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Match Query

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Match Query

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match queries accept text/numerics/dates, analyzes them, and constructs a query. For example:

GET /_search
{
    "query": {
        "match" : {
            "message" : "this is a test"
        }
    }
}

Note, message is the name of a field, you can substitute the name of any field instead.

match

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The match query is of type boolean. It means that the text provided is analyzed and the analysis process constructs a boolean query from the provided text. The operator flag can be set to or or and to control the boolean clauses (defaults to or). The minimum number of optional should clauses to match can be set using the minimum_should_match parameter.

Here is an example when providing additional parameters (note the slight change in structure, message is the field name):

GET /_search
{
    "query": {
        "match" : {
            "message" : {
                "query" : "this is a test",
                "operator" : "and"
            }
        }
    }
}

The analyzer can be set to control which analyzer will perform the analysis process on the text. It defaults to the field explicit mapping definition, or the default search analyzer.

The lenient parameter can be set to true to ignore exceptions caused by data-type mismatches, such as trying to query a numeric field with a text query string. Defaults to false.

Fuzziness

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fuzziness allows fuzzy matching based on the type of field being queried. See Fuzziness for allowed settings.

The prefix_length and max_expansions can be set in this case to control the fuzzy process. If the fuzzy option is set the query will use top_terms_blended_freqs_${max_expansions} as its rewrite method the fuzzy_rewrite parameter allows to control how the query will get rewritten.

Fuzzy transpositions (ab → ba) are allowed by default but can be disabled by setting fuzzy_transpositions to false.

Note that fuzzy matching is not applied to terms with synonyms, as under the hood these terms are expanded to a special synonym query that blends term frequencies, which does not support fuzzy expansion.

GET /_search
{
    "query": {
        "match" : {
            "message" : {
                "query" : "this is a testt",
                "fuzziness": "AUTO"
            }
        }
    }
}

Zero terms query

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If the analyzer used removes all tokens in a query like a stop filter does, the default behavior is to match no documents at all. In order to change that the zero_terms_query option can be used, which accepts none (default) and all which corresponds to a match_all query.

GET /_search
{
    "query": {
        "match" : {
            "message" : {
                "query" : "to be or not to be",
                "operator" : "and",
                "zero_terms_query": "all"
            }
        }
    }
}

Cutoff frequency

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The match query supports a cutoff_frequency that allows specifying an absolute or relative document frequency where high frequency terms are moved into an optional subquery and are only scored if one of the low frequency (below the cutoff) terms in the case of an or operator or all of the low frequency terms in the case of an and operator match.

This query allows handling stopwords dynamically at runtime, is domain independent and doesn’t require a stopword file. It prevents scoring / iterating high frequency terms and only takes the terms into account if a more significant / lower frequency term matches a document. Yet, if all of the query terms are above the given cutoff_frequency the query is automatically transformed into a pure conjunction (and) query to ensure fast execution.

The cutoff_frequency can either be relative to the total number of documents if in the range [0..1) or absolute if greater or equal to 1.0.

Here is an example showing a query composed of stopwords exclusively:

GET /_search
{
    "query": {
        "match" : {
            "message" : {
                "query" : "to be or not to be",
                "cutoff_frequency" : 0.001
            }
        }
    }
}

The cutoff_frequency option operates on a per-shard-level. This means that when trying it out on test indexes with low document numbers you should follow the advice in Relevance is broken.

Synonyms

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The match query supports multi-terms synonym expansion with the synonym_graph token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms. For example, the following synonym: "ny, new york" would produce:

(ny OR ("new york"))

It is also possible to match multi terms synonyms with conjunctions instead:

GET /_search
{
   "query": {
       "match" : {
           "message": {
               "query" : "ny city",
               "auto_generate_synonyms_phrase_query" : false
           }
       }
   }
}

The example above creates a boolean query:

(ny OR (new AND york)) city

that matches documents with the term ny or the conjunction new AND york. By default the parameter auto_generate_synonyms_phrase_query is set to true.

Comparison to query_string / field

The match family of queries does not go through a "query parsing" process. It does not support field name prefixes, wildcard characters, or other "advanced" features. For this reason, chances of it failing are very small / non existent, and it provides an excellent behavior when it comes to just analyze and run that text as a query behavior (which is usually what a text search box does). Also, the phrase_prefix type can provide a great "as you type" behavior to automatically load search results.

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