我怀疑如果
a事先不知道的确切值，是否可以在一个查询中执行此操作，尽管我认为一种非常有效的方法是可行的。
我建议做一个
percentiles聚合作为第一查询和第二
range查询。
在我的样本索引中，我只有14个文档，因此出于说明性原因，我将尝试查找那些占字段30％到60％的文档，
a并按
b相反的顺序对它们进行排序（以确保排序有效）。
这是我插入的文档：
{"a":1,"b":101}{"a":5,"b":105}{"a":10,"b":110}{"a":2,"b":102}{"a":6,"b":106}{"a":7,"b":107}{"a":9,"b":109}{"a":4,"b":104}{"a":8,"b":108}{"a":12,"b":256}{"a":13,"b":230}{"a":14,"b":215}{"a":3,"b":103}{"a":11,"b":205}让我们找出
a介于30％和60％百分位数之间的字段边界：
POST my_percent/doc/_search{ "size": 0, "aggs" : { "percentiles" : { "percentiles" : { "field" : "a", "percents": [ 30, 60, 90 ] } } }}用我的样本索引看起来像这样：
{... "hits": { "total": 14, "max_score": 0, "hits": [] }, "aggregations": { "percentiles": { "values": { "30.0": 4.9, "60.0": 8.8, "90.0": 12.700000000000001 } } }}现在我们可以使用边界进行
range查询：
POST my_percent/doc/_search{ "query": { "range": { "a" : { "gte" : 4.9, "lte" : 8.8 } } }, "sort": { "b": "desc" }}结果是：
{ "took": 5, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 4, "max_score": null, "hits": [ { "_index": "my_percent", "_type": "doc", "_id": "vkFvYGMB_zM1P5OLcYkS", "_score": null, "_source": { "a": 8, "b": 108 }, "sort": [ 108 ] }, { "_index": "my_percent", "_type": "doc", "_id": "vUFvYGMB_zM1P5OLWYkM", "_score": null, "_source": { "a": 7, "b": 107 }, "sort": [ 107 ] }, { "_index": "my_percent", "_type": "doc", "_id": "vEFvYGMB_zM1P5OLRok1", "_score": null, "_source": { "a": 6, "b": 106 }, "sort": [ 106 ] }, { "_index": "my_percent", "_type": "doc", "_id": "u0FvYGMB_zM1P5OLJImy", "_score": null, "_source": { "a": 5, "b": 105 }, "sort": [ 105 ] } ] }}注意
percentiles聚合的结果是近似的。
通常，这看起来像是通过熊猫或Spark作业可以更好地解决的任务。
希望有帮助！
