Terms aggregation

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).

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.

The default shard_size is (size * 1.5 + 10).

doc_count values for a terms aggregation may be approximate. As a result, any sub-aggregations on the terms aggregation may also be approximate.

To calculate doc_count values, each shard provides its own top terms and document counts. The aggregation combines these shard-level results to calculate its final doc_count values. To measure the accuracy of doc_count values, the aggregation results include the following properties:

To get the doc_count_error_upper_bound for each term, set show_term_doc_count_error to true:

GET /_search
{
  "aggs": {
    "products": {
      "terms": {
        "field": "product",
        "size": 5,
        "show_term_doc_count_error": 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.

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.

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 possible to change this behaviour as documented below:

Ordering the buckets by their doc _count in an ascending manner:

GET /_search
{
  "aggs": {
    "genres": {
      "terms": {
        "field": "genre",
        "order": { "_count": "asc" }
      }
    }
  }
}

Ordering the buckets alphabetically by their terms in an ascending manner:

GET /_search
{
  "aggs": {
    "genres": {
      "terms": {
        "field": "genre",
        "order": { "_key": "asc" }
      }
    }
  }
}

Ordering the buckets by single value metrics sub-aggregation (identified by the aggregation name):

GET /_search
{
  "aggs": {
    "genres": {
      "terms": {
        "field": "genre",
        "order": { "max_play_count": "desc" }
      },
      "aggs": {
        "max_play_count": { "max": { "field": "play_count" } }
      }
    }
  }
}

Ordering the buckets by multi value metrics sub-aggregation (identified by the aggregation name):

GET /_search
{
  "aggs": {
    "genres": {
      "terms": {
        "field": "genre",
        "order": { "playback_stats.max": "desc" }
      },
      "aggs": {
        "playback_stats": { "stats": { "field": "play_count" } }
      }
    }
  }
}

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 be 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> ] ;

GET /_search
{
  "aggs": {
    "countries": {
      "terms": {
        "field": "artist.country",
        "order": { "rock>playback_stats.avg": "desc" }
      },
      "aggs": {
        "rock": {
          "filter": { "term": { "genre": "rock" } },
          "aggs": {
            "playback_stats": { "stats": { "field": "play_count" } }
          }
        }
      }
    }
  }
}

The above will sort the artist’s countries buckets based on the average play count among the rock songs.

Multiple criteria can be used to order the buckets by providing an array of order criteria such as the following:

GET /_search
{
  "aggs": {
    "countries": {
      "terms": {
        "field": "artist.country",
        "order": [ { "rock>playback_stats.avg": "desc" }, { "_count": "desc" } ]
      },
      "aggs": {
        "rock": {
          "filter": { "term": { "genre": "rock" } },
          "aggs": {
            "playback_stats": { "stats": { "field": "play_count" } }
          }
        }
      }
    }
  }
}

The above will sort the artist’s countries buckets based on the average play count among the rock songs and then by their doc_count in descending order.

It is possible to only return terms that match more than a configured number of hits using the min_doc_count option:

GET /_search
{
  "aggs": {
    "tags": {
      "terms": {
        "field": "tags",
        "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.

Use a runtime field if the data in your documents doesn’t exactly match what you’d like to aggregate. If, for example, "anthologies" need to be in a special category then you could run this:

GET /_search
{
  "size": 0,
  "runtime_mappings": {
    "normalized_genre": {
      "type": "keyword",
      "script": """
        String genre = doc['genre'].value;
        if (doc['product'].value.startsWith('Anthology')) {
          emit(genre + ' anthology');
        } else {
          emit(genre);
        }
      """
    }
  },
  "aggs": {
    "genres": {
      "terms": {
        "field": "normalized_genre"
      }
    }
  }
}

Which will look like:

{
  "aggregations": {
    "genres": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "electronic",
          "doc_count": 4
        },
        {
          "key": "rock",
          "doc_count": 3
        },
        {
          "key": "electronic anthology",
          "doc_count": 2
        },
        {
          "key": "jazz",
          "doc_count": 2
        }
      ]
    }
  },
  ...
}

This is a little slower because the runtime field has to access two fields instead of one and because there are some optimizations that work on non-runtime keyword fields that we have to give up for for runtime keyword fields. If you need the speed, you can index the normalized_genre field.

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 expression strings or arrays of exact values. Additionally, include clauses can filter using partition expressions.

GET /_search
{
  "aggs": {
    "tags": {
      "terms": {
        "field": "tags",
        "include": ".*sport.*",
        "exclude": "water_.*"
      }
    }
  }
}

GET /_search
{
  "aggs": {
    "JapaneseCars": {
      "terms": {
        "field": "make",
        "include": [ "mazda", "honda" ]
      }
    },
    "ActiveCarManufacturers": {
      "terms": {
        "field": "make",
        "exclude": [ "rover", "jensen" ]
      }
    }
  }
}

GET /_search
{
   "size": 0,
   "aggs": {
      "expired_sessions": {
         "terms": {
            "field": "account_id",
            "include": {
               "partition": 0,
               "num_partitions": 20
            },
            "size": 10000,
            "order": {
               "last_access": "asc"
            }
         },
         "aggs": {
            "last_access": {
               "max": {
                  "field": "access_date"
               }
            }
         }
      }
   }
}

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 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 three approaches that you can use to perform a terms agg across multiple fields:

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 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:

GET /_search
{
  "aggs": {
    "actors": {
      "terms": {
        "field": "actors",
        "size": 10
      },
      "aggs": {
        "costars": {
          "terms": {
            "field": "actors",
            "size": 5
          }
        }
      }
    }
  }
}

Even though the number of actors may be comparatively small and we want only 50 result buckets there is a combinatorial explosion of buckets during calculation - a single actor can 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 depth_first mode.

GET /_search
{
  "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. 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.

There are different mechanisms by which terms aggregations can be executed:

Elasticsearch tries to have sensible defaults so this is something that generally doesn’t need to be configured.

global_ordinals is the default option for keyword field, it uses global ordinals to allocates buckets dynamically so memory usage is linear to the number of values of the documents that are part of the aggregation scope.

map should only be considered when very few documents match a query. Otherwise the ordinals-based execution mode is significantly faster. By default, map is only used when running an aggregation on scripts, since they don’t have ordinals.

GET /_search
{
  "aggs": {
    "tags": {
      "terms": {
        "field": "tags",
        "execution_hint": "map" 
      }
    }
  }
}

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.

The missing parameter defines how documents that are missing a value should be treated. By default they will be ignored but it is also possible to treat them as if they had a value.

GET /_search
{
  "aggs": {
    "tags": {
      "terms": {
        "field": "tags",
        "missing": "N/A" 
      }
    }
  }
}