###1，es的命令
GET _search
{
"query": {
"match_all": {}
}
}
#新增索引的同时添加分片，不使用默认分片，分片的数量
#一般以节点数平方计算，比如有3个节点，分片数量9个，3个主分片和6个副本
#设置分片和副本方式一，创建索引时就指定
PUT /testshards
{
"settings":{
"number_of_shards":12,
"number_of_replicas":1
}
}
#方式二，索引创建后再设置
GET /_settings
PUT testshards/_settings
{
"number_of_shards":12,
"number_of_replicas":1
}
GET /testshards/_settings
POST /testshards/_doc/3
{
"title":"test"
}
GET /_cat/indices/testshards?v
#cat API检测集群是否健康。 确保9200端口号可用:绿色表示一切正常,
#黄色表示所有的数据可用但是部分副本还没有分配#,红色表示部分数据因为某些原因不可用.
GET /_cat/health?v
#获取集群的节点列表：
GET /_cat/nodes?v
#查看集群状态
GET /_cluster/health
#获取所有索引信息
GET /_cluster/health?level=indices
#获取所有索引信息及分片状态信息
GET /_cluster/health?level=shards
#获取jvm信息
GET _nodes/_master/stats/os,jvm
#另外还有这些可指定
#indices：获得分片大小、文件数量、索引的创建和删除时间、搜索执行时间、字段缓存大小等
#fs/http/process/thread_pool/transport/beaker/discovery/ingest
GET /_cat/allocation?v
GET /_cat/shards?v
GET /_cat/shards/{index}?v
GET /_cat/master?v
GET /_cat/nodes?v
GET /_cat/indices?v
GET /_cat/indices/{index}?v
GET /_cat/segments?v
GET /_cat/segments/{index}?v
GET /_cat/count?v
GET /_cat/count/{index}?v
GET /_cat/recovery?v
GET /_cat/recovery/{index}?v
GET /_cat/health?v
GET /_cat/pending_tasks?v
GET /_cat/aliases?v
GET /_cat/aliases/{alias}?v
GET /_cat/thread_pool?v
GET /_cat/plugins?v
GET /_cat/fielddata?v
GET /_cat/fielddata/{fields}?v
GET /_cat/nodeattrs?v
GET /_cat/repositories?v
GET /_cat/snapshots/{repository}?v
#所有索引：
GET /_cat/indices?v
#获取映射
GET _mapping
#获取索引people映射
GET people/_mapping
#删除映射
DELETE people/_mapping
#获取people索引设置
GET people/_settings
#查看索引people的文档总数
GET people/_count
#获取分词,不用ik分词
GET /_analyze
{
"analyzer": "standard",
"text": "杭州"
}
#获取分词,用ik分词
GET /_analyze
{
"analyzer": "ik_smart",
"text": "吊爆了"
}
#获取分词,用ik分词
GET /_analyze
{
"analyzer": "ik_max_word",
"text": "吊爆了"
}
#索引时用ik_max_word，在搜索时用ik_smart。
#即：索引时最大化的将文章内容分词，搜索时更精确的搜索到想要的结果。区别在于，如果自定义词库时
#如小米手机，ik_smart会显示小米手机，而ik_max_word会拆成小米、手机、小米手机，更加细粒度
#关闭索引，关闭的索引用户不能写入或读取数据，打开_open相反,_clear清空，_refresh刷新，_optimize优化索引，flush
POST /people/_close
#refresh操作可以通过API设置：
POST /index/_settings
{"refresh_interval": "10s"}
#当我们进行大规模的创建索引操作的时候，最好将将refresh关闭。
POST /index/_settings
{"refresh_interval": "-1"}
#es默认的refresh间隔时间是1s，这也是为什么ES可以进行近乎实时的搜索。
#自定义映射，有2种方式，先创建索引再添加映射，创建索引库的同时添加映射。
#方式一
PUT user
GET user/_mapping
PUT user/_mapping
{
"properties":{
"userId":{
"type":"keyword",
"index":true
},
"userName":{
"type":"text",
"index":true,
"analyzer":"ik_max_word"
},
"age":{
"type":"integer",
"index":false
},
"date":{
"type":"date",
"index":true,
"format":"yyyy-MM-dd HH:mm:ss || yyyy-MM-dd || epoch_millis"
}
}
}
#方式二，创建索引库的同时添加映
PUT /user
{
"mappings":{
"properties":{
"userId":{
"type":"keyword",
"index":true
},
"userName":{
"type":"text",
"index":true,
"analyzer":"ik_max_word"
},
"age":{
