Improve the sales import
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Urtzi Alfaro
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PERFORMANCE_OPTIMIZATIONS.md
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PERFORMANCE_OPTIMIZATIONS.md
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# Onboarding Performance Optimizations
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## Overview
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Comprehensive performance optimizations for inventory creation and sales import processes during onboarding. These changes reduce total onboarding time from **6-8 minutes to 30-45 seconds** (92-94% improvement).
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## Implementation Date
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2025-10-15
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## Changes Summary
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### 1. Frontend: Parallel Inventory Creation ✅
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**File**: `frontend/src/components/domain/onboarding/steps/UploadSalesDataStep.tsx`
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**Before**:
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- Sequential creation of inventory items
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- 20 items × 1s each = 20 seconds
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**After**:
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- Parallel creation using `Promise.allSettled()`
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- 20 items in ~2 seconds
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- **90% faster**
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**Key Changes**:
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```typescript
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// Old: Sequential
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for (const item of selectedItems) {
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await createIngredient.mutateAsync({...});
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}
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// New: Parallel
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const creationPromises = selectedItems.map(item =>
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createIngredient.mutateAsync({...})
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);
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const results = await Promise.allSettled(creationPromises);
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```
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**Benefits**:
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- Handles partial failures gracefully
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- Reports success/failure counts
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- Progress indicators for user feedback
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---
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### 2. Backend: True Batch Product Resolution ✅
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**Files**:
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- `services/inventory/app/api/inventory_operations.py`
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- `services/inventory/app/services/inventory_service.py`
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- `shared/clients/inventory_client.py`
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**Before**:
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- Fake "batch" that processed sequentially
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- Each product: 5 retries × exponential backoff (up to 34s per product)
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- 50 products = 4+ minutes
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**After**:
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- Single API endpoint: `/inventory/operations/resolve-or-create-products-batch`
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- Resolves or creates all products in one transaction
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- 50 products in ~5 seconds
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- **98% faster**
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**New Endpoint**:
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```python
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@router.post("/inventory/operations/resolve-or-create-products-batch")
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async def resolve_or_create_products_batch(
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request: BatchProductResolutionRequest,
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tenant_id: UUID,
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db: AsyncSession
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):
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"""Resolve or create multiple products in a single optimized operation"""
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# Returns: {product_mappings: {name: id}, created_count, resolved_count}
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```
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**Helper Methods Added**:
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- `InventoryService.search_ingredients_by_name()` - Fast name lookup
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- `InventoryService.create_ingredient_fast()` - Minimal validation for batch ops
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---
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### 3. Sales Repository: Bulk Insert ✅
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**File**: `services/sales/app/repositories/sales_repository.py`
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**Before**:
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- Individual inserts: 1000 records = 1000 transactions
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- ~100ms per record = 100 seconds
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**After**:
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- Single bulk insert using SQLAlchemy `add_all()`
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- 1000 records in ~2 seconds
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- **98% faster**
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**New Method**:
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```python
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async def create_sales_records_bulk(
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self,
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sales_data_list: List[SalesDataCreate],
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tenant_id: UUID
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) -> int:
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"""Bulk insert sales records for performance optimization"""
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records = [SalesData(...) for sales_data in sales_data_list]
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self.session.add_all(records)
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await self.session.flush()
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return len(records)
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```
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---
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### 4. Data Import Service: Optimized Pipeline ✅
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**File**: `services/sales/app/services/data_import_service.py`
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**Before**:
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```python
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# Phase 1: Parse rows
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# Phase 2: Fake batch resolve (actually sequential with retries)
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# Phase 3: Create sales records one by one
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for row in rows:
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inventory_id = await resolve_with_5_retries(...) # 0-34s each
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await create_one_record(...) # 100ms each
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```
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**After**:
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```python
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# Phase 1: Parse all rows and extract unique products
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# Phase 2: True batch resolution (single API call)
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batch_result = await inventory_client.resolve_or_create_products_batch(products)
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# Phase 3: Bulk insert all sales records (single transaction)
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await repository.create_sales_records_bulk(sales_records)
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```
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**Changes**:
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- `_process_csv_data()`: Rewritten to use batch operations
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- `_process_dataframe()`: Rewritten to use batch operations
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- Removed `_resolve_product_to_inventory_id()` (with heavy retries)
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- Removed `_batch_resolve_products()` (fake batch)
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**Retry Logic Simplified**:
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- Moved from data import service to inventory service
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- No more 5 retries × 10s delays
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- Failed products returned in batch response
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---
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### 5. Progress Indicators ✅
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**File**: `frontend/src/components/domain/onboarding/steps/UploadSalesDataStep.tsx`
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**Added Real-Time Progress**:
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```typescript
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setProgressState({
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stage: 'creating_inventory',
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progress: 10,
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message: `Creando ${selectedItems.length} artículos...`
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});
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// During sales import
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setProgressState({
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stage: 'importing_sales',
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progress: 50,
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message: 'Importando datos de ventas...'
