## Deep Crawling Filters & Scorers Architecture

Visual representations of advanced URL filtering, scoring strategies, and performance optimization workflows for intelligent deep crawling.

### Filter Chain Processing Pipeline

```mermaid
flowchart TD
    A[URL Input] --> B{Domain Filter}
    B -->|✓ Pass| C{Pattern Filter}
    B -->|✗ Fail| X1[Reject: Invalid Domain]
    
    C -->|✓ Pass| D{Content Type Filter}
    C -->|✗ Fail| X2[Reject: Pattern Mismatch]
    
    D -->|✓ Pass| E{SEO Filter}
    D -->|✗ Fail| X3[Reject: Wrong Content Type]
    
    E -->|✓ Pass| F{Content Relevance Filter}
    E -->|✗ Fail| X4[Reject: Low SEO Score]
    
    F -->|✓ Pass| G[URL Accepted]
    F -->|✗ Fail| X5[Reject: Low Relevance]
    
    G --> H[Add to Crawl Queue]
    
    subgraph "Fast Filters"
        B
        C
        D
    end
    
    subgraph "Slow Filters"
        E
        F
    end
    
    style A fill:#e3f2fd
    style G fill:#c8e6c9
    style H fill:#e8f5e8
    style X1 fill:#ffcdd2
    style X2 fill:#ffcdd2
    style X3 fill:#ffcdd2
    style X4 fill:#ffcdd2
    style X5 fill:#ffcdd2
```

### URL Scoring System Architecture

```mermaid
graph TB
    subgraph "Input URL"
        A[https://python.org/tutorial/2024/ml-guide.html]
    end
    
    subgraph "Individual Scorers"
        B[Keyword Relevance Scorer]
        C[Path Depth Scorer]
        D[Content Type Scorer]
        E[Freshness Scorer]
        F[Domain Authority Scorer]
    end
    
    subgraph "Scoring Process"
        B --> B1[Keywords: python, tutorial, ml<br/>Score: 0.85]
        C --> C1[Depth: 4 levels<br/>Optimal: 3<br/>Score: 0.75]
        D --> D1[Content: HTML<br/>Score: 1.0]
        E --> E1[Year: 2024<br/>Score: 1.0]
        F --> F1[Domain: python.org<br/>Score: 1.0]
    end
    
    subgraph "Composite Scoring"
        G[Weighted Combination]
        B1 --> G
        C1 --> G
        D1 --> G
        E1 --> G
        F1 --> G
    end
    
    subgraph "Final Result"
        H[Composite Score: 0.92]
        I{Score > Threshold?}
        J[Accept URL]
        K[Reject URL]
    end
    
    A --> B
    A --> C
    A --> D
    A --> E
    A --> F
    
    G --> H
    H --> I
    I -->|✓ 0.92 > 0.6| J
    I -->|✗ Score too low| K
    
    style A fill:#e3f2fd
    style G fill:#fff3e0
    style H fill:#e8f5e8
    style J fill:#c8e6c9
    style K fill:#ffcdd2
```

### Filter vs Scorer Decision Matrix

```mermaid
flowchart TD
    A[URL Processing Decision] --> B{Binary Decision Needed?}
    
    B -->|Yes - Include/Exclude| C[Use Filters]
    B -->|No - Quality Rating| D[Use Scorers]
    
    C --> C1{Filter Type Needed?}
    C1 -->|Domain Control| C2[DomainFilter]
    C1 -->|Pattern Matching| C3[URLPatternFilter]
    C1 -->|Content Type| C4[ContentTypeFilter]
    C1 -->|SEO Quality| C5[SEOFilter]
    C1 -->|Content Relevance| C6[ContentRelevanceFilter]
    
    D --> D1{Scoring Criteria?}
    D1 -->|Keyword Relevance| D2[KeywordRelevanceScorer]
    D1 -->|URL Structure| D3[PathDepthScorer]
    D1 -->|Content Quality| D4[ContentTypeScorer]
    D1 -->|Time Sensitivity| D5[FreshnessScorer]
    D1 -->|Source Authority| D6[DomainAuthorityScorer]
    
    C2 --> E[Chain Filters]
    C3 --> E
    C4 --> E
    C5 --> E
    C6 --> E
    
    D2 --> F[Composite Scorer]
    D3 --> F
    D4 --> F
    D5 --> F
    D6 --> F
    
    E --> G[Binary Output: Pass/Fail]
    F --> H[Numeric Score: 0.0-1.0]
    
    G --> I[Apply to URL Queue]
    H --> J[Priority Ranking]
    
    style C fill:#e8f5e8
    style D fill:#fff3e0
    style E fill:#f3e5f5
    style F fill:#e3f2fd
    style G fill:#c8e6c9
    style H fill:#ffecb3
```

