Categories:

System Functions

EXTRACT_SEMANTIC_CATEGORIES¶

Returns a set of categories (semantic and privacy) for each supported column in the specified table or view. To return the categories for a column, the column must use a data type that supports classification and does not contain all NULL values.

The categories are derived from the metadata and data contained in the columns, as well as the metadata about the columns and data. The privacy categories rely on the generated semantic categories, if any.

See also:

System tags and categories and System tags with international tag values

Syntax¶

EXTRACT_SEMANTIC_CATEGORIES( '<object_name>' [ , <max_rows_to_scan> ] )
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Arguments¶

Required:

object_name

The name of the table, external table, view, or materialized view containing the columns to be classified. If a database and schema is not in use in the current session, the name must be fully-qualified.

The name must be specified exactly as it is stored in the database. If the name contains special characters, capitalization, or blank spaces, the name must be enclosed first in double-quotes and then in single quotes.

Optional:

max_rows_to_scan

The sample size of rows to use for determining the classification categories in the specified table/view.

Valid values: 1 to 10000

Default: 10000

Returns¶

As a representative example, the JSON object has the following structure:

{
  "valid_value_ratio": 1.0,
  "recommendation": {
    "semantic_category": "PASSPORT",
    "privacy_category": "IDENTIFIER",
    "confidence": "HIGH",
    "coverage": 0.7,
    "details": [
      {
        "semantic_category": "US_PASSPORT",
        "coverage": 0.7
      },
      {
        "semantic_category": "CA_PASSPORT",
        "coverage": 0.1
      }
    ]
  },
  "alternates": [
    {
      "semantic_category": "NATIONAL_IDENTIFIER",
      "privacy_category": "IDENTIFIER",
      "confidence": "LOW",
      "coverage": 0.3,
      "details": [
        {
          "semantic_category": "US_SSN",
          "privacy_category": "IDENTIFIER",
          "coverage": 0.3
        }
      ]
    }
  ]
}
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Where:

valid_value_ratio

Specifies the ratio of valid values in the sample size. Invalid values include NULL, an empty string, and a string with more than 256 characters.

recommendation

Specifies information about each tag and value. This information includes:

semantic_category

Specifies the semantic category tag value.

For possible tag values, see System tags and categories and System tags with international tag values.

privacy_category

Specifies the privacy category tag value.

The possible values are IDENTIFIER, QUASI-IDENTIFIER and SENSITIVE.

confidence

Specifies one of the following values: HIGH, MEDIUM, or LOW. This value indicates the relative confidence that Snowflake has based upon the column sampling process and how the column data aligns with how Snowflake classifies data.

coverage

Specifies the percent of sampled cell values that match the rules for a particular category.

details

Specifies fields and values that can specify a geographical tag value for the SEMANTIC_CATEGORY tag.

alternates

Specifies information about each tag and value to consider other than the recommended tag.

Usage Notes¶

The function requires a running warehouse. The warehouse can impact performance and cost. For details, see the section on classification compute costs.

Examples¶

Extract the semantic and privacy categories for the my_db.my_schema.hr_data table using the default (10000) for the number of rows to scan:

USE ROLE data_engineer;

USE WAREHOUSE classification_wh;

SELECT EXTRACT_SEMANTIC_CATEGORIES('my_db.my_schema.hr_data');
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Same as the previous example, but limited to scanning only 5000 rows in the table:

USE ROLE data_engineer;

SELECT EXTRACT_SEMANTIC_CATEGORIES('my_db.my_schema.hr_data', 5000);
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Same as the first example, but stores results in a table:

USE ROLE data_engineer;

CREATE OR REPLACE TABLE classification_results(v VARIANT) AS
  SELECT EXTRACT_SEMANTIC_CATEGORIES('my_db.my_schema.hr_data');
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Once the results are stored in a table, you can revise them before using ASSOCIATE_SEMANTIC_CATEGORY_TAGS to apply them.

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