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Boolean queries
You can perform a Boolean query with the bool
query type. A Boolean query compounds query clauses so you can combine multiple search queries with Boolean logic. To narrow or broaden your search results, use the bool
query clause rules.
As a compound query type, bool
allows you to construct an advanced query by combining several simple queries.
Use the following rules to define how to combine multiple sub-query clauses within a bool
query:
Clause rule | Behavior |
---|---|
must | Logical and operator. The results must match the queries in this clause. If you have multiple queries, all of them must match. |
must_not | Logical not operator. All matches are excluded from the results. |
should | Logical or operator. The results must match at least one of the queries, but, optionally, they can match more than one query. Each matching should clause increases the relevancy score. You can set the minimum number of queries that must match using the minimum_number_should_match parameter. |
minimum_number_should_match | Optional parameter for use with a should query clause. Specifies the minimum number of queries that the document must match for it to be returned in the results. The default value is 1. |
filter | Logical and operator that is applied first to reduce your dataset before applying the queries. A query within a filter clause is a yes or no option. If a document matches the query, it is returned in the results; otherwise, it is not. The results of a filter query are generally cached to allow for a faster return. Use the filter query to filter the results based on exact matches, ranges, dates, numbers, and so on. |
Boolean query structure
The structure of a Boolean query contains the bool
query type followed by clause rules, as follows:
GET _search
{
"query": {
"bool": {
"must": [
{}
],
"must_not": [
{}
],
"should": [
{}
],
"filter": {}
}
}
}
For example, assume you have the complete works of Shakespeare indexed in an OpenSearch cluster. You want to construct a single query that meets the following requirements:
- The
text_entry
field must contain the wordlove
and should contain eitherlife
orgrace
. - The
speaker
field must not containROMEO
. - Filter these results to the play
Romeo and Juliet
without affecting the relevancy score.
Use the following query:
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"text_entry": "love"
}
}
],
"should": [
{
"match": {
"text_entry": "life"
}
},
{
"match": {
"text_entry": "grace"
}
}
],
"minimum_should_match": 1,
"must_not": [
{
"match": {
"speaker": "ROMEO"
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
Sample output
{
"took": 12,
"timed_out": false,
"_shards": {
"total": 4,
"successful": 4,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 11.356054,
"hits": [
{
"_index": "shakespeare",
"_id": "88020",
"_score": 11.356054,
"_source": {
"type": "line",
"line_id": 88021,
"play_name": "Romeo and Juliet",
"speech_number": 19,
"line_number": "4.5.61",
"speaker": "PARIS",
"text_entry": "O love! O life! not life, but love in death!"
}
}
]
}
}
If you want to identify which of these clauses actually caused the matching results, name each query with the _name
parameter. To add the _name
parameter, change the field name in the match
query to an object:
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"text_entry": {
"query": "love",
"_name": "love-must"
}
}
}
],
"should": [
{
"match": {
"text_entry": {
"query": "life",
"_name": "life-should"
}
}
},
{
"match": {
"text_entry": {
"query": "grace",
"_name": "grace-should"
}
}
}
],
"minimum_should_match": 1,
"must_not": [
{
"match": {
"speaker": {
"query": "ROMEO",
"_name": "ROMEO-must-not"
}
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
OpenSearch returns a matched_queries
array that lists the queries that matched these results:
"matched_queries": [
"love-must",
"life-should"
]
If you remove the queries not in this list, you will still see the exact same result. By examining which should
clause matched, you can better understand the relevancy score of the results.
You can also construct complex Boolean expressions by nesting bool
queries. For example, to find a text_entry
field that matches (love
OR hate
) AND (life
OR grace
) in the play Romeo and Juliet
:
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"bool": {
"should": [
{
"match": {
"text_entry": "love"
}
},
{
"match": {
"text": "hate"
}
}
]
}
},
{
"bool": {
"should": [
{
"match": {
"text_entry": "life"
}
},
{
"match": {
"text": "grace"
}
}
]
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
Sample output
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 2,
"successful": 2,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 11.37006,
"hits": [
{
"_index": "shakespeare",
"_type": "doc",
"_id": "88020",
"_score": 11.37006,
"_source": {
"type": "line",
"line_id": 88021,
"play_name": "Romeo and Juliet",
"speech_number": 19,
"line_number": "4.5.61",
"speaker": "PARIS",
"text_entry": "O love! O life! not life, but love in death!"
}
}
]
}
}