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以下操作基于kibana客户端，数据也是使用kiaba自带添加的数据。
1、获得集群的健康情况
GET /_cat/health?v
说明：v是用来要求在结果中返回表头
状态值说明
Green - everything is good (cluster is fully functional)，即最佳状态
Yellow - all data is available but some replicas are not yet allocated (cluster is fully functional)，即数据和集群可用，但是集群的备份有的是坏的Red - some data is not available for whatever reason (cluster is partially functional)，即数据和集群都不可用2、获取集群所有节点
GET /_cat/nodes?v
3、获取index 列表
GET /_cat/indices?v
4、是否设置缓存加锁（ES建议关闭缓存使用）
GET _nodes?filter_path=**.mlockall
5、创建index
PUT /customer
6、创建Document
PUT /custome/_doc/1?pretty
{
"name":"john doe"}
7、查看Document
GET /custome/_doc/1
8、删除index
DELETE /custome
9、创建文档
PUT /customer/_doc/1
{
"name":"john doe"}
10、查看文档
GET /customer/_doc/1
11、修改文档（覆盖）
POST /customer/_doc/1
{
"name":"joan doe1"}
12、修改文档（部分修改）
POST /customer/_update/1
{
"doc":{ "name":"joon doe","age":5
}
}
13、修改文档（使用脚本）
POST /customer/_update/1
{
"script":"ctx._source.age +=5"}
14、批量操作（注意格式）
POST /customer/_bulk
{"index":{"_id":"2"}}
{"name":"bulk1"}
{"update":{"_id":"2"}}
{"doc":{"age":21}}
15、queryString 查询
GET /kibana_sample_data_ecommerce/_search?q=*&sort=order_date:desc
16、使用 DSL查询（查询所有文档并按 order_id 降序排列，默认展示10个）
GET /kibana_sample_data_ecommerce/_search
{
"query": {"match_all": {}}, "sort": [
{
"order_id": { "order": "desc"}
}
]
}
17、查询所有文档，数量size限制为1
GET /kibana_sample_data_ecommerce/_search
{
"query": {"match_all": {}}, "size": 1
}
18、查询所有文档的 "currency","order_id" 字段，展示 第10个开始后的10个文档
GET /kibana_sample_data_ecommerce/_search
{
"query": {"match_all": {}}, "_source": ["currency","order_id"]
, "from": 10
, "size": 10
}
19、查询文档 匹配customer_full_name 字段前缀是 Eddie的
GET /kibana_sample_data_ecommerce/_search
{
"query": {"match_phrase_prefix": { "customer_full_name": "Eddie"}}
}
20、查询文档 匹配customer_full_name字段中包含 Eddie 和Wolfe
GET /kibana_sample_data_ecommerce/_search
{
"query": { "bool": { "must": [{"match": {
"customer_full_name": "Eddie"}}
,{"match": {
"customer_full_name": "Wolfe"}}
]
}
}
}
21、查询文档 匹配customer_full_name 包含 Eddie 或Wolfe
GET /kibana_sample_data_ecommerce/_search
{
"query": { "bool": { "should": [{"match": {
"customer_full_name": "Eddie"}}
,{"match": {
"customer_full_name": "Wolfe"}}
]
}
}
}
22、查询文档 匹配customer_full_name中不包含Eddie
GET /kibana_sample_data_ecommerce/_search
{
"query": { "bool": { "must_not": [{"match": {
"customer_full_name": "Eddie"}}
]
}
}
}
23、查询文档 匹配customer_full_name 字段包含Eddie与不包含Wolfe的，且只查询customer_full_name字段
GET /kibana_sample_data_ecommerce/_search
{
"query": { "bool": { "must": [{"match": {
"customer_full_name": "Eddie"}}
]
, "must_not": [
{"match": {
"customer_full_name": "Wolfe"}}
]
}
}
,"_source": "customer_full_name"
}
24、查询文档 匹配 customer_full_name包含Eddie并且taxful_total_price的值在50-100之间的
GET /kibana_sample_data_ecommerce/_search
{
"query": { "bool": { "must": [{"match": {
"customer_full_name": "Eddie"}}
]
,"filter": {
"range": { "taxful_total_price": { "gte": 50,"lte": 100
}
}
}
}
}
}
25、按照 day_of_week 分组展示20个 并且求 组内taxful_total_price与taxless_total_price的均值，组间按照taxful_total_price 的均值倒序排列
size0 表示我们只在意分组统计结果，普通查询结果不关心。
GET /kibana_sample_data_ecommerce/_search
{
"aggs": { "agg_by_day_of_week": { "terms": { "field": "day_of_week","size": 20
, "order": {
"avg_taxful_total_price": "desc"}
}
, "aggs": {
"avg_taxful_total_price": { "avg": { "field": "taxful_total_price"}
}
,"avg_taxless_total_price":{
"avg": { "field": "taxless_total_price"}
}
}
}
}
, "size": 0
}
26、total_quantity按区间进行分组，组内再按day_of_week 进行二次分组，再求二次分组后taxless_total_price的均值。
GET /kibana_sample_data_ecommerce/_search
{
"size": 0, "aggs": {
"agg_by_total_quantity": { "range": { "field": "total_quantity","ranges": [
{
"from": 0,"to": 2
}
,{
"from": 2,"to": 4
}
,{
"from": 4, "to": 6
}
]
}
, "aggs": {
"agg_by_day_of_week": { "terms": { "field": "day_of_week"}
, "aggs": {
"avg_taxless_total_price": { "avg": { "field": "taxless_total_price"}
}
}
}
}
}
}
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