In the realm of software development for the Russian market, managing personal identification numbers requires high precision and adherence to local standards. The snils function within the mock-jutsu library is designed to generate realistic, syntactically correct Russian Pension Insurance (SNILS) numbers. This identity-focused generator provides developers with the necessary mock data to simulate user profiles, financial records, and government-related submissions without compromising real user privacy. Every generated string follows the standard eleven-digit format, typically displayed as 112-233-445 95, ensuring it fits perfectly into existing UI components and database schemas during the development lifecycle.
Beyond simple string formatting, the mock-jutsu implementation prioritizes technical accuracy by adhering to the official checksum algorithm used by the Russian Pension Fund. SNILS numbers are not merely random sequences; they are validated through a specific weighted sum calculation that determines the final two check digits. By utilizing this function, engineering teams can produce test data that bypasses complex front-end validation logic and back-end integrity checks. This capability is essential for QA professionals who need to verify that their systems correctly handle, store, and retrieve insurance identifiers during intensive integration testing or comprehensive stress testing cycles.
Integrating the snils generator into a modern workflow is seamless across various environments. Python developers can invoke it via the core library to populate data structures, while performance testers can leverage it directly within Apache JMeter scripts using the provided plugin syntax. For rapid prototyping or manual database seeding, the mock-jutsu command-line interface offers a quick way to produce batches of valid identifiers instantly. This versatility ensures that whether you are building a complex HR portal or a high-concurrency fintech API, you have access to high-quality test data that behaves exactly like real-world production values.
The primary benefit of using mock-jutsu for SNILS generation is the significant reduction in manual data preparation time. Instead of relying on static spreadsheets or manually calculated values, developers can automate the creation of diverse datasets for regression testing and edge-case analysis. By incorporating these realistic identifiers into your CI/CD pipelines, you ensure that your application remains robust against data entry errors and validation failures. Ultimately, mock-jutsu empowers teams to focus on building core features rather than wrestling with the mathematical complexities of regional identification standards.
mockjutsu generate snilsmockjutsu bulk snils --count 10mockjutsu export snils --count 10 --format jsonmockjutsu export snils --count 10 --format csvmockjutsu export snils --count 10 --format sql# --mask: regulation-compliant output (PCI DSS / GDPR / KVKK)mockjutsu generate snils --maskmockjutsu bulk snils --count 5 --maskfrom mockjutsu import jutsujutsu.generate('snils')jutsu.bulk('snils', count=10)jutsu.template(['snils'], count=5)# mask=True: regulation-compliant outputjutsu.generate('snils', mask=True)jutsu.bulk('snils', count=5, mask=True)${__mockjutsu_identity(snils)}# JMeter Function: __mockjutsu_identity# Parameter 1: snils# Parameter 2: (not required for this function)# Add 'mask' keyword to get a regulation-compliant masked value${__mockjutsu_identity(snils,mask)}GET /generate/snils# → {"type":"snils","result":"...","status":"ok"}GET /bulk/snils?count=10POST /template {"types":["snils"],"count":1}# mask=true: regulation-compliant outputGET /generate/snils?mask=trueGET /bulk/snils?count=5&mask=true| Parameter | Values | Description |
|---|---|---|
| --mask | true | false | Return a regulation-compliant masked value (PCI DSS, GDPR, KVKK…) |