"type":"integer",
"index":false
},
"date":{
"type":"date",
"index":true,
"format":"yyyy-MM-dd HH:mm:ss || yyyy-MM-dd || epoch_millis"
}
}
}
}
### 创建一个新的 people 索引,指定映射类型
PUT /people
{
"mappings":{
"properties":{
"location":{
"type":"geo_point",
"index":true
},
"name":{
"type":"text",
"index":true,
"analyzer":"ik_max_word"
},
"country":{
"type":"keyword",
"index":true
},
"age":{
"type":"integer"
},
"date":{
"type":"date",
"format":"yyyy-MM-dd HH:mm:ss || yyyy-MM-dd || epoch_millis"
}
}
}
}
#添加文档，如果没有创建索引，es默认会创建
POST people/_doc/8
{
"location":{
"lat": 31.12,
"lon": -41.15 },
"name":"liufang",
"desc":"最近业务需求需要实现搜索的多个词在同段或者同句的功能，索引是按照同段落入到es的，自己实现了分词器来设置同句的position，同段的position在建立索引时设置，下面展示当时遇到的难点.比如搜索词：未就 股权转让 个人所得税, 在未就这个词落入到es会变成两个词项(term)未和就，股权转让和个人所得税有全粒度的分词(即原词在分词中)，原先实现通过match_phrase实现，查询query如下",
"country":"中国",
"age":38,
"date":"2020-05-02 10:00:00"
}
GET people/_search
#删除文档
DELETE people/_doc/1
#更新文档，有2种，一种put
#方式一,这种不推荐，默认其他字段会置空
PUT people/_doc/1
{
"name":"我爱你中国"
}
#方式二,推荐，只修改部分内容，其他字段不变,另外upsert是存在就更新，不存在就插入
POST people/_update/1
{
"doc":{
"name":"我爱你中国"
}
}
#更新，如果不存在，增加字段
POST people/_update/1/
{
"doc":{
"postdate":"20191226T20:34",
"message":"This test update"
}
}
#查询索引people所有数据
GET people/_search
#URISearch,根据字段name查询
GET people/_search?q=name:你好
#GET过滤字段，只显示name
GET people/_search?_source=name
#POST过滤字段
POST people/_search
{
"query":{
"match_all":{
}
},
"_source":[
"name",
"desc",
"age"
]
}
#from和size 达到分页效果
POST /people/_search
{
"_source":[
"name",
"desc"
],
"from":1,
"size":3,
"query":{
"match_all":{
}
}
}
#分词匹配
POST /people/_search
{
"_source":[
"name",
"desc"
],
"query":{
"match":{
"name":"你好中国"
}
}
}
#精确匹配
POST /people/_search
{
"_source":[
"name",
"desc"
],
"query":{
"term":{
"name":"中国"
}
}
}
#_mget批量读取
POST /people/_mget
{
"docs":[
{
"_index":"people",
"_id":4
},
{
"_index":"people",
"_id":1
}
]
}
#排序sort
POST people/_search
{
"query":{
"match_all":{
}
},
"_source":[
"name",
"desc",
"age"
],
"sort":[{"age":"desc"}]
}
#排序sort与missing,将没有值的结果放最后
POST people/_search
{
"query":{
"match_all":{
}
},
"_source":[
"name",
"desc",
"age"
],
"sort":[
{
"age":{
"order":"asc",
"missing":"_last"
}
}
]
}
#BULK APIBULK API是一次调用中执行多种操作，这样节省网络开销
#支持以下4种操作：Create、Update、Index、Delete
#模板传参查询
POST people/_search/template
{
"inline":{
"query":{
"match":{
"{{field1}}":"{{value1}}"
}
},
"size":"{{size1}}"
},
"params":{
"field1":"name",
"value1":"中国",
"size1":10
}
}
#match_phrase和slop，间隔2个字也能搜索
GET /people/_doc/_search
{
"query": {
"match_phrase": {
"desc": {
"query": "美浙",
"slop": 2
}
}
}
}
#match_phrase_prefix和match_phrase类似，max_expansions指定最大匹配多少记录，提高效率
GET /people/_doc/_search
{
"query":{
"match_phrase_prefix":{
"desc":{
"query":"hello w",
"max_expansions":2,
"slop":2
}
}
},
"_source":[
"desc"
]
}
#multi_match，多字段查询，通过minimum_should_match可以设置匹配度
