上一篇文章中，我们学习了Join类型的父子文档，今天继续学习一下嵌套文档，毕竟嵌套文档也是Elasticsearch推荐的，首先我们看下面这条语句
PUT word_document/_doc/1
{
"title" : "up",
"user" : [
{
"name" : "honghong",
"sex" : "female",
"numberOfLikes":500
},
{
"name" : "mingming",
"sex" : "male",
"numberOfLikes":50
},
{
"name" : "lanlan",
"sex" : "male",
"numberOfLikes":100
}
]
}
对于上面这种格式的数据，user就是嵌套对象数组，那么user在Elasticsearch中是怎么存储的呢？如果我们要对嵌套的子对象进行检索，怎么才能检索出我们所需要的数据呢，下面我们就一起来研究下Nested数据类型
环境
- macos 10.14.6
- Elasticsearch 8.1
- Kibana 8.1
Nested
开头我们还是先了解一下，什么是Nested类型，其实就是字面意思，Nested就是嵌套，也就是文章开头user数据类型那种，所以可以看为是一种特殊的Object类型。还是以文章开头的数据为例
PUT word_document/_doc/1
{
"title" : "up",
"user" : [
{
"name" : "honghong",
"sex" : "female",
"numberOfLikes":500
},
{
"name" : "mingming",
"sex" : "male",
"numberOfLikes":50
},
{
"name" : "lanlan",
"sex" : "male",
"numberOfLikes":100
}
]
}
如果我们没有对word_document索引进行显示设置数据类型，在上面这个语句执行之后，Elasticsearch会默认推断数据类型，在Elasticsearch中内容会转换为可能如下的形式，扁平化的处理数据
{
"title":"up",
"user.name":["honghong","mingming","lanlan"],
"user.sex":["male","male","female"],
"user.numberOfLikes":[500,50,100]
}
相信大家也看出来了，如果被Elasticsearch转换成上面的这种数据结构之后，我们的搜索结果是会被影响的，假如我们使用如下这个语句进行查询，我们想搜索name是honghong，sex是male，预期结果是没有匹配的文档，但是因为Elasticsearch对上述的结果进行了扁平化的处理，造成了错误的匹配
GET word_document/_search
{
"query": {
"bool": {
"must": [
{ "match": { "user.name": "honghong" }},
{ "match": { "user.sex": "male" }}
]
}
}
}
如何避免上述情况的发生呢，那就是使用Elasticsearch提供的Nested数据类型，Nested 数据类型保证了嵌套对象的独立性，也就是让我们可以对嵌套对象的内容进行检索，从而不会发生上述的这种情况
-
首先我们还是以上面文档为例，不过是这次我们优先创建索引，并指定
user字段为nestedPUT word_document { "mappings": { "properties": { "title":{ "type": "keyword" }, "user": { "type": "nested" }, "numberOfLikes":{ "type": "integer" } } } } -
下面加入我们的测试数据，来验证我们的搜索语句
PUT word_document/_doc/1 { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes":500 }, { "name" : "mingming", "sex" : "male", "numberOfLikes":50 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes":100 } ] } PUT word_document/_doc/2 { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes":20 }, { "name" : "mingming", "sex" : "male", "numberOfLikes":30 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes":50 } ] } PUT word_document/_doc/3 { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes":50 }, { "name" : "mingming", "sex" : "male", "numberOfLikes":50 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes":50 } ] } -
下面还是刚才那个搜索语句，此时就不会有匹配的文档返回，返回结果为空
GET word_document/_search { "query": { "nested": { "path": "user", "query": { "bool": { "must": [ { "match": { "user.name": "honghong" }}, { "match": { "user.sex": "male" }} ] } } } } } -
那么对于嵌套文档我们可以怎么查询呢，那就是指定
nested查询类型，使用普通的查询是查询不到的哦，nested查询语句如下所示，此时返回的就是我们GET word_document/_search { "query": { "nested": { "path": "user", "query": { "bool": { "must": [ { "match": { "user.name": "honghong" }}, { "match": { "user.sex": "female" }} ] } }, "inner_hits": { "highlight": { "fields": { "user.name": {} } } } } } } -
此外我们还可以根据嵌套对象中的字段进行排序，升序时获取嵌套对象中最小的值最为比较值，降序时获取嵌套对象最大的值作为比较值
GET word_document/_search { "query": { "nested": { "path": "user", "query": { "match": { "user.sex": "male" } } } }, "sort":[ { "user.numberOfLikes": { "order": "asc", "nested": { "path":"user" } } } ] }返回如下
{ "took" : 101, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "word_document", "_id" : "2", "_score" : null, "_source" : { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes" : 20 }, { "name" : "mingming", "sex" : "male", "numberOfLikes" : 30 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes" : 50 } ] }, "sort" : [ 20 ] }, { "_index" : "word_document", "_id" : "1", "_score" : null, "_source" : { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes" : 500 }, { "name" : "mingming", "sex" : "male", "numberOfLikes" : 50 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes" : 100 } ] }, "sort" : [ 50 ] }, { "_index" : "word_document", "_id" : "3", "_score" : null, "_source" : { "title" : "up", "user" : [ { "name" : "honghong", "sex" : "female", "numberOfLikes" : 50 }, { "name" : "mingming", "sex" : "male", "numberOfLikes" : 50 }, { "name" : "lanlan", "sex" : "male", "numberOfLikes" : 50 } ] }, "sort" : [ 50 ] } ] } } -
