文章目录
1 全局2 索引3 文档4 搜索4.1 普通4.2 匹配4.3 组合4.4 过滤4.5 聚合4.6 原生 SQL4.7 官方搜索例子
通过本文学习 Elasticsearch 常用命令，以下所有命令都在 Kibana 的 Dev Tools 中执行。
1 全局
查看集群健康状态：GET /_cat/health?v查看节点状态：GET /_cat/nodes?v查看所有索引信息：GET /_cat/indices?v
2 索引
新增索引：PUT /user新增索引-自定义settings：
PUT /user
{
"settings": {
"number_of_shards": 1,
"number_of_replicas": 0
}
}
新增索引-自定义mappings：
PUT /user
{
"mappings": {
"properties": {
"username": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"address" : {
"type" : "text",
"analyzer" : "ik_max_word",
"search_analyzer" : "ik_smart"
},
"location": {
"type": "geo_point"
},
......
}
}
}
查看索引：GET /user查看索引中的settings：GET /user/_settings查看索引中的mappings：GET /user/_mappings删除索引：DELETE /user
3 文档
新增文档-指定id（id不存在则新增，存在则更新）：
POST /user/_doc/1
{
"id": 1,
"username": "zhangsan",
"password": "123456",
"enabled": true,
"locked": false,
"address": "浙江杭州",
"nickName": "张三",
"createTime": 1602836232511,
"updateTime": 1602836232511
}
新增文档-指定id（id不存在则新增，存在则报错）：
POST /user/_create/1
{
"id": 1,
"username": "zhangsan",
"password": "123456",
"enabled": true,
"locked": false,
"address": "浙江杭州",
"nickName": "张三",
"createTime": 1602836232511,
"updateTime": 1602836232511
}
新增文档-自动生成id：
POST /user/_doc
{
"id": 2,
"username": "lisi",
"password": "123456",
"enabled": true,
"locked": false,
"address": "浙江宁波",
"nickName": "李四",
"createTime": 1602836232511,
"updateTime": 1602836232511
}
新增文档-批量_bulk：
POST /user/_bulk
{"index":{"_id":"1"}}
{"id":1,"username":"zhangsan"}
{"index":{"_id":"2"}}
{"id":2,"username":"zhangsan2"}
查看文档：GET /user/_doc/1查看文档-批量_mget：
GET /user/_mget
{
"ids": ["1", "2"]
}
修改文档-指定id（id不存在则新增，存在则替换）-整体修改（因此需要传所有字段）：
POST /user/_doc/1
{
"id": 1,
"username": "zhangsan",
"password": "654321",
"enabled": true,
"locked": false,
"address": "浙江杭州",
"nickName": "张三",
"createTime": 1602836232511,
"updateTime": 1602836232511
}
修改文档-指定id（id不存在则报错，存在则合并）-局部修改（因此只需要传修改的字段）：
POST /user/_update/1
{
"doc": {
"password": "121212"
}
}
修改文档-指定id（id不存在则创建，存在则合并）-局部修改（因此只需要传修改的字段）：
POST /user/_update/1
{
"doc": {
"password": "121212"
},
"doc_as_upsert": true
}
修改文档-指定id-局部修改-脚本方式：
POST /user/_update/1
{
"script" : {
"source": "ctx._source.password = params.password;ctx._source.address = params.address",
"lang": "painless",
"params": {
"password": "232323",
"address": "浙江宁波"
}
}
}
修改文档-update_by_query-局部修改-脚本方式：
POST /user/_update_by_query
{
"query": {
"match": {
"username": "zhangsan"
}
},
"script": {
"source": "ctx._source.password = params.password;ctx._source.address = params.address,
"lang": "painless",
"params": {
"password": "654321",
"address": "浙江杭州"
}
}
}
检查文档是否存在：HEAD /user/_doc/1删除文档-指定id：DELETE /user/_doc/1删除文档-批量_bulk：
POST /_bulk
{ "delete" : { "_index" : "user2", "_id": 1 }}
{ "delete" : { "_index" : "user2", "_id": 6 }}
删除文档-delete_by_query：
POST /user/_delete_by_query
{
"query": {
"match": {
"username": "zhangsan"
}
}
}
4 搜索
4.1 普通
搜索-文档总数_count：GET /user/_count搜索-文档数量_count：
GET /user/_count
{
"query": {
"match": {
"address": "浙江杭州"
}
}
}
