Tablestore:Array and Nested data types

Array data type

The Array data type is a composite data type and can be combined with primitive data types, such as Long, Double, Boolean, Keyword, Text, Date, and Geopoint, to construct complex data structures. For example, the combination of the Long data type with the Array data type is used to construct arrays of long integers. A long array can contain multiple long integers. If one of the long integers is matched during a query, the corresponding row is returned. The Array data type is suitable for storing a collection of the same type of data.

Nested data type

Data of the Nested type is nested documents. Nested documents are used when a row of data (document) contains multiple child rows (child documents). Multiple child rows are stored in a nested field. The Nested data type is suitable for storing data that has a hierarchical structure.

You must specify the schema of child rows in a nested field. The schema must include the fields of the child rows and the property of each field. The Nested data type can be used to store multiple values, which is similar to the JSON data type.

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

[
    {
        name:"John",
        "age": 20,
        "phone": "1390000****",
        "address": [
            {
                "province": "Zhejiang",
                "city": "Hangzhou",
                "street": "1201 Xingfu Community, Sunshine Avenue"
            }
        ]
    }
]

// Create schemas for the fields in the child row. 
List<FieldSchema> subFieldSchemas = new ArrayList<FieldSchema>();
subFieldSchemas.add(new FieldSchema("tagName", FieldType.KEYWORD)
    .setIndex(true).setEnableSortAndAgg(true));
subFieldSchemas.add(new FieldSchema("score", FieldType.DOUBLE)
    .setIndex(true).setEnableSortAndAgg(true));
subFieldSchemas.add(new FieldSchema("time", FieldType.DATE)
    .setDateFormats(Arrays.asList("epoch_millis")));
    
// Use the schemas that are created for the child rows as the value of subfieldSchemas for the nested field. 
FieldSchema nestedFieldSchema = new FieldSchema("tags", FieldType.NESTED)
    .setSubFieldSchemas(subFieldSchemas);

// Create schemas for the three fields in the child rows of the address nested field. The path specified by user.address can be used to query data of fields in a child row. 
List<FieldSchema> addressSubFiledSchemas = new ArrayList<>();
addressSubFiledSchemas.add(new FieldSchema("province",FieldType.KEYWORD));
addressSubFiledSchemas.add(new FieldSchema("city",FieldType.KEYWORD));
addressSubFiledSchemas.add(new FieldSchema("street",FieldType.KEYWORD));

// Create a schema for each child row of the user nested field. Each child row contains three fields of different primitive field types and one nested field named address. The path specified by user can be used to query data of fields in a child row. 
List<FieldSchema> subFieldSchemas = new ArrayList<>();
subFieldSchemas.add(new FieldSchema("name",FieldType.KEYWORD));
subFieldSchemas.add(new FieldSchema("age",FieldType.LONG));
subFieldSchemas.add(new FieldSchema("birth",FieldType.DATE).setDateFormats(Arrays.asList("yyyy-MM-dd HH:mm:ss.SSS")));
subFieldSchemas.add(new FieldSchema("phone",FieldType.KEYWORD));
subFieldSchemas.add(new FieldSchema("address",FieldType.NESTED).setSubFieldSchemas(addressSubFiledSchemas));

// Use the schemas that are created for the child rows of the user nested field as the value of subfieldSchemas for the nested field. 
List<FieldSchema> fieldSchemas = new ArrayList<>();
fieldSchemas.add(new FieldSchema("user",FieldType.NESTED).setSubFieldSchemas(subFieldSchemas));

References

• When you use a search index to query data, you can use the following query methods: term query, terms query, match all query, match query, match phrase query, prefix query, range query, wildcard query, fuzzy query, Boolean query, geo query, nested query, KNN vector query, and exists query. You can select query methods based on your business requirements to query data from multiple dimensions.

  You can sort or paginate rows that meet the query conditions by using the sorting and paging features. For more information, see Perform sorting and paging.

  You can use the collapse (distinct) feature to collapse the result set based on a specific column. This way, data of the specified type appears only once in the query results. For more information, see Collapse (distinct).

• If you want to analyze data in a data table, you can use the aggregation feature of the Search operation or execute SQL statements. For example, you can obtain the minimum and maximum values, sum, and total number of rows. For more information, see Aggregation and SQL query.

• If you want to obtain all rows that meet the query conditions without the need to sort the rows, you can call the ParallelScan and ComputeSplits operations to use the parallel scan feature. For more information, see Parallel scan.