Unlike the terms aggregation it is currently not possible to use script-generated terms for counting purposes. Because of the way the significant_terms aggregation must consider both foreground and background frequencies it would be prohibitively expensive to use a script on the entire index to obtain background frequencies for comparisons. Also DocValues are not supported as sources of term data for similar reasons.

Floating point fields are currently not supported as the subject of significant_terms analysis. While integer or long fields can be used to represent concepts like bank account numbers or category numbers which can be interesting to track, floating point fields are usually used to represent quantities of something. As such, individual floating point terms are not useful for this form of frequency analysis.

If there is the equivalent of a match_all query or no query criteria providing a subset of the index the significant_terms aggregation should not be used as the top-most aggregation - in this scenario the foreground set is exactly the same as the background set and so there is no difference in document frequencies to observe and from which to make sensible suggestions.

Another consideration is that the significant_terms aggregation produces many candidate results at shard level that are only later pruned on the reducing node once all statistics from all shards are merged. As a result, it can be inefficient and costly in terms of RAM to embed large child aggregations under a significant_terms aggregation that later discards many candidate terms. It is advisable in these cases to perform two searches - the first to provide a rationalized list of significant_terms and then add this shortlist of terms to a second query to go back and fetch the required child aggregations.

The counts of how many documents contain a term provided in results are based on summing the samples returned from each shard and as such may be:

Like most design decisions, this is the basis of a trade-off in which we have chosen to provide fast performance at the cost of some (typically small) inaccuracies. However, the size and shard size settings covered in the next section provide tools to help control the accuracy levels.

The JLH score can be used as a significance score by adding the parameter

         "jlh": {
         }

The scores are derived from the doc frequencies in foreground and background sets. The absolute change in popularity (foregroundPercent - backgroundPercent) would favor common terms whereas the relative change in popularity (foregroundPercent/ backgroundPercent) would favor rare terms. Rare vs common is essentially a precision vs recall balance and so the absolute and relative changes are multiplied to provide a sweet spot between precision and recall.

Mutual information as described in "Information Retrieval", Manning et al., Chapter 13.5.1 can be used as significance score by adding the parameter

         "mutual_information": {
              "include_negatives": true
         }

Mutual information does not differentiate between terms that are descriptive for the subset or for documents outside the subset. The significant terms therefore can contain terms that appear more or less frequent in the subset than outside the subset. To filter out the terms that appear less often in the subset than in documents outside the subset, include_negatives can be set to false.

Per default, the assumption is that the documents in the bucket are also contained in the background. If instead you defined a custom background filter that represents a different set of documents that you want to compare to, set

"background_is_superset": false

Chi square as described in "Information Retrieval", Manning et al., Chapter 13.5.2 can be used as significance score by adding the parameter

         "chi_square": {
         }

Chi square behaves like mutual information and can be configured with the same parameters include_negatives and background_is_superset.

Google normalized distance as described in "The Google Similarity Distance", Cilibrasi and Vitanyi, 2007 (http://arxiv.org/pdf/cs/0412098v3.pdf) can be used as significance score by adding the parameter

         "gnd": {
         }

gnd also accepts the background_is_superset parameter.

A simple calculation of the number of documents in the foreground sample with a term divided by the number of documents in the background with the term. By default this produces a score greater than zero and less than one.

The benefit of this heuristic is that the scoring logic is simple to explain to anyone familiar with a "per capita" statistic. However, for fields with high cardinality there is a tendency for this heuristic to select the rarest terms such as typos that occur only once because they score 1/1 = 100%.

It would be hard for a seasoned boxer to win a championship if the prize was awarded purely on the basis of percentage of fights won - by these rules a newcomer with only one fight under his belt would be impossible to beat. Multiple observations are typically required to reinforce a view so it is recommended in these cases to set both min_doc_count and shard_min_doc_count to a higher value such as 10 in order to filter out the low-frequency terms that otherwise take precedence.

         "percentage": {
         }

Roughly, mutual_information prefers high frequent terms even if they occur also frequently in the background. For example, in an analysis of natural language text this might lead to selection of stop words. mutual_information is unlikely to select very rare terms like misspellings. gnd prefers terms with a high co-occurrence and avoids selection of stopwords. It might be better suited for synonym detection. However, gnd has a tendency to select very rare terms that are, for example, a result of misspelling. chi_square and jlh are somewhat in-between.

It is hard to say which one of the different heuristics will be the best choice as it depends on what the significant terms are used for (see for example [Yang and Pedersen, "A Comparative Study on Feature Selection in Text Categorization", 1997](http://courses.ischool.berkeley.edu/i256/f06/papers/yang97comparative.pdf) for a study on using significant terms for feature selection for text classification).

If none of the above measures suits your usecase than another option is to implement a custom significance measure:

Customized scores can be implemented via a script:

            "script_heuristic": {
              "script": {
                "lang": "painless",
                "source": "params._subset_freq/(params._superset_freq - params._subset_freq + 1)"
              }
            }

Scripts can be inline (as in above example), indexed or stored on disk. For details on the options, see script documentation.

Available parameters in the script are

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 term buckets and once all shards respond, it will reduce the results to the final list that will then be returned to the client. If the number of unique terms is greater than size, the returned list can be 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).

To ensure better accuracy a multiple of the final size is used as the number of terms to request from each shard (2 * (size * 1.5 + 10)). To take manual control of this setting the shard_size parameter can be used to control the volumes of candidate terms produced by each shard.

Low-frequency terms can turn out to be the most interesting ones once all results are combined so the significant_terms aggregation can produce higher-quality results when the shard_size parameter is set to values significantly higher than the size setting. This ensures that a bigger volume of promising candidate terms are given a consolidated review by the reducing node before the final selection. Obviously large candidate term lists will cause extra network traffic and RAM usage so this is quality/cost trade off that needs to be balanced. If shard_size is set to -1 (the default) then shard_size will be automatically estimated based on the number of shards and the size parameter.

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.

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" : {
            "significant_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 3.

Terms that score highly will be collected 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 term frequencies available. The decision if a term is added to a candidate list depends only on the score computed on the shard using local shard frequencies, not the global frequencies of the word. 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 but high scoring 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.

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 words and you are not interested in these (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 reasonable certainty not reach the required min_doc_count even after merging the local frequencies. shard_min_doc_count is set to 1 per default and has no effect unless you explicitly set it.

Setting min_doc_count to 1 is generally not advised as it tends to return terms that are typos or other bizarre curiosities. Finding more than one instance of a term helps reinforce that, while still rare, the term was not the result of a one-off accident. The default value of 3 is used to provide a minimum weight-of-evidence. Setting shard_min_doc_count too high will cause significant candidate terms to be filtered out on a shard level. This value should be set much lower than min_doc_count/#shards.

The default source of statistical information for background term frequencies is the entire index and this scope can be narrowed through the use of a background_filter to focus in on significant terms within a narrower context:

GET /_search
{
    "query" : {
        "match" : {
            "city" : "madrid"
        }
    },
    "aggs" : {
        "tags" : {
            "significant_terms" : {
                "field" : "tag",
                "background_filter": {
                        "term" : { "text" : "spain"}
                }
            }
        }
    }
}

The above filter would help focus in on terms that were peculiar to the city of Madrid rather than revealing terms like "Spanish" that are unusual in the full index’s worldwide context but commonplace in the subset of documents containing the word "Spain".

Use of background filters will slow the query as each term’s postings must be filtered to determine a frequency

It is possible (although rarely required) to filter the values for which buckets will be created. This can be done using the include and exclude parameters which are based on a regular expression string or arrays of exact terms. This functionality mirrors the features described in the terms aggregation documentation.