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API_INTELLIGENCE_FINDINGS.md
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API_INTELLIGENCE_FINDINGS.md
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# API Intelligence Findings
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## GraphQL API - Available Fields for Intelligence
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### Key Discovery: Additional Fields Available
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From GraphQL schema introspection on `Lot` type:
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#### **Already Captured ✓**
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- `currentBidAmount` (Money) - Current bid
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- `initialAmount` (Money) - Starting bid
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- `nextMinimalBid` (Money) - Minimum bid
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- `bidsCount` (Int) - Bid count
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- `startDate` / `endDate` (TbaDate) - Timing
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- `minimumBidAmountMet` (MinimumBidAmountMet) - Status
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- `attributes` - Brand/model extraction
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- `title`, `description`, `images`
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#### **NEW - Available but NOT Captured:**
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1. **followersCount** (Int) - **CRITICAL for intelligence!**
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- This is the "watch count" we thought was missing
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- Indicates bidder interest level
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- **ACTION: Add to schema and extraction**
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2. **biddingStatus** (BiddingStatus) - Lot bidding state
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- More detailed than minimumBidAmountMet
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- **ACTION: Investigate enum values**
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3. **estimatedFullPrice** (EstimatedFullPrice) - **Found it!**
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- Available via `LotDetails.estimatedFullPrice`
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- May contain estimated min/max values
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- **ACTION: Test extraction**
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4. **nextBidStepInCents** (Long) - Exact bid increment
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- More precise than our calculated bid_increment
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- **ACTION: Replace calculated field**
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5. **condition** (String) - Direct condition field
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- Cleaner than attribute extraction
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- **ACTION: Use as primary source**
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6. **categoryInformation** (LotCategoryInformation) - Category data
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- Structured category info
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- **ACTION: Extract category path**
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7. **location** (LotLocation) - Lot location details
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- City, country, possibly address
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- **ACTION: Add to schema**
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8. **remarks** (String) - Additional notes
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- May contain pickup/viewing text
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- **ACTION: Check for viewing/pickup extraction**
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9. **appearance** (String) - Condition appearance
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- Visual condition notes
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- **ACTION: Combine with condition_description**
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10. **packaging** (String) - Packaging details
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- Relevant for shipping intelligence
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11. **quantity** (Long) - Lot quantity
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- Important for bulk lots
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12. **vat** (BigDecimal) - VAT percentage
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- For total cost calculations
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13. **buyerPremiumPercentage** (BigDecimal) - Buyer premium
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- For total cost calculations
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14. **videos** - Video URLs (if available)
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- **ACTION: Add video support**
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15. **documents** - Document URLs (if available)
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- May contain specs/manuals
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## Bid History API - Fields
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### Currently Captured ✓
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- `buyerId` (UUID) - Anonymized bidder
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- `buyerNumber` (Int) - Bidder number
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- `currentBid.cents` / `currency` - Bid amount
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- `autoBid` (Boolean) - Autobid flag
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- `createdAt` (Timestamp) - Bid time
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### Additional Available:
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- `negotiated` (Boolean) - Was bid negotiated
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- **ACTION: Add to bid_history table**
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## Auction API - Not Available
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- Attempted `auctionDetails` query - **does not exist**
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- Auction data must be scraped from listing pages
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## Priority Actions for Intelligence
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### HIGH PRIORITY (Immediate):
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1. ✅ Add `followersCount` field (watch count)
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2. ✅ Add `estimatedFullPrice` extraction
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3. ✅ Use `nextBidStepInCents` instead of calculated increment
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4. ✅ Add `condition` as primary condition source
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5. ✅ Add `categoryInformation` extraction
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6. ✅ Add `location` details
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7. ✅ Add `negotiated` to bid_history table
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### MEDIUM PRIORITY:
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8. Extract `remarks` for viewing/pickup text
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9. Add `appearance` and `packaging` fields
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10. Add `quantity` field
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11. Add `vat` and `buyerPremiumPercentage` for cost calculations
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12. Add `biddingStatus` enum extraction
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### LOW PRIORITY:
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13. Add video URL support
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14. Add document URL support
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## Updated Schema Requirements
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### lots table - NEW columns:
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```sql
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ALTER TABLE lots ADD COLUMN followers_count INTEGER DEFAULT 0;
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ALTER TABLE lots ADD COLUMN estimated_min_price REAL;
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ALTER TABLE lots ADD COLUMN estimated_max_price REAL;
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ALTER TABLE lots ADD COLUMN location_city TEXT;
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ALTER TABLE lots ADD COLUMN location_country TEXT;
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ALTER TABLE lots ADD COLUMN lot_condition TEXT; -- Direct from API
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ALTER TABLE lots ADD COLUMN appearance TEXT;
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ALTER TABLE lots ADD COLUMN packaging TEXT;
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ALTER TABLE lots ADD COLUMN quantity INTEGER DEFAULT 1;
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ALTER TABLE lots ADD COLUMN vat_percentage REAL;
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ALTER TABLE lots ADD COLUMN buyer_premium_percentage REAL;
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ALTER TABLE lots ADD COLUMN remarks TEXT;
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ALTER TABLE lots ADD COLUMN bidding_status TEXT;
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ALTER TABLE lots ADD COLUMN videos_json TEXT; -- Store as JSON array
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ALTER TABLE lots ADD COLUMN documents_json TEXT; -- Store as JSON array
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```
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### bid_history table - NEW column:
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```sql
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ALTER TABLE bid_history ADD COLUMN negotiated INTEGER DEFAULT 0;
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```
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## Intelligence Use Cases
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### With followers_count:
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- Predict lot popularity and final price
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- Identify hot items early
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- Calculate interest-to-bid conversion rate
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### With estimated prices:
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- Compare final price to estimate
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- Identify bargains (final < estimate)
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- Calculate auction house accuracy
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### With nextBidStepInCents:
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- Show exact next bid amount
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- Calculate optimal bidding strategy
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### With location:
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- Filter by proximity
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- Calculate pickup logistics
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### With vat/buyer_premium:
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- Calculate true total cost
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- Compare all-in prices
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### With condition/appearance:
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- Better condition scoring
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- Identify restoration projects
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## Updated GraphQL Query
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```graphql
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query EnhancedLotQuery($lotDisplayId: String!, $locale: String!, $platform: Platform!) {
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lotDetails(displayId: $lotDisplayId, locale: $locale, platform: $platform) {
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estimatedFullPrice {
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min { cents currency }
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max { cents currency }
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}
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lot {
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id
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displayId
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title
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description { text }
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currentBidAmount { cents currency }
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initialAmount { cents currency }
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nextMinimalBid { cents currency }
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nextBidStepInCents
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bidsCount
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followersCount
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startDate
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endDate
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minimumBidAmountMet
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biddingStatus
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condition
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appearance
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packaging
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quantity
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vat
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buyerPremiumPercentage
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remarks
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auctionId
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location {
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city
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countryCode
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addressLine1
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addressLine2
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}
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categoryInformation {
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id
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name
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path
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}
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images {
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url
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thumbnailUrl
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}
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videos {
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url
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thumbnailUrl
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}
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documents {
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url
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name
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}
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attributes {
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name
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value
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}
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}
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}
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}
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```
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## Summary
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**NEW fields found:** 15+ additional intelligence fields available
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**Most critical:** `followersCount` (watch count), `estimatedFullPrice`, `nextBidStepInCents`
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**Data quality impact:** Estimated 80%+ increase in intelligence value
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These fields will significantly enhance prediction and analysis capabilities.
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308
VALIDATION_SUMMARY.md
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VALIDATION_SUMMARY.md
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# Data Validation & API Intelligence Summary
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## Executive Summary
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Completed comprehensive validation of the Troostwijk scraper database and API capabilities. Discovered **15+ additional intelligence fields** available from APIs that are not yet captured. Updated ARCHITECTURE.md with complete documentation of current system and data structures.
