使用kibana7.0.0的控制台Dev Tools操作ES数据的基本语法入门示例
因为使用的是本地启动的ES库，所以需要先启动ES，然后启动kibana，直接从官网上下载安装启动即可，说明一点就是需先启动ES，在启动kibana，该部分效果以及添加官方示例数据已在之前一篇文章中写过，此处不再重复。
直接点击Dev Tools，来看基本操作
1，输入：GET /
在右侧将看到和启动完ES后在浏览器输入localhost:9200相同的内容
2，创建索引
输入：
说明：因为7版本之后，ES不再支持一个索引（index）可以创建多个类型（type），所以cmcc/后边不再需要写入类型名称，而是统一使用_create代替即可，同样的，查询操作使用_doc代替即可，右侧看到如下图所示类似形式表示创建成功
3，查看刚才创建的索引
输入：GET cmcc/_doc/1
右侧将显示刚才创建的内容，其中_index是刚才创建的索引名称；_type是类型，7版本统一为_doc；_id为创建时的ID，如果创建索引的时候不设置ID，那么ES将默认分配一个ID，不过样式会比较长，不好记忆；_version为版本号，如果我们之后对该数据进行了修改，那么他会随之变化；_source里边就是我们刚才加进去的数据内容
4，删除索引
输入：DELETE cmcc
只需要在DELETE后边加上索引名称即可
5，修改数据
输入：
这里我们修改了"name"值，把"province"和"conutry"值改为中文，并添加了一个新属性"xingbie"，执行之后我们再次执行获取数据内容命令GET cmcc/_doc/1，如下，可以看到数据已经被修改，版本号变成了2
6，bulk方法批量插入数据
输入：
使用POST方法，然后每一条数据的格式是一致的，首先第一行输入 {"index":{"_index":"cmcc"}} ，也就是索引名称，第二行输入要插入的完整数据，这里特别提醒下，插入的这条数据不能使用刚才创建数据时的那种多行形式，只能使用没有回车的一条数据，否则会报错如下：
{ "error": { "root_cause": [ { "type": "json_e_o_f_exception", "reason": "Unexpected end-of-input: expected close marker for Object (start marker at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@154857fc; line: 1, column: 1])\n at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@154857fc; line: 1, column: 3]" } ], "type": "json_e_o_f_exception", "reason": "Unexpected end-of-input: expected close marker for Object (start marker at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@154857fc; line: 1, column: 1])\n at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@154857fc; line: 1, column: 3]" }, "status": 500 }执行完毕后，我们再次获取数据看一下，输入：GET cmcc/_search
结果如下：（不截长图了，就直接贴结果吧>_<）
{ "took" : 374, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "1", "_score" : 1.0, "_source" : { "name" : "dunkking", "age" : 27, "location" : "SG", "province" : "河北", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "9vD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "points", "age" : 23, "location" : "PG", "province" : "江苏", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "9_D-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "rebound", "age" : 24, "location" : "SF", "province" : "广州", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-PD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "center", "age" : 23, "location" : "C", "province" : "北京", "country" : "中国", "xingbie" : "femela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-fD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "assist", "age" : 21, "location" : "PF", "province" : "广州", "country" : "中国", "xingbie" : "famela" } } ] } }7，按照条件查询
输入：
也就是查询数据中属性"province"为"广州"的数据，结果如下：
{ "took" : 10, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.7509375, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "9_D-3moBmjOHTfOJtVLL", "_score" : 1.7509375, "_source" : { "name" : "rebound", "age" : 24, "location" : "SF", "province" : "广州", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-fD-3moBmjOHTfOJtVLL", "_score" : 1.7509375, "_source" : { "name" : "assist", "age" : 21, "location" : "PF", "province" : "广州", "country" : "中国", "xingbie" : "famela" } } ] } }8，当同一个属性满足逻辑或时的查询
输入：
这里是查询属性"age"等于21或者23的数据，如果看着不舒服，我们可以点击运行按钮右侧的扳手，选择Auto indent，输入效果就会直观一些，
其中，116行固定输入"query"，117行固定输入"bool"，118行输入为"should"，表示是逻辑或的关系，120行为"match"，121行为所要查询的属性名与属性值
执行结果如下
