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_entryfield must contain the wordloveand should contain eitherlifeorgrace. - The
speakerfield must not containROMEO. - Filter these results to the play
Romeo and Julietwithout 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!"
}
}
]
}
}