聚合框架收集搜索查询选择的所有数据，并由许多构建块组成，这有助于构建数据的复杂摘要。聚合的基本结构如下所示-
"aggregations" : {
"" : {
"" : {
}
[,"meta" : { [] } ]?
[,"aggregations" : { []+ } ]?
}
[,"" : { ... } ]*
}聚合有不同的类型，每种类型都有自己的目的。本章将详细讨论这些问题。
这些聚合有助于根据聚合文档的字段值计算矩阵，有时还可以从脚本生成一些值。
数值矩阵既可以是单值（如平均聚合），也可以是多值（如统计数据）。
此聚合用于获取聚合文档中存在的任何数字字段的平均值。例如，
POST /schools/_search
{
"aggs":{
"avg_fees":{"avg":{"field":"fees"}}
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 41,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "schools",
"_type" : "school",
"_id" : "5",
"_score" : 1.0,
"_source" : {
"name" : "Central School",
"description" : "CBSE Affiliation",
"street" : "Nagan",
"city" : "paprola",
"state" : "HP",
"zip" : "176115",
"location" : [
31.8955385,
76.8380405
],
"fees" : 2200,
"tags" : [
"Senior Secondary",
"beautiful campus"
],
"rating" : "3.3"
}
},
{
"_index" : "schools",
"_type" : "school",
"_id" : "4",
"_score" : 1.0,
"_source" : {
"name" : "City Best School",
"description" : "ICSE",
"street" : "West End",
"city" : "Meerut",
"state" : "UP",
"zip" : "250002",
"location" : [
28.9926174,
77.692485
],
"fees" : 3500,
"tags" : [
"fully computerized"
],
"rating" : "4.5"
}
}
]
},
"aggregations" : {
"avg_fees" : {
"value" : 2850.0
}
}
}此聚合提供了特定字段的不同值的计数。
POST /schools/_search?size=0
{
"aggs":{
"distinct_name_count":{"cardinality":{"field":"fees"}}
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"distinct_name_count" : {
"value" : 2
}
}
}Note ?基数的值为2，因为费用有两个不同的值。
此聚合将生成有关聚合文档中特定数字字段的所有统计信息。
POST /schools/_search?size=0
{
"aggs" : {
"fees_stats" : { "extended_stats" : { "field" : "fees" } }
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"fees_stats" : {
"count" : 2,
"min" : 2200.0,
"max" : 3500.0,
"avg" : 2850.0,
"sum" : 5700.0,
"sum_of_squares" : 1.709E7,
"variance" : 422500.0,
"std_deviation" : 650.0,
"std_deviation_bounds" : {
"upper" : 4150.0,
"lower" : 1550.0
}
}
}
}此聚合查找聚合文档中特定数字字段的最大值。
POST /schools/_search?size=0
{
"aggs" : {
"max_fees" : { "max" : { "field" : "fees" } }
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 16,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"max_fees" : {
"value" : 3500.0
}
}
}此聚合在聚合的文档中查找特定数字字段的最小值。
POST /schools/_search?size=0
{
"aggs" : {
"min_fees" : { "min" : { "field" : "fees" } }
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"min_fees" : {
"value" : 2200.0
}
}
}此聚合计算聚合文档中特定数值字段的和。
POST /schools/_search?size=0
{
"aggs" : {
"total_fees" : { "sum" : { "field" : "fees" } }
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"total_fees" : {
"value" : 5700.0
}
}
}在特殊情况下还有其他一些度量标准聚合，例如地理边界聚合和地理质心聚合，以实现地理位置。
一种多值度量标准聚合，可根据从聚合文档中提取的数值来计算统计信息。
POST /schools/_search?size=0
{
"aggs" : {
"grades_stats" : { "stats" : { "field" : "fees" } }
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"grades_stats" : {
"count" : 2,
"min" : 2200.0,
"max" : 3500.0,
"avg" : 2850.0,
"sum" : 5700.0
}
}
}您可以在请求时使用meta标记添加一些有关聚合的数据，并作为响应获取。
POST /schools/_search?size=0
{
"aggs" : {
"min_fees" : { "avg" : { "field" : "fees" } ,
"meta" :{
"dsc" :"Lowest Fees This Year"
}
}
}
}在运行上面的代码时，我们得到以下结果-
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"min_fees" : {
"meta" : {
"dsc" : "Lowest Fees This Year"
},
"value" : 2850.0
}
}
}