Creating schema JSON files for Amazon Personalize schemas - Amazon Personalize
Schema formatting requirements
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Creating schema JSON files for Amazon Personalize schemas

After you prepare your data, you are ready to create schema JSON files for each type of data that you are importing. These files outline the structure and content of your data, including column names and their data types.

You use schema JSON files when you create an Amazon Personalize schema in Creating a schema and a dataset. In Amazon Personalize, a schema is a resource that allows Amazon Personalize to parse the data when you import it into your dataset. You create a schema for each dataset you are using.

For custom resources, each dataset has specific schema requirements. For Domain dataset groups, the domain you choose determines your dataset and schema requirements. Each domain has a default schema for each dataset type. When you create a dataset, you can either use the existing domain schema or create a new one by modifying the existing default schema. Use the default schema as a guide for what data to import for your domain.

The following sections provide custom and domain requirements for creating a schema JSON file for each dataset type.

Topics

Schema formatting requirements

When you create a schema for a dataset in a Domain dataset group or Custom dataset group, you must follow these guidelines:

  • You must define the schema in Avro format. For information on the Avro data types we support, see Schema data types.

  • A schema has a name key whose value must match the dataset type.

  • The schema fields can appear in any order, but they must match the order of the corresponding column headers in your CSV file.

  • Schemas must be flat JSON files without nested structures. For example, a field cannot be the parent of multiple sub-fields.

  • Amazon Personalize schemas don't support complex types such as arrays and maps.

  • Schema fields must have unique alphanumeric names. For example, you can't add both a GENRES_FIELD_1 field and a GENRESFIELD1 field.

  • You must define required fields as their required data types. Reserved categorical string fields must have the categorical attribute set to true, while reserved string fields can't be categorical. The keywords can't be in your data.

  • If you add your own metadata field of type string and you want Amazon Personalize to use it when training, it must include the categorical attribute or the textual attribute (only Items schemas support fields with the textual attribute).

  • Amazon Personalize doesn't use boolean type data when training or filtering recommendations. To have Amazon Personalize use boolean data when training or filtering, use a field of type String and use the values "True" and "False" in your data. Or you can use type int or long and values 0 and 1.

  • Textual fields must be of the type string and must have the textual attribute set to true. For more information about unstructured text data, see Unstructured text metadata.

Domain dataset group datasets have additional requirements based on both domain and dataset type. Custom dataset group datasets have additional requirements depending on type.

Schema data types

Amazon Personalize schemas support the following Avro types for fields:

  • float

  • double

  • int

  • long

  • string

  • boolean

  • null

Some required and reserved fields support null data. Adding a null type to a field allows you to use imperfect data (for example, metadata with blank values) to generate recommendations. For information about which fields support null data, see the schema requirements topic for your domain: VIDEO_ON_DEMAND datasets and schemas, ECOMMERCE datasets and schemas, or Custom datasets and schemas. The following example shows how to add a null type for a GENDER field.

{
  "name": "GENDER",
  "type": [
    "null",
    "string"
  ],
  "categorical": true
}