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[[search-aggregations-bucket-terms-aggregation]]
=== Terms Aggregation
A multi-bucket value source based aggregation where buckets are dynamically built - one per unique value.
Example:
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : { "field" : "gender" }
}
}
}
--------------------------------------------------
Response:
[source,js]
--------------------------------------------------
{
...
"aggregations" : {
"genders" : {
"buckets" : [
{
"key" : "male",
"doc_count" : 10
},
{
"key" : "female",
"doc_count" : 10
},
]
}
}
}
--------------------------------------------------
By default, the `terms` aggregation will return the buckets for the top ten terms ordered by the `doc_count`. One can
change this default behaviour by setting the `size` parameter.
==== Size
The `size` parameter can be set to define how many term buckets should be returned out of the overall terms list. By
default, the node coordinating the search process will request each shard to provide its own top `size` term buckets
and once all shards respond, it will reduce the results to the final list that will then be returned to the client.
This means that if the number of unique terms is greater than `size`, the returned list is slightly off and not accurate
(it could be that the term counts are slightly off and it could even be that a term that should have been in the top
size buckets was not returned). If set to `0`, the `size` will be set to `Integer.MAX_VALUE`.
==== Document counts are approximate
As described above, the document counts (and the results of any sub aggregations) in the terms aggregation are not always
accurate. This is because each shard provides its own view of what the ordered list of terms should be and these are
combined to give a final view. Consider the following scenario:
A request is made to obtain the top 5 terms in the field product, ordered by descending document count from an index with
3 shards. In this case each shard is asked to give its top 5 terms.
[source,js]
--------------------------------------------------
{
"aggs" : {
"products" : {
"terms" : {
"field" : "product",
"size" : 5
}
}
}
}
--------------------------------------------------
The terms for each of the three shards are shown below with their
respective document counts in brackets:
[width="100%",cols="^2,^2,^2,^2",options="header"]
|=========================================================
| | Shard A | Shard B | Shard C
| 1 | Product A (25) | Product A (30) | Product A (45)
| 2 | Product B (18) | Product B (25) | Product C (44)
| 3 | Product C (6) | Product F (17) | Product Z (36)
| 4 | Product D (3) | Product Z (16) | Product G (30)
| 5 | Product E (2) | Product G (15) | Product E (29)
| 6 | Product F (2) | Product H (14) | Product H (28)
| 7 | Product G (2) | Product I (10) | Product Q (2)
| 8 | Product H (2) | Product Q (6) | Product D (1)
| 9 | Product I (1) | Product J (8) |
| 10 | Product J (1) | Product C (4) |
|=========================================================
The shards will return their top 5 terms so the results from the shards will be:
[width="100%",cols="^2,^2,^2,^2",options="header"]
|=========================================================
| | Shard A | Shard B | Shard C
| 1 | Product A (25) | Product A (30) | Product A (45)
| 2 | Product B (18) | Product B (25) | Product C (44)
| 3 | Product C (6) | Product F (17) | Product Z (36)
| 4 | Product D (3) | Product Z (16) | Product G (30)
| 5 | Product E (2) | Product G (15) | Product E (29)
|=========================================================
Taking the top 5 results from each of the shards (as requested) and combining them to make a final top 5 list produces
the following:
[width="40%",cols="^2,^2"]
|=========================================================
| 1 | Product A (100)
| 2 | Product Z (52)
| 3 | Product C (50)
| 4 | Product G (45)
| 5 | Product B (43)
|=========================================================
Because Product A was returned from all shards we know that its document count value is accurate. Product C was only
returned by shards A and C so its document count is shown as 50 but this is not an accurate count. Product C exists on
shard B, but its count of 4 was not high enough to put Product C into the top 5 list for that shard. Product Z was also
returned only by 2 shards but the third shard does not contain the term. There is no way of knowing, at the point of
combining the results to produce the final list of terms, that there is an error in the document count for Product C and
not for Product Z. Product H has a document count of 44 across all 3 shards but was not included in the final list of
terms because it did not make it into the top five terms on any of the shards.
