Wave 3: SSE structured logs, crash analyzer, session fingerprint
- Task #3: Update SSE stream to emit structured log events (type: "log" for entries, type: "metrics" every 5s, ?format=legacy for backward compat) - Task #10: Create crash pattern analyzer module (6 patterns: memory_exhaustion, dom_bloat, rate_limited, consent_loop, scroll_timeout, element_stale) (confidence scoring, auto-fix params, summarize_crash_patterns for recurring issues) - Task #13: Capture session fingerprint in backend (user_agent, platform, timezone, webgl, canvas, bot_detection_tests) (saved on success and failure for debugging) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Alejandro Gutiérrez
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modules/crash_analyzer.py
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666
modules/crash_analyzer.py
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"""
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Crash Pattern Analyzer Module
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Provides deep analysis of scraper crashes with pattern detection,
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confidence scoring, and auto-fix parameter suggestions.
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Builds on top of the basic classify_crash function in scraper_clean.py
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with more sophisticated pattern matching and multi-signal analysis.
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"""
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional
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import re
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@dataclass
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class CrashAnalysis:
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"""
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Result of crash pattern analysis.
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Attributes:
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pattern: The identified crash pattern type (e.g., "memory_exhaustion", "dom_bloat")
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confidence: Confidence score from 0.0 to 1.0 based on multiple signals
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description: Human-readable description of the crash cause
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suggested_fix: Recommended action to prevent this crash
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auto_fix_params: Parameters that can be applied automatically to prevent recurrence
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"""
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pattern: str # e.g., "memory_exhaustion", "dom_bloat", "rate_limited"
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confidence: float # 0.0 to 1.0
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description: str
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suggested_fix: str
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auto_fix_params: Optional[Dict[str, Any]]
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# Thresholds for pattern detection
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MEMORY_EXHAUSTION_THRESHOLD_MB = 1500 # 1.5GB in MB
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MEMORY_GROWTH_RATE_THRESHOLD_MB_S = 10 # 10MB/s
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DOM_BLOAT_THRESHOLD = 50000 # 50000 nodes
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SCROLL_TIMEOUT_MIN_SCROLLS = 10 # Minimum scrolls before considering scroll_timeout
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# Auto-fix parameters for each crash pattern
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AUTO_FIX_PARAMS = {
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"memory_exhaustion": {
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"max_reviews": 500,
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"restart_browser_after": 200
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},
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"dom_bloat": {
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"scroll_cleanup": True,
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"lazy_load": True
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},
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"rate_limited": {
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"delay_multiplier": 2.0,
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"use_different_proxy": True
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},
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"consent_loop": {
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"skip_consent_retries": True
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},
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"scroll_timeout": {
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"reduce_target": True,
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"target_reviews": "current - 10%"
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},
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"element_stale": {
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"retry_with_fresh_elements": True
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}
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}
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def _calculate_memory_growth_rate(metrics_history: List[Dict]) -> Optional[float]:
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"""
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Calculate memory growth rate in MB/s from metrics history.
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Args:
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metrics_history: List of metric samples with timestamp_ms and memory_mb
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Returns:
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Growth rate in MB/s, or None if cannot be calculated
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"""
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if not metrics_history or len(metrics_history) < 2:
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return None
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# Filter samples that have valid memory readings
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valid_samples = [
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m for m in metrics_history
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if m.get('memory_mb') is not None and m.get('timestamp_ms') is not None
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]
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if len(valid_samples) < 2:
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return None
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# Use first and last valid samples
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first = valid_samples[0]
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last = valid_samples[-1]
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time_delta_s = (last['timestamp_ms'] - first['timestamp_ms']) / 1000
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if time_delta_s <= 0:
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return None
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memory_delta_mb = last['memory_mb'] - first['memory_mb']
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return memory_delta_mb / time_delta_s
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def _get_max_memory(metrics_history: List[Dict]) -> Optional[int]:
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"""Get maximum memory usage from metrics history."""
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if not metrics_history:
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return None
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memories = [m.get('memory_mb') for m in metrics_history if m.get('memory_mb') is not None]
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return max(memories) if memories else None
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def _get_max_dom_nodes(metrics_history: List[Dict]) -> Optional[int]:
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"""Get maximum DOM node count from metrics history."""
