Handle large datasets using background processing (queues). 🏗️ Technical Implementation 1. Database Schema
Ensure historical "Previous Type" and "New Type" are captured accurately.
POST /api/v1/venues/changes/export : Trigger the file generation. Handle large datasets using background processing (queues)
How many do you expect to export at once? (Dozens, thousands, or millions?) Is this for a public-facing app or an internal admin tool ?
To develop a (Изменение типов участка) download feature, you need a robust pipeline that handles data extraction, file generation, and user notification. 🛠️ Core Functional Requirements Handle large datasets using background processing (queues)
GET /api/v1/venues/changes/export/{job_id} : Check status of large exports. 🎨 User Interface Elements
Use a toast or email notification when the file is ready. 📋 Data Export Structure (Example) Venue Name Change Date Original Type Updated Type Modified By Central Park 2024-05-12 Recreational Protected Zone Admin_User_01 2024-05-14 Industrial Residential Planning_Dept Handle large datasets using background processing (queues)
def export_venue_changes(filters, user_id): # 1. Fetch data based on user filters data = db.query(VenueLogs).filter(filters).all() # 2. Generate file (e.g., using Pandas or ExcelJS) file_path = generate_xlsx(data) # 3. Provide download link return upload_to_s3_and_get_link(file_path) Use code with caution. Copied to clipboard 3. API Endpoints GET /api/v1/venues/changes : Preview the list of changes.