==== Shard Size
The higher the requested `size` is, the more accurate the results will be, but also, the more expensive it will be to
compute the final results (both due to bigger priority queues that are managed on a shard level and due to bigger data
transfers between the nodes and the client).
The `shard_size` parameter can be used to minimize the extra work that comes with bigger requested `size`. When defined,
it will determine how many terms the coordinating node will request from each shard. Once all the shards responded, the
coordinating node will then reduce them to a final result which will be based on the `size` parameter - this way,
one can increase the accuracy of the returned terms and avoid the overhead of streaming a big list of buckets back to
the client. If set to `0`, the `shard_size` will be set to `Integer.MAX_VALUE`.
NOTE: `shard_size` cannot be smaller than `size` (as it doesn't make much sense). When it is, elasticsearch will
override it and reset it to be equal to `size`.
added[1.1.0] It is possible to not limit the number of terms that are returned by setting `size` to `0`. Don't use this
on high-cardinality fields as this will kill both your CPU since terms need to be return sorted, and your network.
==== Calculating Document Count Error
coming[1.4.0]
There are two error values which can be shown on the terms aggregation. The first gives a value for the aggregation as
a whole which represents the maximum potential document count for a term which did not make it into the final list of
terms. This is calculated as the sum of the document count from the last term returned from each shard .For the example
given above the value would be 46 (2 + 15 + 29). This means that in the worst case scenario a term which was not returned
could have the 4th highest document count.
[source,js]
--------------------------------------------------
{
...
"aggregations" : {
"products" : {
"doc_count_error_upper_bound" : 46,
"buckets" : [
{
"key" : "Product A",
"doc_count" : 100
},
{
"key" : "Product Z",
"doc_count" : 52
},
...
]
}
}
}
--------------------------------------------------
The second error value can be enabled by setting the `show_term_doc_count_error` parameter to true. This shows an error value
for each term returned by the aggregation which represents the 'worst case' error in the document count and can be useful when
deciding on a value for the `shard_size` parameter. This is calculated by summing the document counts for the last term returned
by all shards which did not return the term. In the example above the error in the document count for Product C would be 15 as
Shard B was the only shard not to return the term and the document count of the last termit did return was 15. The actual document
count of Product C was 54 so the document count was only actually off by 4 even though the worst case was that it would be off by
15. Product A, however has an error of 0 for its document count, since every shard returned it we can be confident that the count
returned is accurate.
[source,js]
--------------------------------------------------
{
...
"aggregations" : {
"products" : {
"doc_count_error_upper_bound" : 46,
"buckets" : [
{
"key" : "Product A",
"doc_count" : 100,
"doc_count_error_upper_bound" : 0
},
{
"key" : "Product Z",
"doc_count" : 52,
"doc_count_error_upper_bound" : 2
},
...
]
}
}
}
--------------------------------------------------
These errors can only be calculated in this way when the terms are ordered by descending document count. When the aggregation is
ordered by the terms values themselves (either ascending or descending) there is no error in the document count since if a shard
does not return a particular term which appears in the results from another shard, it must not have that term in its index. When the
aggregation is either sorted by a sub aggregation or in order of ascending document count, the error in the document counts cannot be
determined and is given a value of -1 to indicate this.
