ElasticSearch6.x日期類型處理

ElasticSearch6.x只允許在一個(gè)index里對(duì)一個(gè)type進(jìn)行mapping，如果對(duì)一個(gè)以上的type進(jìn)行mapping就會(huì)失敗。比如：

創(chuàng)建mapping

PUT m_index
{
  "mappings": {
    "m_type_1": {
      "properties": {
        "startTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        },
        "endTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        }
      }
    },
    "m_type_2": {
      "properties": {
        "startTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        },
        "endTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        }
      }
    }
  }
}

響應(yīng):

{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "Rejecting mapping update to [m_index] as the final mapping would have more than 1 type: [m_type_2, m_type_1]"
      }
    ],
    "type": "illegal_argument_exception",
    "reason": "Rejecting mapping update to [m_index] as the final mapping would have more than 1 type: [m_type_2, m_type_1]"
  },
  "status": 400
}

我如果不對(duì)每個(gè)type進(jìn)行mapping的話，就不能用日期作為條件進(jìn)行查詢了。
比如這樣的查詢：

POST m_index/_search
{
  "query": {
    "range": {
      "startTime": {
        "gte": "2017-08-01",
        "lte": "2017-12-31"
      }
    }
  }
}

大佬們有什么解決辦法嗎？

回答

編輯回答

撿肥皂

實(shí)際上ES6.0中是取消了type的這個(gè)概念的。具體原因可以看：

Removal of mapping

其中也給出了一些修改的方案。
比如可以給這些文檔放在一起，通過(guò)特定的字段來(lái)區(qū)分到底是哪種類型的文檔。

PUT m_index
{
  "mappings": {
    "doc": {
      "properties": {
        "type": "keyword",
        "startTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        },
        "endTime": {
          "type": "date",
          "format": "strict_date_optional_time||epoch_second"
        }
      }
    }
  }
}

這里給你的m_type_1和m_type_2處理成一個(gè)字段，用于區(qū)分，查詢的時(shí)候增加一個(gè)filter就可以。

或者用比較笨的方法，給原來(lái)的type升級(jí)為index處理。按照官方的說(shuō)法是有兩個(gè)優(yōu)勢(shì)：

This approach has two benefits:

Data is more likely to be dense and so benefit from compression techniques used in Lucene.

The term statistics used for scoring in full text search are more likely to be accurate because all documents in the same index represent a single entity.