Elasticsearch - "星期几"的日期时间映射

Question

我在类中有以下属性:

public DateTime InsertedTimeStamp { get; set; }

使用ES中的以下映射

"insertedTimeStamp ":{
    "type":"date",
    "format":"yyyy-MM-ddTHH:mm:ssZ"
},

我想运行一个聚合来返回按"星期几"分组的所有数据,即"星期一","星期二"......等等

我知道我可以在聚合调用中使用"脚本"来执行此操作,请参阅此处,但是,根据我的理解,如果有大量文档(使用此脚本,请考虑分析日志记录),使用脚本会对性能产生不小的影响).

有没有办法可以用'子属性'映射属性.即我有一个字符串,我可以做:

"somestring":{
    "type":"string",
    "analyzer":"full_word",
    "fields":{
        "partial":{
            "search_analyzer":"full_word",
            "analyzer":"partial_word",
            "type":"string"
        },
        "partial_back":{
            "search_analyzer":"full_word",
            "analyzer":"partial_word_back",
            "type":"string"
        },
        "partial_middle":{
            "search_analyzer":"full_word",
            "analyzer":"partial_word_name",
            "type":"string"
        }
    }
},

所有.net代码中的类都具有单个属性.

我可以做类似的事情来存储'完整日期',然后分别存储'年','月'和'日'等(在索引时某种'脚本'),或者我需要在上课并单独映射？这是变形所做的吗？(现在已折旧,因此似乎表明我需要单独的字段......)

Answer 1

绝对可以使用pattern_capture令牌过滤器在索引时执行此操作.

您首先要为每个日期部件定义一个分析器+令牌过滤器组合,并将每个分配器分配给日期字段的子字段.每个令牌过滤器仅捕获它感兴趣的组.

{
  "settings": {
    "analysis": {
      "analyzer": {
        "year_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "year"
          ]
        },
        "month_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "month"
          ]
        },
        "day_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "day"
          ]
        },
        "hour_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "hour"
          ]
        },
        "minute_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "minute"
          ]
        },
        "second_analyzer": {
          "type": "custom",
          "tokenizer": "keyword",
          "filter": [
            "second"
          ]
        }
      },
      "filter": {
        "year": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "(\\d{4})-\\d{2}-\\d{2}[tT]\\d{2}:\\d{2}:\\d{2}[zZ]"
          ]
        },
        "month": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "\\d{4}-(\\d{2})-\\d{2}[tT]\\d{2}:\\d{2}:\\d{2}[zZ]"
          ]
        },
        "day": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "\\d{4}-\\d{2}-(\\d{2})[tT]\\d{2}:\\d{2}:\\d{2}[zZ]"
          ]
        },
        "hour": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "\\d{4}-\\d{2}-\\d{2}[tT](\\d{2}):\\d{2}:\\d{2}[zZ]"
          ]
        },
        "minute": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "\\d{4}-\\d{2}-\\d{2}[tT]\\d{2}:(\\d{2}):\\d{2}[zZ]"
          ]
        },
        "second": {
          "type": "pattern_capture",
          "preserve_original": false,
          "patterns": [
            "\\d{4}-\\d{2}-\\d{2}[tT]\\d{2}:\\d{2}:(\\d{2})[zZ]"
          ]
        }
      }
    }
  },
  "mappings": {
    "test": {
      "properties": {
        "date": {
          "type": "date",
          "format": "yyyy-MM-dd'T'HH:mm:ssZ",
          "fields": {
            "year": {
              "type": "string",
              "analyzer": "year_analyzer"
            },
            "month": {
              "type": "string",
              "analyzer": "month_analyzer"
            },
            "day": {
              "type": "string",
              "analyzer": "day_analyzer"
            },
            "hour": {
              "type": "string",
              "analyzer": "hour_analyzer"
            },
            "minute": {
              "type": "string",
              "analyzer": "minute_analyzer"
            },
            "second": {
              "type": "string",
              "analyzer": "second_analyzer"
            }
          }
        }
      }
    }
  }
}

然后,当您为某个日期编制索引时2016-01-22T10:01:23Z,您将获得使用相关部分填充的每个日期子字段,即

• date: 2016-01-22T10:01:23Z
• date.year: 2016
• date.month: 01
• date.day: 22
• date.hour: 10
• date.minute: 01
• date.second: 23

然后,您可以自由地聚合任何这些子字段以获得您想要的内容.