导数聚合
编辑导数聚合编辑
一种父管道聚合,用于计算父直方图(或 date_histogram)聚合中指定指标的导数。指定的指标必须是数值型的,并且封闭直方图必须将 min_doc_count
设置为 0
(histogram
聚合的默认值)。
语法编辑
一个 derivative
聚合单独来看是这样的
"derivative": { "buckets_path": "the_sum" }
表 59. derivative
参数
参数名称 | 描述 | 必需 | 默认值 |
---|---|---|---|
|
我们要为其查找导数的桶的路径(有关更多详细信息,请参阅 |
必需 |
|
|
在数据中发现缺口时要应用的策略(有关更多详细信息,请参阅 处理数据中的缺口) |
可选 |
|
|
输出值的 DecimalFormat 模式。如果指定,则格式化的值将在聚合的 |
可选 |
|
一阶导数编辑
以下代码段计算每月总 sales
的导数
response = client.search( index: 'sales', body: { size: 0, aggregations: { sales_per_month: { date_histogram: { field: 'date', calendar_interval: 'month' }, aggregations: { sales: { sum: { field: 'price' } }, sales_deriv: { derivative: { buckets_path: 'sales' } } } } } } ) puts response
POST /sales/_search { "size": 0, "aggs": { "sales_per_month": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "sales": { "sum": { "field": "price" } }, "sales_deriv": { "derivative": { "buckets_path": "sales" } } } } } }
以下可能是响应
{ "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 } } ] } } }
二阶导数编辑
可以通过将导数管道聚合链接到另一个导数管道聚合的结果来计算二阶导数,如下面的示例所示,该示例将计算每月总销售额的一阶和二阶导数
response = client.search( index: 'sales', body: { size: 0, aggregations: { sales_per_month: { date_histogram: { field: 'date', calendar_interval: 'month' }, aggregations: { sales: { sum: { field: 'price' } }, sales_deriv: { derivative: { buckets_path: 'sales' } }, "sales_2nd_deriv": { derivative: { buckets_path: 'sales_deriv' } } } } } } ) puts response
POST /sales/_search { "size": 0, "aggs": { "sales_per_month": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "sales": { "sum": { "field": "price" } }, "sales_deriv": { "derivative": { "buckets_path": "sales" } }, "sales_2nd_deriv": { "derivative": { "buckets_path": "sales_deriv" } } } } } }
以下可能是响应
{ "took": 50, "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 }, "sales_2nd_deriv": { "value": 805.0 } } ] } } }
单位编辑
导数聚合允许指定导数值的单位。这将在响应 normalized_value
中返回一个额外的字段,该字段以所需的 x 轴单位报告导数值。在下面的示例中,我们计算每月总销售额的导数,但要求以每天销售额的单位计算销售额的导数
response = client.search( index: 'sales', body: { size: 0, aggregations: { sales_per_month: { date_histogram: { field: 'date', calendar_interval: 'month' }, aggregations: { sales: { sum: { field: 'price' } }, sales_deriv: { derivative: { buckets_path: 'sales', unit: 'day' } } } } } } ) puts response
POST /sales/_search { "size": 0, "aggs": { "sales_per_month": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "sales": { "sum": { "field": "price" } }, "sales_deriv": { "derivative": { "buckets_path": "sales", "unit": "day" } } } } } }
以下可能是响应
{ "took": 50, "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, "normalized_value": -15.806451612903226 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "sales": { "value": 375.0 }, "sales_deriv": { "value": 315.0, "normalized_value": 11.25 } } ] } } }