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.

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).

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 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 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.

Examples

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

GET /_search
{
    "query": {
        "common": {
            "body": {
                "query": "this is bonsai cool",
                "cutoff_frequency": 0.001
            }
        }
    }
}

The number of terms which should match can be controlled with the 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:

GET /_search
{
    "query": {
        "common": {
            "body": {
                "query": "nelly the elephant as a cartoon",
                "cutoff_frequency": 0.001,
                "low_freq_operator": "and"
            }
        }
    }
}

which is roughly equivalent to:

GET /_search
{
    "query": {
        "bool": {
            "must": [
            { "term": { "body": "nelly"}},
            { "term": { "body": "elephant"}},
            { "term": { "body": "cartoon"}}
            ],
            "should": [
            { "term": { "body": "the"}},
            { "term": { "body": "as"}},
            { "term": { "body": "a"}}
            ]
        }
    }
}

Alternatively use minimum_should_match to specify a minimum number or percentage of low frequency terms which must be present, for instance:

GET /_search
{
    "query": {
        "common": {
            "body": {
                "query": "nelly the elephant as a cartoon",
                "cutoff_frequency": 0.001,
                "minimum_should_match": 2
            }
        }
    }
}

which is roughly equivalent to:

GET /_search
{
    "query": {
        "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"}}
                ]
        }
    }
}

A different 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):

GET /_search
{
    "query": {
        "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:

GET /_search
{
    "query": {
        "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 minimum_should_match for high frequency terms is when there are only high frequency terms:

GET /_search
{
    "query": {
        "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:

GET /_search
{
    "query": {
        "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 and analyzer as parameters.