JSON Sample Generator

JSON Sample Generator

The JSON Sample Generator creates realistic sample JSON from a schema or a lightweight template. This is useful for API documentation, test fixtures, mocks, and QA workflows where you need quick data without writing it by hand. You can paste a JSON Schema or use template placeholders like $string and $uuid to generate consistent sample payloads in seconds. Everything runs in your browser and stays private.

Well-formed sample data helps teams communicate. It makes API docs easier to understand, improves test coverage, and speeds up front-end development by providing realistic payloads. This tool helps you create repeatable examples with minimal effort.

Ways to generate samples

• From JSON Schema: Use a schema to define types, required fields, and constraints.
• From templates: Use placeholders like $string or $date to generate values.
• Hybrid: Combine schema structure with custom placeholder values where needed.
• Batch output: Generate multiple samples with a seed for repeatable data.

Template placeholders

• $string for text values
• $number for numeric values
• $boolean for true/false
• $uuid for UUID-like identifiers
• $date for ISO-style date strings
• $email, $phone, $name for realistic values
• $company, $url, $ip for common API fields
• $enum to pick from a list of values
• $regex to generate values from a pattern
• $array to generate arrays

How to use the generator

1. Paste your schema or template JSON on the left.
2. Select the mode (schema or template) or let auto-detect decide.
3. Set row count, seed, and any placeholder constraints.
4. Choose an output format (JSON, NDJSON, or CSV).
5. Click Generate Sample and copy the output.

Example: template-driven sample

Template input:

{
  "id": "$uuid",
  "name": "$name",
  "email": "$email",
  "active": "$boolean",
  "createdAt": "$date",
  "score": "$number",
  "role": "$enum"
}

Generated output:

{
  "id": "8f6b8b14-3c67-4a4d-9f5a-72c92f6d11a1",
  "name": "Ava Verma",
  "email": "ava123@example.com",
  "active": true,
  "createdAt": "2025-01-15",
  "score": 42,
  "role": "admin"
}

Example: schema-driven sample

Schema input:

{
  "type": "object",
  "required": ["id", "email"],
  "properties": {
    "id": { "type": "integer", "minimum": 1 },
    "email": { "type": "string", "format": "email" },
    "roles": { "type": "array", "items": { "type": "string" } }
  }
}

Generated output:

{
  "id": 1,
  "email": "user@example.com",
  "roles": ["editor"]
}

Common errors and fixes

• Invalid JSON: Templates and schemas must be valid JSON. Use JSON Validator to fix syntax errors.
• Unknown placeholder: If a placeholder is not supported, it will be treated as a literal string. Use the documented placeholders or extend your template.
• Regex too complex: Some complex patterns may not generate reliably. Simplify the pattern or use $enum values.
• Schema too strict: If schema rules are too restrictive, generation may fail or produce empty fields. Loosen constraints like minLength or enum.
• Missing required fields: Ensure your schema includes required fields or your template defines all keys you expect.
• Large or complex schemas: Simplify the schema or generate samples in smaller parts.

Output formats

• JSON: Standard array or object output, pretty or minified.
• NDJSON: One JSON object per line for pipelines.
• CSV: Flat rows with headers derived from the first object.

Best practices for sample data

• Keep samples representative of real payloads so tests are meaningful.
• Use consistent keys and value types across samples.
• Document samples alongside your API so consumers know what to expect.
• Combine with JSON Schema Validator to ensure schema accuracy.
• Format samples with JSON Formatter for readability.

Template design tips

Use templates when you want complete control over output shape. This is useful for API docs and mock servers. Keep your templates stable and version them so your examples stay consistent across releases. If a field should always be present, include it explicitly. If you want variability, generate multiple samples with slightly different templates.

When using placeholders, combine them with real values for more realistic output. For example, keep static fields like "type": "user" while generating random $uuid for identifiers.

Schema-driven tips

Schema generation is faster for large objects because it infers structure from a schema definition. However, schemas may not capture real-world distributions, so consider adding examples or constraints for more realistic samples. Use enum to generate a known set of values and minLength or minimum to avoid edge cases.

Workflow for API documentation

1. Generate a sample from schema.
2. Review the output and replace placeholder values with real-world examples.
3. Format the final JSON and include it in docs.
4. Keep samples updated as the API evolves.

Mocking and testing workflows

For front-end teams, samples become mock API responses. Keep a library of generated samples and version them alongside the API spec. For QA, generate multiple variations to cover edge cases such as empty arrays, missing optional fields, and maximum length strings.

When using samples in automated tests, keep them deterministic. Prefer templates with fixed values for core fields, and use random placeholders only where variability is needed.

Edge cases to consider

• Optional fields: Generate both with and without optional fields.
• Empty arrays: Test how systems handle empty lists.
• Large numbers: Include a few large values to confirm numeric handling.
• Unicode strings: Use non-ASCII examples to validate encoding.

Including edge cases early reduces surprises later. Even a small set of deliberate variations can catch parsing bugs and validation issues before production.

Use cases

API docs: Provide realistic example payloads in documentation.

Front-end development: Use samples as mock data while APIs are under development.

Testing: Generate fixtures for unit tests and integration tests.

Data pipelines: Create sample data to validate transformation logic.

FAQs

Does the generator change my schema?
No. It only reads the schema and outputs a sample JSON object.

Can I generate arrays of objects?
Yes. Define array items in your schema or template, and the tool will generate array samples.

Is my data uploaded?
No. All sample generation happens locally in your browser.

Can I customize output values?
Yes. Use template placeholders or provide fixed values where needed.

What format do dates use?
Dates are generated in ISO-like format, which is widely supported in APIs.

Can I use this with JSON Schema drafts?
Yes. Most common Draft-07 style schemas work well for sample generation.

What if my sample output is too small?
Add more fields or extend arrays in the template or schema.

How do I validate the sample?
Paste the output into JSON Schema Validator or JSON Validator.

Can I generate multiple samples?
Yes. Set the row count to output arrays or NDJSON with multiple entries.

Will output be the same every time?
Provide a seed to generate deterministic results across runs.

Can I control value ranges?
Yes. Use the constraints panel for string length, number ranges, date ranges, enums, and regex patterns.

Does it support NDJSON or CSV?
Yes. Choose NDJSON or CSV in the output format and download menus.

Does it support nested arrays?
Yes. Include nested array structures in your template or schema.

How do I keep samples consistent across teams?
Store templates in version control and agree on naming conventions.

Can I generate samples for error responses?
Yes. Create a separate template for error payloads and include it in docs.

Can I use this for load testing?
You can generate sample payloads, but for large-scale load tests you may want a dedicated data generator.

Keyword‑targeted phrases

• json sample generator
• json mock data
• generate json from schema
• json schema sample
• json template generator