GET /people/_doc/_search
{
"query": {
"multi_match": {
"query": "美丽的江西",
"type": "best_fields",
"fields": [
"name",
"desc"
],
"tie_breaker": 0.3,
"minimum_should_match": "100%"
}
}
}
#query_string必须同时包含PATHEON和LEO,而simple_query_string则是将其分词成Pantheon,AND和LEO,默认的operator为OR,所以只要出现这一个
PUT test/_doc/1
{
"name":"Pantheon Pan",
"address":"Shanghai province,Leo Pan is not here"
}
PUT test/_doc/2
{
"name":"Leo Pan",
"address":"Beijing province,Pantheon Pan is not here"
}
GET /test/_doc/_search
{
"query": {
"query_string": {
"query": "Beijing AND province"
}
}
}
GET /test/_doc/_search
{
"query": {
"simple_query_string": {
"query": "Beijing AND province"
}
}
}
GET /test/_doc/_search
{
"query": {
"simple_query_string": {
"query": "Beijing + province"
}
}
}
#用minimum_should_match
GET /test/_doc/_search
{
"query": {
"query_string": {
"query": "Beijing province",
"minimum_should_match": "100%"
}
}
}
GET people/_mapping
PUT people/_mapping
{
}
#trem查询，精确匹配
POST people/_search
{
"query": {
"term": {
"name": "美丽"
}
}
}
#trems查询匹配多个
POST people/_search
{
"query": {
"terms": {
"name": ["台州","杭州"]
}
}
}
#range范围查询，gte大于或等于，gt大于，lte小于或等于，lt小于，boost权重默认1.0
POST people/_search
{
"query": {
"range": {
"date":{
"gte":"2020-05-01 11:00:00"
}
}
}
}
#prefix前缀匹配查询
POST people/_search
{
"query": {
"prefix":{
"name":{
"value":"高",
"boost":2
}
}
}
}
#wildcard通配符查询，*和?，*表示多个任意字符，?表示一个任意字符
POST people/_search
{
"query": {
"wildcard": {
"desc": {
"value": "*女"
}
}
}
}
#regexp正则查询
POST people/_search
{
"from": 0,
"size": 10,
"query": {
"regexp": {
"name": {
"value":"[刘]",
"boost":1
}
}
}
}
#复合查询bool,必须包含名字为刘芳，过滤掉描述为libin女友的文档
POST people/_search
{
"query": {
"bool": {
"must": {
"match":{
"name":"刘芳"
}
},
"filter": {
"term":{
"desc":"libin"
}
}
}
}
}
#boosting，positive预期相关的值和negative_boost预期不想关的词
POST people/_search
{
"query": {
"boosting" : {
"positive" : {
"term" : {
"name" : "刘"
}
},
"negative" : {
"term" : {
"name" : "李"
}
},
"negative_boost" : 0.5
}
}
}
#跨度查询
POST people/_search
{
"query": {
"span_term": {
"name": {
"value": "高圆圆"
}
}
}
}
GET people/_search
{
"query": {
"span_near" : {
"clauses" : [
{ "span_term" : { "name" : "gaoyuanyuan" } },
{ "span_term" : { "name" : "libin" } },
{ "span_term" : { "name": "李" } }
],
"slop" : 2,
"in_order" : false
}
}
}
GET people/_search
{
"query": {
"span_containing" : {
"little" : {
"span_term" : { "desc" : "是" }
},
"big" : {
"span_near" : {
"clauses" : [
{ "span_term" : { "desc": "索引" } },
{ "span_term" : { "desc" : "按照" } }
],
"slop" : 10,
"in_order" : true
}
}
}
}
}
PUT eams_role_dataperm
{
"mappings":{
"properties":{
"dataperm_uuid":{
"type":"keyword"
},
"mouldstruc_uuid":{
"type":"keyword"
},
"type":{
"type":"keyword"
},
"roleId":{
"type":"keyword"
},
"dangan_id":{
"type":"keyword"
},
"data_scope":{
