# Changelog
All notable changes to the Lark Base Python SDK will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [1.0.0] - 2025-12-09
### Added
#### Core Client (`lark_client.py`)
- `LarkBaseClient` class for managing Lark Base data
- `LarkConfig` dataclass for client configuration
- `RateLimiter` class for API rate limiting
- Full CRUD operations for tables, fields, and records
- Automatic pagination with `get_all_records()` method
- Comprehensive error handling and logging
- Type hints throughout the codebase
#### Table Operations
- `list_tables()` - List all tables in a base
- `create_table()` - Create new tables with optional view names
#### Field Operations
- `list_fields()` - List all fields in a table
- `create_field()` - Create fields with support for 26+ field types
#### Record Operations
- `list_records()` - List records with pagination and filtering
- `create_record()` - Create single records
- `batch_create_records()` - Bulk create up to 1000 records
- `update_record()` - Update single records
- `batch_update_records()` - Bulk update up to 1000 records
- `delete_record()` - Delete single records
- `get_all_records()` - Automatically paginate through all records
#### Data Manager (`data_manager.py`)
- `DataManager` class for high-level data operations
- `batch_upsert_records()` - Smart upsert with automatic create/update logic
- `sync_data_incremental()` - Incremental sync based on timestamps
- `transform_and_load()` - ETL pipeline with custom transformers
- `map_fields()` - Field name mapping for external data sources
- `validate_records()` - Data validation with custom validators
- `aggregate_data()` - Data aggregation with group by
- `export_to_dict()` - Export table data to dictionaries
- Utility functions for datetime conversion
- Parallel processing support for transformations
#### Examples
- `basic_usage.py` - Complete CRUD operations walkthrough
- `sync_tiktok_data.py` - TikTok Ads data synchronization example
- `batch_update.py` - Bulk operations and data transformation demo
#### Documentation
- Comprehensive README.md with installation and usage guide
- API reference documentation
- Field type reference table
- Best practices and troubleshooting guide
- Environment setup instructions
#### Configuration
- `.env.example` - Environment variable template
- `requirements.txt` - Python dependencies
- `setup.py` - Package setup for pip installation
- `.gitignore` - Python-specific ignore patterns
#### Development Tools
- `quickstart.py` - Interactive setup verification script
- Package structure with `__init__.py` files
- Logging configuration
### Features
#### Production-Ready
- Automatic rate limiting (configurable requests per second)
- Comprehensive error handling with detailed error messages
- Request ID tracking for debugging
- Structured logging with configurable levels
- Retry-ready architecture
#### Performance
- Batch operations for efficiency
- Automatic pagination handling
- Parallel processing support
- Field filtering to reduce payload size
#### Data Quality
- Pre-load validation
- Custom validator support
- Required field checking
- Type validation
#### Integration
- Compatible with TypeScript SDK for dashboard creation
- Automatic dashboard updates when data changes
- Support for external data sources (TikTok, APIs, databases)
- Field mapping for schema transformation
### Technical Details
#### Dependencies
- `lark-oapi>=1.2.0` - Official Lark Open API SDK
- `python-dotenv>=1.0.0` - Environment variable management
- `requests>=2.31.0` - HTTP requests (transitive dependency)
#### Compatibility
- Python 3.8+
- Lark/Feishu Open Platform API
- Works with Lark International and Feishu China
#### Code Quality
- Full type hints for IDE support
- Comprehensive docstrings
- PEP 8 compliant code style
- Modular architecture
### Examples Included
1. **Basic Usage** - 225 lines
- Create tables and fields
- CRUD operations
- Batch operations
2. **TikTok Sync** - 263 lines
- External data integration
- Data transformation
- Incremental sync
- Metrics calculation
3. **Batch Update** - 313 lines
- 100+ record operations
- Data validation
- Field mapping
- Aggregation
- Parallel processing
### Documentation
- **README.md** - 460 lines
- Installation guide
- Quick start
- API reference
- Best practices
- Troubleshooting
- **Main Project README** - Updated
- Python SDK section added
- Integration workflow documented
- Installation instructions
### Files Created
```
python/
├── __init__.py # Package initialization
├── lark_client.py # Core client (565 lines)
├── data_manager.py # Data operations (479 lines)
├── requirements.txt # Dependencies
├── setup.py # Package setup
├── .env.example # Environment template
├── .gitignore # Git ignore patterns
├── quickstart.py # Setup verification
├── README.md # Documentation (460 lines)
├── CHANGELOG.md # This file
└── examples/
├── __init__.py
├── basic_usage.py # Basic CRUD (225 lines)
├── sync_tiktok_data.py # TikTok sync (263 lines)
└── batch_update.py # Batch ops (313 lines)
```
Total: 2,300+ lines of production-ready code and documentation
## [Unreleased]
### Planned Features
- Async/await support for concurrent operations
- Pandas DataFrame integration
- CSV/Excel import/export utilities
- Data visualization helpers
- Webhook support for real-time updates
- Advanced filtering with query builder
- Transaction support for atomic operations
- Caching layer for improved performance
---
[1.0.0]: https://github.com/hypelab/hype-dash/releases/tag/python-v1.0.0
