(1). 概述
范式设计:范式设计的最主要目标是:减少不必要的更新,相比反范式化设计还节省了磁盘空间(现在的存储空间越来越便宜了),自然,也存在缺陷:造成Join查询.
反范式设计的:它把数据打平(Flattening),不使用关联关系,而是在文档中保存冗余的数据拷贝(磁盘空间消耗比较大).最主要的目的是:无需处理Join操作,数据读取性能好.缺点:不适合在数据频繁修改的场景(一条数据的改动,可能会引起很多数据的更新.)
关系型数据库,一般会考虑范式化设计,而ES往往考虑的是反范式化设计数据.反范式化的好处在于:读的速度变快,无需Join,无需行锁.
ES并不擅长处理关联关系,一般采用四种方法处理关联:
- 对象类型
- 嵌套对象(Nested Object)
- 父子关联关系(Parent/Child)
- 应用程序处理
(2). 对象类型(博客与作者关联关系)
# 删除博客
DELETE /blog
# 博客与作者信息
PUT /blog
{
"mappings": {
"properties": {
"content": {
"type": "text"
},
"time" : {
"type" :"date"
},
"user": {
"properties": {
"city" : {
"type" : "text"
},
"userid" : {
"type" : "long"
},
"username" : {
"type" : "keyword"
}
}
}
}
}
}
# 添加文档
PUT /blog/_doc/1
{
"content":"I like ElasticSearch",
"time" : "2019-01-01T00:00:00",
"user" : {
"userid" : 1,
"username" : "Jack",
"city" : "Shanghai"
}
}
# 检索文档
POST /blog/_search
{
"query": {
"bool": {
"must": [
{
"match": { "content": "ElasticSearch" }
},
{
"match": { "user.username": "Jack" }
}
]
}
}
}
(3). 对象类型(电影名称与演员关系)
对象类型存储时,JSON格式处理成扁平式键值对的结构,当对多个这段进行查询时,导致意外的搜索结果.
可以使用Nested Data Type解决这个问题.
DELETE my_movies
PUT /my_movies
{
"mappings": {
"properties": {
"actors" : {
"properties": {
"first_name" : { "type" : "keyword" },
"last_name" : { "type" : "keyword" }
}
},
"title" : {
"type" : "text",
"fields": {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
POST /my_movies/_doc/1
{
"title" : "Speed",
"actors" :[
{
"first_name" : "Keanu",
"last_name" : "Reeves"
},
{
"first_name" : "Dennis",
"last_name" : "Hopper"
}
]
}
# SELECT * FROM my_movies WHERE actors.first_name = 'Keanu' AND actors.last_name = 'Hopper';
# 实际来说:按关系型的理解,是查询不到该数据的
POST /my_movies/_search
{
"query": {
"bool": {
"must": [
{ "match": {
"actors.first_name": "Keanu"
} },
{
"match": {
"actors.last_name": "Hopper"
}
}
]
}
}
}
##########################检索结果##############################
# 按理来说:是不应该被检索到的,可通过(Nested解决这个问题)
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.723315,
"hits" : [
{
"_index" : "my_movies",
"_type" : "_doc",
"_id" : "1",
"_score" : 0.723315,
"_source" : {
"title" : "Speed",
"actors" : [
{
"first_name" : "Keanu",
"last_name" : "Reeves"
},
{
"first_name" : "Dennis",
"last_name" : "Hopper"
}
]
}
}
]
}
}
(4). Nested Data Type
DELETE my_movies
# 注意,增加了:"type" : "nested",
PUT /my_movies
{
"mappings": {
"properties": {
"actors" : {
"type" : "nested",
"properties": {
"first_name" : { "type" : "keyword" },
"last_name" : { "type" : "keyword" }
}
},
"title" : {
"type" : "text",
"fields": {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
POST /my_movies/_doc/1
{
"title" : "Speed",
"actors" :[
{
"first_name" : "Keanu",
"last_name" : "Reeves"
},
{
"first_name" : "Dennis",
"last_name" : "Hopper"
}
]
}
POST /my_movies/_search
{
"query": {
"bool": {
"must": [
{"match": {
"title": "Speed"
}},
{
"nested": {
"path": "actors",
"query": {
"bool": {
"must": [
{ "match": {
"actors.first_name": "Keanu"
} },
{
"match": {
"actors.last_name": "Hopper"
}
}
]
}
}
}
}
]
}
}
}
##########################检索结果##############################
# SELECT * FROM my_movies WHERE actors.first_name='Keanu' AND actors.last_name = 'Hopper';
# 这时,确实是检索不到数据了(因为:Keanu对应的lastName为:Reeves)
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
# 测试正确检索
# SELECT * FROM my_movies WHERE actors.first_name='Keanu' AND actors.last_name = 'Reeves';
# 这时,确实是检索到数据了(因为:Keanu对应的lastName为:Reeves)
POST /my_movies/_search
{
"query": {
"bool": {
"must": [
{"match": {
"title": "Speed"
}},
{
"nested": {
"path": "actors",
"query": {
"bool": {
"must": [
{ "match": {
"actors.first_name": "Keanu"
} },
{
"match": {
"actors.last_name": "Reeves"
}
}
]
}
}
}
}
]
}
}
}
##########################检索结果##############################
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.6739764,
"hits" : [
{
"_index" : "my_movies",
"_type" : "_doc",
"_id" : "1",
"_score" : 1.6739764,
"_source" : {
"title" : "Speed",
"actors" : [
{
"first_name" : "Keanu",
"last_name" : "Reeves"
},
{
"first_name" : "Dennis",
"last_name" : "Hopper"
}
]
}
}
]
}
}
(5). 总结
在ES中Nested数据类型是允许对象数组中的对象被独立索引.
使用nested和properties关键字,将所有的actors索引到多个分隔的文档.
Nested文档会被保存在两个在Lucene内部,在查询时做join处理.
Nested文档优点:文档存储在一起,因此读取性能高,缺点:更新父或者子文档时,需要对整个文档进行更新.适合,读多写少情况.
