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mForm Bulk Questionnaire Upload Backend

A comprehensive FastAPI backend for bulk Excel-based questionnaire uploads, featuring advanced validation, parsing, and storage capabilities. Designed for robust integration with the Angular frontend and built for extensibility.

Quick Start

Installation

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\\Scripts\\activate
pip install -r requirements.txt
cp .env.example .env
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Environment

MONGODB_URL=mongodb://localhost:27017
DATABASE_NAME=mform_bulk_upload
API_HOST=0.0.0.0
API_PORT=8000
FRONTEND_URL=http://localhost:4200

Architecture Overview

Core Components

Main Application (main.py)

  • FastAPI Application: Central application instance with comprehensive middleware
  • CORS Configuration: Configured for Angular frontend integration
  • API Routes: RESTful endpoints for file processing and form management
  • Lifecycle Management: Startup/shutdown events for MongoDB connection handling
  • Metrics Logging: Performance tracking and cold start monitoring
  • Error Handling: Structured error responses with detailed suggestions

Business Logic (services/xlsform_parser.py)

  • Excel File Validation: Comprehensive structural and content validation
  • TempData JSON Generation: Convert Excel data to comprehensive tempData.json format
  • Dynamic Form Configuration: Extract form settings from Excel sheets with sensible defaults
  • Cross-Reference Validation: Ensures data consistency between sheets
  • Error Classification: Detailed error categorization with actionable suggestions
  • Performance Optimization: Concurrent processing for bulk uploads
  • ObjectId Generation: Mock ObjectId creation for mobile app compatibility

Database Layer (services/database_service.py)

  • Async MongoDB Operations: Full CRUD operations with error handling
  • Data Integrity: Atomic operations and cascade deletions
  • Performance Tracking: Database operation timing and metrics
  • Connection Management: Proper resource handling and cleanup

Data Models (models/)

  • Pydantic Validation: Request/response models with type safety
  • API Schemas: Structured data contracts for frontend integration
  • Validation Models: Comprehensive validation result structures

API Reference

File Processing Endpoints

POST /api/validate

Validates Excel file structure and content without processing.

Request: multipart/form-data

file: UploadFile  # .xls or .xlsx file

Response: json { "valid": true, "message": "File format is valid.", "sheets": [ { "name": "Forms", "exists": true, "columns": ["Language", "Title"], "required_columns": ["Language", "Title"], "missing_columns": [], "row_count": 1 } ], "form_metadata": { "language": "en", "title": "Sample Form" }, "questions_count": 5, "options_count": 15, "errors": [], "warnings": [] }

POST /api/forms/parse

Parses Excel file and returns comprehensive tempData.json format without database storage.

Request: multipart/form-data

file: UploadFile  # .xls or .xlsx file

Response: Array containing questionnaire response and form definition

[
  {
    "_id": "ObjectId(\"66c2e4aca61889ab24b58407\")",
    "formId": 123456789,
    "version": "1.0.0",
    "language": [{"lng": "en", "default": true}],
    "question": [
      {
        "order": 1,
        "input_type": 1,
        "answer": "",
        "initialAnswer": ""
      }
    ],
    "responseUpdateHistory": [],
    "appVersion": "1.0.0",
    "responseIds": {
      "formResponseId": "ObjectId(\"66c2e4aca61889ab24b58408\")",
      "tempResponseId": "ObjectId(\"66c2e4aca61889ab24b58409\")"
    },
    "syncStatus": {
      "questions": [{"order": 1, "synced": false}]
    },
    "keyInfoOrders": [1, 2, 3],
    "copiedFormId": 987654321,
    "title": "Sample Questionnaire",
    "subtitle": "Form subtitle",
    "description": "Form description"
  }
]

POST /api/upload

Processes and stores multiple Excel files concurrently.

Request: multipart/form-data

files: List[UploadFile]  # Multiple .xls or .xlsx files

Response: Array of tempData.json format objects with database IDs and comprehensive form configuration.

Form Management Endpoints

GET /api/forms

Retrieves all forms with summary information.

Response: ```json { "forms": [ { "id": "507f1f77bcf86cd799439011", "title": "Sample Form", "language": "en", "version": "1.0.0", "created_at": "2024-01-15T10:30:00Z" } ], "count": 1 }


#### **GET `/api/forms/{form_id}`**
Retrieves complete form data in tempData.json format including questions and options.

