# Fixing Malformed Database Entries

## Problem

After the initial scrape run with less strict validation, the database contains entries with incomplete or incorrect data:

### Examples of Malformed Data

```csv
A1-34327,"",https://...,"",€Huidig ​​bod,0,gap,"","","",...
A1-39577,"",https://...,"",€Huidig ​​bod,0,gap,"","","",...
```

**Issues identified:**
1. ❌ Missing `auction_id` (empty string)
2. ❌ Missing `title` (empty string)
3. ❌ Invalid bid value: `€Huidig ​​bod` (Dutch for "Current bid" - placeholder text)
4. ❌ Invalid timestamp: `gap` (should be empty or valid date)
5. ❌ Missing `viewing_time`, `pickup_date`, and other fields

## Root Cause

Earlier scraping runs:
- Used less strict validation
- Fell back to HTML parsing when `__NEXT_DATA__` JSON extraction failed
- HTML parser extracted placeholder text as actual values
- Continued on errors instead of flagging incomplete data

## Solution

### Step 1: Parser Improvements ✅

**Fixed in `src/parse.py`:**

1. **Timestamp parsing** (lines 37-70):
   - Filters invalid strings like "gap", "materieel wegens vereffening"
   - Returns empty string instead of invalid value
   - Handles Unix timestamps in seconds and milliseconds

2. **Bid extraction** (lines 246-280):
   - Rejects placeholder text like "€Huidig ​​bod", "€Huidig bod"
   - Removes zero-width Unicode spaces
   - Returns "No bids" instead of invalid placeholder text

### Step 2: Detection and Repair Scripts ✅

Created two scripts to fix existing data:

#### A. `script/migrate_reparse_lots.py`
**Purpose:** Re-parse ALL cached entries with improved JSON extraction


```bash
# Preview what would be changed
# python script/fix_malformed_entries.py --db C:/mnt/okcomputer/output/cache.db
python script/migrate_reparse_lots.py --db C:/mnt/okcomputer/output/cache.db
```

```bash
# Preview what would be changed
python script/migrate_reparse_lots.py --dry-run
# Apply changes
python script/migrate_reparse_lots.py

# Use custom database path
python script/migrate_reparse_lots.py --db /path/to/cache.db
```

**What it does:**
- Reads all cached HTML pages from `cache` table
- Re-parses using improved `__NEXT_DATA__` JSON extraction
- Updates existing database entries with newly extracted fields
- Populates missing `auction_id`, `viewing_time`, `pickup_date`, etc.

#### B. `script/fix_malformed_entries.py` ⭐ **RECOMMENDED**
**Purpose:** Detect and fix ONLY malformed entries

```bash
# Preview malformed entries and fixes
python script/fix_malformed_entries.py --dry-run

# Fix malformed entries
python script/fix_malformed_entries.py

# Use custom database path
python script/fix_malformed_entries.py --db /path/to/cache.db
```

**What it detects:**
```sql
-- Auctions with issues
SELECT * FROM auctions WHERE
    auction_id = '' OR auction_id IS NULL
    OR title = '' OR title IS NULL
    OR first_lot_closing_time = 'gap'

-- Lots with issues
SELECT * FROM lots WHERE
    auction_id = '' OR auction_id IS NULL
    OR title = '' OR title IS NULL
    OR current_bid LIKE '%Huidig%bod%'
    OR closing_time = 'gap' OR closing_time = ''
```

**Example output:**
```
=================================================================
MALFORMED ENTRY DETECTION AND REPAIR
=================================================================

1. CHECKING AUCTIONS...
   Found 23 malformed auction entries

  Fixing auction: A1-39577
    URL: https://www.troostwijkauctions.com/a/...-A1-39577
    ✓ Parsed successfully:
       auction_id: A1-39577
       title: Bootveiling Rotterdam - Console boten, RIB, speedboten...
       location: Rotterdam, NL
       lots: 45
    ✓ Database updated

2. CHECKING LOTS...
   Found 127 malformed lot entries

  Fixing lot: A1-39529-10
    URL: https://www.troostwijkauctions.com/l/...-A1-39529-10
    ✓ Parsed successfully:
       lot_id: A1-39529-10
       auction_id: A1-39529
       title: Audi A7 Sportback Personenauto
       bid: No bids
       closing: 2024-12-08 15:30:00
    ✓ Database updated

=================================================================
SUMMARY
=================================================================
Auctions:
  - Found:  23
  - Fixed:  21
  - Failed: 2

Lots:
  - Found:  127
  - Fixed:  124
  - Failed: 3
```

