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不介绍安装方式，主要是用来记录在学习elasticsearch时的一些基础入门命令和CRUD操作。
以下的例子，都是经过测试的，本文的例子使用的时kibana工具来进行测试
1、检查elasticsearch是否启动成功
会返回当前elasticsearch的服务名称，版本号等相关信息
2、查看当前elasticsearch的状态信息
GET /_cat/health?v
会返回当前服务的状态索引，elasticsearch使用几个颜色状态来表示集群的状态
green:每个索引的primary shard 和replica shard 都是active
yellow:每个索引的primary shard都是active，但是部分replica shard处于不可用状态
red:不是所有的primary shard都是active 状态
Q：为什么我们本地服务状态是yellow的？
A:当前电脑上启动了一个es进程，相当于只有一个node。es当中只有一个index，是kibana内置的index。由于默认的配置是给每个index分配5个primary shard 和replica shard,而且primary shard 和 replica shard不能在同一台机器上。目前kibana自己创建的index是一个primary shard和一个replica shard 。而我们当前机器上只有一个node,所以只有一个primary shard 被分配了，replica shard 没有第二台机器去启动。
3、GET /_cat/indices/?v
查看当前机器上有哪些索引
1、创建商品信息 put命令，如果索引，type不存在，则创建
PUT /test_index1/test_type1/1{ "name":"test", "age":12, "date":"2018-10-11"}返回结果：{ "_index" : "test_index1", "_type" : "test_type1", "_id" : "1", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 0, "_primary_term" : 1} 2、查询信息
使用get命令，根据index/type/id的方式，可以精确查询到具体的数据
GET /test_index1/test_type1/1返回结果{ "_index" : "test_index1", "_type" : "test_type1", "_id" : "1", "_version" : 1, "found" : true, "_source" : { "name" : "test", "age" : 12, "date" : "2018-10-11" }} 3、修改信息
使用put命令
PUT /test_index1/test_type1/1{ "tags":"test1", "brand":"no", "price":12}执行结果：{ "_index" : "test_index1", "_type" : "test_type1", "_id" : "1", "_version" : 2, "result" : "updated", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 1, "_primary_term" : 1} 需要注意的是版本号加一，并且put命令会把原来的所有属性都替换掉，数据当中是当前显示的所有put当中的内容，在实际开发使用这个命令需要慎重，
4、只更新部分字段
post命令只更新部分字段，注意其格式
POST /test_index1/test_type1/1/_update{ "doc": { "name":"wozaiceshi" }}---------返回结果---------{ "_index" : "test_index1", "_type" : "test_type1", "_id" : "1", "_version" : 3, "result" : "updated", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 2, "_primary_term" : 1} 5、删除命令
格式 delete /index/type/id
DELETE /test_index1/test_type1/1--------------返回结果-----------{ "_index" : "test_index1", "_type" : "test_type1", "_id" : "1", "_version" : 4, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 3, "_primary_term" : 1} 注意这里在删除时，版本号也是会加一，es并不会在此刻立即删除数据，后面会介绍es的删除逻辑
1、查询所有商品信息
格式 GET /index/type/_search
GET /ecommerce/product/_search返回结果{ "took" : 49, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 1.0, "hits" : [ { "_index" : "ecommerce", "_type" : "product", "_id" : "2", "_score" : 1.0, "_source" : { "name" : "jiajieshi yagao", "desc" : "youxiao fangzhu", "price" : 25, "producer" : "jiajieshi producer", "tags" : [ "fangzhu" ] } }, 对于search的返回结果
took:表示本次查询耗费的时间，49毫秒
timed_out:表示是否超时，false未超时
shards:表示分片信息，total表示总共5个分片，成功了5个
hits:表示的本次查询的结果信息，total表示查询总数，max_score:score的含义，就是document对于一个search的相关度的匹配分数，越相关，就越匹配，分数也高
如果想要在search的基础上进行条件的筛选，可以使用如下命令
GET /ecommerce/product/_search?q=name:yagao&sort=price:desc
这种方式的查询在实际开发当中使用的不多，主要是在进行相关数据的查询时，用于快速查询
2、复杂条件下的查询条件
查询所有信息
GET /ecommerce/product/_search{ "query": { "match_all": {} }} 结合一些条件的查询
GET /ecommerce/product/_search{ "query" : { "match" : { "name" : "yagao" } }, "sort": [ { "price": "desc" } ]} 分页查询：
GET /ecommerce/product/_search{ "query": { "match_all": {} }, "from": 1, "size": 1} from 和size 类似于mysql当中的 limit m,n 查询
查询结果只返回特定的filed字段，添加_source，筛选列
GET /ecommerce/product/_search{ "query": { "match_all": {} }, "_source": ["name", "price"]} 3、query fileter
查询名称等于牙膏，并且价格大于25的商品信息，注意和上述只筛选名称的区别
GET /ecommerce/product/_search{ "query" : { "bool" : { "must" : { "match" : { "name" : "yagao" } }, "filter" : { "range" : { "price" : { "gt" : 25 } } } } }} 4、全文检索
GET /ecommerce/product/_search{ "query" : { "match" : { "producer" : "yagao producer" } }} 注意，producer这个字段会被拆解，建立倒排索引
对于上述的筛选条件，会被拆分成yagao producer ---> yagao和producer，进行索引匹配
5、短语检索
和上述全文检索不同的是，短语检索不会进行拆分，会对某个连续的字段进行检索
GET /ecommerce/product/_search{ "query" : { "match_phrase" : { "producer" : "yagao producer" } }}------为查询到数据 聚合搜索的理解就是类似于mysql当中的group by ,已经count, avg函数的使用
1、根据tags进行分组查询
aggs是es的一个分组标签，group_by_tags是我自己定义的名称，terms是es的标签，
需要注意的是，在使用tags进行分组查询之前，需要设置这个tags的属性，将文本内容的fielddata设置为true，只有在开启之后，才能进行使用，否则会报错
PUT /ecommerce/_mapping/product{ "properties": { "tags": { "type": "text", "fielddata": true } }} 以下截取了部分的分组信息
