The rvn function within the mock-jutsu library is a specialized tool designed to generate high-quality mock data representing the German Pension Insurance Number, known as the Rentenversicherungsnummer (RVNR). This identifier is a cornerstone of the German social security system, used to track insurance contributions and pension entitlements for millions of citizens. By utilizing this function, developers can produce realistic test data that mimics the exact 12-character alphanumeric format required for processing personal identification records in Central European enterprise applications.
To ensure the highest level of accuracy, the rvn generator follows the official algorithmic standards established by the German pension insurance authorities. Each generated string consists of a two-digit area number, a six-digit birth date (DDMMYY), the first letter of the individual's birth name, a serial number that distinguishes between genders, and a final check digit. This check digit is calculated using a specific weighted modulo 10 algorithm, which ensures that the mock data behaves exactly like real-world production values. This level of precision is essential for bypassing front-end validation logic and ensuring that backend systems process the information without throwing format errors.
Engineering teams working on payroll software, human resources management systems (HRMS), or healthcare platforms will find the rvn function indispensable for various testing scenarios. It allows for the simulation of diverse user profiles while maintaining data integrity across complex database schemas. Whether you are performing stress tests on a backend API or verifying the input masks on a registration form, having access to valid mock-jutsu outputs helps identify edge cases in data processing without the legal and ethical risks associated with using actual sensitive personal information (PII).
Integrating the rvn function into your workflow is seamless, regardless of your preferred development environment. Developers can quickly invoke the generator via the mock-jutsu CLI for rapid prototyping, utilize the Python API for automated test suites, or implement it within JMeter for performance benchmarking. This cross-platform flexibility ensures that teams can maintain high velocity while ensuring their software is fully compliant with German data formatting requirements. By automating the creation of these complex identifiers, mock-jutsu empowers developers to focus on core application logic rather than the manual curation of test data.
mockjutsu generate rvnmockjutsu bulk rvn --count 10mockjutsu export rvn --count 10 --format jsonmockjutsu export rvn --count 10 --format csvmockjutsu export rvn --count 10 --format sql# --mask: regulation-compliant output (PCI DSS / GDPR / KVKK)mockjutsu generate rvn --maskmockjutsu bulk rvn --count 5 --maskfrom mockjutsu import jutsujutsu.generate('rvn')jutsu.bulk('rvn', count=10)jutsu.template(['rvn'], count=5)# mask=True: regulation-compliant outputjutsu.generate('rvn', mask=True)jutsu.bulk('rvn', count=5, mask=True)${__mockjutsu_identity(rvn)}# JMeter Function: __mockjutsu_identity# Parameter 1: rvn# Parameter 2: (not required for this function)# Add 'mask' keyword to get a regulation-compliant masked value${__mockjutsu_identity(rvn,mask)}GET /generate/rvn# → {"type":"rvn","result":"...","status":"ok"}GET /bulk/rvn?count=10POST /template {"types":["rvn"],"count":1}# mask=true: regulation-compliant outputGET /generate/rvn?mask=trueGET /bulk/rvn?count=5&mask=true| Parameter | Values | Description |
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| --mask | true | false | Return a regulation-compliant masked value (PCI DSS, GDPR, KVKK…) |