nodes would utilize too many resources of the cluster.
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This `cardinality` aggregation is based on the
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http://static.googleusercontent.com/media/research.google.com/fr//pubs/archive/40671.pdf[HyperLogLog++]
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algorithm, which counts based on the hashes of the values with some interesting
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properties:
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* configurable precision, which decides on how to trade memory for accuracy,
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* excellent accuracy on low-cardinality sets,
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* fixed memory usage: no matter if there are tens or billions of unique values,
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memory usage only depends on the configured precision.
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For a precision threshold of `c`, the implementation that we are using requires
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about `c * 8` bytes.
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The following chart shows how the error varies before and after the threshold:
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image:images/cardinality_error.png[]
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For all 3 thresholds, counts have been accurate up to the configured threshold
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(although not guaranteed, this is likely to be the case). Please also note that
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even with a threshold as low as 100, the error remains under 5%, even when
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counting millions of items.
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==== Pre-computed hashes
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If you don't want Elasticsearch to re-compute hashes on every run of this
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aggregation, it is possible to use pre-computed hashes, either by computing a
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hash on client-side, indexing it and specifying `rehash: false`, or by using
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the special `murmur3` field mapper, typically in the context of a `multi-field`
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in the mapping:
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[source,js]
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--------------------------------------------------
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{
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"author": {
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"type": "string",
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"fields": {
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"hash": {
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"type": "murmur3"
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}
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}
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}
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}
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--------------------------------------------------
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With such a mapping, Elasticsearch is going to compute hashes of the `author`
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field at indexing time and store them in the `author.hash` field. This
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way, unique counts can be computed using the cardinality aggregation by only
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loading the hashes into memory, not the values of the `author` field, and
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without computing hashes on the fly:
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[source,js]
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--------------------------------------------------
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{
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"aggs" : {
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"author_count" : {
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"cardinality" : {
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"field" : "author.hash"
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}
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}
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}
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}
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--------------------------------------------------
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NOTE: `rehash` is automatically set to `false` when computing unique counts on
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a `murmur3` field.
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NOTE: Pre-computing hashes is usually only useful on very large and/or
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high-cardinality fields as it saves CPU and memory. However, on numeric
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fields, hashing is very fast and storing the original values requires as much
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or less memory than storing the hashes. This is also true on low-cardinality
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string fields, especially given that those have an optimization in order to
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make sure that hashes are computed at most once per unique value per segment.
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==== Script
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The `cardinality` metric supports scripting, with a noticeable performance hit
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however since hashes need to be computed on the fly.
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[source,js]
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--------------------------------------------------
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{
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"aggs" : {
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"author_count" : {
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"cardinality" : {
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"script": "doc['author.first_name'].value + ' ' + doc['author.last_name'].value"
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}
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}
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}
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}
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--------------------------------------------------
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TIP: The `script` parameter expects an inline script. Use `script_id` for indexed scripts and `script_file` for scripts in the `config/scripts/` directory.
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==== Missing value
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The `missing` parameter defines how documents that are missing a value should be treated.
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By default they will be ignored but it is also possible to treat them as if they
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had a value.
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[source,js]
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--------------------------------------------------
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{
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"aggs" : {
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"tag_cardinality" : {
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"cardinality" : {
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"field" : "tag",
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"missing": "N/A" <1>
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}
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}
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}
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}
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--------------------------------------------------
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<1> Documents without a value in the `tag` field will fall into the same bucket as documents that have the value `N/A`.
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