搜索_search：
GET /_search
GET /user,user2/_search
GET /user/_search
GET /user/_search?size=20
GET /user/_search?size=20&from=3
GET /user/_search?q=address:"浙江杭州"
GET /user/_search?filter_path=hits.total
GET /user/_search?_source=username,address
搜索-所有文档match_all：
GET /user/_search
{
"query": {
"match_all": {}
}
}
搜索-分页from/size：
GET /user/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 2
}
搜索-排序sort：
GET /user/_search
{
"query": {
"match_all": {}
},
"sort": {
"updateTime": {
"order": "desc"
}
}
}
搜索-指定返回字段_source：
GET /user/_search
{
"query": {
"match_all": {}
},
"_source": ["username", "address"]
}
4.2 匹配
搜索-匹配match（数值类型是精确匹配，文本类型是模糊匹配）：
GET /user/_search
{
"query": {
"match": {
"enabled": 1
}
}
}
@ GET /user/_search?q=enabled:1
搜索-多字段匹配multi_match（或）：
POST /user/_search
{
"query": {
"multi_match": {
"query": "zhang",
"fields": [
"username",
"nickName"
]
}
}
}
搜索-短语匹配match_phrase（同时包含多个短语）：
GET /user/_search
{
"query": {
"match_phrase": {
"address": "zhejiang hangzhou"
}
}
}
4.3 组合
搜索-组合bool/must（同时满足多个条件）：
GET /user/_search
{
"query": {
"bool": {
"must": [
{ "match": { "address": "zhejiang" } },
{ "match": { "address": "hangzhou" } }
]
}
}
}
搜索-组合bool/should（满足其中任意一个条件）：
GET /user/_search
{
"query": {
"bool": {
"should": [
{ "match": { "address": "zhejiang" } },
{ "match": { "address": "hangzhou" } }
]
}
}
}
搜索-组合bool/must_not（同时不满足多个条件）：
GET /user/_search
{
"query": {
"bool": {
"must_not": [
{ "match": { "address": "zhejiang" } },
{ "match": { "address": "hangzhou" } }
]
}
}
}
搜索-组合bool/filter（过滤，keyword 字段用于一般用于精确搜索、聚合、排序）：
GET /user/_search
{
"query": {
"bool": {
"filter": {
"term": {
"address.keyword": "浙江杭州"
}
}
}
}
}
搜索-多种组合：
GET /user/_search
{
"query": {
"bool": {
"must": [
{ "match": { "enabled": 1 } }
],
"must_not": [
{ "match": { "address": "hangzhou" } }
]
}
}
}
4.4 过滤
搜索-过滤filter：
GET /user/_search
{
"query": {
"bool": {
"must": { "match_all": {} },
"filter": {
"range": {
"id": {
"gte": 3,
"lte": 6
}
}
}
}
}
}
4.5 聚合
搜索-聚合aggs（类似于 MySQL 中的 group by）：
GET /user/_search
{
"size": 0,
"aggs": {
"myagg_enabled": {
"terms": {
"field": "enabled.keyword"
}
}
}
}
搜索-嵌套聚合aggs：
GET /user/_search
{
"size": 0,
"aggs": {
"myagg_enabled": {
"terms": {
"field": "enabled.keyword"
},
"aggs": {
"myagg_age": {
"avg": {
"field": "age"
}
}
}
}
}
}
搜索-嵌套聚合aggs并对结果进行排序order：
GET /user/_search
{
"size": 0,
"aggs": {
"myagg_enabled": {
"terms": {
"field": "enabled.keyword",
"order": {
"myagg_age": "desc"
}
},
"aggs": {
"myagg_age": {
"avg": {
"field": "age"
}
}
}
}
}
}
搜索-分段聚合aggs/range：
GET /user/_search
{
"size": 0,
"aggs": {
"myagg_id": {
"range": {
"field": "id",
"ranges": [
{
"from": 20,
"to": 30
},
{
"from": 30,
"to": 40
},
{
"from": 40,
"to": 50
}
]
},
"aggs": {
"myagg_enabled": {
"terms": {
"field": "enabled.keyword"
},
"aggs": {
"myagg_age": {
"avg": {
"field": "age"
}
}
}
}
}
}
}
}
4.6 原生 SQL
GET /_sql?format=txt
{
"query": "SHOW TABLES"
}
GET /_sql?format=txt
{
"query": "SELECT * FROM user"
}
GET /_sql?format=txt
{
