Terms aggregation

The terms aggregation dynamically creates a bucket for each unique term of a field.

Parameters

The terms aggregation takes the following parameters.

Example

The following example uses the terms aggregation to find the number of documents per response code in web log data:

GET opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "response_codes": {
      "terms": {
        "field": "response.keyword",
        "size": 10
      }
    }
  }
}

Example response

...
"aggregations" : {
  "response_codes" : {
    "doc_count_error_upper_bound" : 0,
    "sum_other_doc_count" : 0,
    "buckets" : [
      {
        "key" : "200",
        "doc_count" : 12832
      },
      {
        "key" : "404",
        "doc_count" : 801
      },
      {
        "key" : "503",
        "doc_count" : 441
      }
    ]
  }
 }
}

The values are returned with the key key. doc_count specifies the number of documents in each bucket. By default, the buckets are sorted in descending order of doc-count.

It is possible to use terms to search for infrequent values by ordering the returned values by ascending count ("order": {"count": "asc"}). However, we strongly discourage this practice because it can lead to inaccurate results when multiple shards are involved. A term that is globally infrequent might not appear as infrequent on every individual shard or might be entirely absent from the least frequent results returned by some shards. Conversely, a term that appears infrequently on one shard might be common on another. In both scenarios, rare terms can be missed during shard-level aggregation, resulting in incorrect overall results. Instead of the terms aggregation, we recommend using the rare_terms aggregation, which is specifically designed to handle these cases more accurately.

The size and shard_size parameters

The number of buckets returned by the terms aggregation is controlled by the size parameter, which is 10 by default.

Additionally, the coordinating node responsible for the aggregation will prompt each shard for its top unique terms. The number of buckets returned by each shard is controlled by the shard_size parameter. This parameter is distinct from the size parameter and exists as a mechanism to increase the accuracy of the bucket document counts.

For example, imagine a scenario in which the size and shard_size parameters both have a value of 3. The terms aggregation prompts each shard for its top three unique terms. The coordinating node aggregates the results to compute the final result. If a shard contains an object that is not included in the top three, then it won’t show up in the response. However, increasing the shard_size value for this request will allow each shard to return a larger number of unique terms, increasing the likelihood that the coordinating node will receive all relevant results.

By default, the shard_size parameter is set to size * 1.5 + 10.

When using concurrent segment search, the shard_size parameter is also applied to each segment slice.

The shard_size parameter serves as a way to balance the performance and document count accuracy of the terms aggregation. Higher shard_size values will ensure higher document count accuracy but will result in higher memory and compute usage. Lower shard_size values will be more performant but will result in lower document count accuracy.

Document count error

The response also includes two keys named doc_count_error_upper_bound and sum_other_doc_count.

The terms aggregation returns the top unique terms. Therefore, if the data contains many unique terms, then some of them might not appear in the results. The sum_other_doc_count field represents the sum of the documents that are excluded from the response. In this case, the number is 0 because all of the unique values appear in the response.

The doc_count_error_upper_bound field represents the maximum possible count for a unique value that is excluded from the final results. Use this field to estimate the margin of error for the count.

The doc_count_error_upper_bound value and the concept of accuracy are only applicable to aggregations using the default sort order—by document count, descending. This is because when you sort by descending document count, any terms that were not returned are guaranteed to include equal or fewer documents than those terms that were returned. Based on this, you can compute the doc_count_error_upper_bound.

If the show_term_doc_count_error parameter is set to true, then the terms aggregation will show the doc_count_error_upper_bound computed for each unique bucket in addition to the overall value.

The min_doc_count and shard_min_doc_count parameters

You can use the min_doc_count parameter to filter out any unique terms with fewer than min_doc_count results. The min_doc_count threshold is applied only after merging the results retrieved from all of the shards. Each shard is unaware of the global document count for a given term. If there is a significant difference between the top shard_size globally frequent terms and the top terms local to a shard, you may receive unexpected results when using the min_doc_count parameter.

Separately, the shard_min_doc_count parameter is used to filter out the unique terms that a shard returns back to the coordinator with fewer than shard_min_doc_count results.

When using concurrent segment search, the shard_min_doc_count parameter is not applied to each segment slice. For more information, see the related GitHub issue.

Filtering values to a subset

You can use the include and exclude parameters to filter the term values that appear in the aggregation buckets. When both parameters are specified, include is evaluated first and then exclude is applied to the result.

