Skip to main content

Create Column

Add new columns to existing tables with full control over data types, constraints, and properties.

create()​

Create a single column in an existing table.

client.columns.create(tableName: string, column: FieldDefinition): Promise<BolticSuccessResponse<ColumnRecord> | BolticErrorResponse>

Parameters​

ParameterTypeRequiredDescription
tableNamestring✅Name of the target table
columnFieldDefinition✅Column definition object

FieldDefinition Properties​

PropertyTypeRequiredDescription
namestring✅Column name (alphanumeric + underscore)
typeFieldType✅Column data type
descriptionstring❌Human-readable description
is_nullableboolean❌Allow null values (default: true)
is_uniqueboolean❌Enforce unique values (default: false)
is_indexedboolean❌Create database index (default: false)
is_visibleboolean❌Show in UI (default: true)
default_valueany❌Default value for new records

Examples​

Basic Text Column​

const { data, error } = await client.columns.create("products", {
    name: "title",
    type: "text",
    is_nullable: false,
    is_indexed: true,
    description: "Product title",
});

if (!error) {
    console.log("Column created:", data.name);
}

Currency Column​

const { data, error } = await client.columns.create("products", {
    name: "price",
    type: "currency",
    currency_format: "USD",
    decimals: "0.00",
    is_nullable: false,
    default_value: 0.0,
    description: "Product price in USD",
});
const { data, error } = await client.columns.create("products", {
    name: "category",
    type: "dropdown",
    selectable_items: ["Electronics", "Clothing", "Books", "Home & Garden"],
    multiple_selections: false,
    is_nullable: false,
    description: "Product category",
});

Date-Time Column​

const { data, error } = await client.columns.create("events", {
    name: "event_date",
    type: "date-time",
    date_format: "%d/%m/%Y",
    time_format: "HH:mm:ss",
    is_nullable: true,
    description: "Event start date and time",
});

Vector Column for AI/ML​

const { data, error } = await client.columns.create("documents", {
    name: "embeddings",
    type: "vector",
    vector_dimension: 1536,
    is_indexed: true,
    description: "Document embeddings for similarity search",
});

Response Format​

{
  data: {
    id: "26b364ef-0401-49b2-aae3-ebe43ae14eca",
  },
   "message": "Column embeddings added successfully"
}

createMany()​

Create multiple columns in a single operation for better performance.

client.columns.createMany(tableName: string, columns: FieldDefinition[]): Promise<BolticListResponse<ColumnRecord> | BolticErrorResponse>

Parameters​

ParameterTypeRequiredDescription
tableNamestring✅Name of the target table
columnsFieldDefinition[]✅Array of column definitions

Example: User Profile Schema​

const userColumns = [
    {
        name: "first_name",
        type: "text",
        is_nullable: false,
        description: "User first name",
    },
    {
        name: "last_name",
        type: "text",
        is_nullable: false,
        description: "User last name",
    },
    {
        name: "email",
        type: "email",
        is_unique: true,
        is_nullable: false,
        is_indexed: true,
        description: "User email address",
    },
    {
        name: "phone",
        type: "phone-number",
        phone_format: "+1 (123) 456-7890",
        is_nullable: true,
        description: "User phone number",
    },
    {
        name: "age",
        type: "number",
        decimals: "0",
        is_nullable: true,
        description: "User age",
    },
    {
        name: "is_verified",
        type: "checkbox",
        default_value: false,
        description: "Whether user is verified",
    },
    {
        name: "profile_data",
        type: "json",
        is_nullable: true,
        description: "Additional profile information",
    },
];

const { data, error } = await client.columns.createMany("users", userColumns);

if (!error) {
    console.log(`Created ${data.length} columns successfully`);
    data.forEach((column) => {
        console.log(`- ${column.name} (${column.type})`);
    });
}

Example: E-commerce Product Schema​

const productColumns = [
    {
        name: "title",
        type: "text",
        is_nullable: false,
        is_unique: true,
        is_indexed: true,
        description: "Product title",
    },
    {
        name: "description",
        type: "long-text",
        is_nullable: true,
        description: "Product description",
    },
    {
        name: "price",
        type: "currency",
        currency_format: "USD",
        decimals: "0.00",
        is_nullable: false,
        is_indexed: true,
        description: "Product price",
    },
    {
        name: "sale_price",
        type: "currency",
        currency_format: "USD",
        decimals: "0.00",
        is_nullable: true,
        description: "Sale price if on discount",
    },
    {
        name: "sku",
        type: "text",
        is_unique: true,
        is_nullable: false,
        is_indexed: true,
        description: "Stock keeping unit",
    },
    {
        name: "category",
        type: "dropdown",
        selection_source: "provide-static-list",
        selectable_items: ["Electronics", "Clothing", "Books", "Home & Garden"],
        multiple_selections: false,
        is_nullable: false,
        description: "Product category",
    },
    {
        name: "tags",
        type: "dropdown",
        selection_source: "provide-static-list",
        selectable_items: ["new", "bestseller", "sale", "limited"],
        multiple_selections: true,
        is_nullable: true,
        description: "Product tags",
    },
    {
        name: "is_active",
        type: "checkbox",
        default_value: true,
        description: "Whether product is active",
    },
    {
        name: "launch_date",
        type: "date-time",
        timezone: "UTC",
        date_format: "MM/DD/YYYY",
        is_nullable: true,
        description: "Product launch date",
    },
    {
        name: "specifications",
        type: "json",
        is_nullable: true,
        description: "Product specifications and features",
    },
];

const { data, error } = await client.columns.createMany("products", productColumns);

Type-Specific Properties​

Different field types require additional properties:

Currency Fields​

{
  name: 'price',
  type: 'currency',
  currency_format: 'USD', // Required: ISO currency code
  decimals: '0.00',       // Optional: decimal precision
}

Phone Number Fields​

{
  name: 'phone',
  type: 'phone-number',
  phone_format: '+1 (123) 456-7890' // Required: display format
}
{
  name: 'status',
  type: 'dropdown',
  selectable_items: ['active', 'inactive'], // Required
  multiple_selections: false                // Required
}

Date-Time Fields​

{
  name: 'created_date',
  type: 'date-time',
  date_format: 'MM/DD/YYYY',         // Optional
  time_format: 'HH:mm:ss'            // Optional
}

Vector Fields​

{
  name: 'embeddings',
  type: 'vector',
  vector_dimension: 1536 // Required: dimension size
}

Validation Rules​

Column Names​

  • Must contain only letters, numbers, and underscores
  • Cannot start with a number
  • Cannot be a reserved keyword (id, created_at, updated_at)
  • Maximum length: 63 characters

Non-Nullable Columns​

info

Cannot create columns with is_nullable: false on tables that already contain rows

To add a non-nullable column to a table with existing data, you must first delete all rows from the table.

Best Practices​

Column Design​

  • Use descriptive names that clearly indicate the column's purpose
  • Set appropriate constraints (is_nullable, is_unique, is_indexed)
  • Choose the most specific data type for your use case
  • Consider performance impact of indexing

Default Values​

  • Use null as default for optional fields
  • Ensure default values match the column type

Bulk Creation​

  • Use createMany() for multiple columns to improve performance
  • Group related columns together logically
  • Validate all column definitions before bulk creation