opensearch-docs-cn/_opensearch/query-dsl/bool.md

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---
layout: default
title: Boolean queries
parent: Query DSL
nav_order: 45
---
# 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:
```json
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:
1. The `text_entry` field must contain the word `love` and should contain either `life` or `grace`.
2. The `speaker` field must not contain `ROMEO`.
3. Filter these results to the play `Romeo and Juliet` without affecting the relevancy score.
Use the following query:
```json
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
```json
{
"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:
```json
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:
```json
"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`:
```json
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
```json
{
"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!"
}
}
]
}
}
```