elasticsearch 获取平均子聚合
我想得到一个子集合的平均值。例如,我有每个分行的每日利润。我想把它们加起来,这样我就可以得到每天的总利润。然后我想得到每日利润的月平均值或周平均值。到目前为止,我已经做到了这一点
elasticsearch 获取平均子聚合,
elasticsearch,search,lucene,
elasticsearch,Search,Lucene,我想得到一个子集合的平均值。例如,我有每个分行的每日利润。我想把它们加起来,这样我就可以得到每天的总利润。然后我想得到每日利润的月平均值或周平均值。到目前为止,我已经做到了这一点 { "size" : 0, "aggs" : { "group_by_month": { "date_histogram": { "field": "Profit_Day", "interval": "month",
{
"size" : 0,
"aggs" : {
"group_by_month": {
"date_histogram": {
"field": "Profit_Day",
"interval": "month",
"format" : "MM-yyyy"
},
"aggs": {
"avgProf": {
"avg": {
"field": "ProfitValue"
}
},
"group_by_day": {
"date_histogram": {
"field": "Profit_Day",
"interval": "day",
"format" : "yyyy-MM-dd"
},
"aggs": {
"prof": {
"sum": {
"field": "ProfitValue"
}
}
}
}
}
}
}
}
问题是我得到的是一个正确的每日金额
但不是得到每日总和的月平均值
我从每个分公司每月平均得到利润 您需要使用
查询:
GET sales1/_search
{
"size": 0,
"aggs": {
"group_by_month": {
"date_histogram": {
"field": "proffit_day",
"interval": "month",
"format": "MM-yyyy"
},
"aggs": {
"group_by_day": {
"date_histogram": {
"field": "proffit_day",
"interval": "day",
"format": "yyyy-MM-dd"
},
"aggs": {
"prof": {
"sum": {
"field": "proffit_value"
}
}
}
},
"avg_monthly_sales": {
"avg_bucket": {
"buckets_path": "group_by_day>prof"
}
}
}
}
}
}
答复:
{
"group_by_month" : {
"buckets" : [
{
"key_as_string" : "09-2019",
"key" : 1567296000000,
"doc_count" : 2,
"group_by_day" : {
"buckets" : [
{
"key_as_string" : "2019-09-25",
"key" : 1569369600000,
"doc_count" : 2,
"prof" : {
"value" : 15.0
}
}
]
},
"avg_monthly_sales" : {
"value" : 15.0
}
},
{
"key_as_string" : "10-2019",
"key" : 1569888000000,
"doc_count" : 2,
"group_by_day" : {
"buckets" : [
{
"key_as_string" : "2019-10-01",
"key" : 1569888000000,
"doc_count" : 1,
"prof" : {
"value" : 10.0
}
},
{
"key_as_string" : "2019-10-02",
"key" : 1569974400000,
"doc_count" : 0,
"prof" : {
"value" : 0.0
}
},
{
"key_as_string" : "2019-10-03",
"key" : 1570060800000,
"doc_count" : 1,
"prof" : {
"value" : 15.0
}
}
]
},
"avg_monthly_sales" : {
"value" : 12.5
}
}
]
}
}
}
你需要使用
查询:
GET sales1/_search
{
"size": 0,
"aggs": {
"group_by_month": {
"date_histogram": {
"field": "proffit_day",
"interval": "month",
"format": "MM-yyyy"
},
"aggs": {
"group_by_day": {
"date_histogram": {
"field": "proffit_day",
"interval": "day",
"format": "yyyy-MM-dd"
},
"aggs": {
"prof": {
"sum": {
"field": "proffit_value"
}
}
}
},
"avg_monthly_sales": {
"avg_bucket": {
"buckets_path": "group_by_day>prof"
}
}
}
}
}
}
答复:
{
"group_by_month" : {
"buckets" : [
{
"key_as_string" : "09-2019",
"key" : 1567296000000,
"doc_count" : 2,
"group_by_day" : {
"buckets" : [
{
"key_as_string" : "2019-09-25",
"key" : 1569369600000,
"doc_count" : 2,
"prof" : {
"value" : 15.0
}
}
]
},
"avg_monthly_sales" : {
"value" : 15.0
}
},
{
"key_as_string" : "10-2019",
"key" : 1569888000000,
"doc_count" : 2,
"group_by_day" : {
"buckets" : [
{
"key_as_string" : "2019-10-01",
"key" : 1569888000000,
"doc_count" : 1,
"prof" : {
"value" : 10.0
}
},
{
"key_as_string" : "2019-10-02",
"key" : 1569974400000,
"doc_count" : 0,
"prof" : {
"value" : 0.0
}
},
{
"key_as_string" : "2019-10-03",
"key" : 1570060800000,
"doc_count" : 1,
"prof" : {
"value" : 15.0
}
}
]
},
"avg_monthly_sales" : {
"value" : 12.5
}
}
]
}
}
}