| Name | stlin JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | A Streamlit component for rendering data lineage from Cognite Data Fusion |
| upload_time | 2025-08-26 18:39:56 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
streamlit
lineage
cognite
visualization
component
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
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|
# Stlin - Streamlit Data Lineage Component
A Streamlit component for rendering interactive data lineage graphs from Cognite Data Fusion. Built with React, TypeScript, and React Flow.
## Installation
```bash
pip install stlin
```
## Quick Start
```python
import streamlit as st
from stlin import render_lineage
# Your lineage data (list of data processes with sources and destinations)
lineage_data = [
{
"externalId": "transformation_1",
"name": "Process Raw Assets",
"query": "SELECT * FROM raw_assets WHERE status = 'active'",
"destination": {"type": "raw", "database": "processed", "table": "assets"},
"sources": ["raw_assets", "_cdf.assets"],
"destinations": ["processed.assets"],
"lastFinishedJob": {
"status": "success",
"startedTime": 1692345600000,
"finishedTime": 1692345660000
}
}
# ... more transformations
]
# Render the component
selected_data = render_lineage(
data=lineage_data,
focus_mode=True,
side_bar_width=300,
height=800
)
# Handle selection
if selected_data:
st.write("Selected:", selected_data)
# Example of returned data structure:
# For transformation: [{"type": "Data Process", "subType": "Transformation", "address": "transformation_1", "sources": [...], "destinations": [...], "query": "SELECT ..."}]
# For data object: [{"type": "Data Object", "subType": "Staging", "address": "raw_assets", "producedBy": [...], "consumedBy": [...]}]
```
## API Reference
### `render_lineage`
The main component function for rendering data lineage.
**Parameters:**
- `data` *(list)*: List of transformation dictionaries containing lineage information
- `focus_mode` *(bool, default=True)*: Whether to show only direct lineage path or full graph
- `side_bar_width` *(int, default=300)*: Initial width of navigation sidebar in pixels
- `height` *(int, default=800)*: Height of the component in pixels
- `key` *(str, optional)*: Unique component key for Streamlit
**Returns:**
- For **data process nodes**: Returns a structured record with:
- `type`: "Data Process"
- `subType`: "Transformation"
- `address`: transformation external ID
- `sources`: list of source identifiers
- `destinations`: list of destination identifiers
- `query`: SQL query or transformation logic
- For **data object nodes**: Returns a structured record with:
- `type`: "Data Object"
- `subType`: specific data object type (e.g., "Staging", "Assets", "Data Model View", etc.)
- `address`: data object identifier
- `producedBy`: list of transformation IDs that produce this data object
- `consumedBy`: list of transformation IDs that consume this data object
- Returns **empty list** if nothing is selected
## Data Format
The component expects transformation data in the following format:
```python
{
"externalId": "unique_transformation_id",
"name": "Human Readable Name",
"query": "SELECT * FROM source_table",
"destination": {
"type": "raw",
"database": "target_db",
"table": "target_table"
},
"sources": ["source1", "source2"], # List of source identifiers
"destinations": ["dest1", "dest2"], # List of destination identifiers
"lastFinishedJob": {
"status": "success",
"startedTime": 1692345600000,
"finishedTime": 1692345660000
}
}
```
### Supported Data Object Types
The component automatically categorizes data objects based on their identifiers:
- **Legacy CDF Resources**: `_cdf.assets`, `_cdf.events`, `_cdf.timeseries`, etc.
- **Data Model Instances**: `cdf_data_models()`, `cdf_nodes()`, `cdf_edges()`
- **Raw/Staging Tables**: `database.table` format
- **Unknown**: Any unrecognized format
## Development
### Building from Source
```bash
git clone https://github.com/evertoncolling/stlin.git
cd stlin
# Install dependencies
uv sync
# Build frontend
cd stlin/frontend
npm install
npm run build
# Build Python package
cd ../..
python -m build
```
### Running the Example
```bash
# Install in development mode
uv pip install -e .
# Run the example app
streamlit run example_app.py
```
## License
MIT License - see LICENSE file for details.
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