ElasticSearch 聚合
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聚合(aggregations)
聚合框架收集由搜索查询选择的所有数据，并由许多构件组成，这些构件有助于构建复杂的数据摘要。聚合的基本结构如下所示-"aggregations" : { "" : { "" : { } [,"meta" : { [] } ]? [,"aggregations" : { []+ } ]? } [,"" : { ... } ]* }有不同类型的聚合，每种聚合都有自己的用途。本章将详细讨论它们。 -
指标(Metrics)聚合
这些汇总有助于根据汇总文档的字段值计算矩阵，有时可以从脚本生成某些值。数值矩阵既可以是单值（例如平均聚合），也可以是多值（例如统计数据）。 -
平均聚合
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 /school/_search { "aggs":{ "avg_fees":{"avg":{"field":"fees"}} } }运行上面的代码，我们得到以下结果-{ "took": 46, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 3, "relation": "eq" }, "max_score": 1.0, "hits": [ { "_index": "school", "_type": "_doc", "_id": "10", "_score": 1.0, "_source": { "name": "Saint Paul School", "description": "ICSE Afiliation", "street": "Dawarka", "city": "Delhi", "state": "Delhi", "zip": "110075", "location": [ 28.5733056, 77.0122136 ], "fees": 5000, "tags": [ "Good Faculty", "Great Sports" ], "rating": "4.5" } }, { "_index": "school", "_type": "_doc", "_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": "school", "_type": "_doc", "_id": "16", "_score": 1.0, "_source": { "name": "Crescent School", "description": "State Board Affiliation", "street": "beijing", "city": "Jaipur", "state": "RJ", "zip": "176114", "location": [ 26.8535922, 75.7923988 ], "fees": 2500, "tags": [ "Well equipped labs" ], "rating": "4.5" } } ] }, "aggregations": { "avg_fees": { "value": 3233.3333333333335 } } } -
基数聚合
此聚合提供了特定字段的不同值的计数。POST /school/_search?size=0 { "aggs":{ "distinct_name_count":{"cardinality":{"field":"fees"}} } }运行上面的代码，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 } } }注意-基数的值为2，因为费用有两个不同的值。
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扩展统计汇总
此聚合生成有关聚合文档中特定数字字段的所有统计信息。POST /school/_search?size=0 { "aggs" : { "fees_stats" : { "extended_stats" : { "field" : "fees" } } } }运行上面的代码，我们得到以下结果-{ "took": 13, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 3, "relation": "eq" }, "max_score": null, "hits": [] }, "aggregations": { "fees_stats": { "count": 3, "min": 2200.0, "max": 5000.0, "avg": 3233.3333333333335, "sum": 9700.0, "sum_of_squares": 3.609E7, "variance": 1575555.555555556, "variance_population": 1575555.555555556, "variance_sampling": 2363333.333333334, "std_deviation": 1255.2113589175156, "std_deviation_population": 1255.2113589175156, "std_deviation_sampling": 1537.3136743466944, "std_deviation_bounds": { "upper": 5743.756051168364, "lower": 722.9106154983024, "upper_population": 5743.756051168364, "lower_population": 722.9106154983024, "upper_sampling": 6307.960682026722, "lower_sampling": 158.70598463994475 } } } } -
最大(Max)聚合
此聚合在聚合的文档中查找特定数字字段的最大值。POST /school/_search?size=0 { "aggs" : { "max_fees" : { "max" : { "field" : "fees" } } } }运行上面的代码，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 } } } -
最小(Min)聚合
此聚合查找聚合文档中特定数字字段的最小值。POST /school/_search?size=0 { "aggs" : { "min_fees" : { "min" : { "field" : "fees" } } } }运行上面的代码，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 } } } -
总和(SUM)
此聚合计算聚合文档中特定数字字段的总和。POST /school/_search?size=0 { "aggs" : { "total_fees" : { "sum" : { "field" : "fees" } } } }运行上面的代码，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。 -
统计汇总
一种多值指标聚合，可根据从聚合文档中提取的数值计算统计信息。POST /school/_search?size=0 { "aggs" : { "grades_stats" : { "stats" : { "field" : "fees" } } } }运行上面的代码，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 } } } -
聚合元数据
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 /school/_search?size=0 { "aggs" : { "min_fees" : { "avg" : { "field" : "fees" } , "meta" :{ "dsc" :"Lowest Fees This Year" } } } }运行上面的代码，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 } } }