GET /ecommerce/product/_search{ "aggs": { "group_by_tags": { "terms": { "field": "tags" } } }}----------查询结果--------------"aggregations" : { "group_by_tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "fangzhu", "doc_count" : 2 }, { "key" : "meibai", "doc_count" : 1 }, { "key" : "qingxin", "doc_count" : 1 } ] } } 2、对于名称当中包含yahao的商品，按照tags进行过滤操作
即先进行条件过滤，在进行分组查询
GET /ecommerce/product/_search{ "query": { "match": { "name": "yangao" } }, "aggs": { "all_tags": { "terms": { "field": "tags" } } }}----------返回结果{ "took" : 3, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 0.2876821, "hits" : [ { "_index" : "ecommerce", "_type" : "product", "_id" : "2", "_score" : 0.2876821, "_source" : { "name" : "jiajieshi yagao", "desc" : "youxiao fangzhu", "price" : 25, "producer" : "jiajieshi producer", "tags" : [ "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "1", "_score" : 0.2876821, "_source" : { "name" : "gaolujie yagao", "desc" : "gaoxiao meibai", "price" : 30, "producer" : "gaolujie producer", "tags" : [ "meibai", "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "3", "_score" : 0.2876821, "_source" : { "name" : "zhonghua yagao", "desc" : "caoben zhiwu", "price" : 40, "producer" : "zhonghua producer", "tags" : [ "qingxin" ] } } ] }, "aggregations" : { "all_tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "fangzhu", "doc_count" : 2 }, { "key" : "meibai", "doc_count" : 1 }, { "key" : "qingxin", "doc_count" : 1 } ] } }} 3、先进行分组，再计算每个分组下的商品平均价格
es提供了avg函数来计算平均值
GET /ecommerce/product/_search{ "aggs": { "group_by_terms": { "terms": { "field": "tags" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } }}--------结果---------------{ "took" : 47, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 1.0, "hits" : [ { "_index" : "ecommerce", "_type" : "product", "_id" : "2", "_score" : 1.0, "_source" : { "name" : "jiajieshi yagao", "desc" : "youxiao fangzhu", "price" : 25, "producer" : "jiajieshi producer", "tags" : [ "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "1", "_score" : 1.0, "_source" : { "name" : "gaolujie yagao", "desc" : "gaoxiao meibai", "price" : 30, "producer" : "gaolujie producer", "tags" : [ "meibai", "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "3", "_score" : 1.0, "_source" : { "name" : "zhonghua yagao", "desc" : "caoben zhiwu", "price" : 40, "producer" : "zhonghua producer", "tags" : [ "qingxin" ] } } ] }, "aggregations" : { "group_by_terms" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "fangzhu", "doc_count" : 2, "avg_price" : { "value" : 27.5 } }, { "key" : "meibai", "doc_count" : 1, "avg_price" : { "value" : 30.0 } }, { "key" : "qingxin", "doc_count" : 1, "avg_price" : { "value" : 40.0 } } ] } }} 4、计算每个tags下的商品平均价格，并按照价格进行降序排序
GET /ecommerce/product/_search{ "aggs": { "group_by_terms": { "terms": { "field": "tags", "order": { "avg_price": "desc" } }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } }}--------返回结果------------{ "took" : 12, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 3, "max_score" : 1.0, "hits" : [ { "_index" : "ecommerce", "_type" : "product", "_id" : "2", "_score" : 1.0, "_source" : { "name" : "jiajieshi yagao", "desc" : "youxiao fangzhu", "price" : 25, "producer" : "jiajieshi producer", "tags" : [ "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "1", "_score" : 1.0, "_source" : { "name" : "gaolujie yagao", "desc" : "gaoxiao meibai", "price" : 30, "producer" : "gaolujie producer", "tags" : [ "meibai", "fangzhu" ] } }, { "_index" : "ecommerce", "_type" : "product", "_id" : "3", "_score" : 1.0, "_source" : { "name" : "zhonghua yagao", "desc" : "caoben zhiwu", "price" : 40, "producer" : "zhonghua producer", "tags" : [ "qingxin" ] } } ] }, "aggregations" : { "group_by_terms" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "qingxin", "doc_count" : 1, "avg_price" : { "value" : 40.0 } }, { "key" : "meibai", "doc_count" : 1, "avg_price" : { "value" : 30.0 } }, { "key" : "fangzhu", "doc_count" : 2, "avg_price" : { "value" : 27.5 } } ] } }} 5、先按照价格区间进行分组，组内数据按照tag进行分组，最后计算每组的价格
GET /ecommerce/product/_search{ "aggs": { "group_by_price": { "range": { "field": "price", "ranges": [ { "from": 0, "to": 20 }, { "from": 20, "to":40 }, { "from": 40, "to":60 } ] }, "aggs": { "group_by_tags": { "terms": { "field": "tags" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } } } }}
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