**Response**: Array containing comprehensive form configuration
```json
[
  {
    "_id": "ObjectId(\"507f1f77bcf86cd799439011\")",
    "formId": 123456789,
    "version": "1.0.0",
    "language": [{"lng": "en", "default": true}],
    "question": [
      {
        "order": 1,
        "input_type": 1,
        "answer": "",
        "initialAnswer": ""
      }
    ],
    "responseUpdateHistory": [],
    "appVersion": "1.0.0",
    "responseIds": {
      "formResponseId": "ObjectId(\"66c2e4aca61889ab24b58408\")",
      "tempResponseId": "ObjectId(\"66c2e4aca61889ab24b58409\")"
    },
    "syncStatus": {
      "questions": [{"order": 1, "synced": false}]
    },
    "keyInfoOrders": [1, 2, 3],
    "copiedFormId": 987654321,
    "title": "Sample Form",
    "created_at": "2024-01-15T10:30:00Z"
  }
]

PUT /api/forms/{form_id}/update

Updates an existing form with new Excel file data.

Request: multipart/form-data

file: UploadFile  # Updated .xls or .xlsx file

DELETE /api/forms/{form_id}

Deletes a form and all related questions and options.

DELETE /api/forms

Deletes all forms and related data (bulk operation).

Data Models & Validation

Core Data Structures

Supported Question Types

SUPPORTED_QUESTION_TYPES = {
    1: 'text',           # Text input
    2: 'select_one',     # Single choice
    3: 'select_multiple', # Multiple choice
    4: 'integer',        # Whole numbers
    5: 'decimal',        # Decimal numbers
    6: 'date',           # Date picker
    7: 'time',           # Time picker
    8: 'datetime',       # Date and time
    9: 'note',           # Display text
    10: 'calculate'      # Computed value
}

Validation Rules

Forms Sheet Requirements:

  • Required columns: Language, Title
  • Supported languages: en, fr, es, de, it, pt, ar, zh, ja, ko, hi, ru
  • Title length: ≤ 255 characters
  • Only first row is processed

Questions Info Sheet Requirements:

  • Required columns: Order, Title, View Sequence, Input Type
  • Order: Positive integers, unique
  • View Sequence: Positive integers
  • Input Type: 1-10 (see supported types above)
  • Title length: ≤ 1000 characters

Answer Options Sheet Requirements:

  • Required columns: Order, Id, Label
  • Order: Positive integers
  • Id: Positive integers, unique per Order
  • Label length: ≤ 500 characters

Cross-Reference Validation

  • Choice questions (types 2, 3) must have corresponding options
  • All option orders must map to existing question orders
  • No orphaned options without corresponding questions

Database Schema

Collections Overview

forms

{
  _id: ObjectId,
  title: String,
  language: String,
  version: String,
  created_at: ISODate
}

questions

{
  _id: ObjectId,
  form_id: String,
  order: Number,
  title: String,
  view_sequence: Number,
  input_type: Number,
  created_at: ISODate
}

options

{
  _id: ObjectId,
  form_id: String,
  order: Number,
  option_id: Number,
  label: String,
  created_at: ISODate
}

Indexes & Performance

  • Forms: {created_at: -1} (recent forms first)
  • Questions: {form_id: 1} (efficient form retrieval)
  • Options: {form_id: 1} (efficient form retrieval)

Configuration & Setup

Environment Variables

# MongoDB Configuration
  MONGODB_URL=mongodb://localhost:27017
  DATABASE_NAME=mform_bulk_upload

# API Configuration
  API_HOST=0.0.0.0
  API_PORT=8000

# CORS Configuration
FRONTEND_URL=http://localhost:4200

Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
cp .env.example .env

# Start the application
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Error Handling & Validation

Error Classification System

File-Level Errors

  • MISSING_FILE: No file uploaded
  • INVALID_FILE_FORMAT: Wrong file extension or format
  • EMPTY_FILE: Zero-byte file
  • FILE_ACCESS_ERROR: Cannot read file content
  • CORRUPTED_FILE: Invalid Excel structure

Structure-Level Errors

  • MISSING_SHEET: Required sheet not found
  • MISSING_COLUMNS: Required columns missing
  • EMPTY_SHEET: Sheet contains no data

Content-Level Errors

  • INVALID_DATA_TYPE: Wrong data type in cells
  • MISSING_VALUE: Required field is empty
  • INVALID_VALUE: Value outside acceptable range
  • DUPLICATE_VALUE: Non-unique value where uniqueness required

Cross-Reference Errors

  • MISSING_REFERENCE: Choice question without options
  • ORPHANED_REFERENCE: Options without corresponding question

Enhanced Error Responses

{
  "detail": {
    "error": "Validation failed",
    "message": "Found 3 error(s) and 2 warning(s).",
    "error_type": "VALIDATION_ERROR",
    "file_name": "questionnaire.xlsx",
    "errors": [
      {
        "type": "missing_column",
        "message": "Required column 'Title' is missing",
        "location": "Forms sheet",
        "row": null,
        "column": "Title"
      }
    ],
    "warnings": [...],
    "suggestions": [
      "Ensure your Excel file contains sheets named: 'Forms', 'Questions Info', 'Answer Options'",
      "Check that all required columns are present in each sheet"
    ]
  }
}

Performance & Monitoring

Metrics Tracked

  • Validation Performance: File validation times
  • Parse Performance: Schema parsing without DB operations
  • Upload Performance: Complete form processing with storage
  • Database Operations: Individual CRUD operation times
