OpenSearch/docs/reference/query-dsl/common-terms-query.asciidoc

[[query-dsl-common-terms-query]]
=== Common Terms Query

The `common` terms query is a modern alternative to stopwords which
improves the precision and recall of search results (by taking stopwords
into account), without sacrificing performance.

[float]
==== The problem

Every term in a query has a cost. A search for `"The brown fox"`
requires three term queries, one for each of `"the"`, `"brown"` and
`"fox"`, all of which are executed against all documents in the index.
The query for `"the"` is likely to match many documents and thus has a
much smaller impact on relevance than the other two terms.

Previously, the solution to this problem was to ignore terms with high
frequency. By treating `"the"` as a _stopword_, we reduce the index size
and reduce the number of term queries that need to be executed.

The problem with this approach is that, while stopwords have a small
impact on relevance, they are still important. If we remove stopwords,
we lose precision, (eg we are unable to distinguish between `"happy"`
and `"not happy"`) and we lose recall (eg text like `"The The"` or
`"To be or not to be"` would simply not exist in the index).

[float]
==== The solution

The `common` terms query divides the query terms into two groups: more
important (ie _low frequency_ terms) and less important (ie _high
frequency_ terms which would previously have been stopwords).

First it searches for documents which match the more important terms.
These are the terms which appear in fewer documents and have a greater
impact on relevance.

Then, it executes a second query for the less important terms -- terms
which appear frequently and have a low impact on relevance. But instead
of calculating the relevance score for *all* matching documents, it only
calculates the `_score` for documents already matched by the first
query. In this way the high frequency terms can improve the relevance
calculation without paying the cost of poor performance.

If a query consists only of high frequency terms, then a single query is
executed as an `AND` (conjunction) query, in other words all terms are
required. Even though each individual term will match many documents,
the combination of terms narrows down the resultset to only the most
relevant. The single query can also be executed as an `OR` with a
specific
<<query-dsl-minimum-should-match,`minimum_should_match`>>,
in this case a high enough value should probably be used.

Terms are allocated to the high or low frequency groups based on the
`cutoff_frequency`, which can be specified as an absolute frequency
(`>=1`) or as a relative frequency (`0.0 .. 1.0`). (Remember that document
frequencies are computed on a per shard level as explained in the blog post
{defguide}/relevance-is-broken.html[Relevance is broken].)

Perhaps the most interesting property of this query is that it adapts to
domain specific stopwords automatically. For example, on a video hosting
site, common terms like `"clip"` or `"video"` will automatically behave
as stopwords without the need to maintain a manual list.

[float]
==== Examples

In this example, words that have a document frequency greater than 0.1%
(eg `"this"` and `"is"`) will be treated as _common terms_.

[source,js]
--------------------------------------------------
{
  "common": {
    "body": {
      "query":            "this is bonsai cool",
      "cutoff_frequency": 0.001
    }
  }
}
--------------------------------------------------

The number of terms which should match can be controlled with the
<<query-dsl-minimum-should-match,`minimum_should_match`>>
(`high_freq`, `low_freq`), `low_freq_operator` (default `"or"`) and
`high_freq_operator` (default `"or"`) parameters.

For low frequency terms, set the `low_freq_operator` to `"and"` to make
all terms required:

[source,js]
--------------------------------------------------
{
  "common": {
    "body": {
      "query":            "nelly the elephant as a cartoon",
      "cutoff_frequency": 0.001,
      "low_freq_operator" "and"
    }
  }
}
--------------------------------------------------

which is roughly equivalent to:

[source,js]
--------------------------------------------------
{
  "bool": {
    "must": [
      { "term": { "body": "nelly"}},
      { "term": { "body": "elephant"}},
      { "term": { "body": "cartoon"}}
    ],
    "should": [
      { "term": { "body": "the"}}
      { "term": { "body": "as"}}
      { "term": { "body": "a"}}
    ]
  }
}
--------------------------------------------------

Alternatively use
<<query-dsl-minimum-should-match,`minimum_should_match`>>
to specify a minimum number or percentage of low frequency terms which
must be present, for instance:

[source,js]
--------------------------------------------------
{
  "common": {
    "body": {
      "query":                "nelly the elephant as a cartoon",
      "cutoff_frequency":     0.001,
      "minimum_should_match": 2
    }
  }
}
--------------------------------------------------

which is roughly equivalent to:

[source,js]
--------------------------------------------------
{
  "bool": {
    "must": {
      "bool": {
        "should": [
          { "term": { "body": "nelly"}},
          { "term": { "body": "elephant"}},
          { "term": { "body": "cartoon"}}
        ],
        "minimum_should_match": 2
      }
    },
    "should": [
      { "term": { "body": "the"}}
      { "term": { "body": "as"}}
      { "term": { "body": "a"}}
    ]
  }
}
--------------------------------------------------

minimum_should_match

A different
<<query-dsl-minimum-should-match,`minimum_should_match`>>
can be applied for low and high frequency terms with the additional
`low_freq` and `high_freq` parameters Here is an example when providing
additional parameters (note the change in structure):

[source,js]
--------------------------------------------------
{
  "common": {
    "body": {
      "query":                "nelly the elephant not as a cartoon",
      "cutoff_frequency":     0.001,
      "minimum_should_match": {
          "low_freq" : 2,
          "high_freq" : 3
       }
    }
  }
}
--------------------------------------------------

which is roughly equivalent to:

[source,js]
--------------------------------------------------
{
  "bool": {
    "must": {
      "bool": {
        "should": [
          { "term": { "body": "nelly"}},
          { "term": { "body": "elephant"}},
          { "term": { "body": "cartoon"}}
        ],
        "minimum_should_match": 2
      }
    },
    "should": {
      "bool": {
        "should": [
          { "term": { "body": "the"}},
          { "term": { "body": "not"}},
          { "term": { "body": "as"}},
          { "term": { "body": "a"}}
        ],
        "minimum_should_match": 3
      }
    }
  }
}
--------------------------------------------------

In this case it means the high frequency terms have only an impact on
relevance when there are at least three of them. But the most
interesting use of the
<<query-dsl-minimum-should-match,`minimum_should_match`>>
for high frequency terms is when there are only high frequency terms:

[source,js]
--------------------------------------------------
{
  "common": {
    "body": {
      "query":                "how not to be",
      "cutoff_frequency":     0.001,
      "minimum_should_match": {
          "low_freq" : 2,
          "high_freq" : 3
       }
    }
  }
}
--------------------------------------------------

which is roughly equivalent to:

[source,js]
--------------------------------------------------
{
  "bool": {
    "should": [
      { "term": { "body": "how"}},
      { "term": { "body": "not"}},
      { "term": { "body": "to"}},
      { "term": { "body": "be"}}
    ],
    "minimum_should_match": "3<50%"
  }
}
--------------------------------------------------

The high frequency generated query is then slightly less restrictive
than with an `AND`.

The `common` terms query also supports `boost`, `analyzer` and
`disable_coord` as parameters.