==== Order
The order of the buckets can be customized by setting the `order` parameter. By default, the buckets are ordered by
their `doc_count` descending. It is also possible to change this behaviour as follows:
Ordering the buckets by their `doc_count` in an ascending manner:
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"field" : "gender",
"order" : { "_count" : "asc" }
}
}
}
}
--------------------------------------------------
Ordering the buckets alphabetically by their terms in an ascending manner:
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"field" : "gender",
"order" : { "_term" : "asc" }
}
}
}
}
--------------------------------------------------
Ordering the buckets by single value metrics sub-aggregation (identified by the aggregation name):
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"field" : "gender",
"order" : { "avg_height" : "desc" }
},
"aggs" : {
"avg_height" : { "avg" : { "field" : "height" } }
}
}
}
}
--------------------------------------------------
Ordering the buckets by multi value metrics sub-aggregation (identified by the aggregation name):
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"field" : "gender",
"order" : { "height_stats.avg" : "desc" }
},
"aggs" : {
"height_stats" : { "stats" : { "field" : "height" } }
}
}
}
}
--------------------------------------------------
It is also possible to order the buckets based on a "deeper" aggregation in the hierarchy. This is supported as long
as the aggregations path are of a single-bucket type, where the last aggregation in the path may either by a single-bucket
one or a metrics one. If it's a single-bucket type, the order will be defined by the number of docs in the bucket (i.e. `doc_count`),
in case it's a metrics one, the same rules as above apply (where the path must indicate the metric name to sort by in case of
a multi-value metrics aggregation, and in case of a single-value metrics aggregation the sort will be applied on that value).
The path must be defined in the following form:
--------------------------------------------------
AGG_SEPARATOR := '>'
METRIC_SEPARATOR := '.'
AGG_NAME := <the name of the aggregation>
METRIC := <the name of the metric (in case of multi-value metrics aggregation)>
PATH := <AGG_NAME>[<AGG_SEPARATOR><AGG_NAME>]*[<METRIC_SEPARATOR><METRIC>]
--------------------------------------------------
[source,js]
--------------------------------------------------
{
"aggs" : {
"countries" : {
"terms" : {
"field" : "address.country",
"order" : { "females>height_stats.avg" : "desc" }
},
"aggs" : {
"females" : {
"filter" : { "term" : { "gender" : { "female" }}},
"aggs" : {
"height_stats" : { "stats" : { "field" : "height" }}
}
}
}
}
}
}
--------------------------------------------------
The above will sort the countries buckets based on the average height among the female population.
==== Minimum document count
It is possible to only return terms that match more than a configured number of hits using the `min_doc_count` option:
[source,js]
--------------------------------------------------
{
"aggs" : {
"tags" : {
"terms" : {
"field" : "tag",
"min_doc_count": 10
}
}
}
}
--------------------------------------------------
The above aggregation would only return tags which have been found in 10 hits or more. Default value is `1`.
Terms are collected and ordered on a shard level and merged with the terms collected from other shards in a second step. However, the shard does not have the information about the global document count available. The decision if a term is added to a candidate list depends only on the order computed on the shard using local shard frequencies. The `min_doc_count` criterion is only applied after merging local terms statistics of all shards. In a way the decision to add the term as a candidate is made without being very _certain_ about if the term will actually reach the required `min_doc_count`. This might cause many (globally) high frequent terms to be missing in the final result if low frequent terms populated the candidate lists. To avoid this, the `shard_size` parameter can be increased to allow more candidate terms on the shards. However, this increases memory consumption and network traffic.
added[1.2.0] `shard_min_doc_count` parameter
The parameter `shard_min_doc_count` regulates the _certainty_ a shard has if the term should actually be added to the candidate list or not with respect to the `min_doc_count`. Terms will only be considered if their local shard frequency within the set is higher than the `shard_min_doc_count`. If your dictionary contains many low frequent terms and you are not interested in those (for example misspellings), then you can set the `shard_min_doc_count` parameter to filter out candidate terms on a shard level that will with a resonable certainty not reach the required `min_doc_count` even after merging the local counts. `shard_min_doc_count` is set to `0` per default and has no effect unless you explicitly set it.
NOTE: Setting `min_doc_count`=`0` will also return buckets for terms that didn't match any hit. However, some of
the returned terms which have a document count of zero might only belong to deleted documents, so there is
no warranty that a `match_all` query would find a positive document count for those terms.
WARNING: When NOT sorting on `doc_count` descending, high values of `min_doc_count` may return a number of buckets
which is less than `size` because not enough data was gathered from the shards. Missing buckets can be
back by increasing `shard_size`.
Setting `shard_min_doc_count` too high will cause terms to be filtered out on a shard level. This value should be set much lower than `min_doc_count/#shards`.
