70_Important_clauses.asciidoc

Most Important Queries

While Elasticsearch comes with many queries, you will use just a few frequently. We discuss them in much greater detail in [search-in-depth] but next we give you a quick introduction to the most important queries.

match_all Query

The match_all query simply matches all documents. It is the default query that is used if no query has been specified:

{ "match_all": {}}

This query is frequently used in combination with a filter—​for instance, to retrieve all emails in the inbox folder. All documents are considered to be equally relevant, so they all receive a neutral _score of 1.

match Query

The match query should be the standard query that you reach for whenever you want to query for a full-text or exact value in almost any field.

If you run a match query against a full-text field, it will analyze the query string by using the correct analyzer for that field before executing the search:

{ "match": { "tweet": "About Search" }}

If you use it on a field containing an exact value, such as a number, a date, a Boolean, or a not_analyzed string field, then it will search for that exact value:

{ "match": { "age":    26           }}
{ "match": { "date":   "2014-09-01" }}
{ "match": { "public": true         }}
{ "match": { "tag":    "full_text"  }}

Tip	For exact-value searches, you probably want to use a filter clause instead of a query, as a filter will be cached. We’ll see some filtering examples soon.

Unlike the query-string search that we showed in [search-lite], the match query does not use a query syntax like +user_id:2 +tweet:search. It just looks for the words that are specified. This means that it is safe to expose to your users via a search field; you control what fields they can query, and it is not prone to throwing syntax errors.

multi_match Query

The multi_match query allows you to run the same match query on multiple fields:

{
    "multi_match": {
        "query":    "full text search",
        "fields":   [ "title", "body" ]
    }
}

range Query

The range query allows you to find numbers or dates that fall into a specified range:

{
    "range": {
        "age": {
            "gte":  20,
            "lt":   30
        }
    }
}

The operators that it accepts are as follows:

gt

Greater than

gte

Greater than or equal to

lt

Less than

lte

Less than or equal to

term Query

The term query is used to search by exact values, be they numbers, dates, Booleans, or not_analyzed exact-value string fields:

{ "term": { "age":    26           }}
{ "term": { "date":   "2014-09-01" }}
{ "term": { "public": true         }}
{ "term": { "tag":    "full_text"  }}

The term query performs no analysis on the input text, so it will look for exactly the value that is supplied.

terms Query

The terms query is the same as the term query, but allows you to specify multiple values to match. If the field contains any of the specified values, the document matches:

{ "terms": { "tag": [ "search", "full_text", "nosql" ] }}

Like the term query, no analysis is performed on the input text. It is looking for exact matches (including differences in case, accents, spaces, etc).

exists and missing Queries

The exists and missing queries are used to find documents in which the specified field either has one or more values (exists) or doesn’t have any values (missing). It is similar in nature to IS_NULL (missing) and NOT IS_NULL (exists) in SQL:

{
    "exists":   {
        "field":    "title"
    }
}

These queries are frequently used to apply a condition only if a field is present, and to apply a different condition if it is missing.