Regular expression filtering

Both include and exclude parameters accept a regular expression string in Lucene’s regular expression syntax:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "success_codes": {
      "terms": {
        "field": "response.keyword",
        "include": "2.*",
        "exclude": "204"
      }
    }
  }
}

By default, the maximum regex string length is 1000 characters. You can change this limit using the index.max_regex_length index setting. For more information, see Index settings.

Exact value filtering

Both include and exclude parameters accept arrays of exact values:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "specific_codes": {
      "terms": {
        "field": "response.keyword",
        "include": ["200", "404", "503"]
      }
    }
  }
}

The include and exclude parameters must use the same format: either regex strings or arrays of exact values.

Paginating through all terms

When a field contains too many unique terms to retrieve in a single request, you can use partition-based filtering to retrieve all terms by sending multiple requests. Terms are assigned to partitions using a hash function, so the distribution is approximately even.

To retrieve all unique terms, send a number of requests equal to num_partitions. Specify partition and num_partitions in the include parameter. For each request, keep num_partitions the same and increment partition from 0 to num_partitions - 1:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "partitioned_terms": {
      "terms": {
        "field": "response.keyword",
        "size": 10000,
        "include": {
          "partition": 0,
          "num_partitions": 5
        }
      }
    }
  }
}

The include parameter can use only one format per request: a regex string, an array of values, or a partition object. Thus, you cannot combine filtering (regex- or array-based) with partition-based retrieval. When using partition-based retrieval, the exclude parameter is not supported.

Collect mode

There are two collect modes available: depth_first and breadth_first. The depth_first collection mode expands all branches of the aggregation tree in a depth-first manner and only performs pruning after the expansion is complete.

However, when using nested terms aggregations, the cardinality of the number of buckets returned is multiplied by the cardinality of the field at each level of nesting, making it easy to see combinatorial explosion in the bucket count as you nest aggregations.

You can use the breadth_first collection mode to address this issue. In this case, pruning will be applied to the first level of the aggregation tree before it is expanded to the next level, potentially greatly reducing the number of buckets computed.

Additionally, there is memory overhead associated with performing breadth_first collection, which is linearly related to the number of matching documents. This is because breadth_first collection works by caching and replaying the pruned set of buckets from the parent level.

Sort order

By default, buckets are sorted by _count descending (most documents first). You can change the sort order using the order parameter. The following example sorts response codes alphabetically:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "response_codes": {
      "terms": {
        "field": "response.keyword",
        "order": { "_key": "asc" }
      }
    }
  }
}

The response returns buckets sorted by key in ascending order:

{
  ...
  "aggregations" : {
    "response_codes" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "200",
          "doc_count" : 12832
        },
        {
          "key" : "404",
          "doc_count" : 801
        },
        {
          "key" : "503",
          "doc_count" : 441
        }
      ]
    }
  }
}

You can also sort by a subaggregation metric. The following example sorts response codes by average bytes descending:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "response_codes": {
      "terms": {
        "field": "response.keyword",
        "order": { "avg_bytes": "desc" }
      },
      "aggs": {
        "avg_bytes": {
          "avg": { "field": "bytes" }
        }
      }
    }
  }
}

To order by a metric nested deeper in the aggregation tree, use the > separator to specify the path. The following example sorts operating systems by average bytes of only their successful (200) responses:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "os": {
      "terms": {
        "field": "machine.os.keyword",
        "size": 3,
        "order": { "successful>avg_bytes": "desc" }
      },
      "aggs": {
        "successful": {
          "filter": { "term": { "response.keyword": "200" } },
          "aggs": {
            "avg_bytes": {
              "avg": { "field": "bytes" }
            }
          }
        }
      }
    }
  }
}

The response sorts OS buckets by the nested avg_bytes metric:

{
  ...
  "aggregations" : {
    "os" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 5639,
      "buckets" : [
        {
          "key" : "win xp",
          "doc_count" : 2885,
          "successful" : {
            "doc_count" : 2549,
            "avg_bytes" : {
              "value" : 6083.602196939976
            }
          }
        },
        {
          "key" : "ios",
          "doc_count" : 2737,
          "successful" : {
            "doc_count" : 2512,
            "avg_bytes" : {
              "value" : 5954.860270700637
            }
          }
        },
        {
          "key" : "win 8",
          "doc_count" : 2813,
          "successful" : {
            "doc_count" : 2585,
            "avg_bytes" : {
              "value" : 5910.602321083172
            }
          }
        }
      ]
    }
  }
}

Pipeline aggregations cannot be used for sorting.