[[search-aggregations-bucket-terms-aggregation-script]]
==== Script
Generating the terms using a script:
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"script" : "doc['gender'].value"
}
}
}
}
--------------------------------------------------
==== Value Script
[source,js]
--------------------------------------------------
{
"aggs" : {
"genders" : {
"terms" : {
"field" : "gender",
"script" : "'Gender: ' +_value"
}
}
}
}
--------------------------------------------------
==== Filtering Values
It is possible to filter the values for which buckets will be created. This can be done using the `include` and
`exclude` parameters which are based on regular expressions.
[source,js]
--------------------------------------------------
{
"aggs" : {
"tags" : {
"terms" : {
"field" : "tags",
"include" : ".*sport.*",
"exclude" : "water_.*"
}
}
}
}
--------------------------------------------------
In the above example, buckets will be created for all the tags that has the word `sport` in them, except those starting
with `water_` (so the tag `water_sports` will no be aggregated). The `include` regular expression will determine what
values are "allowed" to be aggregated, while the `exclude` determines the values that should not be aggregated. When
both are defined, the `exclude` has precedence, meaning, the `include` is evaluated first and only then the `exclude`.
The regular expression are based on the Java(TM) http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html[Pattern],
and as such, they it is also possible to pass in flags that will determine how the compiled regular expression will work:
[source,js]
--------------------------------------------------
{
"aggs" : {
"tags" : {
"terms" : {
"field" : "tags",
"include" : {
"pattern" : ".*sport.*",
"flags" : "CANON_EQ|CASE_INSENSITIVE" <1>
},
"exclude" : {
"pattern" : "water_.*",
"flags" : "CANON_EQ|CASE_INSENSITIVE"
}
}
}
}
}
--------------------------------------------------
<1> the flags are concatenated using the `|` character as a separator
The possible flags that can be used are:
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#CANON_EQ[`CANON_EQ`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#CASE_INSENSITIVE[`CASE_INSENSITIVE`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#COMMENTS[`COMMENTS`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#DOTALL[`DOTALL`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#LITERAL[`LITERAL`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#MULTILINE[`MULTILINE`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#UNICODE_CASE[`UNICODE_CASE`],
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#UNICODE_CHARACTER_CLASS[`UNICODE_CHARACTER_CLASS`] and
http://docs.oracle.com/javase/7/docs/api/java/util/regex/Pattern.html#UNIX_LINES[`UNIX_LINES`]
==== Multi-field terms aggregation
The `terms` aggregation does not support collecting terms from multiple fields
in the same document. The reason is that the `terms` agg doesn't collect the
string term values themselves, but rather uses
<<search-aggregations-bucket-terms-aggregation-execution-hint,global ordinals>>
to produce a list of all of the unique values in the field. Global ordinals
results in an important performance boost which would not be possible across
multiple fields.
There are two approaches that you can use to perform a `terms` agg across
multiple fields:
<<search-aggregations-bucket-terms-aggregation-script,Script>>::
Use a script to retrieve terms from multiple fields. This disables the global
ordinals optimization and will be slower than collecting terms from a single
field, but it gives you the flexibility to implement this option at search
time.
<<copy-to,`copy_to` field>>::
If you know ahead of time that you want to collect the terms from two or more
fields, then use `copy_to` in your mapping to create a new dedicated field at
index time which contains the values from both fields. You can aggregate on
this single field, which will benefit from the global ordinals optimization.
==== Collect mode
added[1.3.0] Deferring calculation of child aggregations
For fields with many unique terms and a small number of required results it can be more efficient to delay the calculation
of child aggregations until the top parent-level aggs have been pruned. Ordinarily, all branches of the aggregation tree
are expanded in one depth-first pass and only then any pruning occurs. In some rare scenarios this can be very wasteful and can hit memory constraints.