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if not metrics_history:
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return None
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nodes = [m.get('dom_nodes') for m in metrics_history if m.get('dom_nodes') is not None]
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return max(nodes) if nodes else None
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def _check_memory_exhaustion(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict]
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) -> tuple[float, str]:
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"""
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Check for memory exhaustion pattern.
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check for high memory usage
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max_memory = _get_max_memory(metrics_history)
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if max_memory is not None:
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if max_memory >= MEMORY_EXHAUSTION_THRESHOLD_MB:
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confidence += 0.5
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signals.append(f"Memory reached {max_memory}MB (threshold: {MEMORY_EXHAUSTION_THRESHOLD_MB}MB)")
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elif max_memory >= MEMORY_EXHAUSTION_THRESHOLD_MB * 0.8:
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confidence += 0.3
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signals.append(f"Memory at {max_memory}MB approaching threshold")
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# Check for rapid memory growth
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growth_rate = _calculate_memory_growth_rate(metrics_history)
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if growth_rate is not None and growth_rate >= MEMORY_GROWTH_RATE_THRESHOLD_MB_S:
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confidence += 0.3
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signals.append(f"Memory growing at {growth_rate:.1f}MB/s (threshold: {MEMORY_GROWTH_RATE_THRESHOLD_MB_S}MB/s)")
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# Check error message for memory-related keywords
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error_lower = error_message.lower()
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memory_keywords = ['memory', 'heap', 'out of memory', 'oom', 'aw, snap', 'status_access_violation']
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for keyword in memory_keywords:
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if keyword in error_lower:
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confidence += 0.2
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signals.append(f"Error contains '{keyword}'")
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break
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# Check logs for memory warnings
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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if 'memory' in msg and ('high' in msg or 'warning' in msg or 'exceeded' in msg):
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confidence += 0.1
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signals.append("Memory warning found in logs")
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break
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description = "; ".join(signals) if signals else "No memory exhaustion signals detected"
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return min(confidence, 1.0), description
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def _check_dom_bloat(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict]
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) -> tuple[float, str]:
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"""
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Check for DOM bloat pattern.
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check for high DOM node count
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max_nodes = _get_max_dom_nodes(metrics_history)
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if max_nodes is not None:
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if max_nodes >= DOM_BLOAT_THRESHOLD:
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confidence += 0.6
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signals.append(f"DOM nodes reached {max_nodes} (threshold: {DOM_BLOAT_THRESHOLD})")
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elif max_nodes >= DOM_BLOAT_THRESHOLD * 0.8:
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confidence += 0.3
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signals.append(f"DOM nodes at {max_nodes} approaching threshold")
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# Check error message for DOM-related keywords
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error_lower = error_message.lower()
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dom_keywords = ['dom', 'element', 'node', 'render', 'paint', 'layout']
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for keyword in dom_keywords:
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if keyword in error_lower:
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confidence += 0.2
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signals.append(f"Error contains '{keyword}'")
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break
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# Check if memory is high too (DOM bloat often causes memory issues)
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max_memory = _get_max_memory(metrics_history)
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if max_memory is not None and max_memory >= 800: # 800MB
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confidence += 0.1
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signals.append(f"Memory also elevated ({max_memory}MB)")
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# Check logs for DOM-related messages
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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if 'dom' in msg and ('large' in msg or 'cleanup' in msg or 'remove' in msg):
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confidence += 0.1
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signals.append("DOM warning found in logs")
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break
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description = "; ".join(signals) if signals else "No DOM bloat signals detected"
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return min(confidence, 1.0), description
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def _check_rate_limited(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict]
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) -> tuple[float, str]:
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"""
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Check for rate limiting pattern.
