下面先简单描述一下mapping是什么？
当我们插入几条数据，让ES自动为我们建立一个索引
PUT /website/_doc/1{ "post_date": "2017-01-01", "title": "my first article", "content": "this is my first article in this website", "author_id": 11400}PUT /website/_doc/2{ "post_date": "2017-01-02", "title": "my second article", "content": "this is my second article in this website", "author_id": 11400}PUT /website/_doc/3{ "post_date": "2017-01-03", "title": "my third article", "content": "this is my third article in this website", "author_id": 11400}查看mapping
GET /website/_mapping{ "website" : { "mappings" : { "properties" : { "author_id" : { "type" : "long" }, "content" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "post_date" : { "type" : "date" }, "title" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } } } } }}上面是插入数据自动生成的mapping，还有手动生成的mapping。这种自动或手动为index中的type建立的一种数据结构和相关配置，称为mapping。
下面是手动创建的mapping。
PUT /test_mapping{ "mappings" : { "properties" : { "author_id" : { "type" : "long" }, "content" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "post_date" : { "type" : "date" }, "title" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } } } }}1、精确匹配与全文搜索的对比分析
（1）exact value
也就是某个field必须全部匹配才能返回相应的document
示例:
GET /website/_search?q=post_date:2017{ "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 0, "relation" : "eq" }, "max_score" : null, "hits" : [ ] }}GET /website/_search?q=post_date:2017-01-01{ "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "website", "_type" : "doc", "_id" : "1", "_score" : 1.0, "_source" : { "post_date" : "2017-01-01", "title" : "my first article", "content" : "this is my first article in this website", "author_id" : 11400 } } ] }}（2）full text
full text与exact value不一样，不是说单纯的只是匹配完整的一个值，而是可以对值进行拆分词语后（分词）进行匹配，也可以通过缩写、时态、大小写、同义词等进行匹配。
示例：
GET /website/_search?q=title:article{ "took" : 7, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 0.087011375, "hits" : [ { "_index" : "website", "_type" : "doc", "_id" : "1", "_score" : 0.087011375, "_source" : { "post_date" : "2017-01-01", "title" : "my first article", "content" : "this is my first article in this website", "author_id" : 11400 } }, { "_index" : "website", "_type" : "doc", "_id" : "2", "_score" : 0.087011375, "_source" : { "post_date" : "2017-01-02", "title" : "my second article", "content" : "this is my second in this website", "author_id" : 11400 } }, { "_index" : "website", "_type" : "doc", "_id" : "3", "_score" : 0.087011375, "_source" : { "post_date" : "2017-01-03", "title" : "my third article", "content" : "this is my third in this website", "author_id" : 11400 } } ] }}2、倒排索引核心原理
下面演示一下倒排索引简单建立的过程，当然实际中倒排索引的建立过程会非常的复杂。
doc1: I really liked my small dogs, and I think my mom also liked them.
doc2: He never liked any dogs, so I hope that my mom will not expect me to liked him.
分词，初步的倒排索引的建立
word doc1 doc2I * *really *liked * *my * *small *dogs *and *think *mom * *also * them *He *never *any *so *hope *that *will *not *expect *me *to *him *搜索 mother like little dog, 不会有任何结果
mother
like
little
dog
这肯定不是我们想要的结果。比如mother和mom其实根本就没有区别。但是却检索不到。但是做下测试发现ES是可以查到的。实际上ES在建立倒排索引的时候，还会执行一个操作，就是会对拆分的各个单词进行相应的处理，以提升后面搜索的时候能够搜索到相关联的文档的概率。像时态的转换，单复数的转换，同义词的转换，大小写的转换。这个过程称为正则化（normalization）
mother-> mom
liked -> like
small -> little
dogs -> dog
这样重新建立倒排索引：
word doc1 doc2I * *really *like * *my * *little *dog *and *think *mom * *also * them *He *never *any *so *hope *that *will *not *expect *me *to *him *查询：mother like little dog 分词正则化
mother -> mom
like -> like
little -> little
dog -> dog
doc1和doc2都会搜索出来
doc1：I really liked my small dogs, and I think my mom also liked them.
doc2：He never liked any dogs, so I hope that my mom will not expect me to liked him.
