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CSV Generator

Reads data from CSV files for data generation.

Basic Usage​

{
  "data": {"gen": "csv", "file": "data/users.csv"}
}

Output: Returns an object with CSV column headers as keys

Options​

OptionTypeDefaultDescription
filestring-Path to CSV file (required)
sequentialbooleantrueRead sequentially or randomly

CSV Format​

  • First row must contain headers
  • Each row becomes an object with header names as keys
  • All values are returned as strings

Examples​

Basic CSV Reading​

CSV file (data/users.csv):

name,email,age
John Doe,john@example.com,30
Jane Smith,jane@example.com,25
Bob Johnson,bob@example.com,35

DSL:

{
  "users": {
    "count": 3,
    "item": {
      "id": {"gen": "uuid"},
      "userData": {"gen": "csv", "file": "data/users.csv"}
    }
  }
}

Output:

{
  "users": [
    {
      "id": "550e8400-...",
      "userData": {
        "name": "John Doe",
        "email": "john@example.com",
        "age": "30"
      }
    }
  ]
}

Sequential Reading​

{
  "records": {
    "count": 10,
    "item": {
      "id": {"gen": "uuid"},
      "data": {"gen": "csv", "file": "data/products.csv", "sequential": true}
    }
  }
}

Reads CSV rows in order: row 1, row 2, row 3, etc.

Random Reading​

{
  "records": {
    "count": 10,
    "item": {
      "id": {"gen": "uuid"},
      "data": {"gen": "csv", "file": "data/products.csv", "sequential": false}
    }
  }
}

Reads random rows from the CSV file.

Common Patterns​

Product Catalog​

CSV file (data/products.csv):

sku,name,category,price
PROD001,Widget,Electronics,29.99
PROD002,Gadget,Electronics,49.99
PROD003,Tool,Hardware,19.99

DSL:

{
  "products": {
    "count": 100,
    "item": {
      "id": {"gen": "uuid"},
      "productData": {"gen": "csv", "file": "data/products.csv"},
      "stock": {"gen": "number", "min": 0, "max": 100}
    }
  }
}

User Seed Data​

CSV file (data/seed-users.csv):

firstName,lastName,department
John,Doe,Engineering
Jane,Smith,Marketing
Bob,Johnson,Sales

DSL:

{
  "users": {
    "count": 3,
    "item": {
      "id": {"gen": "uuid"},
      "userData": {"gen": "csv", "file": "data/seed-users.csv", "sequential": true},
      "email": {"gen": "internet.emailAddress"},
      "createdAt": {"gen": "date"}
    }
  }
}

Test Data Import​

{
  "testCases": {
    "count": 50,
    "item": {
      "id": {"gen": "uuid"},
      "testData": {"gen": "csv", "file": "data/test-inputs.csv", "sequential": true},
      "executedAt": {"gen": "date", "format": "iso_datetime"}
    }
  }
}

Best Practices​

  1. Use Relative Paths: Paths are relative to where you run the generator
  2. Sequential for Ordered Data: Use sequential for test data that must be in order
  3. Random for Variety: Use random for more varied datasets
  4. All Strings: Remember CSV values are strings - convert if needed
  5. Cache Friendly: CSV files are cached automatically

Limitations​

  • All CSV values are returned as strings
  • CSV file must exist and be readable
  • First row must be headers
  • Large CSV files are loaded into memory

Next Steps​