JSONL Gazelle
Fast JSONL viewer / editor for VS Code with advanced features including Table View and Pretty Print view. JSONL (JSON Lines) is increasingly important for machine learning datasets, log analysis, data streaming, and LLM training. Unlike regular JSON, it can be processed line-by-line making it perfect for large datasets.
New
- v0.3.1: For extrememly large JSONL files over 100MB, this version adds a new "Split" function available by right-clicking on the file
- v0.3.0: Add a much improved Pretty Print View with syntax highlighting
Features
- Fast Table View: Automatically detects common JSON paths and displays them as table columns
- Smart Column Detection: Maps common subpaths of each JSONL row into table columns automatically
- Column Expansion: Click ▼ to expand objects/arrays into separate columns (e.g.,
user.name, orders[0])
- Column Management: Right-click context menu on table headers to add, remove, or toggle column visibility
- Memory Efficient: All processing happens in-memory without creating separate files
Usage
- Open any
.jsonl file in VS Code
- The file will automatically open in the JSONL Gazelle viewer
- Table View: Click ▼ buttons in column headers or double-click expandable cells to expand objects/arrays into separate columns
- Pretty Print view: You can edit inline
Extension Development
- Clone this repository
- Run
npm install to install dependencies
- Run
npm run compile to build the extension
- Press F5 to run the extension in a new Extension Development Host window
Development
# Install dependencies
npm install
# Compile TypeScript
npm run compile
# Watch for changes
npm run watch
Test Data Generation
For testing with large datasets, you can generate a comprehensive test file with 45,000 lines (~64MB) containing varied fields and nested structures:
# Generate large test data file
cd test-data
node generate-large.js
This will create test-data/large.jsonl with:
- User profiles with nested addresses, preferences, and social media links
- Orders with items, pricing, shipping, and tracking information
- Analytics data with metrics, device info, and campaign details
- Log entries with request details, performance metrics, and error information
- Mixed data types: strings, numbers, booleans, arrays, objects
- Nested structures up to 4-5 levels deep
The generated file is automatically excluded from git via .gitignore to keep the repository lightweight.
What's next / Roadmap
- [ ] AI column dialog: Remember recent values (along with any enum settings), and allow the user to prefill them from a dropdown. (Don't show the dropdown if the user has never used the feature.)
- [ ] AI column dialog: port the enum option to OpenAI's structured outputs from the current prompt-based implementation
- [ ] Test the AI column generation feature, e.g. for when you give it
{{row.paths}} that exist in some rows but not in others
- [ ] Test the AI row generation feature
- [ ] Suggest feature in AI add column dialog: Add a "Suggest new column" to the column menu. It will prompt OpenAI with some of the example data to make suggestions for new columns to add, with the provided template language. It will suggest a bunch of column names + prompts to choose from. The user can then run that and add a new column.
License
MIT License. See LICENSE file for details.
