Question

我想将bibtex条目的month字段索引到elasticsearch中，并使其可以通过range查询进行搜索。这要求基础字段类型是某种数字数据类型。就我而言，short就足够了。

bibtex month字段的规范形式需要三个字符的缩写，因此我尝试像这样使用char_filter：

...
"char_filter": {
    "month_char_filter": {
        "type": "mapping",
        "mappings": [
            "jan => 1",
            "feb => 2",
            "mar => 3",
            ...
            "nov => 11",
            "dec => 12"
        ]
    }
...
"normalizer": {
    "month_normalizer": {
        "type": "custom",
        "char_filter": [ "month_char_filter" ],
    },

并建立这样的映射：

...
"month": {
    "type": "short",
    "normalizer": "month_normalizer"
},
...

但是它似乎不起作用，因为type字段不支持像这样的规范化器，也不支持分析器。

那么char_filter部分中所示的实现这种映射的方法将是什么，从而有范围查询的可能性呢？

Answer 1

您的方法在直觉上是有意义的，但是，规范化器只能应用于keyword字段，而分析器只能应用于text字段。

另一种方法是在索引编制时利用ingest processors并使用script processor进行映射。

下面您可以找到这种script处理器的仿真，该处理器将根据monthNum字段中存在的月份创建一个名为month的新字段。

POST _ingest/pipeline/_simulate
{
  "pipeline": {
    "processors": [
      {
        "script": {
          "source": """
          def mapping = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'];
          ctx.monthNum = mapping.indexOf(ctx.month) + 1;
          """
        }
      }
    ]
  },
  "docs": [
    {
      "_source": {
        "month": "feb"
      }
    },
    {
      "_source": {
        "month": "mar"
      }
    },
    {
      "_source": {
        "month": "jul"
      }
    },
    {
      "_source": {
        "month": "aug"
      }
    },
    {
      "_source": {
        "month": "nov"
      }
    },
    {
      "_source": {
        "month": "dec"
      }
    },
    {
      "_source": {
        "month": "xyz"
      }
    }
  ]
}

生成的文件

{
  "docs" : [
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 2,
          "month" : "feb"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 3,
          "month" : "mar"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 7,
          "month" : "jul"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 8,
          "month" : "aug"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 11,
          "month" : "nov"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 12,
          "month" : "dec"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    },
    {
      "doc" : {
        "_index" : "_index",
        "_type" : "_type",
        "_id" : "_id",
        "_source" : {
          "monthNum" : 0,
          "month" : "xyz"
        },
        "_ingest" : {
          "timestamp" : "2019-05-08T12:28:27.006Z"
        }
      }
    }
  ]
}

Elasticsearch映射：如何分析或映射到数字字段？

1 个答案: