文章目录
一，Mac上Elasticsearch和Kibana的安装
Elasticsearch是一个基于Apache Lucene的搜索服务器，适用于所有类型的数据，包括文本、数字、地理空间、结构化和非结构化数据，是是ELK的一个组成部分（ELK代表的是：E就是ElasticSearch，L就是Logstach，K就是kibana）。
它提供了分布式可扩展的实时搜索和分析引擎，它以其简单的 REST 风格 API、分布式特性、速度和可扩展性而闻名，是一个非常强大的搜索引擎全文检索。
Elasticsearch 是由Elastic公司创建并开源维护的，该 公司也拥有 Logstash 及 Kibana 开源项目。
三个开源项目共同形成了一个强大的生态圈。简单地说，Logstash 负责数据的采集，处理（丰富数据，数据转型等），Kibana 负责数据展示，分析及管理。Elasticsearch 处于最核心的位置，它可以帮我们对数据进行快速地搜索及分析。
1.1 环境与下载
在安装之前，提前了解本地PC的java版本，因为java版本和的elasticsearch，kibana的对应关系是有严格要求的
我本地Mac使用的是：
java version “1.8.0_121”
elasticsearch-6.8.2 下载地址：https://www.elastic.co/cn/downloads/elasticsearch
kibana-6.8.23 下载地址：https://www.elastic.co/cn/downloads/kibana
1.2 安装与运行
下载完成之后，在Mac上找个目录解压以上两个压缩包
然后进入各自的bin目录下
elasticsearch的启动命令：
./elasticsearch
kibana的启动命令：
./kibana
输出日志运行完毕后，分别访问 http://localhost:9200（返回json格式的数据）和http://localhost:5601（返回一个页面），若两个页面都显示正常，则运行成功
注意：kibana启动花费时间较长，当执行命令后没有立即看到日志输出为正常情况
1.3 问题
1.3.1 elasticsearch安装后其他机器不能访问
在Mac上运行成功后，同一网段的Windows访问不了时，到Mac上安装目录下的config/elasticsearch.yml下，添加或修改一行
network.bind_host: 0.0.0.0
重新启动，验证
1.3.2 kibana安装后其他机器不能访问
同上，到安装目录下的config/kibana.yml下，添加或修改两行
server.port:5602server.host: 0.0.0.0
重新启动，验证
二，Elasticsearch在Kibana的常见命令
首先，在使用命令之前，需要知道以下的命令可以在哪里运行
打开kibana的首页，点击左边栏的【Dev Tools】，右边栏下面的【Console】分为左右两栏，在左边栏输入命令，然后点击三角形绿色按钮，就可以在右边栏呈现结果，如下所示：
2.1 查看集群的健康状态
GET _cat/health
================================ 结果 ================================1673923769 02:49:29 elasticsearch yellow 11770050 - 58.3%
若想知道每个值的含义
GET _cat/health?v
================================ 结果 ================================
epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent
1673923848 02:50:48 elasticsearch yellow 11770050 - 58.3%
常见属性解读：
- epoch：当前时间的时间戳（默认与东八区差八个小时）
- timestamp：当前时间
- cluster：集群名称
- status：集群状态，green代表健康，yellow代表当前为单机，没有副本
- node.total：在线节点个数
- node.data：在线数据节点个数
- …
获取更加详细的内容
GET _all
================================ 结果 ================================#! Deprecation: [types removal] The parameter include_type_name should be explicitly specified in get indices requests to prepare for 7.0. In 7.0 include_type_name will default to 'false', which means responses will omit the type name in mapping definitions.{".kibana_1":{"aliases":{".kibana":{}},
"mappings":{"doc":{"dynamic":"strict",
"properties":{......
