TestDataX 0.1.0

A flexible test data generation toolkit

TestDataX

This command-line interface application enables quick and customizable test data generation across various formats. It leverages Faker for realistic data fields, offers flexible schema configurations, and simplifies output to multiple database dialects or file types. Users can define precise parameters for data volume, types, and constraints for each target data set.

Requirements

• Python 3.11+
• Additional dependencies are handled automatically by poetry

Installation

Prerequisites

# Install Python 3.11+ if not already installed
brew install python@3.11

# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -

# Verify Poetry installation
poetry --version

Install

# Clone the repository
git clone https://github.com/JamesPBrett/testdatax.git
cd testdatax

# Install dependencies
poetry install

Common Issues

• If Poetry is not found in PATH:

  export PATH="$HOME/.local/bin:$PATH"
  

Features

• Generate realistic test data using Data providers
• Support for multiple output formats (CSV, JSON, SQL, etc.)
• Customizable schema definitions
• Configurable data generation parameters
• CLI tool for easy data generation

Supported Formats

• JSON
• CSV
• ORC
• Parquet
• MySQL
• MSSQL
• Oracle

CLI Usage

testdatax -o <output_file> -f <format> -s <schema_file> -r <num_rows> [-d]

Options:

• -o, --output: Output file path (table_name for sql exports)
• -f, --format: Output format (csv, json, orc, parquet, mysql, mssql, oracle)
• -r, --rows: Number of rows to generate (default: 10)
• -s, --schema: Path to schema file
• -d, --debug: Enable debug output

Usage Examples

Generate 10 rows of CSV data:

testdatax -o users.csv -f csv -s schema.json -r 10

Generate 1000 rows of Parquet data with debug output:

testdatax -o large_dataset.parquet -f parquet -s users_schema.json -r 1000 -d

Generate JSON data with default row count (10):

testdatax -o data.json -f json -s schema.json

Generate ORC file with specific schema:

testdatax -o analytics.orc -f orc -s analytics_schema.json -r 100

Generate MySQL with default row count (1000), table_name as 'default':

testdatax -o default.sql -f mysql -r 1000

Generate MSSQL with default row count (1000), table_name as 'mstest':

testdatax -o mstest.sql -f mssql -r 1000

Generate Oracle with default row count (1000), table_name as 'oracle':

datagen -o oracle.sql -f oracle -r 1000

Each command consists of:

• -o, --output: Specify the output file path and name
• -f, --format: Output format (csv, json, orc, parquet, mysql, mssql, oracle)
• -s, --schema: Path to your schema definition file
• -r, --rows: Number of rows to generate (optional, defaults to 10)
• -d, --debug: Enable debug logging (optional)

Schema Example

{
  "username": {
    "type": "string",
    "faker": "name"
  },
  "date_joined": {
    "type": "datetime"
  },
  "date": {
    "type": "date"
  },
  "age": {
    "type": "integer",
    "min": 18,
    "max": 99
  },
  "is_active": {
    "type": "boolean"
  },
  "float": {
    "type": "float"
  },
  "uuid": {
    "type": "uuid"
  },
  "status": {
    "type": "enum",
    "values": ["active", "inactive", "pending"]
  }
}

Schema Configuration

The schema file defines the structure and constraints of your generated data. Each field in the schema can have the following properties:

Basic Field Properties

• type: (required) The data type of the field
• nullable: (optional) Boolean to allow null values (default: false)
• unique: (optional) Boolean to ensure unique values (default: false)

Type-Specific Properties

String Fields

{
  "username": {
    "type": "string",
    "min_length": 5,
    "max_length": 20,
    "faker": "user_name"  // Use faker to generate realistic data
  },
  "description": {
    "type": "text",
    "min_length": 100,
    "max_length": 500
  }
}

Numeric Fields

{
  "age": {
    "type": "integer",
    "min": 18,
    "max": 99
  },
  "score": {
    "type": "float",
    "min": 0.0,
    "max": 100.0,
    "precision": 2
  }
}

Date and Time Fields

{
  "created_at": {
    "type": "datetime",
    "start_date": "2020-01-01",
    "end_date": "2023-12-31"
  },
  "birth_date": {
    "type": "date",
    "format": "%Y-%m-%d"
  }
}

Enum Fields

{
  "status": {
    "type": "enum",
    "values": ["pending", "active", "suspended"],
    "weights": [0.2, 0.7, 0.1]  // Optional probability weights
  }
}

Using Faker

The generator supports Faker providers for generating realistic data:

{
  "name": {
    "type": "string",
    "faker": "name"
  },
  "email": {
    "type": "string",
    "faker": "email"
  },
  "address": {
    "type": "string",
    "faker": "address"
  },
  "company": {
    "type": "string",
    "faker": "company"
  }
}

Complete Example

{
  "user_id": {
    "type": "uuid",
    "unique": true
  },
  "username": {
    "type": "string",
    "faker": "user_name",
    "unique": true
  },
  "email": {
    "type": "string",
    "faker": "email",
    "unique": true
  },
  "age": {
    "type": "integer",
    "min": 18,
    "max": 99
  },
  "status": {
    "type": "enum",
    "values": ["active", "inactive"],
    "weights": [0.8, 0.2]
  },
  "created_at": {
    "type": "datetime",
    "start_date": "2020-01-01",
    "end_date": "2023-12-31"
  },
  "is_verified": {
    "type": "boolean",
    "nullable": true
  }
}

Supported Data Types

• string
• text
• integer
• bigint
• float
• decimal
• boolean
• date
• datetime
• blob
• uuid
• enum

Database Type Mappings

Generic Type	MySQL	MSSQL	Oracle
string	VARCHAR(255)	NVARCHAR(255)	VARCHAR2(255)
text	TEXT	NVARCHAR(MAX)	CLOB
integer	INT	INT	NUMBER(10)
bigint	BIGINT	BIGINT	NUMBER(19)
float	FLOAT	FLOAT	FLOAT
decimal	DECIMAL(18,2)	DECIMAL(18,2)	NUMBER(18,2)
boolean	TINYINT(1)	BIT	NUMBER(1)
date	DATE	DATE	DATE
datetime	DATETIME	DATETIME2	TIMESTAMP
blob	LONGBLOB	VARBINARY(MAX)	BLOB
uuid	VARCHAR(36)	UNIQUEIDENTIFIER	VARCHAR2(36)
enum	ENUM	NVARCHAR(255)	VARCHAR2(255)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Test change