我们也可以对嵌套对象进行聚合操作，如下我们获取索引中
user.name=honghong,user.sex=female的所有文档，聚合统计numberOfLikes的最小值GET word_document/_search { "query": { "nested": { "path": "user", "query": { "bool": { "must": [ { "match": { "user.name": "honghong" } }, { "match": { "user.sex": "female" } } ] } } } }, "aggs": { "my_min_value": { "nested": { "path": "user" }, "aggs": { "min_value": { "min": { "field": "user.numberOfLikes" } } } } } } -
上面的聚合统计只是对外部的文档过滤，那如果我们有这么一个需求，聚合统计嵌套对象
user内容sex=male的最小值，那么我们可以使用如下filter，下面这个语句优先过滤title=up的文档，聚合统计user.sex=male的numberOfLikes最小值GET /word_document/_search?size=0 { "query": { "match": { "title": "up" } }, "aggs": { "my_user": { "nested": { "path": "user" }, "aggs": { "filter_my_user": { "filter": { "bool": { "filter": [ { "match": { "user.sex": "male" } } ] } }, "aggs": { "min_price": { "min": { "field": "user.numberOfLikes" } } } }, "no_filter_my_user":{ "min": { "field": "user.numberOfLikes" } } } } } } -
最后还有一种就是反向嵌套聚合，通过嵌套对象聚合父文档，返回父文档信息
首先我们还是先创建一个索引添加几条数据用来测试
PUT /issues { "mappings": { "properties": { "tags": { "type": "keyword" }, "comments": { "type": "nested", "properties": { "username": { "type": "keyword" }, "comment": { "type": "text" } } } } } } PUT /issues/_doc/1 { "tags":"跳舞", "comments":[{ "username":"小李", "comment":"小李想学跳舞" }, { "username":"小红", "comment":"小红跳舞很有天赋" } ] } PUT /issues/_doc/2 { "tags":"唱歌", "comments":[{ "username":"小李", "comment":"小李会唱歌" }, { "username":"小李", "comment":"小李唱歌有天赋" }, { "username":"小红", "comment":"小红是歌手" } ] } PUT /issues/_doc/3 { "tags":"跳舞", "comments":[ { "username":"小红", "comment":"小红会跳舞" }, { "username":"小红", "comment":"小红是舞神" } ] } PUT /issues/_doc/4 { "tags":"唱歌", "comments":[ { "username":"小李", "comment":"小李简直就是天生歌手" } ] } PUT /issues/_doc/5 { "tags":"跳舞", "comments":[ { "username":"小红", "comment":"小红舞姿很美" } ] }issues 问题；tags 标签；username 名字；comment 评论；
下面我们使用反向嵌套聚合父文档，需求如下：
1、先聚合统计出评论最多的
username2、在聚合统计
username中comment最多的tagGET /issues/_search?size=0 { "query": { "match_all": {} }, "aggs": { "comments": { "nested": { "path": "comments" }, "aggs": { "top_usernames": { "terms": { "field": "comments.username" }, "aggs": { "comment_to_issue": { "reverse_nested": {}, "aggs": { "top_tags_per_comment": { "terms": { "field": "tags" } } } } } } } } } }结果如下，得出结论：
小红评论次数最多，评论了5次，小红评论最多的标签是跳舞，有3次{ "aggregations" : { "comments" : { "doc_count" : 9, "top_usernames" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "小红", "doc_count" : 5, "comment_to_issue" : { "doc_count" : 4, "top_tags_per_comment" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "跳舞", "doc_count" : 3 }, { "key" : "唱歌", "doc_count" : 1 } ] } } }, { "key" : "小李", "doc_count" : 4, "comment_to_issue" : { "doc_count" : 3, "top_tags_per_comment" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "唱歌", "doc_count" : 2 }, { "key" : "跳舞", "doc_count" : 1 } ] } } } ] } } } }
Nested 支持的参数有哪些
Nested也只是特殊的Object的一种，也是有支持的几种参数
dynamic: (可选参数) 是否允许在索引mapping文件未定义字段的情况下对新字段的处理，默认是加入到现有的嵌套对象中(true),还支持false，strictproperties: (可选参数) 嵌套对象字段内容属性设置include_in_parent:(可选参数) 默认false，如果为true，嵌套对象的字段也会作为普通字段的形式(flat)添加到父文档include_in_root:(可选参数) 默认false，如果为true，嵌套对象的字段也会作为普通字段的形式(flat)添加到根文档
Nested 类型的约束
通过前面的学习，我们也知道了nested类型可以作为一个单独的Lucene文档进行所有，当我们有100个嵌套对象的时候我们需要101个文档来存储映射关系，一个用于父文档，一个用于嵌套文档，所以这一部分的开销，ELasticsearch来通过一下设置进行了约束
-
index.mapping.nested_fields.limit一个索引中，嵌套类型字段(nested)最多存在多个限制，默认
50个，如我们上面的例子中，也就是只占用了一个 -
index.mapping.nested_objects.limit一个索引中，单个嵌套类型字段包含的嵌套
JSON对象的最大数量，默认10000
总结
通过上面的学习实践，我们可以知道Nested嵌套类型是Elasticsearch推荐的相对于Join类型，并且Nested可以实现查询，聚合，排序等，基本满足了工作的需要。好了，到这就结束吧，有什么需要深入了解的，留言哦，也可以去官网查看，毕竟官网还是一手资料，博主的也只能算是入门启蒙笔记，实践起来吧，加油！
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标签：male,name,sex,实用,嵌套,user,numberOfLikes,Nested From： https://www.cnblogs.com/zuiyu/p/16729249.html