2.2 索引
2.2.1 查看所有索引
GET _cat/indices
================================ 结果 ================================
yellow open human_index Mf-9YNYrSdyiLZFgZCP7ow 514022.6kb 22.6kb
green open .kibana_task_manager J9YFrgfOS1W2N3dvqXwxOg 102012.5kb 12.5kb
green open .kibana_1 hgDx6B-6QmC0KjWLWB3wgQ 105126.5kb 26.5kb
若想知道每个值的含义
GET _cat/indices?v
================================ 结果 ================================
health status index uuid pri rep docs.count docs.deleted store.size pri.store.size
yellow open human_index Mf-9YNYrSdyiLZFgZCP7ow 514022.6kb 22.6kb
green open .kibana_task_manager J9YFrgfOS1W2N3dvqXwxOg 102012.5kb 12.5kb
green open .kibana_1 hgDx6B-6QmC0KjWLWB3wgQ 105126.5kb 26.5kb
常见属性解读：
- health：索引健康状态
- status：索引启动状态
- index：索引名称
- uuid：索引的唯一标识
- pri：索引主分片数
- rep：索引副本分片数
- docs.count：索引中文档数
- docs.deleted：索引中删除状态的文档
2.2.2 新增索引
PUT /human_index1
================================ 结果 ================================#! Deprecation: the default number of shards will change from [5] to [1] in 7.0.0; if you wish to continue using the default of [5] shards, you must manage this on the create index request or with an index template{"acknowledged": true,
"shards_acknowledged": true,
"index":"human_index1"}
2.2.3 查看单个索引
GET /human_index1
================================ 结果 ================================#! Deprecation: [types removal] The parameter include_type_name should be explicitly specified in get indices requests to prepare for 7.0. In 7.0 include_type_name will default to 'false', which means responses will omit the type name in mapping definitions.{"human_index1":{"aliases":{},
"mappings":{},
"settings":{"index":{"creation_date":"1673926295232",
"number_of_shards":"5",
"number_of_replicas":"1",
"uuid":"i9ESnW6ETN2n5C6V5PLZ8Q",
"version":{"created":"6082399"},
"provided_name":"human_index1"}}}}
2.2.4 删除单个索引
DELETE /human_index1
================================ 结果 ================================{"acknowledged":true}
2.3 查看节点列表
GET _cat/nodes
================================ 结果 ================================10.197.29.203 214592.11 mdi * 2FgJQbJ
或
GET _cat/nodes?v
================================ 结果 ================================ip heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
10.197.29.203 234591.99 mdi * 2FgJQbJ
常见属性解读：
- ip：部署的ip地址
- heap.percent：堆内存占用百分比
- ram.percent：内存占用百分比
- cup：CPU占用百分比
- load_1m：1分钟的系统负载
- node.role：节点的角色
- master：是否为master节点
- name：节点名称
2.4 文档的增删查改
2.4.1 新增文档
put /human_index/user/1
{"name":"hh",
"desc":"my name is hh",
"age":25,
"country":"China GuangDong",
"sex":"female"}================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":1,
"result":"created",
"_shards":{"total":2,
"successful":1,
"failed":0},
"created": true,
"_seq_no":1,
"_primary_term":2}
在以上的新增方式中，已经指定了该文档的id（1），如果不需要自定义id的话，可以使用以下方式：
POST /human_index/user
{"name":"id_test",
"desc":"test no id"}================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"MnS2woUBq_u6VYKKJjno",
"_version":1,
"result":"created",
"_shards":{"total":2,
"successful":1,
"failed":0},
"_seq_no":10,
"_primary_term":3}
可以看到，默认随机生成的id为MnS2woUBq_u6VYKKJjno
在创建document的时候，如果命令行的索引index（human_index）和类型type（user）不存在，默认会自动创建。
2.4.2 查询文档
查询单条
get /human_index/user/1
================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":1,
"_seq_no":1,
"_primary_term":2,
"found": true,
"_source":{"name":"hh",
"desc":"my name is hh",
"age":25,
"country":"China GuangDong",
"sex":"female"}}
查询所有
get /human_index/user/_search
================================ 结果 ================================{"took":1,
