如何编写查询以找到Elasticsearch中的百分比? [英] How to write query to find percentage in elasticsearch?
问题描述
我在Elasticsearch中有数据.
这是我的实际文档 https://docs.google.com/document/d/1DKID90I9ulUcut-S8UfrnSjY-3citEwmyfnJJmrIRU8/edit?usp=sharing
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 13:00:05",
event_type:"heartbeat"
}
我在输入中有store_id，日期范围和事件类型.在输出中，我需要设备在该小时内在线的时间百分比.
这就是我们在线考虑设备的方式. 如果一个小时内store_id发生event ="heartbeat"，则表示商店在线.
示例1.
因此，如果范围是从"2019-05-07"到"2019-05-08"，并且有14个文档的小时数不同，则百分比将是(14/(2 * 24))* 100
示例2.
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 13:00:05",
event_type:"heartbeat"
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 14:00:05",
event_type:"heartbeat"
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 14:00:05",
event_type:"heartbeat"
}
如果输入是store_id ="abc"和date_range ="2019-06-05"到" 2019-06-05和event_type =" heartbeat，则输出为(2/(1 * 24))，因为该商店的event = heartbeat只有两个不同的小时.
这是我对累计总和的查询.如果有一些如何将最终的累计总和除以日期之间的差异的话.
GET /internship38/_search
{
"query":
{
"bool":
{
"must":
[
{
"match" :
{
"attributes.store_id" : "41b15888-0c2f-48f9-89d0-dc7aad19f52b"
}
},
{
"match":
{
"event_type":"app_sent_heartbeat"
}
}
]
}
},
"aggs":
{
"my_date_histo":{
"date_histogram":{
"field":"arrival_timestamp",
"interval":"day"
},
"aggs":
{
"distinct_hours": {
"cardinality": {
"script": {
"lang": "painless",
"source": "doc[params.date_field].value.hourOfDay;",
"params": {
"date_field": "arrival_timestamp"
}
}
}
},
"cumulative_hours": {
"cumulative_sum": {
"buckets_path": "distinct_hours"
}
}
}
}
}
}
可以用Java完成吗?例如 https://www.programcreek.com /java-api-examples/?api=org.elasticsearch.script.Script
来自链接: I have data in elasticsearch. this is my actual doc https://docs.google.com/document/d/1DKID90I9ulUcut-S8UfrnSjY-3citEwmyfnJJmrIRU8/edit?usp=sharing I have store_id, range of dates and event type in the input.in output, I need the percentage amount of time device was online for that hour. This is how we consider device online.
If there is an event="heartbeat" for a store_id in an hour then we say the store is online. example 1. so if the range is of "2019-05-07" to "2019-05-08" and there are 14 docs with different hour then the percentage will be (14/(2*24))*100 example 2. if input was store_id="abc" and date_range="2019-06-05" to ""2019-06-05" and event_type="heartbeat" then output would be (2/(1*24)) because there are only two different hour with event=heartbeat of that store. this is my query for the cumulative sum.If some How I can divide the final cumulative sum with difference between dates. Can It be done in java? for example https://www.programcreek.com/java-api-examples/?api=org.elasticsearch.script.Script Above link in the elasticsearch documentation would help if you can reformat your query into "buckets" using the "aggs" functionality. from link:
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"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"total_sales": {
"sum": {
"field": "price"
}
},
"t-shirts": {
"filter": {
"term": {
"type": "t-shirt"
}
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"t-shirt-percentage": {
"bucket_script": {
"buckets_path": {
"tShirtSales": "t-shirts>sales",
"totalSales": "total_sales"
},
"script": "params.tShirtSales / params.totalSales * 100"
}
}
}
}
}
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 13:00:05",
event_type:"heartbeat"
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 13:00:05",
event_type:"heartbeat"
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 14:00:05",
event_type:"heartbeat"
}
doc:
{
store_id:"abc",
event_timestamp:"2019-06-05 14:00:05",
event_type:"heartbeat"
}
GET /internship38/_search
{
"query":
{
"bool":
{
"must":
[
{
"match" :
{
"attributes.store_id" : "41b15888-0c2f-48f9-89d0-dc7aad19f52b"
}
},
{
"match":
{
"event_type":"app_sent_heartbeat"
}
}
]
}
},
"aggs":
{
"my_date_histo":{
"date_histogram":{
"field":"arrival_timestamp",
"interval":"day"
},
"aggs":
{
"distinct_hours": {
"cardinality": {
"script": {
"lang": "painless",
"source": "doc[params.date_field].value.hourOfDay;",
"params": {
"date_field": "arrival_timestamp"
}
}
}
},
"cumulative_hours": {
"cumulative_sum": {
"buckets_path": "distinct_hours"
}
}
}
}
}
}
{
"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"total_sales": {
"sum": {
"field": "price"
}
},
"t-shirts": {
"filter": {
"term": {
"type": "t-shirt"
}
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"t-shirt-percentage": {
"bucket_script": {
"buckets_path": {
"tShirtSales": "t-shirts>sales",
"totalSales": "total_sales"
},
"script": "params.tShirtSales / params.totalSales * 100"
}
}
}
}
}
}