"query": "DESCRIBE user"
}
POST /_sql?format=txt
{
"query": "SHOW FUNCTIONS LIKE '%DATE%'"
}
POST /_sql?format=txt
{
"query": "SELECT id,username,nick_name,address,SCORE() FROM user WHERE MATCH(address,'杭州') LIMIT 10"
}
4.7 官方搜索例子
@ 准备数据
POST twitter/_bulk
{ "index" : { "_id": 1 } }
{"user":"双榆树-张三","message":"今儿天气不错啊，出去转转去","uid":2,"age":20,"city":"北京","province":"北京","country":"中国","address":"中国北京市海淀区","location":{"lat":"39.970718","lon":"116.325747"}}
{ "index" : { "_id": 2 } }
{"user":"东城区-老刘","message":"出发，下一站云南！","uid":3,"age":30,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区台基厂三条3号","location":{"lat":"39.904313","lon":"116.412754"}}
{ "index" : { "_id": 3 } }
{"user":"东城区-李四","message":"happy birthday!","uid":4,"age":30,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区","location":{"lat":"39.893801","lon":"116.408986"}}
{ "index" : { "_id": 4 } }
{"user":"朝阳区-老贾","message":"123,gogogo","uid":5,"age":35,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区建国门","location":{"lat":"39.718256","lon":"116.367910"}}
{ "index" : { "_id": 5 } }
{"user":"朝阳区-老王","message":"Happy BirthDay My Friend!","uid":6,"age":50,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区国贸","location":{"lat":"39.918256","lon":"116.467910"}}
{ "index" : { "_id": 6 } }
{"user":"虹桥-老吴","message":"好友来了都今天我生日，好友来了,什么 birthday happy 就成!","uid":7,"age":90,"city":"上海","province":"上海","country":"中国","address":"中国上海市闵行区","location":{"lat":"31.175927","lon":"121.383328"}}
@ 查询
@@ _search
GET _search
GET /twitter,twitter2/_search
GET /twitter/_search
GET /twitter/_search?size=20
GET /twitter/_search?size=20&from=3
GET /twitter/_search?filter_path=hits.total
GET /twitter/_search?_source=user,city
@@ _count
GET /twitter/_count
GET /twitter/_count
{
"query": {
"match": {
"city": "北京"
}
}
}
@@ 查看/设置 settings
GET /twitter/_settings
PUT twitter
{
"settings": {
"number_of_shards": 1,
"number_of_replicas": 1
}
}
@@ 查看/设置 mapping
GET /twitter/_mapping
PUT twitter/_mapping
{
"properties": {
"address": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"age": {
"type": "long"
},
"city": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"country": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"location": {
"type": "geo_point"
},
"message": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"province": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"uid": {
"type": "long"
},
"user": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
@@ 普通查询
@@@ match
GET /twitter/_search
{
"query": {
"match": {
"city": "北京"
}
}
}
GET /twitter/_search?q=city:"北京"
@@@ keyword字段用于精确搜索，aggregation和排序（sorting）
GET /twitter/_search
{
"query": {
"bool": {
"filter": {
"term": {
"city.keyword": "北京"
}
}
}
}
}
GET /twitter/_search
{
"query": {
"constant_score": {
"filter": {
"term": {
"city.keyword": {
"value": "北京"
}
}
}
}
}
}
@@@ 匹配“朝”，“阳”，“区”，“老”及“贾”这5个字中的任何一个将被显示
GET /twitter/_search
{
"query": {
"match": {
"user": {
"query": "朝阳区-老贾",
"operator": "or"
}
}
}
}