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check error message for rate limit indicators
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error_lower = error_message.lower()
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if '429' in error_message:
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confidence += 0.6
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signals.append("HTTP 429 status code in error")
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rate_keywords = ['rate limit', 'too many requests', 'unusual traffic', 'captcha', 'blocked']
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for keyword in rate_keywords:
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if keyword in error_lower:
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confidence += 0.4
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signals.append(f"Error contains '{keyword}'")
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break
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# Check logs for rate limiting signals
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rate_log_count = 0
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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network = log_entry.get('network', {})
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status = network.get('status')
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if status == 429:
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rate_log_count += 1
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confidence += 0.2
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if 'unusual traffic' in msg or 'rate' in msg or 'blocked' in msg:
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rate_log_count += 1
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confidence += 0.1
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if rate_log_count > 0:
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signals.append(f"Found {rate_log_count} rate-limiting indicators in logs")
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description = "; ".join(signals) if signals else "No rate limiting signals detected"
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return min(confidence, 1.0), description
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def _check_consent_loop(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict]
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) -> tuple[float, str]:
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"""
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Check for consent popup loop pattern.
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check error message for consent keywords
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error_lower = error_message.lower()
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if 'consent' in error_lower:
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confidence += 0.3
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signals.append("Error mentions consent")
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# Count consent-related log entries
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consent_count = 0
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consent_messages = []
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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if 'consent' in msg:
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consent_count += 1
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consent_messages.append(msg[:50])
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# Multiple consent messages indicate a loop
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if consent_count >= 3:
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confidence += 0.5
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signals.append(f"Consent popup appeared {consent_count} times in logs")
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elif consent_count >= 2:
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confidence += 0.3
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signals.append(f"Consent popup appeared {consent_count} times")
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elif consent_count == 1:
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confidence += 0.1
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signals.append("Single consent popup detected")
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# Check for timeout after consent handling
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if 'timeout' in error_lower and consent_count > 0:
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confidence += 0.2
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signals.append("Timeout occurred with consent activity")
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description = "; ".join(signals) if signals else "No consent loop signals detected"
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return min(confidence, 1.0), description
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def _check_scroll_timeout(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict],
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state: Optional[Dict] = None
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) -> tuple[float, str]:
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"""
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Check for scroll timeout pattern (no new reviews after many scrolls).
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check state for scroll count
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scroll_count = 0
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reviews_count = 0
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if state:
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scroll_count = state.get('scroll_count', 0)
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reviews_count = state.get('reviews_extracted', 0)
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# Check error for timeout indicators
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error_lower = error_message.lower()
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if 'timeout' in error_lower:
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confidence += 0.2
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signals.append("Timeout in error message")
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# Count recovery attempts in logs (indicate stuck scrolling)
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recovery_count = 0
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no_new_count = 0
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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if 'recovery attempt' in msg:
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recovery_count += 1
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if 'no new' in msg or 'stuck' in msg:
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no_new_count += 1
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if recovery_count >= SCROLL_TIMEOUT_MIN_SCROLLS:
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confidence += 0.5
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signals.append(f"Made {recovery_count} recovery attempts")
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elif recovery_count >= 5:
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confidence += 0.3
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signals.append(f"Made {recovery_count} recovery attempts")
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if no_new_count > 0:
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confidence += 0.2
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signals.append(f"Found {no_new_count} 'no new reviews' log entries")
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# Check if reviews stopped growing
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if metrics_history and len(metrics_history) >= 5:
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# Check if reviews count plateaued
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recent_counts = [m.get('reviews_count', 0) for m in metrics_history[-5:] if m.get('reviews_count')]
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if recent_counts and len(set(recent_counts)) == 1:
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confidence += 0.2
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signals.append(f"Review count stuck at {recent_counts[0]}")
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description = "; ".join(signals) if signals else "No scroll timeout signals detected"
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return min(confidence, 1.0), description
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def _check_element_stale(
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error_message: str,
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metrics_history: List[Dict],
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logs: List[Dict]
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) -> tuple[float, str]:
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"""
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Check for stale element reference pattern.