### Step 3: Verification

After running the fix script, verify the data:

```bash
# Check if malformed entries still exist
python -c "
import sqlite3
conn = sqlite3.connect('path/to/cache.db')

print('Auctions with empty auction_id:')
print(conn.execute('SELECT COUNT(*) FROM auctions WHERE auction_id = \"\" OR auction_id IS NULL').fetchone()[0])

print('Lots with invalid bids:')
print(conn.execute('SELECT COUNT(*) FROM lots WHERE current_bid LIKE \"%Huidig%bod%\"').fetchone()[0])

print('Lots with \"gap\" timestamps:')
print(conn.execute('SELECT COUNT(*) FROM lots WHERE closing_time = \"gap\"').fetchone()[0])
"
```

Expected result after fix: **All counts should be 0**

### Step 4: Prevention

To prevent future occurrences:

1. **Validation in scraper** - Add validation before saving to database:

```python
def validate_lot_data(lot_data: Dict) -> bool:
    """Validate lot data before saving"""
    required_fields = ['lot_id', 'title', 'url']
    invalid_values = ['gap', '€Huidig bod', '€Huidig ​​bod', '']

    for field in required_fields:
        value = lot_data.get(field, '')
        if not value or value in invalid_values:
            print(f"  ⚠️  Invalid {field}: {value}")
            return False

    return True

# In save_lot method:
if not validate_lot_data(lot_data):
    print(f"  ❌ Skipping invalid lot: {lot_data.get('url')}")
    return
```

2. **Prefer JSON over HTML** - Ensure `__NEXT_DATA__` parsing is tried first (already implemented)

3. **Logging** - Add logging for fallback to HTML parsing:

```python
if next_data:
    return next_data
else:
    print(f"  ⚠️  No __NEXT_DATA__ found, falling back to HTML parsing: {url}")
    # HTML parsing...
```

## Recommended Workflow

```bash
# 1. First, run dry-run to see what will be fixed
python script/fix_malformed_entries.py --dry-run

# 2. Review the output - check if fixes look correct

# 3. Run the actual fix
python script/fix_malformed_entries.py

# 4. Verify the results
python script/fix_malformed_entries.py --dry-run
# Should show "Found 0 malformed auction entries" and "Found 0 malformed lot entries"

# 5. (Optional) Run full migration to ensure all fields are populated
python script/migrate_reparse_lots.py
```

## Files Modified/Created

### Modified:
- ✅ `src/parse.py` - Improved timestamp and bid parsing with validation

### Created:
- ✅ `script/fix_malformed_entries.py` - Targeted fix for malformed entries
- ✅ `script/migrate_reparse_lots.py` - Full re-parse migration
- ✅ `_wiki/JAVA_FIXES_NEEDED.md` - Java-side fixes documentation
- ✅ `_wiki/FIXING_MALFORMED_ENTRIES.md` - This file

## Database Location

If you get "no such table" errors, find your actual database:

```bash
# Find all .db files
find . -name "*.db"

# Check which one has data
sqlite3 path/to/cache.db "SELECT COUNT(*) FROM lots"

# Use that path with --db flag
python script/fix_malformed_entries.py --db /actual/path/to/cache.db
```

## Next Steps

After fixing malformed entries:
1. ✅ Run `fix_malformed_entries.py` to repair bad data
2. ⏳ Apply Java-side fixes (see `_wiki/JAVA_FIXES_NEEDED.md`)
3. ⏳ Re-run Java monitoring process
4. ✅ Add validation to prevent future issues