@@@ 至少要匹配“朝”，“阳”，“区”，“老”及“贾这5个中的3个字才可以
GET /twitter/_search
{
"query": {
"match": {
"user": {
"query": "朝阳区-老贾",
"operator": "or",
"minimum_should_match": 3
}
}
}
}
@@@ 需要同时匹配索引的5个字才可以
GET /twitter/_search
{
"query": {
"match": {
"user": {
"query": "朝阳区-老贾",
"operator": "and"
}
}
}
}
@@@ multi：同时对三个fields: user，adress及message进行搜索
GET /twitter/_search
{
"query": {
"multi_match": {
"query": "朝阳",
"fields": [
"user",
"address^3",
"message"
],
"type": "best_fields"
}
}
}
@@@ prefix：查询user字段里以“朝”为开头的所有文档
GET /twitter/_search
{
"query": {
"prefix": {
"user": {
"value": "朝"
}
}
}
}
@@@ term：使用user.keyword来对“朝阳区-老贾”进行精确匹配查询相应的文档
GET /twitter/_search
{
"query": {
"term": {
"user.keyword": {
"value": "朝阳区-老贾"
}
}
}
}
@@@ terms：查询user.keyword里含有“双榆树-张三”或“东城区-老刘”的所有文档
GET /twitter/_search
{
"query": {
"terms": {
"user.keyword": [
"双榆树-张三",
"东城区-老刘"
]
}
}
}
@@ 复合查询
@@@ must：查询的是必须是 北京城市的，并且年刚好是30岁的
GET /twitter/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"city": "北京"
}
},
{
"match": {
"age": "30"
}
}
]
}
}
}
@@@ must_not：寻找不在北京的所有的文档
GET /twitter/_search
{
"query": {
"bool": {
"must_not": [
{
"match": {
"city": "北京"
}
}
]
}
}
}
@@@ should：表述“或”的意思，也就是有就更好，没有就算了。age必须是30岁，但是如果文档里含有“Hanppy birthday”，相关性会更高
GET /twitter/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"age": "30"
}
}
],
"should": [
{
"match_phrase": {
"message": "Happy birthday"
}
}
]
}
}
}
@@@ 位置查询 geo_distance ：查找在地址栏里有“北京”，并且在以位置(116.454182, 39.920086)为中心的5公里以内的所有文档，并按照远近大小进行排序
GET /twitter/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"address": "北京"
}
}
]
}
},
"post_filter": {
"geo_distance": {
"distance": "5km",
"location": {
"lat": 39.920086,
"lon": 116.454182
}
}
},
"sort": [
{
"_geo_distance": {
"location": "39.920086,116.454182",
"order": "asc",
"unit": "km"
}
}
]
}
@@@ 范围查询 range ：查询年龄介于30到40岁的文档
GET /twitter/_search
{
"query": {
"range": {
"age": {
"gte": 30,
"lte": 40
}
}
}
}
@@@ 范围查询 range ：查询年龄介于30到40岁的文档，并对它们进行排序
GET /twitter/_search
{
"query": {
"range": {
"age": {
"gte": 30,
"lte": 40
}
}
},"sort": [
{
"age": {
"order": "desc"
}
}
]
}
@@@ Exists 查询：查询 district 字段是否存在
GET /twitter/_search
{
"query": {
"exists": {
"field": "district"
}
}
}
@@@ 匹配查询
PUT twitter/_doc/8
{
"user": "朝阳区-老王",
"message": "Happy",
"uid": 6,
"age": 50,
"city": "北京",
"province": "北京",
"country": "中国",
"address": "中国北京市朝阳区国贸",
"location": {
"lat": "39.918256",
"lon": "116.467910"
}
}
@@@ 匹配查询：match 查询时不分大小写，不分先后顺序
@@@ 默认“或”，匹配到一个单词即可
GET /twitter/_search
{
"query": {
"match": {
"message": "happy birthday"
}
}
}
@@@ “与”，需要同时匹配两个单词
GET /twitter/_search
{
"query": {
"match": {
"message": {
"query": "happy birthday",
"operator": "and"
}
}
}
}
@@@ minimum_should_match 至少应该要匹配两个单词
GET /twitter/_search
{
"query": {
"match": {
"message": {
"query": "happy birthday",
"minimum_should_match": 2
}
}
}
}
@@@ 匹配查询：match_phrase 查询时不分大小写，但区分先后顺序
GET /twitter/_search
{
"query": {
"match_phrase": {
"message": "happy birthday"
}
},
"highlight": {
"fields": {
"message": {}
}
}
}
@@@ SQL 查询
GET /_sql?
