# mapping 的创建以及复杂 mapping 详解
本章会记录 3个 章节的笔记
- 初识搜索引擎_ mapping 的核心数据类型以及 dynamic mapping
- 初识搜索引擎_手动建立和修改 mapping 以及定制 string 类型数据是否分词
- 初识搜索引擎_ mapping 复杂数据类型以及 object 类型数据底层结构大揭秘
# 核心的数据类型
- string
- byte，short，integer，long
- float，double
- boolean
- date
# dynamic mapping 规则
就是自动识别类型
- true or false --> boolean
- 123 --> long
- 123.45 --> double
- 2017-01-01 --> date
- "hello world" --> string/text
# 查看 mapping
语法
GET /index/_mapping/type
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# 如何建立索引时指定 mapping
语法如下
PUT /index
{
"index": {
"type":{
"properties": {
"field":{
"type": "text", // 数据类型
"index":"", // 索引类型
"analyzer": "english" // 分词类型
}
}
}
}
}
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索引类型有如下值：
- analyzed : 全文 full text
- not_analyzed : 精准匹配 exact value
- no ：不索引
TIP
只能创建 index 时手动建立 mapping，或者新增 field mapping，但是不能 update field mapping
PUT /website
{
"mappings": {
"article": {
"properties": {
"author_id": {
"type": "long"
},
"title": {
"type": "text",
"analyzer": "english"
},
"content": {
"type": "text"
},
"post_date": {
"type": "date"
},
"publisher_id": {
"type": "text",
"index": "not_analyzed"
}
}
}
}
}
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如果你尝试再次执行上面的语句就会看到报错了,不能修改
{
"error": {
"root_cause": [
{
"type": "index_already_exists_exception",
"reason": "index [website/icXrvvkcRj6z4uNaNhf6uA] already exists",
"index_uuid": "icXrvvkcRj6z4uNaNhf6uA",
"index": "website"
}
],
"type": "index_already_exists_exception",
"reason": "index [website/icXrvvkcRj6z4uNaNhf6uA] already exists",
"index_uuid": "icXrvvkcRj6z4uNaNhf6uA",
"index": "website"
},
"status": 400
}
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# 新增字段 mapping
对于已经存在的字段不能修改 mapping，新增字段则可以指定
PUT /website/_mapping/article
{
"properties" : {
"new_field" : {
"type" : "string",
"index": "not_analyzed"
}
}
}
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# 测试 mapping
可以使用如下语法进行查看 mapping 的分词效果，如
title 的 analyzer 是 english，可以看到把 a 干掉了
GET /website/_analyze
{
"field": "title",
"text": "a dogs"
}
--------------- 响应
{
"tokens": [
{
"token": "dog",
"start_offset": 2,
"end_offset": 6,
"type": "<ALPHANUM>",
"position": 1
}
]
}
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GET /website/_analyze
{
"field": "content",
"text": "my-dogs"
}
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如果 index = not_analyzed 的话。使用该 api 就会报错；如 new_field 字段
GET website/_analyze
{
"field": "new_field",
"text": "my dogs"
}
--------------------------------- 响应
{
"error": {
"root_cause": [
{
"type": "remote_transport_exception",
"reason": "[sEvAlYx][127.0.0.1:9300][indices:admin/analyze[s]]"
}
],
"type": "illegal_argument_exception",
"reason": "Can't process field [new_field], Analysis requests are only supported on tokenized fields"
},
"status": 400
}
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mapping 复杂数据类型以及 object 类型数据底层结构大揭秘
# multivalue field
建立索引时与 string 是一样的，数据类型不能混
{ "tags": [ "tag1", "tag2" ]}
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# empty field
主要是空值：null，[]，[null]
# object field
对象类型的就比较复杂了，先来创建一个文档，再查看 es 自动为我们创建的 mapping 是什么样的
PUT /company/employee/1
{
"address": {
"country": "china",
"province": "guangdong",
"city": "guangzhou"
},
"name": "jack",
"age": 27,
"join_date": "2017-01-01"
}
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GET /company/_mapping/employee 可以看到返回的数据嵌套很复杂了。
address 下面还有一个 properties ，那么 address 就是一个 object field
{
"company": {
"mappings": {
"employee": {
"properties": {
"address": {
"properties": {
"city": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"country": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"province": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
},
"age": {
"type": "long"
},
"join_date": {
"type": "date"
},
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
}
}
}
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比如这一条数据，它在 es 里面被分词之后，可能是这样的
{
"address": {
"country": "china",
"province": "guangdong",
"city": "guangzhou"
},
"name": "jack",
"age": 27,
"join_date": "2017-01-01"
}
-----------------------------------
{
"name": [jack],
"age": [27],
"join_date": [2017-01-01],
"address.country": [china],
"address.province": [guangdong],
"address.city": [guangzhou]
}
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上面的都是比较简单的内容，如果是稍微复杂一点的，就可能是下面这样了
{
"authors": [
{ "age": 26, "name": "Jack White"},
{ "age": 55, "name": "Tom Jones"},
{ "age": 39, "name": "Kitty Smith"}
]
}
-------------------------------
{
"authors.age": [26, 55, 39],
"authors.name": [jack, white, tom, jones, kitty, smith]
}
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