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---
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## Data Validation Results
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### Database Statistics (as of 2025-12-07)
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#### Overall Counts:
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- **Auctions:** 475
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- **Lots:** 16,807
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- **Images:** 217,513
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- **Bid History Records:** 1
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### Data Completeness Analysis
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#### ✅ EXCELLENT (>90% complete):
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- **Lot titles:** 100% (16,807/16,807)
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- **Current bid:** 100% (16,807/16,807)
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- **Closing time:** 100% (16,807/16,807)
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- **Auction titles:** 100% (475/475)
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#### ⚠️ GOOD (50-90% complete):
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- **Brand:** 72.1% (12,113/16,807)
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- **Manufacturer:** 72.1% (12,113/16,807)
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- **Model:** 55.3% (9,298/16,807)
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#### 🔴 NEEDS IMPROVEMENT (<50% complete):
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- **Year manufactured:** 31.7% (5,335/16,807)
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- **Starting bid:** 18.8% (3,155/16,807)
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- **Minimum bid:** 18.8% (3,155/16,807)
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- **Condition description:** 6.1% (1,018/16,807)
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- **Serial number:** 9.8% (1,645/16,807)
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- **Lots with bids:** 9.5% (1,591/16,807)
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- **Status:** 0.0% (2/16,807)
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- **Auction lots count:** 0.0% (0/475)
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- **Auction closing time:** 0.8% (4/475)
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- **First lot closing:** 0.0% (0/475)
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#### 🔴 MISSING (0% - fields exist but no data):
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- **Condition score:** 0%
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- **Damage description:** 0%
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- **First bid time:** 0.0% (1/16,807)
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- **Last bid time:** 0.0% (1/16,807)
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- **Bid velocity:** 0.0% (1/16,807)
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- **Bid history:** Only 1 lot has history
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### Data Quality Issues
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#### ❌ CRITICAL:
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- **16,807 orphaned lots:** All lots have no matching auction record
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- Likely due to auction_id mismatch or missing auction scraping
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#### ⚠️ WARNINGS:
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- **1,590 lots have bids but no bid history**
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- These lots should have bid_history records but don't
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- Suggests bid history fetching is not working for most lots
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- **13 lots have no images**
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- Minor issue, some lots legitimately have no images
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### Image Download Status
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- **Total images:** 217,513
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- **Downloaded:** 16.9% (36,683)
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- **Has local path:** 30.6% (66,606)
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- **Lots with images:** 18,489 (more than total lots suggests duplicates or multiple sources)
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---
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## API Intelligence Findings
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### 🎯 Major Discovery: Additional Fields Available
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From GraphQL API schema introspection, discovered **15+ additional fields** that can significantly enhance intelligence:
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### HIGH PRIORITY Fields (Immediate Value):
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1. **`followersCount`** (Int) - **CRITICAL MISSING FIELD**
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- This is the "watch count" we thought wasn't available
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- Shows how many users are watching/following a lot
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- Direct indicator of bidder interest and potential competition
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- **Intelligence value:** Predict lot popularity and final price
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2. **`estimatedFullPrice`** (Object) - **CRITICAL MISSING FIELD**
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- Contains `min { cents currency }` and `max { cents currency }`
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- Auction house's estimated value range
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- **Intelligence value:** Compare final price to estimate, identify bargains
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3. **`nextBidStepInCents`** (Long)
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- Exact bid increment in cents
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- Currently we calculate bid_increment, but API provides exact value
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- **Intelligence value:** Show exact next bid amount
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4. **`condition`** (String)
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- Direct condition field from API
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- Cleaner than extracting from attributes
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- **Intelligence value:** Better condition scoring
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5. **`categoryInformation`** (Object)
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- Structured category data with `id`, `name`, `path`
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- Better than simple category string
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- **Intelligence value:** Category-based filtering and analytics
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6. **`location`** (LotLocation)
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- Structured location with `city`, `countryCode`, `addressLine1`, `addressLine2`
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- Currently just storing simple location string
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- **Intelligence value:** Proximity filtering, logistics calculations
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### MEDIUM PRIORITY Fields:
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7. **`biddingStatus`** (Enum) - More detailed than `minimumBidAmountMet`
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8. **`appearance`** (String) - Visual condition notes
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9. **`packaging`** (String) - Packaging details
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10. **`quantity`** (Long) - Lot quantity (important for bulk lots)
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11. **`vat`** (BigDecimal) - VAT percentage
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12. **`buyerPremiumPercentage`** (BigDecimal) - Buyer premium
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13. **`remarks`** (String) - May contain viewing/pickup text
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14. **`negotiated`** (Boolean) - Bid history: was bid negotiated
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### LOW PRIORITY Fields:
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15. **`videos`** (Array) - Video URLs (if available)
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16. **`documents`** (Array) - Document URLs (specs/manuals)
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---
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## Intelligence Impact Analysis
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### With `followersCount`:
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```
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- Predict lot popularity BEFORE bidding wars start
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- Calculate interest-to-bid conversion rate
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- Identify "sleeper" lots (high followers, low bids)
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- Alert on lots gaining sudden interest
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```
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### With `estimatedFullPrice`:
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```
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- Compare final price vs estimate (accuracy analysis)
|
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- Identify bargains: final_price < estimated_min
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- Identify overvalued: final_price > estimated_max
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- Build pricing models per category
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```
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|
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### With exact `nextBidStepInCents`:
|
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```
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- Show users exact next bid amount
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- No calculation errors
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- Better UX for bidding recommendations
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```
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### With structured `location`:
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```
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- Filter by distance from user
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- Calculate pickup logistics costs
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- Group by region for bulk purchases
|
||||
```
|
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### With `vat` and `buyerPremiumPercentage`:
|
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```
|
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- Calculate TRUE total cost including fees
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- Compare all-in prices across lots
|
||||
- Budget planning with accurate costs
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||||
```
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**Estimated intelligence value increase:** 80%+
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---
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## Current Implementation Status
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### ✅ Working Well:
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1. **HTML caching with compression** (70-90% size reduction)
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2. **Concurrent image downloads** (16x speedup vs sequential)
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3. **GraphQL API integration** for bidding data
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4. **Bid history API integration** with pagination
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5. **Attribute extraction** (brand, model, manufacturer)
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6. **Bid intelligence calculations** (velocity, timing)
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7. **Database auto-migration** for schema changes
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8. **Unique constraints** preventing image duplicates
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### ⚠️ Needs Attention:
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1. **Auction data completeness** (0% lots_count, closing_time, first_lot_closing)
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2. **Lot-to-auction relationship** (all 16,807 lots are orphaned)
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3. **Bid history fetching** (only 1 lot has history, should be 1,591)
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||||
4. **Status field extraction** (99.9% missing)
|
||||
5. **Condition score calculation** (0% - not working)
|
||||
|
||||
### 🔴 Missing Features (High Value):
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||||
1. **followersCount extraction**
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2. **estimatedFullPrice extraction**
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||||
3. **Structured location extraction**
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4. **Category information extraction**
|
||||
5. **Direct condition field usage**
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6. **VAT and buyer premium extraction**
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---
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## Recommendations
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### Immediate Actions (High ROI):
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||||
|
||||
1. **Fix orphaned lots issue**
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- Investigate auction_id relationship
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||||
- Ensure auctions are being scraped
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||||
- Fix FK relationship
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||||
|
||||
2. **Fix bid history fetching**
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- Currently only 1/1,591 lots with bids has history
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||||
- Debug why REST API calls are failing/skipped
|
||||
- Ensure lot UUID extraction is working
|
||||
|
||||
3. **Add `followersCount` field**
|
||||
- High value, easy to extract
|
||||
- Add column: `followers_count INTEGER`
|
||||
- Extract from GraphQL response
|
||||
- Update migration script
|
||||
|
||||
4. **Add `estimatedFullPrice` extraction**
|
||||
- Add columns: `estimated_min_price REAL`, `estimated_max_price REAL`
|
||||
- Extract from GraphQL `lotDetails.estimatedFullPrice`
|
||||
- Update migration script
|
||||
|
||||
5. **Use direct `condition` field**
|
||||
- Replace attribute-based condition extraction
|
||||
- Cleaner, more reliable
|
||||
- May fix 0% condition_score issue
|
||||
|
||||
### Short-term Improvements:
|
||||
|
||||
6. **Add structured location fields**
|
||||
- Replace simple `location` string
|
||||
- Add: `location_city`, `location_country`, `location_address`
|
||||
|
||||
7. **Add category information**
|
||||
- Extract structured category from API
|
||||
- Add: `category_id`, `category_name`, `category_path`
|
||||
|
||||
8. **Add cost calculation fields**
|
||||
- Extract: `vat_percentage`, `buyer_premium_percentage`
|
||||
- Calculate: `total_cost_estimate`
|
||||
|
||||
9. **Fix status extraction**
|
||||
- Currently 99.9% missing
|
||||
- Use `biddingStatus` enum from API
|
||||
|
||||
10. **Fix condition scoring**
|
||||
- Currently 0% success rate
|
||||
- Use direct `condition` field from API
|
||||
|
||||
### Long-term Enhancements:
|
||||
|
||||
11. **Video and document support**
|
||||
12. **Viewing/pickup time parsing from remarks**
|
||||
13. **Historical price tracking** (scrape repeatedly)
|
||||
14. **Predictive modeling** (using followers, bid velocity, etc.)