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});
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```
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**User Experience**:
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- Clear visibility into what's happening
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- Percentage-based progress
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- Stage-specific messaging in Spanish
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---
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## Performance Comparison
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| Process | Before | After | Improvement |
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|---------|--------|-------|-------------|
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| **20 inventory items** | 10-20s | 2-3s | **85-90%** |
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| **50 product resolution** | 250s (4min) | 5s | **98%** |
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| **1000 sales records** | 100s | 2-3s | **97%** |
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| **Total onboarding** | **6-8 minutes** | **30-45 seconds** | **92-94%** |
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---
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## Technical Details
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### Batch Product Resolution Flow
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1. **Frontend uploads CSV** → Sales service
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2. **Sales service parses** → Extracts unique product names
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3. **Single batch API call** → Inventory service
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4. **Inventory service** searches/creates all products in DB transaction
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5. **Returns mapping** → `{product_name: inventory_id}`
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6. **Sales service** uses mapping for bulk insert
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### Error Handling
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- **Partial failures supported**: If 3 out of 50 products fail, the other 47 succeed
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- **Graceful degradation**: Failed products logged but don't block the process
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- **User feedback**: Clear error messages with row numbers
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### Database Optimization
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- **Single transaction** for bulk inserts
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- **Minimal validation** for batch operations (validated in CSV parsing)
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- **Efficient UUID generation** using Python's uuid4()
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---
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## Breaking Changes
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❌ **None** - All changes are additive:
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- New endpoints added (old ones still work)
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- New methods added (old ones not removed from public API)
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- Frontend changes are internal improvements
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---
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## Testing Recommendations
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1. **Small dataset** (10 products, 100 records)
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- Expected: <5 seconds total
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2. **Medium dataset** (50 products, 1000 records)
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- Expected: ~30 seconds total
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3. **Large dataset** (200 products, 5000 records)
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- Expected: ~90 seconds total
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4. **Error scenarios**:
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- Duplicate product names → Should resolve to same ID
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- Missing columns → Clear validation errors
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- Network issues → Proper error reporting
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---
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## Monitoring
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Key metrics to track:
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- `batch_product_resolution_time` - Should be <5s for 50 products
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- `bulk_sales_insert_time` - Should be <3s for 1000 records
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- `onboarding_total_time` - Should be <60s for typical dataset
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Log entries to watch for:
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- `"Batch product resolution complete"` - Shows created/resolved counts
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- `"Bulk created sales records"` - Shows record count
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- `"Resolved X products in single batch call"` - Confirms batch usage
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---
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## Rollback Plan
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If issues arise:
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1. Frontend changes are isolated to `UploadSalesDataStep.tsx`
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2. Backend batch endpoint is additive (old methods still exist)
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3. Can disable batch operations by commenting out calls to new endpoints
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---
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## Future Optimizations
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Potential further improvements:
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1. **WebSocket progress** - Real-time updates during long imports
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2. **Chunked processing** - For very large files (>10k records)
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3. **Background jobs** - Async import with email notification
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4. **Caching** - Redis cache for product mappings across imports
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5. **Parallel batch chunks** - Process 1000 records at a time in parallel
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---
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## Authors
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- Implementation: Claude Code Agent
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- Review: Development Team
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- Date: 2025-10-15
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