TestDataX 0.2.0

A flexible test data generation toolkit

TestDataX

This command-line interface application enables quick and customizable test data generation across various formats. It supports multiple data providers (Mimesis and Faker) for realistic data generation, 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+

Quick Start

# Install from PyPI
pip install testdatax

# Generate sample data
testdatax --rows 1000 --format json --output data.json

Features

• Generate realistic test data using multiple data providers (Mimesis, Faker)
• Support for multiple output formats (CSV, JSON, SQL, etc.)
• Customizable schema definitions
• Configurable data generation parameters
• CLI tool for easy test 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> -p <provider> [-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
• -p, --provider: Data provider (mimesis, faker) - default: mimesis
• -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 10 rows of CSV data using Faker provider:

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

Generate 1000 rows of Parquet data with debug output:

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

Generate 1000 rows of Parquet data using Mimesis provider:

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

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':

testdatax -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)
• -p, --provider: Data provider (mimesis, faker) - default: mimesis
• -d, --debug: Enable debug logging (optional)

Schema Example

{
  "username": {
    "type": "string",
    "provider_field": "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,
    "provider_field": "user_name"  // Use provider-specific field 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"
  }
}

Note: start_date/end_date bound the generated range (inclusive). When format is set, date/datetime values are rendered to a string with strftime; for the SQL exporters this means the column receives a formatted string literal rather than a native date, so format is best suited to the CSV/JSON formats.

Enum Fields

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

Using Data Providers

Both Mimesis and Faker providers support the same schema format. You can specify provider-specific generators using the provider_field field (works with both providers):

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

Complete Example

{
  "user_id": {
    "type": "uuid",
    "unique": true
  },
  "username": {
    "type": "string",
    "provider_field": "user_name",
    "unique": true
  },
  "email": {
    "type": "string",
    "provider_field": "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
  }
}

Data Providers

TestDataX supports two powerful data providers for generating realistic test data:

Mimesis (Default)

Mimesis is a high-performance Python library for generating synthetic data. It provides:

• Fast data generation with excellent performance
• Support for multiple locales and languages
• Wide variety of data providers for different domains
• Lightweight and efficient implementation

Faker

Faker is a popular Python library for generating fake data. It offers:

• Extensive provider ecosystem with community contributions
• Rich set of localized providers
• Well-established and widely used in the Python community
• Comprehensive documentation and examples

You can specify the provider using the -p or --provider option:

# Use Mimesis (default)
testdatax -o data.csv -f csv -p mimesis

# Use Faker
testdatax -o data.csv -f csv -p faker

Both providers support the same schema format and generate compatible data types.

Note: For backward compatibility, the legacy faker field name is still supported, but provider_field is recommended for new schemas.

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.