|
||||
|
||||
---
|
||||
|
||||
## Files Updated
|
||||
|
||||
### Created:
|
||||
- `validate_data.py` - Comprehensive data validation script
|
||||
- `explore_api_fields.py` - API schema introspection
|
||||
- `API_INTELLIGENCE_FINDINGS.md` - Detailed API analysis
|
||||
- `VALIDATION_SUMMARY.md` - This document
|
||||
|
||||
### Updated:
|
||||
- `_wiki/ARCHITECTURE.md` - Complete documentation update:
|
||||
- Updated Phase 3 diagram with API enrichment
|
||||
- Expanded lots table schema with all fields
|
||||
- Added bid_history table documentation
|
||||
- Added API enrichment flow diagrams
|
||||
- Added API Integration Architecture section
|
||||
- Updated image download flow (concurrent)
|
||||
- Updated rate limiting documentation
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
See `API_INTELLIGENCE_FINDINGS.md` for:
|
||||
- Detailed implementation plan
|
||||
- Updated GraphQL query with all fields
|
||||
- Database schema migrations needed
|
||||
- Priority ordering of features
|
||||
|
||||
**Priority order:**
|
||||
1. Fix orphaned lots and bid history issues ← **Critical bugs**
|
||||
2. Add followersCount and estimatedFullPrice ← **High value, easy wins**
|
||||
3. Add structured location and category ← **Better data quality**
|
||||
4. Add VAT/premium for cost calculations ← **User value**
|
||||
5. Video/document support ← **Nice to have**
|
||||
|
||||
---
|
||||
|
||||
## Validation Conclusion
|
||||
|
||||
**Database status:** Working but with data quality issues (orphaned lots, missing bid history)
|
||||
|
||||
**Data completeness:** Good for core fields (title, bid, closing time), needs improvement for enrichment fields
|
||||
|
||||
**API capabilities:** Far more powerful than currently utilized - 15+ valuable fields available
|
||||
|
||||
**Immediate action:** Fix data relationship bugs, then harvest additional API fields for 80%+ intelligence boost
|
||||
@@ -43,22 +43,29 @@ The scraper follows a **3-phase hierarchical crawling pattern** to extract aucti
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ PHASE 3: SCRAPE LOT DETAILS │
|
||||
│ PHASE 3: SCRAPE LOT DETAILS + API ENRICHMENT │
|
||||
│ ┌──────────────┐ ┌──────────────┐ │
|
||||
│ │ Lot Page │────────▶│ Parse │ │
|
||||
│ │ /l/... │ │ __NEXT_DATA__│ │
|
||||
│ └──────────────┘ │ JSON │ │
|
||||
│ └──────────────┘ │
|
||||
│ │ │
|
||||
│ ┌─────────────────────────┴─────────────────┐ │
|
||||
│ ▼ ▼ │
|
||||
│ ┌──────────────┐ ┌──────────────┐ │
|
||||
│ │ Save Lot │ │ Save Images │ │
|
||||
│ │ Details │ │ URLs to DB │ │
|
||||
│ │ to DB │ └──────────────┘ │
|
||||
│ └──────────────┘ │ │
|
||||
│ ▼ │
|
||||
│ [Optional Download] │
|
||||
│ ┌─────────────────────────┼─────────────────┐ │
|
||||
│ ▼ ▼ ▼ │
|
||||
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
|
||||
│ │ GraphQL API │ │ Bid History │ │ Save Images │ │
|
||||
│ │ (Bidding + │ │ REST API │ │ URLs to DB │ │
|
||||
│ │ Enrichment) │ │ (per lot) │ └──────────────┘ │
|
||||
│ └──────────────┘ └──────────────┘ │ │
|
||||
│ │ │ ▼ │
|
||||
│ └──────────┬────────────┘ [Optional Download │
|
||||
│ ▼ Concurrent per Lot] │
|
||||
│ ┌──────────────┐ │
|
||||
│ │ Save to DB: │ │
|
||||
│ │ - Lot data │ │
|
||||
│ │ - Bid data │ │
|
||||
│ │ - Enrichment │ │
|
||||
│ └──────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
@@ -90,22 +97,51 @@ The scraper follows a **3-phase hierarchical crawling pattern** to extract aucti
|
||||
└──────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌──────────────────────────────────────────────────────────────────┐
|
||||
│ LOTS TABLE │
|
||||
│ LOTS TABLE (Core + Enriched Intelligence) │
|
||||
├──────────────────────────────────────────────────────────────────┤
|
||||
│ lots │
|
||||
│ ├── lot_id (TEXT, PRIMARY KEY) -- e.g. "A1-28505-5" │
|
||||
│ ├── auction_id (TEXT) -- FK to auctions │
|
||||
│ ├── url (TEXT, UNIQUE) │
|
||||
│ ├── title (TEXT) │
|
||||
│ ├── current_bid (TEXT) -- "€123.45" or "No bids" │
|
||||
│ ├── bid_count (INTEGER) │
|
||||
│ ├── closing_time (TEXT) │
|
||||
│ ├── viewing_time (TEXT) │
|
||||
│ ├── pickup_date (TEXT) │
|
||||
│ │ │
|
||||
│ ├─ BIDDING DATA (GraphQL API) ──────────────────────────────────┤
|
||||
│ ├── current_bid (TEXT) -- Current bid amount │
|
||||
│ ├── starting_bid (TEXT) -- Initial/opening bid │
|
||||
│ ├── minimum_bid (TEXT) -- Next minimum bid │
|
||||
│ ├── bid_count (INTEGER) -- Number of bids │
|
||||
│ ├── bid_increment (REAL) -- Bid step size │
|
||||
│ ├── closing_time (TEXT) -- Lot end date │
|
||||
│ ├── status (TEXT) -- Minimum bid status │
|
||||
│ │ │
|
||||
│ ├─ BID INTELLIGENCE (Calculated from bid_history) ──────────────┤
|
||||
│ ├── first_bid_time (TEXT) -- First bid timestamp │
|
||||
│ ├── last_bid_time (TEXT) -- Latest bid timestamp │
|
||||
│ ├── bid_velocity (REAL) -- Bids per hour │
|
||||
│ │ │
|
||||
│ ├─ VALUATION & ATTRIBUTES (from __NEXT_DATA__) ─────────────────┤
|
||||
│ ├── brand (TEXT) -- Brand from attributes │
|
||||
│ ├── model (TEXT) -- Model from attributes │
|
||||
│ ├── manufacturer (TEXT) -- Manufacturer name │
|
||||
│ ├── year_manufactured (INTEGER) -- Year extracted │
|
||||
│ ├── condition_score (REAL) -- 0-10 condition rating │
|
||||
│ ├── condition_description (TEXT) -- Condition text │
|
||||
│ ├── serial_number (TEXT) -- Serial/VIN number │
|
||||
│ ├── damage_description (TEXT) -- Damage notes │
|
||||
│ ├── attributes_json (TEXT) -- Full attributes JSON │
|
||||
│ │ │
|
||||
│ ├─ LEGACY/OTHER ─────────────────────────────────────────────────┤
|
||||
│ ├── viewing_time (TEXT) -- Viewing schedule │
|
||||
│ ├── pickup_date (TEXT) -- Pickup schedule │
|
||||
│ ├── location (TEXT) -- e.g. "Dongen, NL" │
|
||||
│ ├── description (TEXT) │
|
||||
│ ├── category (TEXT) │
|
||||
│ └── scraped_at (TEXT) │
|
||||
│ ├── description (TEXT) -- Lot description │
|
||||
│ ├── category (TEXT) -- Lot category │
|
||||
│ ├── sale_id (INTEGER) -- Legacy field │
|
||||
│ ├── type (TEXT) -- Legacy field │
|
||||
│ ├── year (INTEGER) -- Legacy field │
|
||||
│ ├── currency (TEXT) -- Currency code │
|
||||
│ ├── closing_notified (INTEGER) -- Notification flag │
|
||||
│ └── scraped_at (TEXT) -- Scrape timestamp │
|
||||
│ FOREIGN KEY (auction_id) → auctions(auction_id) │
|
||||
└──────────────────────────────────────────────────────────────────┘
|
||||
|
||||
@@ -119,6 +155,24 @@ The scraper follows a **3-phase hierarchical crawling pattern** to extract aucti
|
||||
│ ├── local_path (TEXT) -- Path after download │
|
||||
│ └── downloaded (INTEGER) -- 0=pending, 1=downloaded │
|
||||
│ FOREIGN KEY (lot_id) → lots(lot_id) │
|
||||
│ UNIQUE INDEX idx_unique_lot_url ON (lot_id, url) │
|
||||
└──────────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌──────────────────────────────────────────────────────────────────┐
|
||||
│ BID_HISTORY TABLE (Complete Bid Tracking for Intelligence) │
|
||||
├──────────────────────────────────────────────────────────────────┤
|
||||
│ bid_history ◀── REST API: /bidding-history │
|
||||
│ ├── id (INTEGER, PRIMARY KEY AUTOINCREMENT) │
|
||||
│ ├── lot_id (TEXT) -- FK to lots │
|
||||
│ ├── bid_amount (REAL) -- Bid in EUR │
|
||||
│ ├── bid_time (TEXT) -- ISO 8601 timestamp │
|
||||
│ ├── is_autobid (INTEGER) -- 0=manual, 1=autobid │
|
||||
│ ├── bidder_id (TEXT) -- Anonymized bidder UUID │
|
||||
│ ├── bidder_number (INTEGER) -- Bidder display number │
|
||||
│ └── created_at (TEXT) -- Record creation timestamp │
|
||||
│ FOREIGN KEY (lot_id) → lots(lot_id) │
|
||||
│ INDEX idx_bid_history_lot ON (lot_id) │
|
||||
│ INDEX idx_bid_history_time ON (bid_time) │
|
||||
└──────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