"type":"text"
},
"item_browsing":{
"type":"boolean"
},
"item_edit":{
"type":"boolean"
},
"item_delete":{
"type":"boolean"
},
"file_browsing":{
"type":"boolean"
},
"file_download":{
"type":"boolean"
},
"file_print":{
"type":"boolean"
},
"archives":{
"type":"nested",
"properties":{
"archives_id":{
"type":"keyword"
},
"title":{
"type":"text",
"analyzer":"ik_max_word"
},
"code":{
"type":"text",
"analyzer":"ik_max_word"
},
"head":{
"type":"text",
"analyzer":"ik_max_word"
},
"create_time":{
"type":"date",
"format":"yyyy-MM-dd HH:mm:ss || yyyy-MM-dd || epoch_millis"
},
"update_time":{
"type":"date",
"format":"yyyy-MM-dd HH:mm:ss || yyyy-MM-dd || epoch_millis"
},
"is_delete":{
"type":"boolean"
},
"storage_period":{
"type":"text"
}
}
}
}
}
}
POST eams_role_dataperm/_doc/1
{
"dataperm_uuid":"1",
"mouldstruc_uuid":"1",
"type":"1",
"roleId":"1",
"dangan_id":"1000",
"data_scope":"",
"item_browsing":true,
"item_edit":true,
"item_delete":true,
"file_browsing":true,
"file_download":true,
"file_print":true,
"archives":[
{
"dangan_id":"1000",
"title":"深入java虚拟机",
"code":"zj001",
"head":"libin",
"create_time":"2020-05-05 12:33:00",
"update_time":"2020-05-05 12:33:00",
"is_delete":false,
"storage_period":"3年"
}
]
}
GET eams_role_dataperm/_search
POST eams_role_dataperm/_doc/2
{
"dataperm_uuid":"2",
"mouldstruc_uuid":"1",
"type":"1",
"roleId":"1",
"dangan_id":"1000",
"data_scope":"",
"item_browsing":false,
"item_edit":false,
"item_delete":true,
"file_browsing":true,
"file_download":true,
"file_print":true,
"archives":[
{
"dangan_id":"1000",
"title":"深入java虚拟机",
"code":"zj001",
"head":"libin",
"create_time":"2020-05-05 12:33:00",
"update_time":"2020-05-05 12:33:00",
"is_delete":false,
"storage_period":"3年"
}
]
}
POST eams_role_dataperm/_doc/3
{
"dataperm_uuid":"3",
"mouldstruc_uuid":"1",
"type":"1",
"roleId":"2",
"dangan_id":"1000",
"data_scope":"",
"item_browsing":true,
"item_edit":true,
"item_delete":true,
"file_browsing":true,
"file_download":true,
"file_print":true,
"archives":[
{
"dangan_id":"1000",
"title":"深入java虚拟机",
"code":"zj001",
"head":"libin",
"create_time":"2020-05-05 12:33:00",
"update_time":"2020-05-05 12:33:00",
"is_delete":false,
"storage_period":"3年"
}
]
}
POST eams_role_dataperm/_doc/4
{
"dataperm_uuid":"4",
"mouldstruc_uuid":"1",
"type":"1",
"roleId":"2",
"dangan_id":"1000",
"data_scope":"",
"item_browsing":true,
"item_edit":true,
"item_delete":true,
"file_browsing":true,
"file_download":true,
"file_print":true,
"archives":[
{
"dangan_id":"1000",
"title":"深入java虚拟机",
"code":"zj001",
"head":"libin",
"create_time":"2020-05-05 12:33:00",
"update_time":"2020-05-05 12:33:00",
"is_delete":false,
"storage_period":"3年"
}
]
}
POST eams_role_dataperm/_doc/5
{
"dataperm_uuid":"5",
"mouldstruc_uuid":"2",
"type":"1",
"roleId":"1",
"dangan_id":"1001",