Multi-field terms aggregation

The terms aggregation does not natively support multiple fields. To group by a combination of fields, use the multi_terms aggregation or a script that combines field values:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "os_and_response": {
      "terms": {
        "script": {
          "source": "doc['machine.os.keyword'].value + ' - ' + doc['response.keyword'].value"
        },
        "size": 5
      }
    }
  }
}

For better performance, consider using a multi_terms aggregation or creating a combined field at index time using copy_to.

Mixing field types across indexes

When the same field name has different numeric types across indexes (for example, long in one index and double in another), the terms aggregation promotes values to the broader type. Non-decimal values are cast to double, which can result in a loss of precision for values exceeding 2^53.

Missing values

The missing parameter assigns a value to documents that do not have the target field, placing them in the corresponding bucket. By default, documents without the field are excluded from the aggregation. The following example assigns "unknown" to any documents missing the machine.os.keyword field:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "os": {
      "terms": {
        "field": "machine.os.keyword",
        "missing": "unknown"
      }
    }
  }
}

Script

You can use a script instead of a field to compute term values dynamically. The following example classifies log entries into size categories based on the bytes field:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "byte_ranges": {
      "terms": {
        "script": {
          "source": "if (doc['bytes'].value < 5000) return 'small'; if (doc['bytes'].value < 10000) return 'medium'; return 'large';"
        }
      }
    }
  }
}

The response groups documents into computed categories:

{
  ...
  "aggregations" : {
    "byte_ranges" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "medium",
          "doc_count" : 6995
        },
        {
          "key" : "small",
          "doc_count" : 6377
        },
        {
          "key" : "large",
          "doc_count" : 702
        }
      ]
    }
  }
}

Value script

When both field and script are specified, the script acts as a value script and receives the field value as _value. The following example prefixes each response code with “HTTP “:

GET /opensearch_dashboards_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "responses_prefixed": {
      "terms": {
        "field": "response.keyword",
        "script": {
          "source": "'HTTP ' + _value"
        }
      }
    }
  }
}

The response shows the transformed keys:

{
  ...
  "aggregations" : {
    "responses_prefixed" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "HTTP 200",
          "doc_count" : 12832
        },
        {
          "key" : "HTTP 404",
          "doc_count" : 801
        },
        {
          "key" : "HTTP 503",
          "doc_count" : 441
        }
      ]
    }
  }
}

Accounting for preaggregated data

While the doc_count field provides a representation of the number of individual documents aggregated in a bucket, doc_count by itself does not have a way to correctly increment documents that store preaggregated data. To account for preaggregated data and accurately calculate the number of documents in a bucket, you can use the _doc_count field to add the number of documents in a single summary field. When a document includes the _doc_count field, all bucket aggregations recognize its value and increase the bucket doc_count cumulatively. Keep these considerations in mind when using the _doc_count field:

• The field does not support nested arrays; only positive integers can be used.
• If a document does not contain the _doc_count field, aggregation uses the document to increase the count by 1.

OpenSearch features that rely on an accurate document count illustrate the importance of using the _doc_count field. To see how this field can be used to support other search tools, refer to Index rollups, an OpenSearch feature for the Index Management (IM) plugin that stores documents with preaggregated data in rollup indexes.

Example request

PUT /my_index/_doc/1
{
  "response_code": 404,
  "date":"2022-08-05",
  "_doc_count": 20
}

PUT /my_index/_doc/2
{
  "response_code": 404,
  "date":"2022-08-06",
  "_doc_count": 10
}

PUT /my_index/_doc/3
{
  "response_code": 200,
  "date":"2022-08-06",
  "_doc_count": 300
}

GET /my_index/_search
{
  "size": 0,
  "aggs": {
    "response_codes": {
      "terms": {
        "field" : "response_code"
      }
    }
  }
}

Example response

{
  "took" : 20,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "response_codes" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : 200,
          "doc_count" : 300
        },
        {
          "key" : 404,
          "doc_count" : 30
        }
      ]
    }
  }
}