"timed_out": false,
"_shards":{"total":5,
"successful":5,
"skipped":0,
"failed":0},
"hits":{"total":4,
"max_score":1.0,
"hits":[{"_index":"human_index",
"_type":"user",
"_id":"2",
"_score":1.0,
"_source":{"name":"sb",
"desc":"my name is sb",
"age":25,
"country":"China GuangDong Jieyang",
"sex":"female"}},
{"_index":"human_index",
"_type":"user",
"_id":"4",
"_score":1.0,
"_source":{"doc":{"name":"lmc hh",
"country":"China GuangDong Jieyang",
"sex":"male",
"desc":"my name is leemon",
"age":11}}},
{"_index":"human_index",
"_type":"user",
"_id":"1",
"_score":1.0,
"_source":{"name":"hh",
"desc":"my name is hh",
"age":25,
"country":"China GuangDong",
"sex":"female"}},
{"_index":"human_index",
"_type":"user",
"_id":"3",
"_score":1.0,
"_source":{"age":24,
"country":"China GuangDong Shenzhen",
"sex":"male",
"name":"ln",
"desc":"my name is lee nai"}}]}}
或
get /human_index/user/_search
{"query":{"match_all":{}}}
由于我是把流程走过一遍了，因此存在多条记录
字段解释：
- took：耗费时间（毫秒）
- _shards：分片情况
- hits：获取到的数据情况 - total：数据总条数- max_score：数据里面打分最高的分数
2.4.3 修改文档
修改可以通过POST和PUT来处理，但两者有区别
- PUT的修改是全局的修改，会丢数据
- POST的修改是局部更新，别的数据不变；请求体文档内容要包裹在键doc内，
PUT
使用put时，如果原document已存在，则会直接替换成新的
put /human_index/user/1
{"sex":"female"}================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":3,
"result":"updated",
"_shards":{"total":2,
"successful":1,
"failed":0},
"_seq_no":2,
"_primary_term":2}
再继续查看：
get /human_index/user/1
================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":3,
"_seq_no":2,
"_primary_term":2,
"found": true,
"_source":{"sex":"female"}}
可以发现，除了sex字段外，其他都不见了
POST
将该文档重新还原
put /human_index/user/1
{"name":"hh",
"desc":"my name is hh",
"age":25,
"country":"China GuangDong",
"sex":"female"}
然后通过POST进行修改
post /human_index/user/1/_update
{"doc":{"sex":"male"}}================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":7,
"result":"updated",
"_shards":{"total":2,
"successful":1,
"failed":0},
"_seq_no":6,
"_primary_term":2}
再重新查看
get /human_index/user/1
================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":7,
"_seq_no":6,
"_primary_term":2,
"found": true,
"_source":{"name":"hh",
"desc":"my name is hh",
"age":25,
"country":"China GuangDong",
"sex":"male"}}
这个时候，除了sex的其他属性都在存在，为局部修改
2.4.4 删除文档
DELETE /human_index/user/1
================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"_version":8,
"result":"deleted",
"_shards":{"total":2,
"successful":1,
"failed":0},
"_seq_no":7,
"_primary_term":2}
再继续查看
get /human_index/user/1
================================ 结果 ================================{"_index":"human_index",
"_type":"user",
"_id":"1",
"found":false}
已经删除成功
2.5 查询
再进行查询之前，该索引类型user下的所有记录如下所示
{"took":2,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":6,"max_score":1.0,"hits":[{"_index":"human_index","_type":"user","_id":"5","_score":1.0,"_source":{"name":"ln-1","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon-1","age":21}},{"_index":"human_index","_type":"user","_id":"2","_score":1.0,"_source":{"name":"sb","desc":"my name is sb","age":25,"country":"China GuangDong Jieyang","sex":"female"}},{"_index":"human_index","_type":"user","_id":"4","_score":1.0,"_source":{"doc":{"name":"lmc hh","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon","age":11}}},{"_index":"human_index","_type":"user","_id":"6","_score":1.0,"_source":{"name":"ln sb","country":"China GuangDong Jieyang","sex":"male","desc":"my name is sb leemon","age":27}},{"_index":"human_index","_type":"user","_id":"1","_score":1.0,"_source":{"name":"hh","desc":"my name is hh","age":25,"country":"China GuangDong","sex":"female"}},{"_index":"human_index","_type":"user","_id":"3","_score":1.0,"_source":{"age":24,"country":"China GuangDong Shenzhen","sex":"male","name":"ln","desc":"my name is lee nai"}}]}}