{
"query": """
SELECT * FROM twitter
WHERE age = 30
"""
}
@@@ Multi Search：减少请求次数
@@@ 同时获取两个文档
GET /twitter/_doc/_mget
{
"ids": ["1", "2"]
}
@@@ 同时到多个索引中查询
GET _msearch
{"index":"twitter"}
{"query":{"match_all":{}},"from":0,"size":1}
{"index":"twitter"}
{"query":{"bool":{"filter":{"term":{"city.keyword":"北京"}}}}, "size":1}
{"index":"twitter2"}
{"query":{"match_all":{}}}
GET /twitter,twitter2/_search
GET /twitter*/_search
@@@ Profile：调试工具，它添加了有关执行的详细信息搜索请求中的每个组件，它为用户提供有关搜索的每个步骤的洞察力
GET /twitter/_search
{
"profile": "true",
"query": {
"match": {
"city": "北京"
}
}
}
@@ 聚合查询 aggregation 及 analyzer
@@@ 准备数据
DELETE twitter
PUT twitter
{
"mappings": {
"properties": {
"DOB": {
"type": "date"
},
"address": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"age": {
"type": "long"
},
"city": {
"type": "keyword"
},
"country": {
"type": "keyword"
},
"location": {
"type": "geo_point"
},
"message": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"province": {
"type": "keyword"
},
"uid": {
"type": "long"
},
"user": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
}
POST _bulk
{"index":{"_index":"twitter","_id":1}}
{"user":"张三","message":"今儿天气不错啊，出去转转去","uid":2,"age":20,"city":"北京","province":"北京","country":"中国","address":"中国北京市海淀区","location":{"lat":"39.970718","lon":"116.325747"}, "DOB": "1999-04-01"}
{"index":{"_index":"twitter","_id":2}}
{"user":"老刘","message":"出发，下一站云南！","uid":3,"age":22,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区台基厂三条3号","location":{"lat":"39.904313","lon":"116.412754"}, "DOB": "1997-04-01"}
{"index":{"_index":"twitter","_id":3}}
{"user":"李四","message":"happy birthday!","uid":4,"age":25,"city":"北京","province":"北京","country":"中国","address":"中国北京市东城区","location":{"lat":"39.893801","lon":"116.408986"}, "DOB": "1994-04-01"}
{"index":{"_index":"twitter","_id":4}}
{"user":"老贾","message":"123,gogogo","uid":5,"age":30,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区建国门","location":{"lat":"39.718256","lon":"116.367910"}, "DOB": "1989-04-01"}
{"index":{"_index":"twitter","_id":5}}
{"user":"老王","message":"Happy BirthDay My Friend!","uid":6,"age":26,"city":"北京","province":"北京","country":"中国","address":"中国北京市朝阳区国贸","location":{"lat":"39.918256","lon":"116.467910"}, "DOB": "1993-04-01"}
{"index":{"_index":"twitter","_id":6}}
{"user":"老吴","message":"好友来了都今天我生日，好友来了,什么 birthday happy 就成!","uid":7,"age":28,"city":"上海","province":"上海","country":"中国","address":"中国上海市闵行区","location":{"lat":"31.175927","lon":"121.383328"}, "DOB": "1991-04-01"}
@@@ range 聚合：把用户进行年龄分段，查出来在不同的年龄段的用户
GET /twitter/_search
{
"size": 0,
"aggs": {
"age_range": {
"range": {
"field": "age",
"ranges": [
{
"from": 20,
"to": 30
},
{
"from": 30,
"to": 40
},
{
"from": 40,
"to": 50
}
]
}
}
}
}
@@@ date_range 聚合：统计在某个时间段里的文档数
POST twitter/_search
{
"size": 0,
"aggs": {
"birth_range": {
"date_range": {
"field": "DOB",
"format": "yyyy-MM-dd",
"ranges": [
{
"from": "1989-01-01",
"to": "1990-01-01"
},
{
"from": "1991-01-01",
"to": "1992-01-01"
}
]
}
}
}
}
@@@ terms 聚合：寻找在所有的文档出现”happy birthday”里按照城市进行分类的一个聚合
GET /twitter/_search
{
"query": {
"match": {
"message": "happy birthday"
}
},
"size": 0,
"aggs": {
"city": {
"terms": {
"field": "city",
"size": 10
}
}
}
}