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Returns:
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Tuple of (confidence, description)
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"""
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confidence = 0.0
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signals = []
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# Check error message for stale element indicators
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error_lower = error_message.lower()
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stale_keywords = [
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'stale element', 'staleelement', 'stale_element',
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'element is not attached', 'element reference',
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'no such element', 'element not found',
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'element is no longer valid'
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]
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for keyword in stale_keywords:
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if keyword in error_lower:
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confidence += 0.6
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signals.append(f"Error contains '{keyword}'")
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break
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# Check logs for stale element patterns
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stale_log_count = 0
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for log_entry in logs:
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msg = log_entry.get('message', '').lower()
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for keyword in stale_keywords:
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if keyword in msg:
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stale_log_count += 1
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break
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if stale_log_count > 0:
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confidence += 0.2
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signals.append(f"Found {stale_log_count} stale element references in logs")
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# Check if DOM was changing rapidly (indicates dynamic page)
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if metrics_history and len(metrics_history) >= 3:
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dom_counts = [m.get('dom_nodes') for m in metrics_history if m.get('dom_nodes')]
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if len(dom_counts) >= 3:
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# Calculate variance
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avg = sum(dom_counts) / len(dom_counts)
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variance = sum((x - avg) ** 2 for x in dom_counts) / len(dom_counts)
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std_dev = variance ** 0.5
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# High variance indicates rapidly changing DOM
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if std_dev > 1000:
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confidence += 0.2
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signals.append(f"High DOM variability (std dev: {std_dev:.0f})")
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description = "; ".join(signals) if signals else "No stale element signals detected"
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return min(confidence, 1.0), description
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def analyze_crash(crash_report: Dict) -> CrashAnalysis:
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"""
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Analyze a crash report to determine the most likely crash pattern.
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Examines error_message, metrics_history, and logs_before_crash to
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calculate confidence scores for each crash pattern type.
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Args:
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crash_report: Dictionary containing:
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- error_message: str - The exception message
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- metrics_history: List[Dict] - Sampled metrics with timestamp_ms, memory_mb, dom_nodes
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- logs_before_crash: List[Dict] - Recent log entries before the crash
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- state: Optional[Dict] - Scraper state (reviews_extracted, scroll_count, etc.)
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- crash_type: Optional[str] - Basic crash classification from classify_crash()
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Returns:
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CrashAnalysis with the highest-confidence pattern match
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"""
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# Extract data from crash report
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error_message = crash_report.get('error_message', '')
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metrics_history = crash_report.get('metrics_history', [])
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logs = crash_report.get('logs_before_crash', [])
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state = crash_report.get('state', {})
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basic_type = crash_report.get('crash_type', 'unknown')
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# Run all pattern checks
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pattern_results = {}
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# Memory exhaustion
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conf, desc = _check_memory_exhaustion(error_message, metrics_history, logs)
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pattern_results['memory_exhaustion'] = (conf, desc)
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# DOM bloat
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conf, desc = _check_dom_bloat(error_message, metrics_history, logs)
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pattern_results['dom_bloat'] = (conf, desc)
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# Rate limited
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conf, desc = _check_rate_limited(error_message, metrics_history, logs)
|
||||
pattern_results['rate_limited'] = (conf, desc)
|
||||
|
||||
# Consent loop
|
||||
conf, desc = _check_consent_loop(error_message, metrics_history, logs)
|
||||
pattern_results['consent_loop'] = (conf, desc)
|
||||
|
||||
# Scroll timeout
|
||||
conf, desc = _check_scroll_timeout(error_message, metrics_history, logs, state)
|
||||
pattern_results['scroll_timeout'] = (conf, desc)
|
||||
|
||||
# Element stale
|
||||
conf, desc = _check_element_stale(error_message, metrics_history, logs)
|
||||
pattern_results['element_stale'] = (conf, desc)
|
||||
|
||||
# Find the pattern with highest confidence
|
||||
best_pattern = max(pattern_results.items(), key=lambda x: x[1][0])
|
||||
pattern_name = best_pattern[0]
|
||||
confidence = best_pattern[1][0]
|
||||
description = best_pattern[1][1]
|
||||
|
||||
# If confidence is too low, fall back to basic classification
|
||||
if confidence < 0.2:
|
||||
# Map basic crash types to our patterns
|
||||
basic_to_pattern = {
|
||||
'memory_exhaustion': 'memory_exhaustion',
|
||||
'tab_crash': 'memory_exhaustion', # Tab crashes often from memory
|
||||
'timeout': 'scroll_timeout',
|
||||
'element_not_found': 'element_stale',
|
||||
'rate_limited': 'rate_limited',
|
||||
'network_failure': 'rate_limited', # Could be blocking
|
||||
}
|
||||
|
||||
if basic_type in basic_to_pattern:
|
||||
pattern_name = basic_to_pattern[basic_type]
|
||||
confidence = 0.3 # Low confidence fallback
|
||||
description = f"Inferred from basic crash type '{basic_type}'"
|
||||
else:
|
||||
pattern_name = 'unknown'
|
||||
confidence = 0.0
|
||||
description = f"Unable to determine crash pattern (basic type: {basic_type})"
|
||||
|
||||
# Generate suggested fix based on pattern
|
||||
suggested_fixes = {
|
||||
'memory_exhaustion': (
|
||||
"Reduce batch size and restart browser more frequently. "
|
||||
"Consider limiting max_reviews to 500 and restarting browser after every 200 reviews."
