Terms aggregations

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

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.

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.

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.

Account for pre-aggregated 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 pre-aggregated data. To account for pre-aggregated 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 pre-aggregated 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
        }
      ]
    }
  }
}