An example problem scenario is querying a movie database for the 10 most popular actors and their 5 most common co-stars:
[source,js]
--------------------------------------------------
{
"aggs" : {
"actors" : {
"terms" : {
"field" : "actors",
"size" : 10
},
"aggs" : {
"costars" : {
"terms" : {
"field" : "actors",
"size" : 5
}
}
}
}
}
}
--------------------------------------------------
Even though the number of movies may be comparatively small and we want only 50 result buckets there is a combinatorial explosion of buckets
during calculation - a single movie will produce n² buckets where n is the number of actors. The sane option would be to first determine
the 10 most popular actors and only then examine the top co-stars for these 10 actors. This alternative strategy is what we call the `breadth_first` collection
mode as opposed to the default `depth_first` mode:
[source,js]
--------------------------------------------------
{
"aggs" : {
"actors" : {
"terms" : {
"field" : "actors",
"size" : 10,
"collect_mode" : "breadth_first"
},
"aggs" : {
"costars" : {
"terms" : {
"field" : "actors",
"size" : 5
}
}
}
}
}
}
--------------------------------------------------
When using `breadth_first` mode the set of documents that fall into the uppermost buckets are
cached for subsequent replay so there is a memory overhead in doing this which is linear with the number of matching documents.
In most requests the volume of buckets generated is smaller than the number of documents that fall into them so the default `depth_first`
collection mode is normally the best bet but occasionally the `breadth_first` strategy can be significantly more efficient. Currently
elasticsearch will always use the `depth_first` collect_mode unless explicitly instructed to use `breadth_first` as in the above example.
Note that the `order` parameter can still be used to refer to data from a child aggregation when using the `breadth_first` setting - the parent
aggregation understands that this child aggregation will need to be called first before any of the other child aggregations.
WARNING: It is not possible to nest aggregations such as `top_hits` which require access to match score information under an aggregation that uses
the `breadth_first` collection mode. This is because this would require a RAM buffer to hold the float score value for every document and
this would typically be too costly in terms of RAM.
[[search-aggregations-bucket-terms-aggregation-execution-hint]]
==== Execution hint
added[1.2.0] Added the `global_ordinals`, `global_ordinals_hash` and `global_ordinals_low_cardinality` execution modes
deprecated[1.3.0] Removed the `ordinals` execution mode
There are different mechanisms by which terms aggregations can be executed:
- by using field values directly in order to aggregate data per-bucket (`map`)
- by using ordinals of the field and preemptively allocating one bucket per ordinal value (`global_ordinals`)
- by using ordinals of the field and dynamically allocating one bucket per ordinal value (`global_ordinals_hash`)
- by using per-segment ordinals to compute counts and remap these counts to global counts using global ordinals (`global_ordinals_low_cardinality`)
Elasticsearch tries to have sensible defaults so this is something that generally doesn't need to be configured.
`map` should only be considered when very few documents match a query. Otherwise the ordinals-based execution modes
are significantly faster. By default, `map` is only used when running an aggregation on scripts, since they don't have
ordinals.
`global_ordinals_low_cardinality` only works for leaf terms aggregations but is usually the fastest execution mode. Memory
usage is linear with the number of unique values in the field, so it is only enabled by default on low-cardinality fields.
`global_ordinals` is the second fastest option, but the fact that it preemptively allocates buckets can be memory-intensive,
especially if you have one or more sub aggregations. It is used by default on top-level terms aggregations.
`global_ordinals_hash` on the contrary to `global_ordinals` and `global_ordinals_low_cardinality` allocates buckets dynamically
so memory usage is linear to the number of values of the documents that are part of the aggregation scope. It is used by default
in inner aggregations.
[source,js]
--------------------------------------------------
{
"aggs" : {
"tags" : {
"terms" : {
"field" : "tags",
"execution_hint": "map" <1>
}
}
}
}
--------------------------------------------------
<1> the possible values are `map`, `global_ordinals`, `global_ordinals_hash` and `global_ordinals_low_cardinality`
Please note that Elasticsearch will ignore this execution hint if it is not applicable and that there is no backward compatibility guarantee on these hints.
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