Content Extraction:
- Image recognition (CNN models)
- Object detection (YOLO, Faster R-CNN)
- OCR for text in images
- Color histogram
- Image embeddings (512-dim vectors)

Index Structure:
- Vector index for similarity search
- Inverted index for text (OCR, alt text)
- Metadata index (size, format, domain)

Ranking Factors:
- Visual similarity (40%)
- Text relevance (30%)
- Image quality (20%)
- Source authority (10%)

Challenges:
- Large image files (100x text)
- Expensive ML inference
- Similarity search at scale

Content Processing:
- Video transcription (speech-to-text)
- Scene detection
- Object recognition per frame
- Audio fingerprinting

Index:
- Transcript text (inverted index)
- Video metadata (title, description)
- Timestamp index (find specific moments)
- Visual embeddings

Search Features:
- Search within video (jump to timestamp)
- Similar videos
- Clip extraction
- Auto-generated summaries

Data Sources:
- Business listings (Google My Business)
- Maps data (addresses, coordinates)
- Reviews and ratings
- Operating hours, photos

Geospatial Index:
- Quadtree or R-tree for spatial queries
- Find businesses within radius
- Distance calculation
- Route optimization

Ranking:
- Distance (40%)
- Relevance (30%)
- Rating (20%)
- Popularity (10%)

Features:
- "Near me" queries
- Directions and navigation
- Business hours
- Photos and reviews

Speech Recognition:
- Convert audio to text
- Handle accents, noise
- Real-time processing
- 95%+ accuracy needed

Query Understanding:
- Natural language (longer queries)
- Conversational context
- Intent detection
- Entity recognition

Response Format:
- Spoken answers (text-to-speech)
- Featured snippets
- Direct answers
- Concise results

Optimization:
- Low latency (<1 second)
- Mobile-optimized
- Offline capability

Entity Database:
- 5 billion entities
- Relationships between entities
- Attributes (birth date, location, etc.)
- Sources (Wikipedia, Wikidata)

Entity Recognition:
- Identify entities in queries
- Disambiguate (Apple company vs fruit)
- Link to knowledge graph

Answer Generation:
- Direct answers (no click needed)
- Info cards (right side panel)
- Related entities
- Fact verification

Schema:
{
  entity_id: "Q95",  // Google
  type: "Company",
  name: "Google LLC",
  founded: "1998-09-04",
  founders: ["Larry Page", "Sergey Brin"],
  headquarters: "Mountain View, CA",
  industry: "Technology"
}

Solution: Near-Duplicate Detection

1. Content Fingerprinting:
   - Simhash algorithm
   - 64-bit fingerprint per document
   - Hamming distance for similarity

2. Duplicate Detection:
   - If hamming distance < 3: Duplicates
   - Keep highest-quality version
   - Canonical URL selection

3. Clustering:
   - Group similar pages
   - Show best representative
   - "Show omitted results" link

4. Implementation:
   - Process during indexing
   - Store fingerprints in index
   - O(1) duplicate check

Benefits:
- Better user experience
- Reduced index size (20% savings)
- Faster search

Solution: Multi-Layer Spell Correction

1. Dictionary-Based:
   - Check against known words
   - Edit distance (Levenshtein)
   - Suggest corrections

2. Context-Based:
   - N-gram language model
   - "did you mean" suggestions
   - Auto-correct for obvious typos

3. Query Logs:
   - Learn from user corrections
   - Popular misspellings
   - Domain-specific terms

4. Phonetic Matching:
   - Soundex algorithm
   - Handle homophones
   - "their" vs "there"

Implementation:
if (no_results || low_quality_results):
    suggest_correction = spell_checker(query)
    show "Did you mean: {suggestion}?"
    
if (obvious_typo):
    auto_correct(query)
    show "Showing results for: {corrected}"

Solution: Multi-Signal Spam Detection

1. Content Analysis:
   - Keyword stuffing detection
   - Hidden text detection
   - Cloaking detection
   - Thin content detection

2. Link Analysis:
   - Unnatural link patterns
   - Link farms
   - Paid links
   - Link velocity

3. Behavioral Signals:
   - High bounce rate
   - Low dwell time
   - No return visits
   - User reports

4. ML Model:
   - Features: 100+ spam signals
   - Model: Gradient boosting
   - Training: Human-labeled data
   - Inference: At index time

Actions:
- Low spam score: Index normally
- Medium: Rank lower
- High: Don't index (penalty)
- Extreme: Ban domain

Manual Review:
- Sample flagged pages
- Verify false positives
- Update model

Solution: Diversity Algorithms

1. Source Diversity:
   - Limit results per domain (max 2)
   - Show variety of sources
   - Balance perspectives

2. Content Diversity:
   - Different content types (articles, videos, images)
   - Different viewpoints
   - Different formats