2.5.1 单条/全表查询
详见2.4.2
2.5.2 分词查询
get /human_index/user/_search
{"query":{"match":{"name":"ln"}}}
结果会查出三条记录（省略部分结果）
{"_index":"human_index","_type":"user","_id":"6","_score":0.6099695,"_source":{"name":"ln sb","country":"China GuangDong Jieyang","sex":"male","desc":"my name is sb leemon","age":27}},{"_index":"human_index","_type":"user","_id":"5","_score":0.2876821,"_source":{"name":"ln-1","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon-1","age":21}},{"_index":"human_index","_type":"user","_id":"3","_score":0.2876821,"_source":{"age":24,"country":"China GuangDong Shenzhen","sex":"male","name":"ln","desc":"my name is lee nai"}}
可以看到，通过match查询时，当从文档中的name属性值中出现
ln
时，满足条件
2.5.3 子属性分词查询
get /human_index/user/_search
{"query":{"match":{"doc.name":"hh"}}}
结果查出一条记录
{"_index":"human_index","_type":"user","_id":"4","_score":0.2876821,"_source":{"doc":{"name":"lmc hh","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon","age":11}}}
2.5.4 短句查询
前面的是对单个词进行查询，短句指的是多个词组合形成的句子
get /human_index/user/_search
{"query":{"match_phrase":{"country":"GuangDong Jieyang"}}}
结果查出3条记录，id分别为：2，5，6
如果将
match_phrase
改成
match
，相当于只要country中出现
GuangDong
或者
Jieyang
，都会被查出来，相当于查询条件会先被分词，然后返回分词后查询的并集
2.5.5 模糊查询
这里的模糊查询跟关系型数据库的模糊查询有较大的差异，关系型的模糊查询与上面的分词，短句查询类似，Elasticsearch的模糊查询是指查询出参数内容和实际内容的编辑距离在2以内的文档
get /human_index/user/_search
{"query":{"fuzzy":{"country":"Jieyank"}}}
或
get /human_index/user/_search
{"query":{"fuzzy":{"country":"Jieyamg"}}}
等等。
由于
Jieyang
跟
Jieyank
和
Jieyamg
的编辑距离都在2以内，因此能够通过模糊查询得到。结果查出的记录id分别为：2，5，6
2.5.6 排序
get /human_index/user/_search
{"query":{"match":{"country":"Jieyang"}},
"sort":[{"_id":{"order":"desc"}}]}
查询出来的文档数量和
2.5.5
一样，只不过根据id进行降序排序
2.5.7 分页查询
get /human_index/user/_search
{"query":{"match_all":{}},
"sort":[{"age":{"order":"asc"}}],
"from":0,
"size":3}
查找
age
最小的三个文档记录，返回结果的记录id按顺序为：5，3，2
2.5.8 指定字段查询
get /human_index/user/_search
{"query":{"match":{"country":"Jieyang"}},
"_source":["name"]}
查询结果如下所示：
{"took":1,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":3,"max_score":0.2876821,"hits":[{"_index":"human_index","_type":"user","_id":"5","_score":0.2876821,"_source":{"name":"ln-1"}},{"_index":"human_index","_type":"user","_id":"2","_score":0.18232156,"_source":{"name":"sb"}},{"_index":"human_index","_type":"user","_id":"6","_score":0.18232156,"_source":{"name":"ln sb"}}]}}
2.5.9 多条件查询
如果需要多个查询条件拼接在一起就需要使用bool
bool
过滤可以用来合并多个过滤条件查询结果的布尔逻辑，它包含以下操作符：
- must：多个查询条件的完全匹配，相当于 AND
- must_not：多个查询条件的相反匹配，相当于 NOT
- should：至少有一个条件符合匹配，相当于 OR
查找country出现Jieyang，name出现sb，age在24-26中的文档
get /human_index/user/_search
{"query":{"bool":{"must":[{"match":{"country":"Jieyang"}},
{"match":{"name":"sb"}}],
"filter":{"range":{"age":{"gte":24,
"lte":26}}}}}}
结果只查出id为2的文档
关于范围查询：
- gte：大于或大于
- gt：大于
- lte：小于或等于
- le：小于
查找country出现Jieyang或name出现sb，并且age在24-26中的文档
get /human_index/user/_search
{"query":{"bool":{"should":[{"match":{"country":"Jieyang"}},
{"match":{"name":"sb"}}],
"filter":{"range":{"age":{"gte":24,
"lte":26}}}}}}
结果查出id为1，2，3的文档
2.5.10 高亮显示
查询返回结果的时候，将查询条件的内容高亮显示
get /human_index/user/_search
{"query":{"bool":{"must":[{"match":{"country":"Jieyang"}},
{"match":{"name":"sb"}}],
"filter":{"range":{"age":{"gte":24,
"lte":26}}}}},
"highlight":{"fields":{"country":{}}}}
返回结果