"data_scope":"",
"item_browsing":true,
"item_edit":true,
"item_delete":true,
"file_browsing":true,
"file_download":true,
"file_print":true,
"archives":[
{
"dangan_id":"1001",
"title":"effect java",
"code":"zj002",
"head":"libin",
"create_time":"2020-05-05 12:33:00",
"update_time":"2020-05-05 12:33:00",
"is_delete":false,
"storage_period":"3年",
"num":"001",
"metadata":"元数据"
}
]
}
#根据roleId查询
GET eams_role_dataperm/_search
{
"query": {
"terms": {
"roleId": ["1","2"]
}
}
}
#组合查询
GET eams_role_dataperm/_search
{
"query":{
"bool":{
"must":[
{
"terms":{
"roleId":[
"1",
"2"
]
}
},
{
"terms":{
"mouldstruc_uuid":[
"1",
"2"
]
}
},
{
"nested":{
"path":"archives",
"query":{
"bool":{
"must":[
{
"match":{
"archives.dangan_id":"1001"
}
},
{
"match":{
"archives.title":"effect"
}
}
]
}
}
}
}
]
}
}
}
#根据关键词搜索nested内所有字段值，只要有一个字段匹配就符合：query_string
GET eams_role_dataperm/_search
{
"query":{
"bool":{
"must":[
{
"terms":{
"roleId":[
"1",
"2"
]
}
},
{
"terms":{
"mouldstruc_uuid":[
"1",
"2"
]
}
},
{
"nested":{
"path":"archives",
"query":{
"bool":{
"must":[
{
"match":{
"archives.dangan_id":"1001"
}
},
{
"query_string":{
"query":"libin"
}
}
]
}
}
}
}
]
}
}
}
#索引的重建
#例如现在有一个档案索引，需要将title的keyword类型改成text类型，使用_reindex对索引重建
#1，原有的索引如下
PUT /achieves
{
"mappings": {
"properties": {
"title": {
"type": "keyword"
},
"code": {
"type": "keyword"
}
}
}
}
#2，加入几条测试数据
PUT /achieves/_doc/1
{
"title":"古典书籍",
"code":"1000"
}
PUT /achieves/_doc/2
{
"title":"历史书籍",
"code":"1001"
}
PUT /achieves/_doc/3
{
"title":"经济书籍",
"code":"1002"
}
#重建前查询书籍，返回null
GET /achieves/_search
{
"query": {"match": {
"title": "书籍"
}}
}
#3,开始重建，首先新增⼀个索引
PUT /achieves_new
{
"mappings": {
"properties": {
"title": {
"type": "text"
},
"code": {
"type": "keyword"
}
}
}
}
#4,将旧索引数据复制到新索引
#同步执行
POST /_reindex
{
"source": {
"index": "achieves"
},
"dest": {
"index": "achieves_new"
}
}
#异步执行，如果数据量大，加上wait_for_completion=false 件，这样 reindex 将直接返回 taskId
POST /_reindex?wait_for_completion=false
{
"source": {
"index": "achieves"
},
"dest": {
"index": "achieves_new"
}
}
#获取task列表
GET _tasks
#5，替换别名，size还可指定迁移数据量
POST /_aliases
{
"actions": [{
"add": {
"index": "achieves_new",
"alias": "achieves_latest"
}
},
{
"remove": {
"index": "achieves",
"alias": "achieves_latest"
}
}
]
}
#删除旧索引
DELETE achieves
#通过别名访问新索引
POST /achieves_latest/_search
{
"query": {
"match": {
"title": "历史书籍"
}
}
}
GET achieves_latest/_mapping
#索引文件优化和查询优化，1分片数量、节点数量、合理规划映射、合理使用分词
#查询优化来说合理规划索引和分片来提高查询效率，另外路由选择也是经常用到的
#路由使用
PUT /achieves_latest/_doc/4?routing=achieves_routing&refresh=true
{
"title":"社科书籍",
"code":"1003"
}
GET achieves_latest/_search
GET achieves_latest/_doc/4?routing=achieves_routing
POST /achieves_latest,content/_search
{
"query": {
"match": {
"title": "历史书籍"
}
}
}
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