|
||||
),
|
||||
'dom_bloat': (
|
||||
"Enable DOM cleanup during scrolling. "
|
||||
"Hide processed review cards and remove separator elements to keep DOM light."
|
||||
),
|
||||
'rate_limited': (
|
||||
"Increase delays between requests and consider rotating proxies. "
|
||||
"Double the delay multiplier and switch to a different proxy if available."
|
||||
),
|
||||
'consent_loop': (
|
||||
"Skip consent handling after initial attempt to avoid infinite loops. "
|
||||
"The consent popup may be appearing due to cookie clearing or navigation issues."
|
||||
),
|
||||
'scroll_timeout': (
|
||||
"The page may have stopped loading new reviews. "
|
||||
"Try reducing the target review count by 10% and accepting partial results."
|
||||
),
|
||||
'element_stale': (
|
||||
"Page elements are being removed/replaced during scraping. "
|
||||
"Retry operations with freshly-located elements and add defensive waits."
|
||||
),
|
||||
'unknown': (
|
||||
"Unable to determine specific crash cause. "
|
||||
"Review logs and consider restarting with fresh browser session."
|
||||
)
|
||||
}
|
||||
|
||||
suggested_fix = suggested_fixes.get(pattern_name, suggested_fixes['unknown'])
|
||||
auto_fix_params = AUTO_FIX_PARAMS.get(pattern_name)
|
||||
|
||||
return CrashAnalysis(
|
||||
pattern=pattern_name,
|
||||
confidence=confidence,
|
||||
description=description,
|
||||
suggested_fix=suggested_fix,
|
||||
auto_fix_params=auto_fix_params
|
||||
)
|
||||
|
||||
|
||||
def get_auto_fix_params(pattern: str) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Get auto-fix parameters for a specific crash pattern.
|
||||
|
||||
Args:
|
||||
pattern: The crash pattern name
|
||||
|
||||
Returns:
|
||||
Dictionary of auto-fix parameters, or None if pattern not recognized
|
||||
"""
|
||||
return AUTO_FIX_PARAMS.get(pattern)
|
||||
|
||||
|
||||
def apply_auto_fix(pattern: str, current_params: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Apply auto-fix parameters to current scraper parameters.
|
||||
|
||||
Args:
|
||||
pattern: The crash pattern name
|
||||
current_params: Current scraper parameters to modify
|
||||
|
||||
Returns:
|
||||
Updated parameters dictionary with fixes applied
|
||||
"""
|
||||
fix_params = AUTO_FIX_PARAMS.get(pattern, {})
|
||||
updated = current_params.copy()
|
||||
|
||||
for key, value in fix_params.items():
|
||||
if key == 'target_reviews' and value == 'current - 10%':
|
||||
# Special case: reduce target by 10%
|
||||
current_target = updated.get('max_reviews', 1000)
|
||||
updated['max_reviews'] = int(current_target * 0.9)
|
||||
elif key == 'delay_multiplier':
|
||||
# Multiply existing delay
|
||||
current_delay = updated.get('scroll_delay', 1.0)
|
||||
updated['scroll_delay'] = current_delay * value
|
||||
else:
|
||||
updated[key] = value
|
||||
|
||||
return updated
|
||||
|
||||
|
||||
def summarize_crash_patterns(crash_reports: List[Dict]) -> Dict[str, Any]:
|
||||
"""
|
||||
Analyze multiple crash reports to identify recurring patterns.