{"took":3,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":1,"max_score":0.39343074,"hits":[{"_index":"human_index","_type":"user","_id":"2","_score":0.39343074,"_source":{"name":"sb","desc":"my name is sb","age":25,"country":"China GuangDong Jieyang","sex":"female"},"highlight":{"country":["China GuangDong <em>Jieyang</em>"]}}]}}
2.6 聚合分析
2.6.1 简单分组
对
country
的每个词进行分组，统计出现的文档数量（用户
user
数量）
get /human_index/user/_search
{"aggs":{"group_by_tag":{"terms":{"field":"country"}}}}
返回结果
{"error":{"root_cause":[{"type":"illegal_argument_exception","reason":"Fielddata is disabled on text fields by default. Set fielddata=true on [country] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."}],"type":"search_phase_execution_exception","reason":"all shards failed","phase":"query","grouped":true,"failed_shards":[{"shard":0,"index":"human_index","node":"2FgJQbJ5QhWVXfvoaI2kqQ","reason":{"type":"illegal_argument_exception","reason":"Fielddata is disabled on text fields by default. Set fielddata=true on [country] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."}}],"caused_by":{"type":"illegal_argument_exception","reason":"Fielddata is disabled on text fields by default. Set fielddata=true on [country] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead.","caused_by":{"type":"illegal_argument_exception","reason":"Fielddata is disabled on text fields by default. Set fielddata=true on [country] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."}}},"status":400}
这里发现报错了，但是原因不是执行命令的问题，是因为elasticsearch默认
fielddata
的值为false，此时先要对分组的字段进行处理，将
fielddata
值修改为
true
get /human_index/_mapping/user
{"properties":{"country":{"type":"text",
"fielddata":true}}}================================ 结果 ================================{"acknowledged":true}
再重新执行一遍统计命令，得到结果：
{"took":4,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":6,"max_score":1.0,"hits":[{"_index":"human_index","_type":"user","_id":"5","_score":1.0,"_source":{"name":"ln-1","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon-1","age":21}},{"_index":"human_index","_type":"user","_id":"2","_score":1.0,"_source":{"name":"sb","desc":"my name is sb","age":25,"country":"China GuangDong Jieyang","sex":"female"}},{"_index":"human_index","_type":"user","_id":"4","_score":1.0,"_source":{"doc":{"name":"lmc hh","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon","age":11}}},{"_index":"human_index","_type":"user","_id":"6","_score":1.0,"_source":{"name":"ln sb","country":"China GuangDong Jieyang","sex":"male","desc":"my name is sb leemon","age":27}},{"_index":"human_index","_type":"user","_id":"1","_score":1.0,"_source":{"name":"hh","desc":"my name is hh","age":25,"country":"China GuangDong","sex":"female"}},{"_index":"human_index","_type":"user","_id":"3","_score":1.0,"_source":{"age":24,"country":"China GuangDong Shenzhen","sex":"male","name":"ln","desc":"my name is lee nai"}}]},"aggregations":{"group_by_tag":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"china","doc_count":5},{"key":"guangdong","doc_count":5},{"key":"jieyang","doc_count":3},{"key":"shenzhen","doc_count":1}]}}}
可以看到
aggregations
中，对
country
每个词出现的文档数量
2.6.2 分组统计
对
sex
进行分组，计算每个分组的平均age，再按照平均
age
降序排序。在查询之前，记得先对
sex
的
fielddata
进行设置
get /human_index/user/_search
{"aggs":{"group_by_tag":{"terms":{"field":"sex",
"order":{"avg_age":"desc"}},
"aggs":{"avg_age":{"avg":{"field":"age"}}}}}}
结果如下所示：