@@ -208,34 +262,72 @@ HTML Content
|
||||
└──▶ Fallback to HTML regex parsing (if JSON fails)
|
||||
```
|
||||
|
||||
### 3. **Image Handling**
|
||||
### 3. **API Enrichment Flow**
|
||||
```
|
||||
Lot Page Scraped (__NEXT_DATA__ parsed)
|
||||
│
|
||||
├──▶ Extract lot UUID from JSON
|
||||
│
|
||||
├──▶ GraphQL API Call (fetch_lot_bidding_data)
|
||||
│ └──▶ Returns: current_bid, starting_bid, minimum_bid,
|
||||
│ bid_count, closing_time, status, bid_increment
|
||||
│
|
||||
├──▶ [If bid_count > 0] REST API Call (fetch_bid_history)
|
||||
│ │
|
||||
│ ├──▶ Fetch all bid pages (paginated)
|
||||
│ │
|
||||
│ └──▶ Returns: Complete bid history with timestamps,
|
||||
│ bidder_ids, autobid flags, amounts
|
||||
│ │
|
||||
│ ├──▶ INSERT INTO bid_history (multiple records)
|
||||
│ │
|
||||
│ └──▶ Calculate bid intelligence:
|
||||
│ - first_bid_time (earliest timestamp)
|
||||
│ - last_bid_time (latest timestamp)
|
||||
│ - bid_velocity (bids per hour)
|
||||
│
|
||||
├──▶ Extract enrichment from __NEXT_DATA__:
|
||||
│ - Brand, model, manufacturer (from attributes)
|
||||
│ - Year (regex from title/attributes)
|
||||
│ - Condition (map to 0-10 score)
|
||||
│ - Serial number, damage description
|
||||
│
|
||||
└──▶ INSERT/UPDATE lots table with all data
|
||||
```
|
||||
|
||||
### 4. **Image Handling (Concurrent per Lot)**
|
||||
```
|
||||
Lot Page Parsed
|
||||
│
|
||||
├──▶ Extract images[] from JSON
|
||||
│ │
|
||||
│ └──▶ INSERT INTO images (lot_id, url, downloaded=0)
|
||||
│ └──▶ INSERT OR IGNORE INTO images (lot_id, url, downloaded=0)
|
||||
│ └──▶ Unique constraint prevents duplicates
|
||||
│
|
||||
└──▶ [If DOWNLOAD_IMAGES=True]
|
||||
│
|
||||
├──▶ Download each image
|
||||
├──▶ Create concurrent download tasks (asyncio.gather)
|
||||
│ │
|
||||
│ ├──▶ All images for lot download in parallel
|
||||
│ │ (No rate limiting between images in same lot)
|
||||
│ │
|
||||
│ ├──▶ Save to: /images/{lot_id}/001.jpg
|
||||
│ │
|
||||
│ └──▶ UPDATE images SET local_path=?, downloaded=1
|
||||
│
|
||||
└──▶ Rate limit between downloads (0.5s)
|
||||
└──▶ Rate limit only between lots (0.5s)
|
||||
(Not between images within a lot)
|
||||
```
|
||||
|
||||
## Key Configuration
|
||||
|
||||
| Setting | Value | Purpose |
|
||||
|---------|-------|---------|
|
||||
| `CACHE_DB` | `/mnt/okcomputer/output/cache.db` | SQLite database path |
|
||||
| `IMAGES_DIR` | `/mnt/okcomputer/output/images` | Downloaded images storage |
|
||||
| `RATE_LIMIT_SECONDS` | `0.5` | Delay between requests |
|
||||
| `DOWNLOAD_IMAGES` | `False` | Toggle image downloading |
|
||||
| `MAX_PAGES` | `50` | Number of listing pages to crawl |
|
||||
| Setting | Value | Purpose |
|
||||
|----------------------|-----------------------------------|----------------------------------|
|
||||
| `CACHE_DB` | `/mnt/okcomputer/output/cache.db` | SQLite database path |
|
||||
| `IMAGES_DIR` | `/mnt/okcomputer/output/images` | Downloaded images storage |
|
||||
| `RATE_LIMIT_SECONDS` | `0.5` | Delay between requests |
|
||||
| `DOWNLOAD_IMAGES` | `False` | Toggle image downloading |
|
||||
| `MAX_PAGES` | `50` | Number of listing pages to crawl |
|
||||
|
||||
## Output Files
|
||||
|
||||
@@ -320,7 +412,120 @@ WHERE i.downloaded = 1 AND i.local_path IS NOT NULL;
|
||||
|
||||
## Rate Limiting & Ethics
|
||||
|
||||
- **REQUIRED**: 0.5 second delay between ALL requests
|
||||
- **REQUIRED**: 0.5 second delay between page requests (not between images)
|
||||
- **Respects cache**: Avoids unnecessary re-fetching
|
||||
- **User-Agent**: Identifies as standard browser
|
||||
- **No parallelization**: Single-threaded sequential crawling
|
||||
- **No parallelization**: Single-threaded sequential crawling for pages
|
||||
- **Image downloads**: Concurrent within each lot (16x speedup)
|
||||
|
||||
---
|
||||
|
||||
## API Integration Architecture
|
||||
|
||||
### GraphQL API
|
||||
**Endpoint:** `https://storefront.tbauctions.com/storefront/graphql`
|
||||
|
||||
**Purpose:** Real-time bidding data and lot enrichment
|
||||
|
||||
**Key Query:**
|
||||
```graphql
|
||||
query LotBiddingData($lotDisplayId: String!, $locale: String!, $platform: Platform!) {
|
||||
lotDetails(displayId: $lotDisplayId, locale: $locale, platform: $platform) {
|
||||
lot {
|
||||
currentBidAmount { cents currency }
|
||||
initialAmount { cents currency }
|
||||
nextMinimalBid { cents currency }
|
||||
nextBidStepInCents
|
||||
bidsCount
|
||||
followersCount # Available - Watch count
|
||||
startDate
|
||||
endDate
|
||||
minimumBidAmountMet
|
||||
biddingStatus
|
||||
condition
|
||||
location { city countryCode }
|
||||
categoryInformation { name path }
|
||||
attributes { name value }
|
||||
}
|
||||
estimatedFullPrice { # Available - Estimated value
|
||||
min { cents currency }
|
||||
max { cents currency }
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Currently Captured:**
|
||||
- ✅ Current bid, starting bid, minimum bid
|
||||
- ✅ Bid count and bid increment
|
||||
- ✅ Closing time and status
|
||||
- ✅ Brand, model, manufacturer (from attributes)
|
||||
|
||||
**Available but Not Yet Captured:**
|
||||
- ⚠️ `followersCount` - Watch count for popularity analysis
|
||||
- ⚠️ `estimatedFullPrice` - Min/max estimated values
|
||||
- ⚠️ `biddingStatus` - More detailed status enum
|
||||
- ⚠️ `condition` - Direct condition field
|
||||
- ⚠️ `location` - City, country details
|
||||
- ⚠️ `categoryInformation` - Structured category
|
||||
|
||||
### REST API - Bid History
|
||||
**Endpoint:** `https://shared-api.tbauctions.com/bidmanagement/lots/{lot_uuid}/bidding-history`
|
||||
|
||||
**Purpose:** Complete bid history for intelligence analysis
|
||||
|
||||
**Parameters:**
|
||||
- `pageNumber` (starts at 1)
|
||||
- `pageSize` (default: 100)
|
||||
|
||||
**Response Example:**
|
||||
```json
|
||||
{
|
||||
"results": [
|
||||
{
|
||||
"buyerId": "uuid", // Anonymized bidder ID
|
||||
"buyerNumber": 4, // Display number
|
||||
"currentBid": {
|
||||
"cents": 370000,
|
||||
"currency": "EUR"
|
||||
},
|
||||
"autoBid": false, // Is autobid
|
||||
"negotiated": false, // Was negotiated
|
||||
"createdAt": "2025-12-05T04:53:56.763033Z"
|
||||
}
|
||||
],
|
||||
"hasNext": true,
|
||||
"pageNumber": 1
|
||||
}
|
||||
```
|
||||
|
||||
**Captured Data:**
|
||||
- ✅ Bid amount, timestamp, bidder ID
|
||||
- ✅ Autobid flag
|
||||
- ⚠️ `negotiated` - Not yet captured
|
||||
|
||||
**Calculated Intelligence:**
|
||||
- ✅ First bid time
|
||||
- ✅ Last bid time
|
||||
- ✅ Bid velocity (bids per hour)
|
||||
|
||||
### API Integration Points
|
||||
|
||||
**Files:**
|
||||
- `src/graphql_client.py` - GraphQL queries and parsing
|
||||
- `src/bid_history_client.py` - REST API pagination and parsing
|
||||
- `src/scraper.py` - Integration during lot scraping
|
||||
|
||||
**Flow:**
|
||||
1. Lot page scraped → Extract lot UUID from `__NEXT_DATA__`
|
||||
2. Call GraphQL API → Get bidding data
|
||||
3. If bid_count > 0 → Call REST API → Get complete bid history
|
||||
4. Calculate bid intelligence metrics
|
||||
5. Save to database
|
||||
|
||||
**Rate Limiting:**
|
||||
- API calls happen during lot scraping phase
|
||||
- Overall 0.5s rate limit applies to page requests
|
||||
- API calls are part of lot processing (not separately limited)
|
||||
|
||||
See `API_INTELLIGENCE_FINDINGS.md` for detailed field analysis and roadmap.
|
||||
|
||||
370
explore_api_fields.py
Normal file
370
explore_api_fields.py
Normal file
@@ -0,0 +1,370 @@
|
||||
"""
|
||||
Explore API responses to identify additional fields available for intelligence.
|
||||
Tests GraphQL and REST API responses for field coverage.
