You can add a column to your importer directly through the UI of your Fuse account, or you can do so in code using the documentation below.

The String column is the most versatile column type. It accepts any strings, and has a number of different ways to validate or transform values.

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addColumn Function

The addColumn function accepts an object as an argument.

  • internal_key (String) - required: The internal key used to access a column’s value.
  • label (String) - required: The user-facing column label that is shown in the interface.
  • column_type (String) - required: Should be “string”.
  • required (String) - required: Whether or not the column is required.
  • position (Optional): The position or order of the column.
  • unique (Optional): Whether values should be unique.
  • validations (Optional): An array of built-in data validations. See Built-in String Validations.
  • transformations (Optional): An array of built-in data transformations. See Built-in String Transformations.

​
Example Code

  const importer = new FuseImporter(organizationApiKey, importerId);

  const validations = [
    {
      validation_type: "max_length",
      message: "My Label should not be longer than 18 characters",
      options: {
        max_length: 18,
      },
    },
  ];

  const transformations = [
    {
      transformation_type: "lowercase",
    },
  ];

  importer.addColumn({
    internal_key: "my_key",
    label: "My Label",
    column_type: "string",
    required: true,
    position: 1,
    unique: true,
    validations: validations,
    transformations: transformations,
  });

  importer.show();

​
Built-in String Validations

The addColumn object accepts an array of validations. Each validation has three properties:

  • validation_type: the name of the validation
  • message: the message showed to the end user if the validation fails
  • options: options for the specific validation type

The validations property accepts an array of objects that contains these possible values: validation_type, message and options. As the example below:

validations: [
  {
    validation_type: "cannot_contain",
    message: "Cannot contain 'john'",
    options: {
      pattern: "john",
    },
  },
];

​
cannot_contain

Specifies that the column cannot contain a specific pattern.

validations: [
  {
    validation_type: "cannot_contain",
    message: "Cannot contain 'john'",
    options: {
      pattern: "john",
    },
  },
];

​
contain

Specifies that column must contain a specific pattern.

validations: [
  {
    validation_type: "contain",
    message: "Must contain 'john'",
    options: {
      pattern: "john",
    },
  },
];

​
max_length

Specifies the maximum character length of the column.

validations: [
  {
    validation_type: "max_length",
    message: "Must be less than 10 characters",
    options: {
      max_length: 9,
    },
  },
];

​
min_length

Specifies the minimum character length of the column.

validations: [
  {
    validation_type: "min_length",
    message: "Must be at least 10 characters",
    options: {
      min_length: 10,
    },
  },
];

​
length_exactly

Specifies the exact length that the column must have.

validations: [
  {
    validation_type: "length_exactly",
    message: "Must be exactly 10 characters",
    options: {
      length: 10,
    },
  },
];

​
unique_case_sensitive

Specifies that the value must be unique using case-sensitive comparison.

validations: [
  {
    validation_type: "unique_case_sensitive",
    message: "Must be unique (case sensitive)",
  },
];

​
unique_case_insensitive

Specifies that the value must be unique using case-insensitive comparison.

validations: [
  {
    validation_type: "unique_case_insensitive",
    message: "Must be unique (case insensitive)",
  },
];

​
regex

Specifies that the value must match a regex.

validations: [
  {
    validation_type: "regex",
    message: "Must be a valid domain name",
    options: {
      pattern: /^[a-zA-Z0-9][a-zA-Z0-9-]{0,61}[a-zA-Z0-9](?:\.[a-zA-Z]{2,})+$/,
    },
  },
];

​
Built-in String Transformations

The options property of addColumn accepts an array of transformations. For the string transformations, you can specify one property:

  • transformation_type: the name of the transformation

The transformations property accepts an array of objects that contains these possible values: transformation_type. As the example below:

transformations: [
  {
    transformation_type: "trim_whitespace",
  },
];

​
sentence_case

This transformation converts all the strings in the column to sentence case. For example, “hello world” becomes “Hello world”.

transformations: [
  {
    transformation_type: "sentence_case",
  },
];

​
capitalize

This transformation capitalizes the first letter of each string in the column while making the remaining letters lowercase. For example, “apple” becomes “Apple”.

transformations: [
  {
    transformation_type: "capitalize",
  },
];

​
uppercase

This transformation converts all the strings in the column to uppercase. For example, “hello” becomes “HELLO”.

transformations: [
  {
    transformation_type: "uppercase",
  },
];

​
lowercase

This transformation converts all the strings in the column to lowercase. For example, “WORLD” becomes “world”.

transformations: [
  {
    transformation_type: "lowercase",
  },
];

​
trim_whitespace

This transformation removes any leading or trailing whitespace characters (spaces, tabs, etc.) from each string in the column. For example, ” hello ” becomes “hello”.

transformations: [
  {
    transformation_type: "trim_whitespace",
  },
];
Bulk EditingBoolean Columns