{"took":4,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":6,"max_score":1.0,"hits":[{"_index":"human_index","_type":"user","_id":"5","_score":1.0,"_source":{"name":"ln-1","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon-1","age":21}},{"_index":"human_index","_type":"user","_id":"2","_score":1.0,"_source":{"name":"sb","desc":"my name is sb","age":25,"country":"China GuangDong Jieyang","sex":"female"}},{"_index":"human_index","_type":"user","_id":"4","_score":1.0,"_source":{"doc":{"name":"lmc hh","country":"China GuangDong Jieyang","sex":"male","desc":"my name is leemon","age":11}}},{"_index":"human_index","_type":"user","_id":"6","_score":1.0,"_source":{"name":"ln sb","country":"China GuangDong Jieyang","sex":"male","desc":"my name is sb leemon","age":27}},{"_index":"human_index","_type":"user","_id":"1","_score":1.0,"_source":{"name":"hh","desc":"my name is hh","age":25,"country":"China GuangDong","sex":"female"}},{"_index":"human_index","_type":"user","_id":"3","_score":1.0,"_source":{"age":24,"country":"China GuangDong Shenzhen","sex":"male","name":"ln","desc":"my name is lee nai"}}]},"aggregations":{"group_by_tag":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"female","doc_count":2,"avg_age":{"value":25.0}},{"key":"male","doc_count":3,"avg_age":{"value":24.0}}]}}}
2.6.3 区间分组
划分
age
范围区间，按照年龄区间进行分组，在每个分组内再按照
sex
进行分组，然后计算每个分组的平均年龄，降序排序
get /human_index/user/_search
{"aggs":{"group_age_range":{"range":{"field":"age",
"ranges":[{"from":0,
"to":10},{"from":11,
"to":20},{"from":21,
"to":25},{"from":25,
"to":30}]},
"aggs":{"group_by_sex":{"terms":{"field":"sex",
"order":{"avg_age":"desc"}},
"aggs":{"avg_age":{"avg":{"field":"age"}}}}}}}}
输出结果的aggregations如下所示：
{"group_age_range":{"buckets":[{"key":"0.0-10.0","from":0.0,"to":10.0,"doc_count":0,"group_by_sex":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[]}},{"key":"11.0-20.0","from":11.0,"to":20.0,"doc_count":0,"group_by_sex":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[]}},{"key":"21.0-25.0","from":21.0,"to":25.0,"doc_count":2,"group_by_sex":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"male","doc_count":2,"avg_age":{"value":22.5}}]}},{"key":"25.0-30.0","from":25.0,"to":30.0,"doc_count":3,"group_by_sex":{"doc_count_error_upper_bound":0,"sum_other_doc_count":0,"buckets":[{"key":"male","doc_count":1,"avg_age":{"value":27.0}},{"key":"female","doc_count":2,"avg_age":{"value":25.0}}]}}]}}
2.7 Mapping
通过
_mapping
可以设置和查看每个类型每个字段的数据类型等等
2.7.1 查看所有类型type的mapping
get /human_index/_mapping
================================ 结果 ================================#! Deprecation: [types removal] The parameter include_type_name should be explicitly specified in get mapping requests to prepare for 7.0. In 7.0 include_type_name will default to 'false', which means responses will omit the type name in mapping definitions.{"human_index":{"mappings":{"user":{"properties":{"age":{"type":"long"},
"country":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}},
"fielddata":true},
"desc":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}},
"doc":{"properties":{"age":{"type":"long"},
"country":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}},
"desc":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}},
"name":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}},
"sex":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}}}},
"name":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}}},
"sex":{"type":"text",
"fields":{"keyword":{"type":"keyword",
"ignore_above":256}},
"fielddata":true},
"tags":{"type":"text",
"fielddata":true}}}}}}
2.7.2 查看单个类型type的mapping
get /human_index/_mapping/user
由于当前只有一个索引
human_index
，且索引下只有一个类型
user
，因此结果与
2.7.1
基本一致
2.7.3 修改mapping
参考
2.6.1
的修改fielddata属性
本文转载自: https://blog.csdn.net/lmchhh/article/details/128724113
版权归原作者 李奈 - Leemon 所有， 如有侵权，请联系我们删除。
版权归原作者 李奈 - Leemon 所有， 如有侵权，请联系我们删除。