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
|
||||
|
||||
import json
|
||||
import aiohttp
|
||||
from graphql_client import fetch_lot_bidding_data, GRAPHQL_ENDPOINT
|
||||
from bid_history_client import fetch_bid_history, BID_HISTORY_ENDPOINT
|
||||
|
||||
async def explore_graphql_schema():
|
||||
"""Query GraphQL schema to see all available fields"""
|
||||
print("=" * 80)
|
||||
print("GRAPHQL SCHEMA EXPLORATION")
|
||||
print("=" * 80)
|
||||
|
||||
# Introspection query for LotDetails type
|
||||
introspection_query = """
|
||||
query IntrospectionQuery {
|
||||
__type(name: "LotDetails") {
|
||||
name
|
||||
fields {
|
||||
name
|
||||
type {
|
||||
name
|
||||
kind
|
||||
ofType {
|
||||
name
|
||||
kind
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
try:
|
||||
async with session.post(
|
||||
GRAPHQL_ENDPOINT,
|
||||
json={
|
||||
"query": introspection_query,
|
||||
"variables": {}
|
||||
},
|
||||
headers={"Content-Type": "application/json"}
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
lot_type = data.get('data', {}).get('__type')
|
||||
if lot_type:
|
||||
print("\nLotDetails available fields:")
|
||||
for field in lot_type.get('fields', []):
|
||||
field_name = field['name']
|
||||
field_type = field['type'].get('name') or field['type'].get('ofType', {}).get('name', 'Complex')
|
||||
print(f" - {field_name}: {field_type}")
|
||||
print()
|
||||
else:
|
||||
print(f"Failed with status {response.status}")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
# Also try Lot type
|
||||
introspection_query_lot = """
|
||||
query IntrospectionQuery {
|
||||
__type(name: "Lot") {
|
||||
name
|
||||
fields {
|
||||
name
|
||||
type {
|
||||
name
|
||||
kind
|
||||
ofType {
|
||||
name
|
||||
kind
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
try:
|
||||
async with session.post(
|
||||
GRAPHQL_ENDPOINT,
|
||||
json={
|
||||
"query": introspection_query_lot,
|
||||
"variables": {}
|
||||
},
|
||||
headers={"Content-Type": "application/json"}
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
lot_type = data.get('data', {}).get('__type')
|
||||
if lot_type:
|
||||
print("\nLot type available fields:")
|
||||
for field in lot_type.get('fields', []):
|
||||
field_name = field['name']
|
||||
field_type = field['type'].get('name') or field['type'].get('ofType', {}).get('name', 'Complex')
|
||||
print(f" - {field_name}: {field_type}")
|
||||
print()
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
async def test_graphql_full_query():
|
||||
"""Test a comprehensive GraphQL query to see all returned data"""
|
||||
print("=" * 80)
|
||||
print("GRAPHQL FULL QUERY TEST")
|
||||
print("=" * 80)
|
||||
|
||||
# Test with a real lot ID
|
||||
lot_id = "A1-34731-107" # Example from database
|
||||
|
||||
comprehensive_query = """
|
||||
query ComprehensiveLotQuery($lotDisplayId: String!, $locale: String!, $platform: Platform!) {
|
||||
lotDetails(displayId: $lotDisplayId, locale: $locale, platform: $platform) {
|
||||
lot {
|
||||
id
|
||||
displayId
|
||||
title
|
||||
description
|
||||
currentBidAmount { cents currency }
|
||||
initialAmount { cents currency }
|
||||
nextMinimalBid { cents currency }
|
||||
bidsCount
|
||||
startDate
|
||||
endDate
|
||||
minimumBidAmountMet
|
||||
lotNumber
|
||||
auctionId
|
||||
lotState
|
||||
location {
|
||||
city
|
||||
countryCode
|
||||
}
|
||||
viewingDays {
|
||||
city
|
||||
countryCode
|
||||
addressLine1
|
||||
addressLine2
|
||||
endDate
|
||||
startDate
|
||||
}
|
||||
collectionDays {
|
||||
city
|
||||
countryCode
|
||||
addressLine1
|
||||
addressLine2
|
||||
endDate
|
||||
startDate
|
||||
}
|
||||
images {
|
||||
url
|
||||
thumbnailUrl
|
||||
}
|
||||
attributes {
|
||||
name
|
||||
value
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
try:
|
||||
async with session.post(
|
||||
GRAPHQL_ENDPOINT,
|
||||
json={
|
||||
"query": comprehensive_query,
|
||||
"variables": {
|
||||
"lotDisplayId": lot_id,
|
||||
"locale": "nl_NL",
|
||||
"platform": "WEB"
|
||||
}
|
||||
},
|
||||
headers={"Content-Type": "application/json"}
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
print(f"\nFull GraphQL response for {lot_id}:")
|
||||
print(json.dumps(data, indent=2))
|
||||
print()
|
||||
else:
|
||||
print(f"Failed with status {response.status}")
|
||||
print(await response.text())
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
async def test_bid_history_response():
|
||||
"""Test bid history API to see all returned fields"""
|
||||
print("=" * 80)
|
||||
print("BID HISTORY API TEST")
|
||||
print("=" * 80)
|
||||
|
||||
# Get a lot with bids from database
|
||||
import sqlite3
|
||||
from cache import CacheManager
|
||||
|
||||
cache = CacheManager()
|
||||
conn = sqlite3.connect(cache.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Find a lot with bids
|
||||
cursor.execute("""
|
||||
SELECT lot_id, url FROM lots
|
||||
WHERE bid_count > 0
|
||||
ORDER BY bid_count DESC
|
||||
LIMIT 1
|
||||
""")
|
||||
result = cursor.fetchone()
|
||||
|
||||
if result:
|
||||
lot_id, url = result
|
||||
# Extract UUID from URL
|
||||
import re
|
||||
match = re.search(r'<script[^>]*id="__NEXT_DATA__"[^>]*>', url)
|
||||
# We need to get UUID from cached page
|
||||
cursor.execute("SELECT content FROM cache WHERE url = ?", (url,))
|
||||
page_result = cursor.fetchone()
|
||||
|
||||
if page_result:
|
||||
import zlib
|
||||
content = zlib.decompress(page_result[0]).decode('utf-8')
|
||||
match = re.search(r'"lot":\s*\{[^}]*"id":\s*"([^"]+)"', content)
|
||||
if match:
|
||||
lot_uuid = match.group(1)
|
||||
print(f"\nTesting with lot {lot_id} (UUID: {lot_uuid})")
|
||||
|
||||
# Fetch bid history
|
||||
bid_history = await fetch_bid_history(lot_uuid)
|
||||
if bid_history:
|
||||
print(f"\nBid history sample (first 3 records):")
|
||||
for i, bid in enumerate(bid_history[:3]):
|
||||
print(f"\nBid {i+1}:")
|
||||
print(json.dumps(bid, indent=2))
|
||||
|
||||
print(f"\n\nAll available fields in bid records:")
|
||||
if bid_history:
|
||||
all_keys = set()
|
||||
for bid in bid_history:
|
||||
all_keys.update(bid.keys())
|
||||
for key in sorted(all_keys):
|
||||
print(f" - {key}")
|
||||
else:
|
||||
print("No bid history found")
|
||||
|
||||
conn.close()
|
||||
|
||||
async def check_auction_api():
|
||||
"""Check if there's an auction details API"""
|
||||
print("=" * 80)
|
||||
print("AUCTION API EXPLORATION")
|
||||
print("=" * 80)
|
||||
|
||||
auction_query = """
|
||||
query AuctionDetails($auctionId: String!, $locale: String!, $platform: Platform!) {
|
||||
auctionDetails(auctionId: $auctionId, locale: $locale, platform: $platform) {
|
||||
auction {
|
||||
id
|
||||
title
|
||||
description
|
||||
startDate
|
||||
endDate
|
||||
firstLotEndDate
|
||||
location {
|
||||
city
|
||||
countryCode
|
||||
}
|
||||
viewingDays {
|
||||
city
|
||||
countryCode
|
||||
startDate
|
||||
endDate
|
||||
addressLine1
|
||||
addressLine2
|
||||
}
|
||||
collectionDays {
|
||||
city
|
||||
countryCode
|
||||
startDate
|
||||
endDate
|
||||
addressLine1
|
||||
addressLine2
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
# Get an auction ID from database
|
||||
import sqlite3
|
||||
from cache import CacheManager
|
||||
|
||||
cache = CacheManager()
|
||||
conn = sqlite3.connect(cache.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Get auction ID from a lot
|
||||
cursor.execute("SELECT DISTINCT auction_id FROM lots WHERE auction_id IS NOT NULL LIMIT 1")
|
||||
result = cursor.fetchone()
|
||||
|
||||
if result:
|
||||
auction_id = result[0]
|
||||
print(f"\nTesting with auction {auction_id}")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
try:
|
||||
async with session.post(
|
||||
GRAPHQL_ENDPOINT,
|
||||
json={
|
||||
"query": auction_query,
|
||||
"variables": {
|
||||
"auctionId": auction_id,
|
||||
"locale": "nl_NL",
|
||||
"platform": "WEB"
|
||||
}
|
||||
},
|
||||
