Mock Data Generator

A comprehensive mock data generator that creates realistic test data based on field definitions and types. Supports multiple output formats including JSON, CSV, JavaScript, and TypeScript.

Features

• Type-based generation: Define field types and get realistic data
• Multiple output formats: JSON, CSV, JS, TS
• Customizable: Support for custom field types and parameters
• Seeded generation: Reproducible data with seed values
• Business-specific: Pre-built generators for common business data
• Auto-save: Automatically save to files with directory creation

Quick Start

Basic Usage

import { generateData } from './index.mjs';

const fields = {
  id: 'id',
  name: 'name',
  email: 'email',
  amount: 'amount',
  status: 'status'
};

const data = generateData(10, fields, {
  outputPath: './output.json',
  format: 'json',
  seed: 12345
});

Field Types

Basic Types

• 'string' - Random alphanumeric string
• 'number' - Random integer (1-10000)
• 'boolean' - Random boolean value
• 'date' - Recent date in ISO format

Business-Specific Types

• 'terminalId' - Terminal ID (T + 8 chars)
• 'merchantId' - Merchant ID (M + 8 chars)
• 'card_pan' - Credit card number
• 'rrn' - Retrieval Reference Number
• 'transactionId' - Transaction ID (TXN + 10 chars)

Financial Types

• 'amount' - Float amount (100-100000)
• 'totalValue' - Large float value (1000-1000000)
• 'settledValue' - Settlement amount (500-500000)
• 'charge' - Fee amount (10-5000)

Status Types

• 'status' - Transaction status (pending, completed, failed, processing)
• 'settlementStatus' - Settlement status (settled, pending, failed, processing)
• 'type' - Transaction type (credit, debit, transfer, withdrawal)

Personal Information

• 'name' - Full name
• 'firstName' - First name
• 'lastName' - Last name
• 'email' - Email address
• 'phone' - Phone number
• 'address' - Street address
• 'city' - City name
• 'country' - Country name
• 'zipCode' - ZIP/Postal code

Company Information

• 'companyName' - Company name
• 'jobTitle' - Job title
• 'department' - Department name

Product Information

• 'productName' - Product name
• 'productDescription' - Product description
• 'price' - Product price (10-1000)

Media & URLs

• 'url' - Website URL
• 'imageUrl' - Image URL
• 'avatar' - Avatar image URL

Text Content

• 'description' - Sentence description
• 'title' - Short title (3 words)
• 'text' - Paragraph text

IDs and References

• 'id' - Sequential integer ID
• 'uuid' - UUID string

Complex Field Types

Enum with Options

{
  status: { type: 'enum', options: ['active', 'inactive', 'suspended'] }
}

Number Range

{
  quantity: { type: 'numberRange', min: 1, max: 100 }
}

Float Range with Precision

{
  price: { type: 'floatRange', min: 10.50, max: 999.99, precision: 0.01 }
}

Date Range

{
  eventDate: { type: 'dateRange', startDate: '2024-01-01', endDate: '2024-12-31' }
}

String with Length

{
  code: { type: 'stringLength', length: 8 }
}

Nested Objects

{
  status: {
    name: 'statusName',
    description: 'statusDescription',
    context: 'statusContext'
  }
}

This will generate:

{
  "status": {
    "name": "processing",
    "description": "Transaction is being processed",
    "context": "warning"
  }
}

API Reference

generateData(count, fields, options)

Generates mock data based on field definitions.

Parameters

• count (number): Number of records to generate
• fields (object): Field definitions with types
• options (object): Optional configuration

Options

• seed (number): Seed for reproducible generation
• format (string): Output format ('json', 'csv', 'js', 'ts')
• outputPath (string): File path to save output
• additionalData (object): Additional data to merge with each record

Returns

Array of generated records

generateRecord(fields, index)

Generates a single record.