3、对mapping进一步总结
（1）往ES里面直接插入数据，ES会自动建立索引，同时建立type以及对应的mapping
（2）mapping中自动定义了每个fieldd的数据类型
（3）不同的数据类型（比如说text和date），可能有的是exact value，有的是full text
（4）exact value，在建立倒排索引的时候，分词的时候，都是将整个值一起作为关键字建立到倒排索引中；full text会经历各种各样的处理，分词，normalization（时态转换，同义词转换，大小写转换），才会建立到倒排索引中
（5）在搜索的时候，exact value和full text类型就决定了，对exact value和full text field进行搜索的行为也是不一样的，会跟建立倒排索引的行为保持一致；比如说exact value搜索的时候，就是直接按照整个值进行匹配，full text也会进行分词和正则化normalization再去倒排索引中去搜索。
（6）可以用 ES的dynamic mapping，让其自动建立mapping,包括自动设置数据类型；也可以提前手动创建index和type的mapping,自己对各个field进行设置，包括数据类型，包括索引行为，包括分析器等等。
mapping本质上就是index的type的元数据，决定了数据类型，建立倒排索引的行为，还有进行搜索的行为。
4、mapping核心数据类型以及dynamic mapping
（1）核心数据类型
string text：字符串类型
byte:字节类型
short：短整型
integer：整型
long:长整型
float:浮点型
boolean:布尔类型
date:时间类型
当然还有一些高级类型，像数组，对象object，但其底层都是text字符串类型
（2） dynamic mapping
true or false -> boolean
123 -> long
123.45 -> float
2017-01-01 -> date
"hello world" -> string text
（3）查看mapping
GET /{index}/mappingGET /test/_mapping{ "test" : { "mappings" : { "properties" : { "field1" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "field2" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } } } } }}5、手动建立和修改mapping以及定制string类型是否分词
注意：只能创建index时手动建立mapping，或者新增field mapping，但是不能update field mapping。
# 创建索引PUT /website{ "mappings": { "properties": { "author_id": { "type": "long" }, "title": { "type": "text", "analyzer": "standard" }, "content": { "type": "text" }, "post_date": { "type": "date" }, "publisher_id": { "type": "text", "index": false } } }}#修改字段的mappingPUT /website{ "mappings": { "properties": { "author_id": { "type": "text" } } }}{ "error": { "root_cause": [ { "type": "resource_already_exists_exception", "reason": "index [website/5xLohnJITHqCwRYInmBFmA] already exists", "index_uuid": "5xLohnJITHqCwRYInmBFmA", "index": "website" } ], "type": "resource_already_exists_exception", "reason": "index [website/5xLohnJITHqCwRYInmBFmA] already exists", "index_uuid": "5xLohnJITHqCwRYInmBFmA", "index": "website" }, "status": 400}#增加mapping的字段PUT /website/_mapping{ "properties": { "new_field": { "type": "text" } }}{ "acknowledged" : true}6、mapping复杂类型y以及object类型数据底层结构
（1）multivalue field
{ "tags": ["tag1", "tag2"]}（2）empty field
null, []
（3）object field
PUT /test/_create/1{ "address": { "country": "china", "province": "guangdong", "city": "guangzhou" }, "name": "jack", "age": 27, "join_date": "2017-01-01"}GET /test/_mapping{ "test" : { "mappings" : { "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 } } } } } }}GET /test/_doc/1{ "_index" : "test", "_type" : "_doc", "_id" : "1", "_version" : 1, "_seq_no" : 0, "_primary_term" : 1, "found" : true, "_source" : { "address" : { "country" : "china", "province" : "guangdong", "city" : "guangzhou" }, "name" : "jack", "age" : 27, "join_date" : "2017-01-01" }}注意：建立索引的时候与string时一样的，数据类型不能混