### Performance Optimization Strategy

```mermaid
sequenceDiagram
    participant Queue as URL Queue
    participant Fast as Fast Filters
    participant Slow as Slow Filters
    participant Score as Scorers
    participant Output as Filtered URLs
    
    Note over Queue, Output: Batch Processing (1000 URLs)
    
    Queue->>Fast: Apply Domain Filter
    Fast-->>Queue: 60% passed (600 URLs)
    
    Queue->>Fast: Apply Pattern Filter
    Fast-->>Queue: 70% passed (420 URLs)
    
    Queue->>Fast: Apply Content Type Filter
    Fast-->>Queue: 90% passed (378 URLs)
    
    Note over Fast: Fast filters eliminate 62% of URLs
    
    Queue->>Slow: Apply SEO Filter (378 URLs)
    Slow-->>Queue: 80% passed (302 URLs)
    
    Queue->>Slow: Apply Relevance Filter
    Slow-->>Queue: 75% passed (227 URLs)
    
    Note over Slow: Content analysis on remaining URLs
    
    Queue->>Score: Calculate Composite Scores
    Score-->>Queue: Scored and ranked
    
    Queue->>Output: Top 100 URLs by score
    Output-->>Queue: Processing complete
    
    Note over Queue, Output: Total: 90% filtered out, 10% high-quality URLs retained
```

### Custom Filter Implementation Flow

```mermaid
stateDiagram-v2
    [*] --> Planning
    
    Planning --> IdentifyNeeds: Define filtering criteria
    IdentifyNeeds --> ChooseType: Binary vs Scoring decision
    
    ChooseType --> FilterImpl: Binary decision needed
    ChooseType --> ScorerImpl: Quality rating needed
    
    FilterImpl --> InheritURLFilter: Extend URLFilter base class
    ScorerImpl --> InheritURLScorer: Extend URLScorer base class
    
    InheritURLFilter --> ImplementApply: def apply(url) -> bool
    InheritURLScorer --> ImplementScore: def _calculate_score(url) -> float
    
    ImplementApply --> AddLogic: Add custom filtering logic
    ImplementScore --> AddLogic
    
    AddLogic --> TestFilter: Unit testing
    TestFilter --> OptimizePerf: Performance optimization
    
    OptimizePerf --> Integration: Integrate with FilterChain
    Integration --> Production: Deploy to production
    
    Production --> Monitor: Monitor performance
    Monitor --> Tune: Tune parameters
    Tune --> Production
    
    note right of Planning : Consider performance impact
    note right of AddLogic : Handle edge cases
    note right of OptimizePerf : Cache frequently accessed data
```

### Filter Chain Optimization Patterns

```mermaid
graph TB
    subgraph "Naive Approach - Poor Performance"
        A1[All URLs] --> B1[Slow Filter 1]
        B1 --> C1[Slow Filter 2]
        C1 --> D1[Fast Filter 1]
        D1 --> E1[Fast Filter 2]
        E1 --> F1[Final Results]
        
        B1 -.->|High CPU| G1[Performance Issues]
        C1 -.->|Network Calls| G1
    end
    
    subgraph "Optimized Approach - High Performance"
        A2[All URLs] --> B2[Fast Filter 1]
        B2 --> C2[Fast Filter 2]
        C2 --> D2[Batch Process]
        D2 --> E2[Slow Filter 1]
        E2 --> F2[Slow Filter 2]
        F2 --> G2[Final Results]
        
        D2 --> H2[Concurrent Processing]
        H2 --> I2[Semaphore Control]
    end
    
    subgraph "Performance Metrics"
        J[Processing Time]
        K[Memory Usage]
        L[CPU Utilization]
        M[Network Requests]
    end
    
    G1 -.-> J
    G1 -.-> K
    G1 -.-> L
    G1 -.-> M
    
    G2 -.-> J
    G2 -.-> K
    G2 -.-> L
    G2 -.-> M
    
    style A1 fill:#ffcdd2
    style G1 fill:#ffcdd2
    style A2 fill:#c8e6c9
    style G2 fill:#c8e6c9
    style H2 fill:#e8f5e8
    style I2 fill:#e8f5e8
```