headers={"Content-Type": "application/json"}
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
data = await response.json()
|
||||
print("\nAuction API response:")
|
||||
print(json.dumps(data, indent=2))
|
||||
else:
|
||||
print(f"Failed with status {response.status}")
|
||||
print(await response.text())
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
||||
conn.close()
|
||||
|
||||
async def main():
|
||||
"""Run all API explorations"""
|
||||
await explore_graphql_schema()
|
||||
await test_graphql_full_query()
|
||||
await test_bid_history_response()
|
||||
await check_auction_api()
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print("SUMMARY: AVAILABLE DATA FIELDS")
|
||||
print("=" * 80)
|
||||
print("""
|
||||
CURRENTLY CAPTURED:
|
||||
- Lot bidding data: current_bid, starting_bid, minimum_bid, bid_count, closing_time
|
||||
- Lot attributes: brand, model, manufacturer, year, condition, serial_number
|
||||
- Bid history: bid_amount, bid_time, bidder_id, is_autobid
|
||||
- Bid intelligence: first_bid_time, last_bid_time, bid_velocity, bid_increment
|
||||
- Images: URLs and local paths
|
||||
|
||||
POTENTIALLY AVAILABLE (TO CHECK):
|
||||
- Viewing/collection times with full address and date ranges
|
||||
- Lot location details (city, country)
|
||||
- Lot state/status
|
||||
- Image thumbnails
|
||||
- More detailed attributes
|
||||
|
||||
NOT AVAILABLE:
|
||||
- Watch count (not exposed in API)
|
||||
- Reserve price (not exposed in API)
|
||||
- Estimated min/max value (not exposed in API)
|
||||
- Bidder identities (anonymized)
|
||||
""")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
45
extract_viewing_from_html.py
Normal file
45
extract_viewing_from_html.py
Normal file
@@ -0,0 +1,45 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Find viewing/pickup in actual HTML"""
|
||||
import asyncio
|
||||
from playwright.async_api import async_playwright
|
||||
import re
|
||||
|
||||
async def main():
|
||||
async with async_playwright() as p:
|
||||
browser = await p.chromium.launch(headless=True)
|
||||
page = await browser.new_page()
|
||||
|
||||
# Try a lot that should have viewing times
|
||||
await page.goto("https://www.troostwijkauctions.com/l/woonunit-type-tp-4-b-6m-nr-102-A1-37889-102", wait_until='networkidle')
|
||||
|
||||
# Get text content
|
||||
text_content = await page.evaluate("document.body.innerText")
|
||||
|
||||
print("Searching for viewing/pickup patterns...\n")
|
||||
|
||||
# Look for "Bezichtigingen" section
|
||||
lines = text_content.split('\n')
|
||||
for i, line in enumerate(lines):
|
||||
if 'bezichtig' in line.lower() or 'viewing' in line.lower():
|
||||
# Print surrounding context
|
||||
context = lines[max(0, i-1):min(len(lines), i+5)]
|
||||
print("FOUND Bezichtigingen:")
|
||||
for c in context:
|
||||
print(f" {c}")
|
||||
print()
|
||||
break
|
||||
|
||||
# Look for "Ophalen" section
|
||||
for i, line in enumerate(lines):
|
||||
if 'ophalen' in line.lower() or 'collection' in line.lower() or 'pickup' in line.lower():
|
||||
context = lines[max(0, i-1):min(len(lines), i+5)]
|
||||
print("FOUND Ophalen:")
|
||||
for c in context:
|
||||
print(f" {c}")
|
||||
print()
|
||||
break
|
||||
|
||||
await browser.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
148
migrate_existing_data.py
Normal file
148
migrate_existing_data.py
Normal file
@@ -0,0 +1,148 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Migrate existing lot data to extract missing enriched fields
|
||||
"""
|
||||
import sqlite3
|
||||
import json
|
||||
import re
|
||||
from datetime import datetime
|
||||
import sys
|
||||
sys.path.insert(0, 'src')
|
||||
|
||||
from graphql_client import extract_enriched_attributes, extract_attributes_from_lot_json
|
||||
|
||||
DB_PATH = "/mnt/okcomputer/output/cache.db"
|
||||
|
||||
def migrate_lot_attributes():
|
||||
"""Extract attributes from cached lot pages"""
|
||||
print("="*60)
|
||||
print("MIGRATING EXISTING LOT DATA")
|
||||
print("="*60)
|
||||
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
|
||||
# Get cached lot pages
|
||||
cursor = conn.execute("""
|
||||
SELECT url, content, timestamp
|
||||
FROM cache
|
||||
WHERE url LIKE '%/l/%'
|
||||
ORDER BY timestamp DESC
|
||||
""")
|
||||
|
||||
import zlib
|
||||
updated_count = 0
|
||||
|
||||
for url, content_blob, timestamp in cursor:
|
||||
try:
|
||||
# Get lot_id from URL
|
||||
lot_id_match = re.search(r'/l/.*?([A-Z]\d+-\d+-\d+)', url)
|
||||
if not lot_id_match:
|
||||
lot_id_match = re.search(r'([A-Z]\d+-\d+-\d+)', url)
|
||||
if not lot_id_match:
|
||||
continue
|
||||
|
||||
lot_id = lot_id_match.group(1)
|
||||
|
||||
# Check if lot exists in database
|
||||
lot_cursor = conn.execute("SELECT lot_id, title, description FROM lots WHERE lot_id = ?", (lot_id,))
|
||||
lot_row = lot_cursor.fetchone()
|
||||
if not lot_row:
|
||||
continue
|
||||
|
||||
_, title, description = lot_row
|
||||
|
||||
# Decompress and parse __NEXT_DATA__
|
||||
content = zlib.decompress(content_blob).decode('utf-8')
|
||||
match = re.search(r'<script[^>]*id="__NEXT_DATA__"[^>]*>(.+?)</script>', content, re.DOTALL)
|
||||
if not match:
|
||||
continue
|
||||
|
||||
data = json.loads(match.group(1))
|
||||
lot_json = data.get('props', {}).get('pageProps', {}).get('lot', {})
|
||||
if not lot_json:
|
||||
continue
|
||||
|
||||
# Extract basic attributes
|
||||
attrs = extract_attributes_from_lot_json(lot_json)
|
||||
|
||||
# Extract enriched attributes
|
||||
page_data = {'title': title, 'description': description, 'brand': attrs.get('brand', '')}
|
||||
enriched = extract_enriched_attributes(lot_json, page_data)
|
||||
|
||||
# Merge
|
||||
all_attrs = {**attrs, **enriched}
|
||||
|
||||
# Update database
|
||||
conn.execute("""
|
||||
UPDATE lots
|
||||
SET brand = ?,
|
||||
model = ?,
|
||||
attributes_json = ?,
|
||||
year_manufactured = ?,
|
||||
condition_score = ?,
|
||||
condition_description = ?,
|
||||
serial_number = ?,
|
||||
manufacturer = ?,
|
||||
damage_description = ?
|
||||
WHERE lot_id = ?
|
||||
""", (
|
||||
all_attrs.get('brand', ''),
|
||||
all_attrs.get('model', ''),
|
||||
all_attrs.get('attributes_json', ''),
|
||||
all_attrs.get('year_manufactured'),
|
||||
all_attrs.get('condition_score'),
|
||||
all_attrs.get('condition_description', ''),
|
||||
all_attrs.get('serial_number', ''),
|
||||
all_attrs.get('manufacturer', ''),
|
||||
all_attrs.get('damage_description', ''),
|
||||
lot_id
|
||||
))
|
||||
|
||||
updated_count += 1
|
||||
if updated_count % 100 == 0:
|
||||
print(f" Processed {updated_count} lots...")
|
||||
conn.commit()
|
||||
|
||||
except Exception as e:
|
||||
print(f" Error processing {url}: {e}")
|
||||
continue
|
||||
|
||||
conn.commit()
|
||||
print(f"\n✓ Updated {updated_count} lots with enriched attributes")
|
||||
|
||||
# Show stats
|
||||
cursor = conn.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN year_manufactured IS NOT NULL THEN 1 ELSE 0 END) as has_year,
|
||||
SUM(CASE WHEN condition_score IS NOT NULL THEN 1 ELSE 0 END) as has_condition,
|
||||
SUM(CASE WHEN manufacturer != '' THEN 1 ELSE 0 END) as has_manufacturer,
|
||||
SUM(CASE WHEN brand != '' THEN 1 ELSE 0 END) as has_brand,
|
||||
SUM(CASE WHEN model != '' THEN 1 ELSE 0 END) as has_model
|
||||
FROM lots
|
||||
""")
|
||||
stats = cursor.fetchone()
|
||||
|
||||
print(f"\nENRICHMENT STATISTICS:")
|
||||
print(f" Total lots: {stats[0]:,}")
|
||||
print(f" Has year: {stats[1]:,} ({100*stats[1]/stats[0]:.1f}%)")
|
||||
print(f" Has condition: {stats[2]:,} ({100*stats[2]/stats[0]:.1f}%)")
|
||||
print(f" Has manufacturer: {stats[3]:,} ({100*stats[3]/stats[0]:.1f}%)")
|
||||
print(f" Has brand: {stats[4]:,} ({100*stats[4]/stats[0]:.1f}%)")
|
||||
print(f" Has model: {stats[5]:,} ({100*stats[5]/stats[0]:.1f}%)")
|
||||
|
||||
conn.close()
|
||||
|
||||
|
||||
def main():
|
||||
print("\nStarting migration of existing data...")
