elasticsearch painless 排序脚本中使用 Map (HashMap)

梦里梦外; 2022-11-21 01:14 243阅读 0赞

参考资料[https://www.elastic.co/guide/en/elasticsearch/painless/master/painless-casting.html][https_www.elastic.co_guide_en_elasticsearch_painless_master_painless-casting.html]

[https://blog.csdn.net/weixin\_38809962/article/details/79768002][https_blog.csdn.net_weixin_38809962_article_details_79768002]

[https://blog.csdn.net/hechaojie\_com/article/details/88881503][https_blog.csdn.net_hechaojie_com_article_details_88881503]

有一个业务需求，在给 es 中传入一个 id 集合，然后根据条件去过滤掉一些记录。然后分页返回 10 条记录，但是有个条件，排序要按照传入的 id 的顺序进行排序，传入的时候，哪个 id 在前，返回的时候这个 id 也得在前面。只能根据脚本写排序。

GET xxxxx/_doc/_search?size=1000
    {
      "query": {
        "bool": {
          "filter": [
            {
              "terms": {
                "exerciseId": [
                  268704949713821702,
                  268704949713821698,
                  268704949713821705,
                  330379959905607682,
                  330379959905607681,
                  330379959905607680,
                  330379959905607687,
                  330379959905607690,
                  330354511029002255,
                  330354511029002245,
                  330354511029002247,
                  330354511029002240,
                  330354511029002243,
                  330354511029002253,
                  268704949713821707,
                  395591013664874499,
                  329589022711406596,
                  329589022711406597,
                  395591013664874500,
                  268704949713821709,
                  395943043096961027,
                  268704949713821706,
                  268704949713821704,
                  330379959905607693,
                  329589022711406598,
                  329589022711406613,
                  395943043096961025,
                  395943043096961024,
                  395591013664874498,
                  329589022711406612,
                  329589022711406601,
                  268704949713821696,
                  268704949713821700,
                  268704949713821697,
                  329589022711406611,
                  329589022711406594,
                  395591013664874497,
                  329589022711406600,
                  330379959905607683,
                  330379959905607685,
                  330379959905607684,
                  330379959905607691,
                  330379959905607689,
                  330379959905607688,
                  330379959905607692,
                  330354511029002248,
                  330354511029002249,
                  330354511029002250,
                  330354511029002251,
                  330354511029002241,
                  330354511029002242,
                  329589022711406595,
                  395943043096961026,
                  330379959905607686,
                  330354511029002246,
                  395591013664874496,
                  329589022711406602,
                  268704949713821701,
                  268704949713821708,
                  329589022711406610,
                  329589022711406599,
                  330354511029002254,
                  329589022711406609,
                  329589022711406614
                ],
                "boost": 1.0
              }
            }
          ],
          "adjust_pure_negative": true,
          "boost": 1.0
        }
      },
      "sort": [
        {
          "_script": {
            "script": {
              "source": "def exerciseId = doc['exerciseId'].value;def str = String.valueOf(exerciseId);params.exerciseIdToScoreMap.get(str);",
              "lang": "painless",
              "params": {
                "exerciseIdToScoreMap": {
                  "329589022711406599": 4,
                  "329589022711406598": 40,
                  "329589022711406597": 47,
                  "329589022711406596": 48,
                  "329589022711406595": 13,
                  "329589022711406594": 29,
                  "395591013664874497": 28,
                  "330354511029002250": 17,
                  "395591013664874496": 9,
                  "395591013664874499": 49,
                  "395591013664874498": 36,
                  "330354511029002242": 14,
                  "330354511029002243": 52,
                  "330354511029002240": 53,
                  "330354511029002241": 15,
                  "330354511029002246": 10,
                  "330354511029002247": 54,
                  "330354511029002245": 55,
                  "330354511029002248": 19,
                  "330354511029002249": 18,
                  "268704949713821697": 31,
                  "268704949713821696": 33,
                  "268704949713821698": 63,
                  "330354511029002253": 51,
                  "330354511029002254": 3,
                  "330354511029002251": 16,
                  "330354511029002255": 56,
                  "329589022711406614": 1,
                  "329589022711406613": 39,
                  "329589022711406612": 35,
                  "329589022711406611": 30,
                  "329589022711406610": 5,
                  "268704949713821701": 7,
                  "268704949713821700": 32,
                  "268704949713821702": 64,
                  "268704949713821705": 62,
                  "268704949713821704": 42,
                  "268704949713821707": 50,
                  "268704949713821706": 43,
                  "268704949713821709": 45,
                  "268704949713821708": 6,
                  "330379959905607687": 58,
                  "395943043096961027": 44,
                  "395591013664874500": 46,
                  "330379959905607688": 21,
                  "330379959905607685": 25,
                  "330379959905607686": 11,
                  "330379959905607683": 26,
                  "330379959905607684": 24,
                  "330379959905607681": 60,
                  "330379959905607682": 61,
                  "329589022711406609": 2,
                  "330379959905607680": 59,
                  "395943043096961024": 37,
                  "395943043096961025": 38,
                  "329589022711406602": 8,
                  "329589022711406601": 34,
                  "395943043096961026": 12,
                  "329589022711406600": 27,
                  "330379959905607689": 22,
                  "330379959905607692": 20,
                  "330379959905607693": 41,
                  "330379959905607690": 57,
                  "330379959905607691": 23
                }
              }
            },
            "type": "number",
            "order": "desc"
          }
        }
      ]
    }

map 在使用的时候有两种方式:

1.  一种是 map.key 这样
2.  另一种是 map.get(key) 这种形式。

如果 key 是脚本中定义的变量，则只能 map.get(key) 这种形式，今天尝试了好久终于调好了，特此记录一下，以飨后来人。

[https_www.elastic.co_guide_en_elasticsearch_painless_master_painless-casting.html]: https://www.elastic.co/guide/en/elasticsearch/painless/master/painless-casting.html
[https_blog.csdn.net_weixin_38809962_article_details_79768002]: https://blog.csdn.net/weixin_38809962/article/details/79768002
[https_blog.csdn.net_hechaojie_com_article_details_88881503]: https://blog.csdn.net/hechaojie_com/article/details/88881503