ElasticSearch的数据类型 - 郑志彬的博客

ES使用JSON作为数据交互格式，所以简单来说，JSON支持的数据类型，ES都支持。

1. core simple field types

String: string
Whole number: byte, short, integer, long
Floating point: float, double
Boolean: boolean
Date: date

ES对于没有定义mapping的未知字段会采用dynamic mapping进行类型猜测，并且将其加入到type mapping中。这个行为可以通过 dynamic 配置项进行控制。

true: Add new fields dynamically — the default
false: Ignore new fields
strict: Throw an exception if an unknown field is encountered

相应的mappings配置如下：

PUT /my_index
{
    "mappings": {
        "my_type": {
            "dynamic":      "strict", 
            "properties": {
                "title":  { "type": "string"},
                "stash":  {
                    "type":     "object",
                    "dynamic":  true 
                }
            }
        }
    }
}

TIPS 日期格式

JSON并没有日期类型，日期是以特定字符串格式形式表示。比如postDate: "2014-09-24"。ES能够根据字符串的格式猜测出这是一个Date类型。日期字符串格式可以通过format和dynamic_date_formats配置项指定（date-format），而且支持多个格式如：yyyy/MM/dd HH:mm:ss yyyy/MM/dd。

{
    "tweet" : {
        "_timestamp" : {
            "enabled" : true,
            "path" : "post_date",
            "format" : "YYYY-MM-dd",
            "default" : "1970-01-01"
        }
    }
}

可以通过date_detection配置项单独关闭日期类型检查：

curl -XPUT "http://localhost:9200/myindex" -d
{
   "mappings": {
      "tweet": {
         "date_detection": false
      }
   }
}

2. complex core field types

除了简单的基本类型，ES还支持如下复杂数据类型：

multi-value fields

对应JSON中的数组。如tag

{ "tag": [ "search", "nosql" ]}

但是事实上ES并没有array类型，因为默认就是支持的。具体参见array type。

empty fields

empty string: “”
null value: null
empty array: []
array_with_null_value: [null]

multi-level objects

对应JSON中的嵌套对象，例如：

{
    "tweet":            "Elasticsearch is very flexible",
    "user": {
        "id":           "@johnsmith",
        "gender":       "male",
        "age":          26,
        "name": {
            "full":     "John Smith",
            "first":    "John",
            "last":     "Smith"
        }
    }
}

ES会自动检测和映射为object类型，也可以通过如下mapping配置：

{
  "gb": {
    "tweet": { 
      "properties": {
        "tweet":            { "type": "string" },
        "user": { 
          "type":             "object",
          "properties": {
            "id":           { "type": "string" },
            "gender":       { "type": "string" },
            "age":          { "type": "long"   },
            "name":   { 
              "type":         "object",
              "properties": {
                "full":     { "type": "string" },
                "first":    { "type": "string" },
                "last":     { "type": "string" }
              }
            }
          }
        }
      }
    }
  }
}

NOTES && TIPS how inner objects are indexed

Lucene并没有嵌套对象的概念，事实上，A Lucene document consists of a flat list of key-value pairs. Lucene only indexes scalar or simple values, not complex datastructures. ES其实是做了如下平坦化处理：

{
    "tweet":            [elasticsearch, flexible, very],
    "user.id":          [@johnsmith],
    "user.gender":      [male],
    "user.age":         [26],
    "user.name.full":   [john, smith],
    "user.name.first":  [john],
    "user.name.last":   [smith]
}

arrays of inner objects

Finally, consider how an array containing inner objects would be indexed. Let’s say we have a followers array which looks like this:

{
    "followers": [
        { "age": 35, "name": "Mary White"},
        { "age": 26, "name": "Alex Jones"},
        { "age": 19, "name": "Lisa Smith"}
    ]
}

This document will be flattened as we described above, but the result will look like this:

{
    "followers.age":    [19, 26, 35],
    "followers.name":   [alex, jones, lisa, smith, mary, white]
}

这其实是有问题的，ES提供了一个称之为nested objects的解决方案。比较恶心，这里不讨论。