为什么升序地理距离排序比降序地理距离排序快答案

【问题标题】：Why is ascending geo distance sorting faster than descending geo distance sorting为什么升序地理距离排序比降序地理距离排序快
【发布时间】：2019-03-22 21:18:39
【问题描述】：

我使用的是 Elasticsearch 6.6，并且有一个索引（1 个分片，1 个副本），其中包含地理名称 (https://www.geonames.org/) 数据集的索引（索引大小 =1.3 gb，11.8 个 mio 地理点）。我正在玩一些地理距离排序查询，对某些原点的整个索引进行排序。所以经过一些测试后，我发现升序排序总是比降序排序快。这是一个示例查询（我还使用更大的“大小”参数进行了测试）：

POST /geonames/_search?request_cache=false
{   
    "size":1,
    "sort" : [
        {
            "_geo_distance" : {
                "location" : [8, 49],
                "order" : "asc",
                "unit" : "m",
                "mode" : "min",
                "distance_type" : "arc",
                "ignore_unmapped": true
            }
        }
    ]
}

这是升序排序的答案（解释和配置文件为真）：

{
  "took" : 1374,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 11858060,
    "max_score" : null,
    "hits" : [
      {
        "_shard" : "[geonames][0]",
        "_node" : "qXTymyB9QLmxhPtGEtA_mA",
        "_index" : "geonames",
        "_type" : "doc",
        "_id" : "L781LmkBrQo0YN4qP48D",
        "_score" : null,
        "_source" : {
          "id" : "3034701",
          "name" : "Forêt de Wissembourg",
          "location" : {
            "lat" : "49.00924",
            "lon" : "8.01542"
          }
        },
        "sort" : [
          1523.4121312414704
        ],
        "_explanation" : {
          "value" : 1.0,
          "description" : "*:*",
          "details" : [ ]
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[qXTymyB9QLmxhPtGEtA_mA][geonames][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "MatchAllDocsQuery",
                "description" : "*:*",
                "time_in_nanos" : 265223567,
                "breakdown" : {
                  "score" : 0,
                  "build_scorer_count" : 54,
                  "match_count" : 0,
                  "create_weight" : 10209,
                  "next_doc" : 253091268,
                  "match" : 0,
                  "create_weight_count" : 1,
                  "next_doc_count" : 11858087,
                  "score_count" : 0,
                  "build_scorer" : 263948,
                  "advance" : 0,
                  "advance_count" : 0
                }
              }
            ],
            "rewrite_time" : 1097,
            "collector" : [
              {
                "name" : "CancellableCollector",
                "reason" : "search_cancelled",
                "time_in_nanos" : 1044167746,
                "children" : [
                  {
                    "name" : "SimpleFieldCollector",
                    "reason" : "search_top_hits",
                    "time_in_nanos" : 508296683
                  }
                ]
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

这里是降序，只是将参数从 asc 切换到 desc（也带有 profile 和 explain）：

{
  "took" : 2226,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 11858060,
    "max_score" : null,
    "hits" : [
      {
        "_shard" : "[geonames][0]",
        "_node" : "qXTymyB9QLmxhPtGEtA_mA",
        "_index" : "geonames",
        "_type" : "doc",
        "_id" : "Mq80LmkBrQo0YN4q11bA",
        "_score" : null,
        "_source" : {
          "id" : "4036351",
          "name" : "Bollons Seamount",
          "location" : {
            "lat" : "-49.66667",
            "lon" : "-176.16667"
          }
        },
        "sort" : [
          1.970427111052182E7
        ],
        "_explanation" : {
          "value" : 1.0,
          "description" : "*:*",
          "details" : [ ]
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[qXTymyB9QLmxhPtGEtA_mA][geonames][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "MatchAllDocsQuery",
                "description" : "*:*",
                "time_in_nanos" : 268521404,
                "breakdown" : {
                  "score" : 0,
                  "build_scorer_count" : 54,
                  "match_count" : 0,
                  "create_weight" : 9333,
                  "next_doc" : 256458664,
                  "match" : 0,
                  "create_weight_count" : 1,
                  "next_doc_count" : 11858087,
                  "score_count" : 0,
                  "build_scorer" : 195265,
                  "advance" : 0,
                  "advance_count" : 0
                }
              }
            ],
            "rewrite_time" : 1142,
            "collector" : [
              {
                "name" : "CancellableCollector",
                "reason" : "search_cancelled",
                "time_in_nanos" : 1898324618,
                "children" : [
                  {
                    "name" : "SimpleFieldCollector",
                    "reason" : "search_top_hits",
                    "time_in_nanos" : 1368306442
                  }
                ]
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

所以我的问题是，为什么会这样？据我了解， Es 计算从原点到每个其他点的距离，然后对它们进行排序。那么为什么降序排序这么慢呢？

【问题讨论】：

标签： sorting elasticsearch distance

【解决方案1】：

在 Elasticsearch 板上提出相同的问题并获得 answer。因此，显然 Elasticsearch 使用不同的搜索策略/算法进行升序降序距离排序。

对于降序排序，它计算从原点到每个点的距离然后排序。对于升序排序，它使用边界框来过滤原点附近的点，并且只计算边界框内的点的距离。

【讨论】：