Parameters

• fields (object): Field definitions
• index (number): Record index (for sequential IDs)

Returns

Single generated record object

Examples

Transaction Data

const transactionFields = {
  id: 'id',
  transactionId: 'transactionId',
  terminalId: 'terminalId',
  merchantId: 'merchantId',
  amount: 'amount',
  status: 'status',
  type: 'type',
  card_pan: 'card_pan',
  rrn: 'rrn',
  createdAt: 'createdAt'
};

const data = generateData(50, transactionFields, {
  outputPath: './transactions.json',
  format: 'json',
  seed: 12345
});

Merchant Data with Complex Fields

const merchantFields = {
  id: 'id',
  name: 'companyName',
  email: 'email',
  phone: 'phone',
  address: 'address',
  city: 'city',
  country: 'country',
  zipCode: 'zipCode',
  status: { type: 'enum', options: ['active', 'inactive', 'suspended', 'pending'] },
  registrationDate: 'registrationDate',
  totalTransactions: { type: 'numberRange', min: 0, max: 10000 },
  averageAmount: { type: 'floatRange', min: 100, max: 50000, precision: 0.01 }
};

const data = generateData(25, merchantFields, {
  outputPath: './merchants.json',
  format: 'json',
  seed: 67890
});

CSV Output

const csvFields = {
  id: 'id',
  name: 'name',
  email: 'email',
  amount: 'amount',
  status: 'status',
  date: 'date'
};

const data = generateData(15, csvFields, {
  outputPath: './data.csv',
  format: 'csv',
  seed: 44444
});

TypeScript Output

const tsFields = {
  id: 'id',
  title: 'title',
  description: 'description',
  status: { type: 'enum', options: ['draft', 'published', 'archived'] },
  createdAt: 'createdAt'
};

const data = generateData(10, tsFields, {
  outputPath: './data.ts',
  format: 'ts',
  seed: 55555
});

Nested Objects

const disputeFields = {
  id: 'id',
  product: 'product',
  transaction_reference: 'transactionId',
  reason: 'reason',
  merchantId: 'merchantId',
  amount: 'amount',
  status: {
    name: 'statusName',
    description: 'statusDescription',
    context: 'statusContext'
  }
};

const data = generateData(20, disputeFields, {
  outputPath: './disputes.json',
  format: 'json',
  seed: 88888
});

This generates data like:

{
  "id": 1,
  "product": "commissionDispute",
  "transaction_reference": "TXNJBCZAXEKEK",
  "reason": "Transaction dispute raised by merchant",
  "merchantId": "MSKRJZFNA",
  "amount": 2782.24,
  "status": {
    "name": "processing",
    "description": "Dispute is being reviewed",
    "context": "warning"
  }
}

Output Formats

JSON

[
  {
    "id": 1,
    "name": "John Doe",
    "email": "[email protected]",
    "amount": 1250.50,
    "status": "completed"
  }
]

CSV

id,name,email,amount,status
1,John Doe,[email protected],1250.50,completed
2,Jane Smith,[email protected],890.25,pending

JavaScript

export const mockData = [
  {
    "id": 1,
    "name": "John Doe",
    "email": "[email protected]",
    "amount": 1250.50,
    "status": "completed"
  }
];

TypeScript

export const mockData = [
  {
    "id": 1,
    "name": "John Doe",
    "email": "[email protected]",
    "amount": 1250.50,
    "status": "completed"
  }
] as const;

Running Examples

To run the example file:

node data/mock/examples.mjs

This will generate multiple sample files in the data/mock/generated/ directory.

Custom Field Generators

You can extend the FIELD_GENERATORS object to add custom field types:

const FIELD_GENERATORS = {
  // ... existing generators
  
  // Custom generator
  customField: () => 'custom value',
  
  // Generator with parameters
  customRange: (min, max) => Math.floor(Math.random() * (max - min + 1)) + min
};

Best Practices

1. Use seeds for reproducible data during development
2. Define field types that match your actual data structure
3. Use business-specific types when available for more realistic data
4. Test with small counts first before generating large datasets
5. Validate generated data to ensure it meets your requirements

Dependencies

• @faker-js/faker: For generating realistic fake data
• fs: For file system operations
• path: For path manipulation