关于文档的基本操作
基本操作
添加数据
PUT /latte2/user/1
{
"name":"latte",
"age": 23,
"desc": "干",
"tags":["技术","海贼王"]
}
PUT /latte2/user/2
{
"name": "张三",
"age": 28,
"desc": "法外狂徒",
"tags": ["旅游", "渣男", "交友"]
}
PUT /latte2/user/3
{
"name": "李四",
"age": 30,
"desc": "不知道怎么描述",
"tags": ["旅游", "男", "唱歌"]
}
GET latte2/user/1
GET latte2/user/_search?q=name:latte
获取数据 GET
更新数据 PUT
put如果字段传不全，会把原来的字段丢弃，put也就是覆盖操作
Post _update , 推荐使用这种更新方式！
使用 POST 后面没有加 _update
其他没有被提交的字段会被置空，然后新添加了doc.name字段
使用 POST 后面加 _update
提交的字段值被更新了，没有被提交的字段还是原来的值
简单地搜索
GET latte2/user/1
简答的条件查询，可以根据默认的映射规则，产生基本的查询！
权重
复杂操作搜索
select ( 排序，分页，高亮，模糊查询，精准查询！)
# 模糊查询
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
}
}
# 对查询结果进行字段过滤
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
},
"_source": ["name", "desc"]
}
# 排序
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
},
"sort":[{
"age": "asc"
}]
}
第二种写法
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
},
"sort": [
{
"age": {
"order": "asc"
}
}
]
}
# 分页
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
},
"sort":[{
"age": "asc"
}],
"from": 0,
"size": 2
}
json的结构体
hit：索引和文档的信息。查询的总数，然后就是查询出来的具体的文档，数据中的东西都可以遍历出来了，分数：我们可以通过来的判断谁更加符合结果
输出结果，不想要那么多！
我们之后使用Java操作es ，所有的方法和对象就是这里面的 key！
排序！
分页查询！
数据下标还是从0开始的，和学的所有数据结构是一样的！
/search/{current}/{pagesize}
布尔值查询
# 多条件查询 must 相当于and
GET latte2/user/_search
{
"query": {
"bool": {
"must": [
{"match": {
"name": "latte2"
}},
{"match": {
"age": 23
}}
]
}
}
}
# 多条件查询 should 相当于or
GET latte2/user/_search
{
"query": {
"bool": {
"should": [
{"match": {
"name": "latte2"
}},
{"match": {
"age": 25
}}
]
}
}
}
# 多条件查询 must_not 相当于 not
GET latte2/user/_search
{
"query": {
"bool": {
"must_not": [
{"match": {
"age": 25
}}
]
}
}
}
# 过滤查询1 age > 27
GET latte2/user/_search
{
"query": {
"bool": {
"must": [
{"match": {
"name": "latte2"
}}
],
"filter": [
{"range": {
"age": {
"gt": 27
}
}}
]
}
}
}
# 过滤器2 22<age<30
GET latte2/user/_search
{
"query": {
"bool": {
"must": [
{"match": {
"name": "latte2"
}}
],
"filter": [
{"range": {
"age": {
"lt": 30,
"gt": 22
}
}}
]
}
}
}
GET latte2/user/_search
{
"query": {
"match": {
"tags": "技术 男"
}
}
}
must （and），所有的条件都要符合 where id = 1 and name = xxx
should（or），所有的条件都要符合 where id = 1 or name = xxx
must_not （not）
过滤器 filter
- gt 大于
- gte 大于等于
- lt 小于
- lte 小于等于！
匹配多个条件！
精确查询！
# 定义类型term: 精确匹配
PUT latte3
{
"mappings": {
"properties": {
"name": {
"type": "text"
},
"desc": {
"type": "keyword"
}
}
}
}
PUT /latte3/_doc/1
{
"name":"latte3",
"desc":"latte3 desc"
}
PUT /latte3/_doc/2
{
"name":"latte3",
"desc":"desc 2"
}
# 按照keyword类型精准匹配
GET latte3/_search
{
"query": {
"term": {
"desc": {
"value": "desc 2"
}
}
}
}
# 结果：
{
"took" : 174,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.6931471,
"hits" : [
{
"_index" : "latte3",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.6931471,
"_source" : {
"name" : "latte3",
"desc" : "desc 2"
}
}
]
}
}
# 按照text类型匹配
GET latte3/_search
{
"query": {
"term": {
"name":"latte3"
}
}
# 结果：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.18232156,
"hits" : [
{
"_index" : "latte3",
"_type" : "_doc",
"_id" : "1",
"_score" : 0.18232156,
"_source" : {
"name" : "latte3",
"desc" : "latte3 desc"
}
},
{
"_index" : "latte3",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.18232156,
"_source" : {
"name" : "latte3",
"desc" : "desc 2"
}
}
]
}
}
多个值匹配精确查询
PUT /latte3/_doc/3
{
"t1":"22",
"t2":"2021-03-13"
}
PUT /latte3/_doc/4
{
"t1": "33",
"t2": "2021-03-13"
}
GET /latte3/_search
{
"query": {
"bool": {
"should": [
{
"term": {
"t1": "22"
}
},{
"term": {
"t1": "33"
}
}
]
}
}
}
高亮
GET latte2/user/_search
{
"query": {
"match": {
"name": "latte2"
}
},
"highlight": {
"pre_tags": "<p class='key' style='color:red'>",
"post_tags": "</p>",
"fields": {
"name": {}
}
}
}
# 结果显示：
#! Deprecation: [types removal] Specifying types in search requests is deprecated.
{
"took" : 148,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.8405091,
"hits" : [
{
"_index" : "latte2",
"_type" : "user",
"_id" : "1",
"_score" : 0.8405091,
"_source" : {
"doc" : {
"name" : "latte2"
},
"name" : "latte2"
},
"highlight" : {
"name" : [
"<p class='key' style='color:red'>latte2</p>"
]
}
},
{
"_index" : "latte2",
"_type" : "user",
"_id" : "2",
"_score" : 0.8405091,
"_source" : {
"name" : "latte2",
"age" : 28,
"desc" : "法外狂徒",
"tags" : [
"旅游",
"渣男",
"交友"
]
},
"highlight" : {
"name" : [
"<p class='key' style='color:red'>latte2</p>"
]
}
}
]
}
}
term 查询是直接通过倒排索引指定的词条进程精确查找的！
关于分词：
- term ，直接查询精确的
- match，会使用分词器解析！（先分析文档，然后在通过分析的文档进行查询！）
两个类型 text keyword (text类型的会被分词，keyword类型的不会被分词)
多个值匹配精确查询
高亮查询！