  • Cold Start Time: Application initialization duration
  • Batch Processing: Multi-file upload performance

Performance Optimizations

  • Concurrent Processing: Async file processing with asyncio.gather()
  • Connection Pooling: MongoDB connection reuse
  • Efficient Parsing: Pandas DataFrame operations for large datasets
  • Memory Management: File stream handling to prevent memory leaks

Development & Extension Guide

Key Parser Methods

TempData Format Generation

# services/xlsform_parser.py
class XLSFormParser:
    def _extract_form_config(self, forms_df) -> Dict[str, Any]:
        """Extract dynamic form configuration from Forms sheet"""
        # Extracts title, version, language, boolean flags, etc.
        
    def _build_temp_data_format(self, forms_df, questions_df, options_df) -> List[Dict]:
        """Build comprehensive tempData.json structure"""
        # Creates response data and form definition
        
    def _convert_db_to_temp_data_format(self, form, questions, options) -> List[Dict]:
        """Convert database entities to tempData.json format"""
        # Handles database-to-JSON conversion

Dynamic Configuration Features

  • Form Settings Extraction: Automatically extracts configuration from Excel Forms sheet
  • Sensible Defaults: Provides fallback values for missing configuration
  • ObjectId Generation: Creates mock ObjectIds for mobile app compatibility
  • Language Support: Handles single and multi-language configurations
  • Boolean Flags: Processes form-specific feature toggles

Adding New Features

1. New API Endpoint

# main.py
@app.post("/api/forms/export/{form_id}")
async def export_form(form_id: str):
    # Implementation
    pass

2. New Business Logic

# services/xlsform_parser.py
class XLSFormParser:
    def export_to_format(self, form_id: str, format: str) -> Dict[str, Any]:
        # Implementation
        pass

3. New Database Operations

# services/database_service.py
class DatabaseService:
    async def export_form_data(self, form_id: str) -> Dict[str, Any]:
        # Implementation
        pass

4. New Data Models

# models/export.py
from pydantic import BaseModel

class ExportRequest(BaseModel):
    format: str  # 'json', 'csv', 'xml'
    include_metadata: bool = True

Testing Strategy

Unit Tests

# Test individual components
def test_xlsform_parser_validation():
    parser = XLSFormParser()
    # Test validation logic

def test_database_service_operations():
    service = DatabaseService()
    # Test database operations

Integration Tests

# Test complete workflows
def test_file_upload_workflow():
    # Test end-to-end file processing
    pass

Code Quality Guidelines

Error Handling

  • Use structured error responses with specific error types
  • Include actionable suggestions in error messages
  • Log errors with appropriate context
  • Never expose sensitive information in error responses

Performance

  • Use async/await for I/O operations
  • Implement proper connection pooling
  • Monitor and log performance metrics
  • Optimize database queries with appropriate indexes

Security

  • Validate all input data
  • Use parameterized queries
  • Implement proper CORS configuration
  • Never log sensitive information

Troubleshooting

Common Issues

MongoDB Connection Issues

# Check connection
from database import connect_to_mongo
await connect_to_mongo()

File Processing Errors

# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)

Performance Issues

# Check metrics
with open('metrics.txt', 'r') as f:
    print(f.read())

Debug Commands

# Check MongoDB collections
mongo mform_bulk_upload --eval "db.forms.count()"

# Test API endpoints
curl -X POST "http://localhost:8000/api/validate" -F "file=@test.xlsx"

# Monitor logs
tail -f logs/app.log

Additional Resources

Excel File Format Standards

API Documentation

Development Tools

Contributing

  1. Follow the established code structure
  2. Add comprehensive error handling
  3. Include performance metrics for new operations
  4. Update documentation for any API changes
  5. Add tests for new functionality
  6. Use meaningful commit messages

Why you don't do compression with XLS/XLSX files

XLS and XLSX files are already compressed formats (especially XLSX, which is a ZIP archive of XML files). Applying additional co pression (like gzip) typically results in minimal size reduction—often less than 3%. This extra step adds processing overhead wi hout significant storage or transfer benefits. In most cases, it's more efficient to transfer these files as-is.

Compression Metrics for XLSX Files

File Name Compressed Size (bytes) Decompressed Size (bytes) Compression Ratio Bytes Saved
valid_form_07.xlsx.gz 37,009 37,973 2.5% 964
valid_form_08.xlsx.gz 38,975 40,021 2.6% 1,046
valid_form_09.xlsx.gz 37,341 38,420 2.8% 1,079
valid_form_10.xlsx.gz 37,269 38,208 2.5% 939

Observation: The compression ratios are very low (2.5%–2.8%), saving only about 939–1,079 bytes per file. This demonstrates that compressing LSX files provides negligible space savings.

To put this in perspective, this required refactoring half the codebase with over 5,000 changed lines of code.

License

MIT License - see LICENSE file for details.

For questions or support, please refer to the project documentation or create an issue in the repository.