|
||||
|
||||
Args:
|
||||
crash_reports: List of crash report dictionaries
|
||||
|
||||
Returns:
|
||||
Summary dictionary with pattern frequencies and recommendations
|
||||
"""
|
||||
if not crash_reports:
|
||||
return {
|
||||
'total_crashes': 0,
|
||||
'patterns': {},
|
||||
'most_common': None,
|
||||
'recommendations': []
|
||||
}
|
||||
|
||||
pattern_counts: Dict[str, int] = {}
|
||||
pattern_confidences: Dict[str, List[float]] = {}
|
||||
|
||||
for report in crash_reports:
|
||||
analysis = analyze_crash(report)
|
||||
pattern = analysis.pattern
|
||||
|
||||
pattern_counts[pattern] = pattern_counts.get(pattern, 0) + 1
|
||||
if pattern not in pattern_confidences:
|
||||
pattern_confidences[pattern] = []
|
||||
pattern_confidences[pattern].append(analysis.confidence)
|
||||
|
||||
# Calculate average confidence per pattern
|
||||
patterns_summary = {}
|
||||
for pattern, count in pattern_counts.items():
|
||||
avg_confidence = sum(pattern_confidences[pattern]) / len(pattern_confidences[pattern])
|
||||
patterns_summary[pattern] = {
|
||||
'count': count,
|
||||
'percentage': count / len(crash_reports) * 100,
|
||||
'avg_confidence': avg_confidence
|
||||
}
|
||||
|
||||
# Find most common pattern
|
||||
most_common = max(pattern_counts.items(), key=lambda x: x[1])[0] if pattern_counts else None
|
||||
|
||||
# Generate recommendations
|
||||
recommendations = []
|
||||
for pattern, stats in sorted(patterns_summary.items(), key=lambda x: x[1]['count'], reverse=True):
|
||||
if stats['count'] >= 2: # Only recommend for recurring patterns
|
||||
fix_params = AUTO_FIX_PARAMS.get(pattern)
|
||||
if fix_params:
|
||||
recommendations.append({
|
||||
'pattern': pattern,
|
||||
'occurrences': stats['count'],
|
||||
'auto_fix_params': fix_params
|
||||
})
|
||||
|
||||
return {
|
||||
'total_crashes': len(crash_reports),
|
||||
'patterns': patterns_summary,
|
||||
'most_common': most_common,
|
||||
'recommendations': recommendations
|
||||
}
|
||||
@@ -430,6 +430,47 @@ class DatabaseManager:
|
||||
|
||||
log.debug(f"Incremental save: {len(reviews)} reviews for job {job_id}")
|
||||
|
||||
async def update_session_fingerprint(
|
||||
self,
|
||||
job_id: UUID,
|
||||
session_fingerprint: Dict[str, Any]
|
||||
):
|
||||
"""
|
||||
Update the session fingerprint for a job.
|
||||
|
||||
This should be called early in the scraping process after the browser
|
||||
fingerprint is captured, to record browser characteristics for
|
||||
bot detection analysis.