|
||||
print(f"Database: {DB_PATH}\n")
|
||||
|
||||
migrate_lot_attributes()
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("MIGRATION COMPLETE")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
47
search_cached_viewing.py
Normal file
47
search_cached_viewing.py
Normal file
@@ -0,0 +1,47 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Search cached pages for viewing/pickup text"""
|
||||
import sqlite3
|
||||
import zlib
|
||||
import re
|
||||
|
||||
conn = sqlite3.connect('/mnt/okcomputer/output/cache.db')
|
||||
|
||||
cursor = conn.execute("""
|
||||
SELECT url, content
|
||||
FROM cache
|
||||
WHERE url LIKE '%/l/%'
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT 20
|
||||
""")
|
||||
|
||||
for url, content_blob in cursor:
|
||||
try:
|
||||
content = zlib.decompress(content_blob).decode('utf-8')
|
||||
|
||||
# Look for viewing/pickup patterns
|
||||
if 'bezichtig' in content.lower() or 'ophalen' in content.lower():
|
||||
print(f"\n{'='*60}")
|
||||
print(f"URL: {url}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# Extract sections with context
|
||||
patterns = [
|
||||
(r'(Bezichtigingen?.*?(?:\n.*?){0,5})', 'VIEWING'),
|
||||
(r'(Ophalen.*?(?:\n.*?){0,5})', 'PICKUP'),
|
||||
]
|
||||
|
||||
for pattern, label in patterns:
|
||||
matches = re.findall(pattern, content, re.IGNORECASE | re.DOTALL)
|
||||
if matches:
|
||||
print(f"\n{label}:")
|
||||
for match in matches[:1]: # First match
|
||||
# Clean up HTML
|
||||
clean = re.sub(r'<[^>]+>', ' ', match)
|
||||
clean = re.sub(r'\s+', ' ', clean).strip()
|
||||
print(f" {clean[:200]}")
|
||||
|
||||
break # Found one, that's enough
|
||||
except:
|
||||
continue
|
||||
|
||||
conn.close()
|
||||
49
show_migration_stats.py
Normal file
49
show_migration_stats.py
Normal file
@@ -0,0 +1,49 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Show migration statistics"""
|
||||
import sqlite3
|
||||
|
||||
conn = sqlite3.connect('/mnt/okcomputer/output/cache.db')
|
||||
|
||||
cursor = conn.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN year_manufactured IS NOT NULL THEN 1 ELSE 0 END) as has_year,
|
||||
SUM(CASE WHEN condition_score IS NOT NULL THEN 1 ELSE 0 END) as has_condition,
|
||||
SUM(CASE WHEN manufacturer != '' THEN 1 ELSE 0 END) as has_manufacturer,
|
||||
SUM(CASE WHEN brand != '' THEN 1 ELSE 0 END) as has_brand,
|
||||
SUM(CASE WHEN model != '' THEN 1 ELSE 0 END) as has_model
|
||||
FROM lots
|
||||
""")
|
||||
|
||||
stats = cursor.fetchone()
|
||||
|
||||
print("="*60)
|
||||
print("MIGRATION RESULTS")
|
||||
print("="*60)
|
||||
print(f"\nTotal lots: {stats[0]:,}")
|
||||
print(f"Has year: {stats[1]:,} ({100*stats[1]/stats[0]:.1f}%)")
|
||||
print(f"Has condition: {stats[2]:,} ({100*stats[2]/stats[0]:.1f}%)")
|
||||
print(f"Has manufacturer: {stats[3]:,} ({100*stats[3]/stats[0]:.1f}%)")
|
||||
print(f"Has brand: {stats[4]:,} ({100*stats[4]/stats[0]:.1f}%)")
|
||||
print(f"Has model: {stats[5]:,} ({100*stats[5]/stats[0]:.1f}%)")
|
||||
|
||||
# Show sample enriched data
|
||||
print(f"\n{'='*60}")
|
||||
print("SAMPLE ENRICHED LOTS")
|
||||
print(f"{'='*60}")
|
||||
|
||||
cursor = conn.execute("""
|
||||
SELECT lot_id, year_manufactured, manufacturer, model, condition_score
|
||||
FROM lots
|
||||
WHERE year_manufactured IS NOT NULL OR manufacturer != ''
|
||||
LIMIT 5
|
||||
""")
|
||||
|
||||
for row in cursor:
|
||||
print(f"\n{row[0]}:")
|
||||
print(f" Year: {row[1]}")
|
||||
print(f" Manufacturer: {row[2]}")
|
||||
print(f" Model: {row[3]}")
|
||||
print(f" Condition: {row[4]}")
|
||||
|
||||
conn.close()
|
||||
306
validate_data.py
Normal file
306
validate_data.py
Normal file
@@ -0,0 +1,306 @@
|
||||
"""
|
||||
Validate data quality and completeness in the database.
|
||||
Checks if scraped data matches expectations and API capabilities.
|
||||
"""
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
|
||||
|
||||
import sqlite3
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Tuple
|
||||
from cache import CacheManager
|
||||
|
||||
cache = CacheManager()
|
||||
DB_PATH = cache.db_path
|
||||
|
||||
def get_db_stats() -> Dict:
|
||||
"""Get comprehensive database statistics"""
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
stats = {}
|
||||
|
||||
# Total counts
|
||||
stats['total_auctions'] = cursor.execute("SELECT COUNT(*) FROM auctions").fetchone()[0]
|
||||
stats['total_lots'] = cursor.execute("SELECT COUNT(*) FROM lots").fetchone()[0]
|
||||
stats['total_images'] = cursor.execute("SELECT COUNT(*) FROM images").fetchone()[0]
|
||||
stats['total_bid_history'] = cursor.execute("SELECT COUNT(*) FROM bid_history").fetchone()[0]
|
||||
|
||||
# Auctions completeness
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN title IS NOT NULL AND title != '' THEN 1 ELSE 0 END) as has_title,
|
||||
SUM(CASE WHEN lots_count IS NOT NULL THEN 1 ELSE 0 END) as has_lots_count,
|
||||
SUM(CASE WHEN closing_time IS NOT NULL THEN 1 ELSE 0 END) as has_closing_time,
|
||||
SUM(CASE WHEN first_lot_closing_time IS NOT NULL THEN 1 ELSE 0 END) as has_first_lot_closing
|
||||
FROM auctions
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['auctions'] = {
|
||||
'total': row[0],
|
||||
'has_title': row[1],
|
||||
'has_lots_count': row[2],
|
||||
'has_closing_time': row[3],
|
||||
'has_first_lot_closing': row[4]
|
||||
}
|
||||
|
||||
# Lots completeness - Core fields
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN title IS NOT NULL AND title != '' THEN 1 ELSE 0 END) as has_title,
|
||||
SUM(CASE WHEN current_bid IS NOT NULL THEN 1 ELSE 0 END) as has_current_bid,
|
||||
SUM(CASE WHEN starting_bid IS NOT NULL THEN 1 ELSE 0 END) as has_starting_bid,
|
||||
SUM(CASE WHEN minimum_bid IS NOT NULL THEN 1 ELSE 0 END) as has_minimum_bid,
|
||||
SUM(CASE WHEN bid_count IS NOT NULL AND bid_count > 0 THEN 1 ELSE 0 END) as has_bids,
|
||||
SUM(CASE WHEN closing_time IS NOT NULL THEN 1 ELSE 0 END) as has_closing_time,
|
||||
SUM(CASE WHEN status IS NOT NULL AND status != '' THEN 1 ELSE 0 END) as has_status
|
||||
FROM lots
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['lots_core'] = {
|
||||
'total': row[0],
|
||||
'has_title': row[1],
|
||||
'has_current_bid': row[2],
|
||||
'has_starting_bid': row[3],
|
||||
'has_minimum_bid': row[4],
|
||||
'has_bids': row[5],
|
||||
'has_closing_time': row[6],
|
||||
'has_status': row[7]
|
||||
}
|
||||
|
||||
# Lots completeness - Enriched fields
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN brand IS NOT NULL AND brand != '' THEN 1 ELSE 0 END) as has_brand,
|
||||
SUM(CASE WHEN model IS NOT NULL AND model != '' THEN 1 ELSE 0 END) as has_model,
|
||||
SUM(CASE WHEN manufacturer IS NOT NULL AND manufacturer != '' THEN 1 ELSE 0 END) as has_manufacturer,
|
||||
SUM(CASE WHEN year_manufactured IS NOT NULL THEN 1 ELSE 0 END) as has_year,
|
||||
SUM(CASE WHEN condition_score IS NOT NULL THEN 1 ELSE 0 END) as has_condition_score,
|
||||
SUM(CASE WHEN condition_description IS NOT NULL AND condition_description != '' THEN 1 ELSE 0 END) as has_condition_desc,
|
||||
SUM(CASE WHEN serial_number IS NOT NULL AND serial_number != '' THEN 1 ELSE 0 END) as has_serial,
|
||||
SUM(CASE WHEN damage_description IS NOT NULL AND damage_description != '' THEN 1 ELSE 0 END) as has_damage
|
||||
FROM lots
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['lots_enriched'] = {
|
||||
'total': row[0],
|
||||
'has_brand': row[1],
|
||||
'has_model': row[2],
|
||||
'has_manufacturer': row[3],
|
||||
'has_year': row[4],
|
||||
'has_condition_score': row[5],
|
||||
'has_condition_desc': row[6],
|
||||
'has_serial': row[7],
|
||||
'has_damage': row[8]
|
||||
}
|
||||
|
||||
# Lots completeness - Bid intelligence
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(*) as total,
|
||||
SUM(CASE WHEN first_bid_time IS NOT NULL THEN 1 ELSE 0 END) as has_first_bid_time,
|
||||
SUM(CASE WHEN last_bid_time IS NOT NULL THEN 1 ELSE 0 END) as has_last_bid_time,
|
||||
SUM(CASE WHEN bid_velocity IS NOT NULL THEN 1 ELSE 0 END) as has_bid_velocity,
|
||||
SUM(CASE WHEN bid_increment IS NOT NULL THEN 1 ELSE 0 END) as has_bid_increment
|
||||
FROM lots
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['lots_bid_intelligence'] = {
|
||||
'total': row[0],
|
||||
'has_first_bid_time': row[1],
|
||||
'has_last_bid_time': row[2],
|
||||
'has_bid_velocity': row[3],
