- Avg Bucket Aggregation
作用：獲取想要的兩個(gè)字段的平均值
{
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"avg_monthly_sales": {
"avg_bucket": {
"buckets_path": "sales_per_month>sales" 獲取平均銷售量
}
}
}
}
#返回
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
}
}
]
},
"avg_monthly_sales": {
"value": 328.33333333333333
}
}
}
- Derivative Aggregation
作用：計(jì)算每個(gè)間隔內(nèi)的導(dǎo)數(shù)
{
"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_path": "sales"
}
}
}
}
}
}
#返回
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"sales_deriv": {
"value": -490.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
},
"sales_deriv": {
"value": 315.0
}
}
]
}
}
}
- Max Bucket Aggregation,Min Bucket Aggregation，Sun Bucket Aggregation
作用：獲取所有間隔內(nèi)的最大值或最小值或總和
{
"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"max_monthly_sales": {
"max_bucket": {
"buckets_path": "sales_per_month>sales"
}
}
}
}
#返回
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
}
}
]
},
"max_monthly_sales": {
"keys": ["2015/01/01 00:00:00"],
"value": 550.0
}
}
}
- Moving Average Aggregation
作用：移動(dòng)平均聚合，比如我們給的數(shù)據(jù)是[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]，會(huì)不斷的計(jì)算出：
(1 + 2 + 3 + 4 + 5) / 5 = 3
(2 + 3 + 4 + 5 + 6) / 5 = 4
(3 + 4 + 5 + 6 + 7) / 5 = 5
{
“size”：0，
“aggs”：{
“my_date_histo”：{
“date_histogram”：{
“field”：“date”，
“calendar_interval”：“1M”
}，
“aggs”：{
“the_sum”：{
“sum”：{“field”：“price”}
}，
“the_movavg”：{
“moving_avg”：{“buckets_path”：“the_sum”}
}
}
}
}
}
#返回
{
“take”：11，
“timed_out”：false，
“_ shards”：...，
“hits”：...，
“aggregations”：{
“my_date_histo”：{
“buckets”：[
{
“key_as_string”：“ 2015/01/01 00:00:00“，
”key“：1420070400000，
”doc_count“：3，
”the_sum“：{
”value“：550.0
}
}，
{
”key_as_string“：”2015/02/01 00： 00:00“，
”鑰匙“：1422748800000，
“doc_count”：2，
“the_sum”：{
“value”：60.0
}，
“the_movavg”：{
“value”：550.0
}
}，
{
“key_as_string”：“2015/03/01 00:00:00”，
“key”：1425168000000，
“doc_count”：2，
“the_sum”：{
“value”：375.0
}，
“the_movavg”：{
“value”：305。0
}
}
]
}
}
}
Moving Function Aggregation
作用:類似上面的移動(dòng)平均聚合，但功能更多，使用函數(shù)，不斷的移動(dòng)去計(jì)算下個(gè)值Cumulative Sum Aggregation
作用:不斷增加，比如數(shù)據(jù)是[1,2,3,4,5]，那么獲得的數(shù)據(jù)就會(huì)是:
1
1+2
1+2+3
等等
{
"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"cumulative_sales": {
"cumulative_sum": {
"buckets_path": "sales"
}
}
}
}
}
}
#返回
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
},
"cumulative_sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"cumulative_sales": {
"value": 610.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
},
"cumulative_sales": {
"value": 985.0
}
}
]
}
}
}
- Bucket Sort Aggregation
作用：根據(jù)指定的字段排序輸出
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
},
"cumulative_sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"cumulative_sales": {
"value": 610.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
},
"cumulative_sales": {
"value": 985.0
}
}
]
}
}
}{
"size": 0,
"aggs" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"calendar_interval" : "month"
},
"aggs": {
"total_sales": {
"sum": {
"field": "price"
}
},
"sales_bucket_sort": {
"bucket_sort": {
"sort": [
{"total_sales": {"order": "desc"}}#根據(jù)每月的金額倒序輸出
],
"size": 3#只要前三個(gè)月的
}
}
}
}
}
}
#返回
{
"took": 82,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"total_sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"total_sales": {
"value": 375.0
},
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"total_sales": {
"value": 60.0
},
}
]
}
}
}
- Matrix Stats
作用：計(jì)算指定字段的數(shù)量，平均值，均勻分布，不對(duì)稱分步等
{
"aggs": {
"statistics": {
"matrix_stats": {
"fields": ["poverty", "income"]
}
}
}
}
#返回
{
...
"aggregations": {
"statistics": {
"doc_count": 50,
"fields": [{
"name": "income",
"count": 50,
"mean": 51985.1,
"variance": 7.383377037755103E7,
"skewness": 0.5595114003506483,
"kurtosis": 2.5692365287787124,
"covariance": {
"income": 7.383377037755103E7,
"poverty": -21093.65836734694
},
"correlation": {
"income": 1.0,
"poverty": -0.8352655256272504
}
}, {
"name": "poverty",
"count": 50,
"mean": 12.732000000000001,
"variance": 8.637730612244896,
"skewness": 0.4516049811903419,
"kurtosis": 2.8615929677997767,
"covariance": {
"income": -21093.65836734694,
"poverty": 8.637730612244896
},
"correlation": {
"income": -0.8352655256272504,
"poverty": 1.0
}
}]
}
}
}