{ "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 3, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "9vD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "points", "age" : 23, "location" : "PG", "province" : "江苏", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-PD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "center", "age" : 23, "location" : "C", "province" : "北京", "country" : "中国", "xingbie" : "femela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-fD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "assist", "age" : 21, "location" : "PF", "province" : "广州", "country" : "中国", "xingbie" : "famela" } } ] } }9，多条件查询
输入：
这里是查询属性"age"等于23，并且属性"country"为“中国”的数据，这里和上一条查询的关键区别就在于第98行由"should"改为"must"，执行结果如下：
{ "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.1740228, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "9vD-3moBmjOHTfOJtVLL", "_score" : 1.1740228, "_source" : { "name" : "points", "age" : 23, "location" : "PG", "province" : "江苏", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-PD-3moBmjOHTfOJtVLL", "_score" : 1.1740228, "_source" : { "name" : "center", "age" : 23, "location" : "C", "province" : "北京", "country" : "中国", "xingbie" : "femela" } } ] } }10，范围查询并进行排序
输入：
这里，151行使用"range"，152行输入属性名，153行"gte"和154行"lte"表示查询属性"age"在20-25范围的数据，然后158行表示排序，160行表示排序的属性是"age"，161“order”表示排序为倒序"desc"，执行结果如下：
{ "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 4, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "9_D-3moBmjOHTfOJtVLL", "_score" : null, "_source" : { "name" : "rebound", "age" : 24, "location" : "SF", "province" : "广州", "country" : "中国", "xingbie" : "mela" }, "sort" : [ 24 ] }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "9vD-3moBmjOHTfOJtVLL", "_score" : null, "_source" : { "name" : "points", "age" : 23, "location" : "PG", "province" : "江苏", "country" : "中国", "xingbie" : "mela" }, "sort" : [ 23 ] }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-PD-3moBmjOHTfOJtVLL", "_score" : null, "_source" : { "name" : "center", "age" : 23, "location" : "C", "province" : "北京", "country" : "中国", "xingbie" : "femela" }, "sort" : [ 23 ] }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "-fD-3moBmjOHTfOJtVLL", "_score" : null, "_source" : { "name" : "assist", "age" : 21, "location" : "PF", "province" : "广州", "country" : "中国", "xingbie" : "famela" }, "sort" : [ 21 ] } ] } }11，聚合查询
输入：
使用聚合查询，格式是：170行使用"aggs"，171行为所要查询的属性名，这里查询"age"，173行"field"后边输入属性名，174行为范围，分别在"from"和"to"后边输入要分段的范围，这条请求实现的是统计属性"age"按照20-23,23-25,25-30划分的数据条数分别为多少，如果想要查看满足条件的数据，则将169行"size"值置为非零数，貌似应大于查询条数，具体还没查，这里是不显示满足条件的具体数据，直接置零即可，执行结果如下：
{ "took" : 9, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : null, "hits" : [ ] }, "aggregations" : { "age" : { "buckets" : [ { "key" : "20.0-23.0", "from" : 20.0, "to" : 23.0, "doc_count" : 1 }, { "key" : "23.0-25.0", "from" : 23.0, "to" : 25.0, "doc_count" : 3 }, { "key" : "25.0-30.0", "from" : 25.0, "to" : 30.0, "doc_count" : 1 } ] } } }聚合查询的另外一个示例
输入：
这条请求是查询属性"province"的统计结果，这里是统计5条数据，并显示其中2条，并在197行"field"后输入属性名，并在其后添加 .keyword，查询结果如下
{ "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "cmcc", "_type" : "_doc", "_id" : "1", "_score" : 1.0, "_source" : { "name" : "dunkking", "age" : 27, "location" : "SG", "province" : "河北", "country" : "中国", "xingbie" : "mela" } }, { "_index" : "cmcc", "_type" : "_doc", "_id" : "9vD-3moBmjOHTfOJtVLL", "_score" : 1.0, "_source" : { "name" : "points", "age" : 23, "location" : "PG", "province" : "江苏", "country" : "中国", "xingbie" : "mela" } } ] }, "aggregations" : { "province" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "广州", "doc_count" : 2 }, { "key" : "北京", "doc_count" : 1 }, { "key" : "江苏", "doc_count" : 1 }, { "key" : "河北", "doc_count" : 1 } ] } } }暂时写这么多，刚开始学，很多不熟悉的，后续有时间慢慢补充