### Composite Scoring Weight Distribution

```mermaid
pie title Composite Scorer Weight Distribution
    "Keyword Relevance (30%)" : 30
    "Domain Authority (25%)" : 25
    "Content Type (20%)" : 20
    "Freshness (15%)" : 15
    "Path Depth (10%)" : 10
```

### Deep Crawl Integration Architecture

```mermaid
graph TD
    subgraph "Deep Crawl Strategy"
        A[Start URL] --> B[Extract Links]
        B --> C[Apply Filter Chain]
        C --> D[Calculate Scores]
        D --> E[Priority Queue]
        E --> F[Crawl Next URL]
        F --> B
    end
    
    subgraph "Filter Chain Components"
        C --> C1[Domain Filter]
        C --> C2[Pattern Filter]
        C --> C3[Content Filter]
        C --> C4[SEO Filter]
        C --> C5[Relevance Filter]
    end
    
    subgraph "Scoring Components"
        D --> D1[Keyword Scorer]
        D --> D2[Depth Scorer]
        D --> D3[Freshness Scorer]
        D --> D4[Authority Scorer]
        D --> D5[Composite Score]
    end
    
    subgraph "Queue Management"
        E --> E1{Score > Threshold?}
        E1 -->|Yes| E2[High Priority Queue]
        E1 -->|No| E3[Low Priority Queue]
        E2 --> F
        E3 --> G[Delayed Processing]
    end
    
    subgraph "Control Flow"
        H{Max Depth Reached?}
        I{Max Pages Reached?}
        J[Stop Crawling]
    end
    
    F --> H
    H -->|No| I
    H -->|Yes| J
    I -->|No| B
    I -->|Yes| J
    
    style A fill:#e3f2fd
    style E2 fill:#c8e6c9
    style E3 fill:#fff3e0
    style J fill:#ffcdd2
```

### Filter Performance Comparison

```mermaid
xychart-beta
    title "Filter Performance Comparison (1000 URLs)"
    x-axis [Domain, Pattern, ContentType, SEO, Relevance]
    y-axis "Processing Time (ms)" 0 --> 1000
    bar [50, 80, 45, 300, 800]
```

### Scoring Algorithm Workflow

```mermaid
flowchart TD
    A[Input URL] --> B[Parse URL Components]
    B --> C[Extract Features]
    
    C --> D[Domain Analysis]
    C --> E[Path Analysis] 
    C --> F[Content Type Detection]
    C --> G[Keyword Extraction]
    C --> H[Freshness Detection]
    
    D --> I[Domain Authority Score]
    E --> J[Path Depth Score]
    F --> K[Content Type Score]
    G --> L[Keyword Relevance Score]
    H --> M[Freshness Score]
    
    I --> N[Apply Weights]
    J --> N
    K --> N
    L --> N
    M --> N
    
    N --> O[Normalize Scores]
    O --> P[Calculate Final Score]
    P --> Q{Score >= Threshold?}
    
    Q -->|Yes| R[Accept for Crawling]
    Q -->|No| S[Reject URL]
    
    R --> T[Add to Priority Queue]
    S --> U[Log Rejection Reason]
    
    style A fill:#e3f2fd
    style P fill:#fff3e0
    style R fill:#c8e6c9
    style S fill:#ffcdd2
    style T fill:#e8f5e8
```

**📖 Learn more:** [Deep Crawling Strategy](https://docs.crawl4ai.com/core/deep-crawling/), [Performance Optimization](https://docs.crawl4ai.com/advanced/performance-tuning/), [Custom Implementations](https://docs.crawl4ai.com/advanced/custom-filters/)