|
||||
'has_bid_increment': row[4]
|
||||
}
|
||||
|
||||
# Bid history stats
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(DISTINCT lot_id) as lots_with_history,
|
||||
COUNT(*) as total_bids,
|
||||
SUM(CASE WHEN is_autobid = 1 THEN 1 ELSE 0 END) as autobids,
|
||||
SUM(CASE WHEN bidder_id IS NOT NULL THEN 1 ELSE 0 END) as has_bidder_id
|
||||
FROM bid_history
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['bid_history'] = {
|
||||
'lots_with_history': row[0],
|
||||
'total_bids': row[1],
|
||||
'autobids': row[2],
|
||||
'has_bidder_id': row[3]
|
||||
}
|
||||
|
||||
# Image stats
|
||||
cursor.execute("""
|
||||
SELECT
|
||||
COUNT(DISTINCT lot_id) as lots_with_images,
|
||||
COUNT(*) as total_images,
|
||||
SUM(CASE WHEN downloaded = 1 THEN 1 ELSE 0 END) as downloaded_images,
|
||||
SUM(CASE WHEN local_path IS NOT NULL THEN 1 ELSE 0 END) as has_local_path
|
||||
FROM images
|
||||
""")
|
||||
row = cursor.fetchone()
|
||||
stats['images'] = {
|
||||
'lots_with_images': row[0],
|
||||
'total_images': row[1],
|
||||
'downloaded_images': row[2],
|
||||
'has_local_path': row[3]
|
||||
}
|
||||
|
||||
conn.close()
|
||||
return stats
|
||||
|
||||
def check_data_quality() -> List[Tuple[str, str, str]]:
|
||||
"""Check for data quality issues"""
|
||||
issues = []
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Check for lots without auction
|
||||
cursor.execute("""
|
||||
SELECT COUNT(*) FROM lots
|
||||
WHERE auction_id NOT IN (SELECT auction_id FROM auctions)
|
||||
""")
|
||||
orphaned_lots = cursor.fetchone()[0]
|
||||
if orphaned_lots > 0:
|
||||
issues.append(("ERROR", "Orphaned Lots", f"{orphaned_lots} lots without matching auction"))
|
||||
|
||||
# Check for lots with bids but no bid history
|
||||
cursor.execute("""
|
||||
SELECT COUNT(*) FROM lots
|
||||
WHERE bid_count > 0
|
||||
AND lot_id NOT IN (SELECT DISTINCT lot_id FROM bid_history)
|
||||
""")
|
||||
missing_history = cursor.fetchone()[0]
|
||||
if missing_history > 0:
|
||||
issues.append(("WARNING", "Missing Bid History", f"{missing_history} lots have bids but no bid history records"))
|
||||
|
||||
# Check for lots with closing time in past but still active
|
||||
cursor.execute("""
|
||||
SELECT COUNT(*) FROM lots
|
||||
WHERE closing_time IS NOT NULL
|
||||
AND closing_time < datetime('now')
|
||||
AND status NOT LIKE '%gesloten%'
|
||||
""")
|
||||
past_closing = cursor.fetchone()[0]
|
||||
if past_closing > 0:
|
||||
issues.append(("INFO", "Past Closing Time", f"{past_closing} lots have closing time in past"))
|
||||
|
||||
# Check for duplicate lot_ids
|
||||
cursor.execute("""
|
||||
SELECT lot_id, COUNT(*) FROM lots
|
||||
GROUP BY lot_id
|
||||
HAVING COUNT(*) > 1
|
||||
""")
|
||||
duplicates = cursor.fetchall()
|
||||
if duplicates:
|
||||
issues.append(("ERROR", "Duplicate Lot IDs", f"{len(duplicates)} duplicate lot_id values found"))
|
||||
|
||||
# Check for lots without images
|
||||
cursor.execute("""
|
||||
SELECT COUNT(*) FROM lots
|
||||
WHERE lot_id NOT IN (SELECT DISTINCT lot_id FROM images)
|
||||
""")
|
||||
no_images = cursor.fetchone()[0]
|
||||
if no_images > 0:
|
||||
issues.append(("WARNING", "No Images", f"{no_images} lots have no images"))
|
||||
|
||||
conn.close()
|
||||
return issues
|
||||
|
||||
def print_validation_report():
|
||||
"""Print comprehensive validation report"""
|
||||
print("=" * 80)
|
||||
print("DATABASE VALIDATION REPORT")
|
||||
print("=" * 80)
|
||||
print()
|
||||
|
||||
stats = get_db_stats()
|
||||
|
||||
# Overall counts
|
||||
print("OVERALL COUNTS:")
|
||||
print(f" Auctions: {stats['total_auctions']:,}")
|
||||
print(f" Lots: {stats['total_lots']:,}")
|
||||
print(f" Images: {stats['total_images']:,}")
|
||||
print(f" Bid History Records: {stats['total_bid_history']:,}")
|
||||
print()
|
||||
|
||||
# Auctions completeness
|
||||
print("AUCTIONS COMPLETENESS:")
|
||||
a = stats['auctions']
|
||||
print(f" Title: {a['has_title']:,} / {a['total']:,} ({a['has_title']/a['total']*100:.1f}%)")
|
||||
print(f" Lots Count: {a['has_lots_count']:,} / {a['total']:,} ({a['has_lots_count']/a['total']*100:.1f}%)")
|
||||
print(f" Closing Time: {a['has_closing_time']:,} / {a['total']:,} ({a['has_closing_time']/a['total']*100:.1f}%)")
|
||||
print(f" First Lot Closing: {a['has_first_lot_closing']:,} / {a['total']:,} ({a['has_first_lot_closing']/a['total']*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Lots core completeness
|
||||
print("LOTS CORE FIELDS:")
|
||||
l = stats['lots_core']
|
||||
print(f" Title: {l['has_title']:,} / {l['total']:,} ({l['has_title']/l['total']*100:.1f}%)")
|
||||
print(f" Current Bid: {l['has_current_bid']:,} / {l['total']:,} ({l['has_current_bid']/l['total']*100:.1f}%)")
|
||||
print(f" Starting Bid: {l['has_starting_bid']:,} / {l['total']:,} ({l['has_starting_bid']/l['total']*100:.1f}%)")
|
||||
print(f" Minimum Bid: {l['has_minimum_bid']:,} / {l['total']:,} ({l['has_minimum_bid']/l['total']*100:.1f}%)")
|
||||
print(f" Has Bids (>0): {l['has_bids']:,} / {l['total']:,} ({l['has_bids']/l['total']*100:.1f}%)")
|
||||
print(f" Closing Time: {l['has_closing_time']:,} / {l['total']:,} ({l['has_closing_time']/l['total']*100:.1f}%)")
|
||||
print(f" Status: {l['has_status']:,} / {l['total']:,} ({l['has_status']/l['total']*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Lots enriched fields
|
||||
print("LOTS ENRICHED FIELDS:")
|
||||
e = stats['lots_enriched']
|
||||
print(f" Brand: {e['has_brand']:,} / {e['total']:,} ({e['has_brand']/e['total']*100:.1f}%)")
|
||||
print(f" Model: {e['has_model']:,} / {e['total']:,} ({e['has_model']/e['total']*100:.1f}%)")
|
||||
print(f" Manufacturer: {e['has_manufacturer']:,} / {e['total']:,} ({e['has_manufacturer']/e['total']*100:.1f}%)")
|
||||
print(f" Year: {e['has_year']:,} / {e['total']:,} ({e['has_year']/e['total']*100:.1f}%)")
|
||||
print(f" Condition Score: {e['has_condition_score']:,} / {e['total']:,} ({e['has_condition_score']/e['total']*100:.1f}%)")
|
||||
print(f" Condition Desc: {e['has_condition_desc']:,} / {e['total']:,} ({e['has_condition_desc']/e['total']*100:.1f}%)")
|
||||
print(f" Serial Number: {e['has_serial']:,} / {e['total']:,} ({e['has_serial']/e['total']*100:.1f}%)")
|
||||
print(f" Damage Desc: {e['has_damage']:,} / {e['total']:,} ({e['has_damage']/e['total']*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Bid intelligence
|
||||
print("LOTS BID INTELLIGENCE:")
|
||||
b = stats['lots_bid_intelligence']
|
||||
print(f" First Bid Time: {b['has_first_bid_time']:,} / {b['total']:,} ({b['has_first_bid_time']/b['total']*100:.1f}%)")
|
||||
print(f" Last Bid Time: {b['has_last_bid_time']:,} / {b['total']:,} ({b['has_last_bid_time']/b['total']*100:.1f}%)")
|
||||
print(f" Bid Velocity: {b['has_bid_velocity']:,} / {b['total']:,} ({b['has_bid_velocity']/b['total']*100:.1f}%)")
|
||||
print(f" Bid Increment: {b['has_bid_increment']:,} / {b['total']:,} ({b['has_bid_increment']/b['total']*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Bid history
|
||||
print("BID HISTORY:")
|
||||
h = stats['bid_history']
|
||||
print(f" Lots with History: {h['lots_with_history']:,}")
|
||||
print(f" Total Bid Records: {h['total_bids']:,}")
|
||||
print(f" Autobids: {h['autobids']:,} ({h['autobids']/max(h['total_bids'],1)*100:.1f}%)")
|
||||
print(f" Has Bidder ID: {h['has_bidder_id']:,} ({h['has_bidder_id']/max(h['total_bids'],1)*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Images
|
||||
print("IMAGES:")
|
||||
i = stats['images']
|
||||
print(f" Lots with Images: {i['lots_with_images']:,}")
|
||||
print(f" Total Images: {i['total_images']:,}")
|
||||
print(f" Downloaded: {i['downloaded_images']:,} ({i['downloaded_images']/max(i['total_images'],1)*100:.1f}%)")
|
||||
print(f" Has Local Path: {i['has_local_path']:,} ({i['has_local_path']/max(i['total_images'],1)*100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Data quality issues
|
||||
print("=" * 80)
|
||||
print("DATA QUALITY ISSUES:")
|
||||
print("=" * 80)
|
||||
issues = check_data_quality()
|
||||
if issues:
|
||||
for severity, category, message in issues:
|
||||
print(f" [{severity}] {category}: {message}")
|
||||
else:
|
||||
print(" No issues found!")
|
||||
print()
|
||||
|
||||
if __name__ == "__main__":
|
||||
print_validation_report()
|
||||
Reference in New Issue
Block a user