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infer_storage_schema_with_suggestions plugin

  • Artykuł

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

This infer_storage_schema_with_suggestions plugin infers the schema of external data and returns a JSON object. For each column, the object provides inferred type, a recommended type, and the recommended mapping transformation. The recommended type and mapping are provided by the suggestion logic that determines the optimal type using the following logic:

  • Identity columns: If the inferred type for a column is long and the column name ends with id, the suggested type is string since it provides optimized indexing for identity columns where equality filters are common.
  • Unix datetime columns: If the inferred type for a column is long and one of the unix-time to datetime mapping transformations produces a valid datetime value, the suggested type is datetime and the suggested ApplicableTransformationMapping mapping is the one that produced a valid datetime value.

The plugin is invoked with the evaluate operator. To obtain the table schema that uses the inferred schema for Create and alter Azure Storage external tables without suggestions, use the infer_storage_schema plugin.

Authentication and authorization

In the properties of the request, you specify storage connection strings to access. Each storage connection string specifies the authorization method to use for access to the storage. Depending on the authorization method, the principal may need to be granted permissions on the external storage to perform the schema inference.

The following table lists the supported authentication methods and any required permissions by storage type.

Authentication method Azure Blob Storage / Data Lake Storage Gen2 Data Lake Storage Gen1
Impersonation Storage Blob Data Reader Reader
Shared Access (SAS) token List + Read This authentication method isn't supported in Gen1.
Microsoft Entra access token
Storage account access key This authentication method isn't supported in Gen1.

Syntax

evaluate infer_storage_schema_with_suggestions( Options )

Learn more about syntax conventions.

Parameters

Name Type Required Description
Options dynamic ✔️ A property bag specifying the properties of the request.

Supported properties of the request

Name Type Required Description
StorageContainers dynamic ✔️ An array of storage connection strings that represent prefix URI for stored data artifacts.
DataFormat string ✔️ One of the supported Data formats supported for ingestion
FileExtension string If specified, the function only scans files ending with this file extension. Specifying the extension may speed up the process or eliminate data reading issues.
FileNamePrefix string If specified, the function only scans files starting with this prefix. Specifying the prefix may speed up the process.
Mode string The schema inference strategy. A value of: any, last, all. The function infers the data schema from the first found file, from the last written file, or from all files respectively. The default value is last.
InferenceOptions dynamic More inference options. Valid options: UseFirstRowAsHeader for delimited file formats. For example, 'InferenceOptions': {'UseFirstRowAsHeader': true} .

Returns

The infer_storage_schema_with_suggestions plugin returns a single result table containing a single row/column containing a JSON string.

Note

  • Storage container URI secret keys must have the permissions for List in addition to Read.
  • Schema inference strategy 'all' is a very "expensive" operation, as it implies reading from all artifacts found and merging their schema.
  • Some returned types may not be the actual ones as a result of wrong type guess (or, as a result of schema merge process). This is why you should review the result carefully before using them.

Example

let options = dynamic({
  'StorageContainers': [
    h@'https://storageaccount.blob.core.windows.net/MobileEvents;secretKey'
  ],
  'FileExtension': '.json',
  'FileNamePrefix': 'js-',
  'DataFormat': 'json'
});
evaluate infer_storage_schema_with_suggestions(options)

Example input data

    {
        "source": "DataExplorer",
        "created_at": "2022-04-10 15:47:57",
        "author_id": 739144091473215488,
        "time_millisec":1547083647000
    }

Output

{
  "Columns": [
    {
      "OriginalColumn": {
        "Name": "source",
        "CslType": {
          "type": "string",
          "IsNumeric": false,
          "IsSummable": false
        }
      },
      "RecommendedColumn": {
        "Name": "source",
        "CslType": {
          "type": "string",
          "IsNumeric": false,
          "IsSummable": false
        }
      },
      "ApplicableTransformationMapping": "None"
    },
    {
      "OriginalColumn": {
        "Name": "created_at",
        "CslType": {
          "type": "datetime",
          "IsNumeric": false,
          "IsSummable": true
        }
      },
      "RecommendedColumn": {
        "Name": "created_at",
        "CslType": {
          "type": "datetime",
          "IsNumeric": false,
          "IsSummable": true
        }
      },
      "ApplicableTransformationMapping": "None"
    },
    {
      "OriginalColumn": {
        "Name": "author_id",
        "CslType": {
          "type": "long",
          "IsNumeric": true,
          "IsSummable": true
        }
      },
      "RecommendedColumn": {
        "Name": "author_id",
        "CslType": {
          "type": "string",
          "IsNumeric": false,
          "IsSummable": false
        }
      },
      "ApplicableTransformationMapping": "None"
    },
    {
      "OriginalColumn": {
        "Name": "time_millisec",
        "CslType": {
          "type": "long",
          "IsNumeric": true,
          "IsSummable": true
        }
      },
      "RecommendedColumn": {
        "Name": "time_millisec",
        "CslType": {
          "type": "datetime",
          "IsNumeric": false,
          "IsSummable": true
        }
      },
      "ApplicableTransformationMapping": "DateTimeFromUnixMilliseconds"
    }
  ]
}

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