@@@ terms 聚合：我们也可以使用 script 来生成一个在索引里没有的术语来进行统计。比如，我们可以通过如下的script来生成一个对文档人出生年份的统计：
POST twitter/_search
{
"size": 0,
"aggs": {
"birth_year": {
"terms": {
"script": {
"source": "2019 - doc['age'].value"
},
"size": 10
}
}
}
}
@@@ histogram 聚合：根据值动态构建固定大小（也称为间隔）的存储桶
GET /twitter/_search
{
"size": 0,
"aggs": {
"age_distribution": {
"histogram": {
"field": "age",
"interval": 2
}
}
}
}
@@@ date_histogram 聚合：这种聚合类似于正常的直方图，但只能与日期或日期范围值一起使用。这里我们按照每隔一年这样的时间间隔来进行
GET /twitter/_search
{
"size": 0,
"aggs": {
"age_distribution": {
"date_histogram": {
"field": "DOB",
"interval": "year"
}
}
}
}
@@@ cardinality 聚合：统计有多少个城市
GET /twitter/_search
{
"size": 0,
"aggs": {
"number_of_cities": {
"cardinality": {
"field": "city.keyword"
}
}
}
}
@@@ Metric 聚合：我们可以使用Metrics来统计我们的数值数据
@@@ 对年龄字段全方位统计，包括数据总条数、平均值、最大/小值、求和
GET /twitter/_search
{
"size": 0,
"aggs": {
"age_stats": {
"stats": {
"field": "age"
}
}
}
}
@@@ 只得到平均值
GET /twitter/_search
{
"size": 0,
"aggs": {
"average_age": {
"avg": {
"field": "age"
}
}
}
}
POST twitter/_search
{
"size": 0,
"query": {
"match": {
"city": "北京"
}
},
"aggs": {
"average_age_beijing": {
"avg": {
"field": "age"
}
}
}
}
POST twitter/_search
{
"size": 0,
"query": {
"match": {
"city": "北京"
}
},
"aggs": {
"average_age_beijing": {
"avg": {
"field": "age"
}
},
"average_age_all": {
"global": {},
"aggs": {
"age_global_avg": {
"avg": {
"field": "age"
}
}
}
}
}
}
@@@ 使用 script，计算平均值再乘以2倍的结果
GET /twitter/_search
{
"size": 0,
"aggs": {
"average_age_2": {
"avg": {
"field": "age",
"script": {
"source": "_value * params.correction",
"params": {
"correction": 2
}
}
}
}
}
}
@@@ 或者直接使用script的方法来进行聚合
GET /twitter/_search
{
"size": 0,
"aggs": {
"average_2_times_age": {
"avg": {
"script": {
"source": "doc['age'].value * params.times",
"params": {
"times": 2.0
}
}
}
}
}
}
@@@ percentile：得到25%，50%、75%及100%的人在什么年龄范围
GET /twitter/_search
{
"size": 0,
"aggs": {
"age_quartiles": {
"percentiles": {
"field": "age",
"percents": [
25,
50,
75,
100
]
}
}
}
}
@@@ 更为复杂的聚合：结合上面的 bucket 聚合及 metric 聚合
@@@ 我们首先通过terms来生成每个城市的桶聚合，然后在每个桶里计算所有文档的平均年龄，并根据平均年龄来进行降序排序
GET /twitter/_search
{
"size": 0,
"aggs": {
"cities": {
"terms": {
"field": "city",
"order": {
"average_age": "desc"
},
"size": 5
},
"aggs": {
"average_age": {
"avg": {
"field": "age"
}
}
}
}
}
}
@@ Analyzer 分为三个部分：Char Filters, Tokenizer及 Token Filter
@@@ happy、birthday
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"analyzer": "standard"
}
@@@ happi、birthdai（词根）
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"analyzer": "english"
}
@@@ Happy、Birthday
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"analyzer": "whitespace"
}
@@@ happy、birthday
GET /twitter/_analyze
{
"text": [
"Happy.Birthday"
],
"analyzer": "simple"
}
@@@ 生、日、快、乐
GET /twitter/_analyze
{
"text": [
"生日快乐"
],
"analyzer": "standard"
}
@@@ 也可以只使用 Analyzer 中的 Tokenizer 部分
@@@ Happy、Birthday
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"tokenizer": "standard"
}
@@@ Happy Birthday
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"tokenizer": "keyword"
}
@@@ happy birthday
GET /twitter/_analyze
{
"text": [
"Happy Birthday"
],
"tokenizer": "keyword",
"filter": ["lowercase"]
}
扫码关注微信公众号 程序员35 ，获取最新技术干货，畅聊 #程序员的35，35的程序员# 。独立站点：https://cxy35.com