3. Temporal Diversity:
   - Mix recent and evergreen content
   - Balance freshness and authority

4. Implementation:
   - Post-ranking diversification
   - Maximal Marginal Relevance (MMR)
   - Penalize similar results

Algorithm:
selected = []
for result in ranked_results:
    if not too_similar_to(result, selected):
        selected.append(result)
    if len(selected) == 10:
        break

Benefits:
- Better user satisfaction
- Avoid filter bubbles
- Comprehensive coverage

Solution: Fast-Track Indexing Pipeline

1. News Source Detection:
   - Identify news sites
   - Monitor RSS feeds
   - Social media signals
   - Breaking news alerts

2. Priority Crawling:
   - 5-minute crawl cycle
   - Bypass normal queue
   - Dedicated crawler pool

3. Fast Indexing:
   - Lightweight processing
   - Skip heavy analysis
   - Incremental index update

4. Freshness Boost:
   - Recency signal in ranking
   - "Top stories" section
   - Time-based sorting

5. Update Frequency:
   - Re-crawl every 5 minutes
   - Update index in real-time
   - Visible in <10 minutes

Pipeline:
News Event → RSS/Alert → Priority Crawl → Fast Index → 
Freshness Boost → Top Stories

Benefits:
- Breaking news in <10 minutes
- Competitive advantage
- User satisfaction

Current: 92K queries/second
Target: 920K queries/second (10x)

Scaling Strategy:

1. Index Servers:
   - 100K → 1M servers (10x)
   - More shards (10K → 100K)
   - Better parallelization

2. Caching:
   - Larger cache (1 TB → 10 TB)
   - Higher hit rate (60% → 80%)
   - More edge locations

3. Query Optimization:
   - More aggressive early termination
   - Approximate algorithms
   - Pre-computation

4. Infrastructure:
   - More data centers (20 → 50)
   - Better load balancing
   - Geographic distribution

Cost: $10B/year (10x current)

Solution: Rolling Index Updates

1. Dual Index:
   - Active index (serving queries)
   - Shadow index (receiving updates)

2. Update Process:
   - Build updates in shadow index
   - Test shadow index
   - Verify quality

3. Cutover:
   - Gradually shift traffic (1% → 100%)
   - Monitor quality metrics
   - Rollback if issues

4. Cleanup:
   - Old index becomes shadow
   - Repeat for next update

Benefits:
- Zero downtime
- Safe rollback
- Quality verification

Timeline:
- Build: 6 hours
- Test: 1 hour
- Cutover: 2 hours
- Total: 9 hours

Ranking Pipeline:

1. Initial Retrieval (BM25):
   - Fetch 10,000 matching documents
   - Score by term relevance
   - Fast (<50ms)

2. Feature Extraction:
   - 200+ ranking signals
   - Content, links, engagement
   - Freshness, authority

3. ML Re-Ranking:
   - LambdaMART model
   - Trained on click data
   - Top 100 documents
   - <50ms inference

4. Personalization:
   - Location, language
   - Search history (optional)
   - Device type
   - Top 20 documents

5. Diversity:
   - Ensure variety
   - Limit per-domain results
   - Final top 10

Total Time: <200ms

Metrics:

1. User Engagement:
   - Click-through rate (60%+ target)
   - Dwell time (>30 seconds)
   - Return rate (90%+)
   - Bounce rate (<40%)

2. Relevance:
   - Human raters (NDCG score)
   - A/B testing
   - Side-by-side comparison

3. Coverage:
   - Index size (100B pages)
   - Freshness (80% <24h)
   - Zero-result rate (<5%)

4. Performance:
   - Query latency (p95 <500ms)
   - Availability (99.99%)
   - Error rate (<0.01%)

Quality Process:
1. Launch experiment (1% traffic)
2. Measure metrics
3. Human evaluation
4. If better: Gradual rollout
5. If worse: Rollback

Detection and Prevention:

1. Algorithmic Detection:
   - Link schemes
   - Keyword stuffing
   - Cloaking
   - Doorway pages

2. Manual Review:
   - Sample flagged sites
   - Investigate reports
   - Verify violations

3. Penalties:
   - Ranking demotion
   - Partial de-indexing
   - Complete ban

4. Webmaster Guidelines:
   - Clear rules
   - Best practices
   - Reconsideration process

5. Continuous Improvement:
   - Update algorithms
   - Close loopholes
   - Stay ahead of spammers

Prevention:
- Make quality content rank well
- Reduce incentive for spam
- Fast detection and penalties
- Transparent guidelines