How to Use Seedling Dummy File Creator to Generate Sample DataGenerating realistic sample data quickly and reliably is essential for development, testing, demos, and documentation. Seedling Dummy File Creator (hereafter “Seedling”) is a lightweight tool designed to produce configurable dummy files — from simple text files to image placeholders and structured datasets — so you can focus on building features instead of hand-crafting test assets. This guide walks through what Seedling does, when to use it, installation options, key features, practical examples, and best practices for integrating it into development workflows.
What Seedling Dummy File Creator does
Seedling generates dummy files with configurable content, size, format, and metadata. Typical outputs include:
- Plain text files with lorem ipsum or custom templates.
- CSV/JSON files with random or schema-driven records.
- Binary files of arbitrary size to simulate uploads/storage.
- Placeholder images (JPEG/PNG) with configurable dimensions, background/foreground colors, and optional text.
- Nested directories populated with files to simulate project structures or datasets.
Seedling focuses on speed, reproducibility, and flexibility: you can create thousands of files with predictable naming, seeded randomness, and metadata suited to your tests.
When to use Seedling
Use Seedling when you need:
- Load-testing file uploads or storage systems.
- Reproducible datasets for unit/integration tests.
- Placeholder assets for UI layouts or design reviews.
- Large binary files to test streaming, transfer, or memory usage.
- Complex directory trees to validate backup/archival logic.
Installation and setup
Seedling is available as a command-line tool, a library (for Node.js/Python), and a Docker image. Pick the option that best fits your environment.
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CLI installation (example, npm):
npm install -g seedling-dummy-file-creator -
Python package (pip):
pip install seedling -
Docker usage:
docker run --rm -v $(pwd):/out seedling/seedling --generate --type csv --count 100 --out /out
After installation, run seedling --help (or seedling -h) to view options and flags.
Key concepts and options
- Seed/Determinism: Use a numeric seed to make generated data reproducible across runs.
- Templates/Schemas: Define field types, ranges, and formats for structured outputs (CSV/JSON).
- Size/Count: Specify file sizes (bytes/KB/MB) or quantities of files to create.
- Naming patterns: Use prefixes, suffixes, zero-padded counters, or timestamp tokens.
- Metadata: Attach file timestamps, ownership stubs, or custom extended attributes where supported.
- Output targets: Local filesystem, object storage-compatible endpoints (S3), or stdout for piping.
Example workflows
Below are practical examples that show common tasks. Adjust flags to match the exact CLI/library API you have; these examples use representative commands.
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Create 50 lorem ipsum text files:
seedling generate --type text --template lorem --count 50 --name sample_%03d.txt --out ./dummy_texts -
Generate a CSV with 10,000 user records (id, name, email, signup_date) using a schema:
seedling generate --type csv --schema user_schema.json --count 10000 --out ./data/users.csv --seed 42Example schema snippet (JSON):
{ "fields": [ {"name": "id", "type": "integer", "range": [1, 1000000]}, {"name": "name", "type": "name"}, {"name": "email", "type": "email"}, {"name": "signup_date", "type": "date", "between": ["2020-01-01","2025-01-01"]} ] } -
Produce a 100 MB binary file to test uploads:
seedling generate --type binary --size 100MB --name big_test.bin --out ./binaries -
Generate placeholder images with text labels:
seedling generate --type image --width 1200 --height 800 --bg "#cccccc" --fg "#333333" --text "Demo" --count 20 --out ./images -
Create a nested directory tree of mixed assets:
seedling generate --structure project_template.json --out ./project_sampleExample structure spec:
{ "dirs": ["src", "assets/images", "assets/docs"], "files": [ {"path": "README.md", "type": "text", "template": "lorem", "size": "2KB"}, {"path": "assets/images/hero.png", "type": "image", "width": 1920, "height": 1080} ], "repeat": {"dirs": 3, "files_per_dir": 5} }
Integrating Seedling into workflows
- CI/CD: Generate small deterministic datasets for unit tests and larger randomized datasets for staging smoke tests. Cache generated files between CI runs when size is large.
- Local development: Add a dev script (npm/Yarn/Makefile) to create mock assets on project start.
- Back-end load testing: Use Seedling to create many large files and upload them in parallel to simulate real-world traffic.
- UI/UX reviews: Provide designers with a directory of placeholder images and text files matching the real app’s dimensions and naming.
Best practices
- Use a fixed seed in tests to ensure reproducibility.
- Keep schema definitions version-controlled alongside test code.
- For storage/load testing, start with smaller sizes, then progressively increase to find breaking points.
- Clean up generated artifacts in CI to avoid storage costs.
- Validate sample data against the same parsers/validators your application uses to reveal real parsing issues.
Troubleshooting & tips
- If generation is slow, increase concurrency threads or run inside Docker with proper I/O settings.
- For platform-specific metadata (ownership, permissions), run Seedling with appropriate privileges or apply metadata in a separate step.
- When creating very large files, prefer streaming options to avoid exhausting RAM.
- If images look low quality, increase DPI or use vector-based placeholders when supported.
Security and privacy considerations
- Ensure dummy data does not accidentally contain or resemble real personal data. Use built-in fake data generators (names, emails) rather than copying production exports.
- When uploading generated files to cloud storage for tests, use isolated test buckets and lifecycle rules to auto-delete after tests.
Summary
Seedling Dummy File Creator simplifies creating realistic, reproducible sample files across formats and sizes. Use its templating, seeding, and schema features to fit test needs, and integrate generation into CI, local dev, or load testing workflows. With careful use of seeds, schemas, and storage practices, Seedling helps you build robust tests and faster development cycles.