Data types

Basic data types

Search indexes support the following basic data types: Long, Double, Boolean, Keyword, Text, Date, and Geopoint. The following table describes the basic data types.

Array and Nested types

In addition to basic data types, such as Long, Double, Boolean, Keyword, Text, Date, and Geopoint, search indexes support two special data types: Array and Nested. The Array data type is suitable for storing a collection of the same type of data. The Nested data type is similar to the JSON data type and is suitable for storing data that has a hierarchical structure. For more information, see Array and Nested data types.

[
    {
        "tagName": "tag1",
        "score": 0.8
    },
    {
        "tagName": "tag2",
        "score": 0.2
    }
]

[
    {
        "name": "Zhang San",
        "age": 20,
        "phone": "1390000****",
        "address": [
            {
                "province": "Zhejiang Province",
                "city": "Hangzhou City",
                "street": "No. 1201, Xingfu Community, Sunshine Avenue"
            }
        ]
    }
]

Virtual columns

You can use the virtual column feature of search indexes to query new fields and the data of new field types without the need to modify the storage schema and data in the Tablestore tables. For more information, see Virtual columns.

The virtual column feature allows you to map a column in a table to a virtual column in a search index when you create the search index. The type of the virtual column can be different from that of the column in the table. This allows you to create a column without modifying the table schema and data. The new column can be used to accelerate queries or can be configured with different tokenization methods.

• You can configure different tokenization methods for Text fields that are mapped to the same field in a table.

  A single String column can be mapped to multiple Text columns of a search index. Different Text columns use different tokenization methods to meet various business requirements.

• Query acceleration

  You do not need to cleanse data or re-create a table schema. You need to only map required columns of a table to the columns in a search index. The column types can be different between the table and the search index. For example, map the numeric type to the Keyword type to improve the performance of a term query, and map the String type to the numeric type to improve the performance of a range query.