|
||||
|
||||
Args:
|
||||
job_id: Job UUID
|
||||
session_fingerprint: Dictionary containing browser fingerprint data:
|
||||
- user_agent: Browser user agent string
|
||||
- platform: OS platform
|
||||
- language: Primary language
|
||||
- languages: List of accepted languages
|
||||
- timezone: Timezone string
|
||||
- screen: {width, height, colorDepth}
|
||||
- viewport: {width, height}
|
||||
- webgl_vendor: WebGL vendor string
|
||||
- webgl_renderer: WebGL renderer string
|
||||
- canvas_fingerprint: Canvas fingerprint hash
|
||||
- hardware_concurrency: Number of CPU cores
|
||||
- device_memory: Device memory in GB
|
||||
- bot_detection_tests: {webdriver_hidden, chrome_runtime, permissions_query}
|
||||
- captured_at: ISO timestamp when fingerprint was captured
|
||||
"""
|
||||
async with self.pool.acquire() as conn:
|
||||
await conn.execute("""
|
||||
UPDATE jobs
|
||||
SET
|
||||
session_fingerprint = $2::jsonb,
|
||||
updated_at = NOW()
|
||||
WHERE job_id = $1
|
||||
""", job_id, json.dumps(session_fingerprint))
|
||||
|
||||
log.debug(f"Updated session fingerprint for job {job_id}")
|
||||
|
||||
async def mark_job_partial(
|
||||
self,
|
||||
job_id: UUID,
|
||||
|
||||
@@ -35,6 +35,214 @@ def get_dom_node_count(driver) -> Optional[int]:
|
||||
return None
|
||||
|
||||
|
||||
def capture_session_fingerprint(driver) -> dict:
|
||||
"""
|
||||
Capture browser session fingerprint for bot detection analysis.
|
||||
|
||||
This captures various browser attributes that can be used to:
|
||||
1. Verify bot detection evasion is working
|
||||
2. Debug issues when scraping fails
|
||||
3. Track session characteristics for analysis
|
||||
|
||||
Args:
|
||||
driver: Selenium WebDriver instance (must be initialized)
|
||||
|
||||
Returns:
|
||||
Dictionary containing session fingerprint data
|
||||
"""
|
||||
fingerprint = {
|
||||
"user_agent": None,
|
||||
"platform": None,
|
||||
"language": None,
|
||||
"languages": None,
|
||||
"timezone": None,
|
||||
"screen": {
|
||||
"width": None,
|
||||
"height": None,
|
||||
"colorDepth": None
|
||||
},
|
||||
"viewport": {
|
||||
"width": None,
|
||||
"height": None
|
||||
},
|
||||
"webgl_vendor": None,
|
||||
"webgl_renderer": None,
|
||||
"canvas_fingerprint": None,
|
||||
"hardware_concurrency": None,
|
||||
"device_memory": None,
|
||||
"bot_detection_tests": {
|
||||
"webdriver_hidden": None,
|
||||
"chrome_runtime": None,
|
||||
"permissions_query": None
|
||||
},
|
||||
"captured_at": None
|
||||
}
|
||||
|
||||
try:
|
||||
# Navigate to about:blank first to ensure we can execute JS
|
||||
# (in case driver was just created and hasn't navigated yet)
|
||||
current_url = driver.current_url
|
||||
if not current_url or current_url == "data:,":
|
||||
driver.get("about:blank")
|
||||
|
||||
# Capture timestamp
|
||||
fingerprint["captured_at"] = datetime.now().isoformat()
|
||||
|
||||
# Basic navigator properties
|
||||
try:
|
||||
fingerprint["user_agent"] = driver.execute_script("return navigator.userAgent")
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
fingerprint["platform"] = driver.execute_script("return navigator.platform")
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
fingerprint["language"] = driver.execute_script("return navigator.language")
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
fingerprint["languages"] = driver.execute_script("return navigator.languages")
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
fingerprint["timezone"] = driver.execute_script(
|
||||
"return Intl.DateTimeFormat().resolvedOptions().timeZone"
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
# Screen properties
|
||||
try:
|
||||
fingerprint["screen"]["width"] = driver.execute_script("return screen.width")
|
||||
fingerprint["screen"]["height"] = driver.execute_script("return screen.height")
|
||||
fingerprint["screen"]["colorDepth"] = driver.execute_script("return screen.colorDepth")
|
||||
except:
|
||||
pass
|
||||
|
||||
# Viewport properties
|
||||
try:
|
||||
fingerprint["viewport"]["width"] = driver.execute_script("return window.innerWidth")
|
||||
fingerprint["viewport"]["height"] = driver.execute_script("return window.innerHeight")
|
||||
except:
|
||||
pass
|
||||
|
||||
# WebGL vendor and renderer (important for fingerprinting)
|
||||
try:
|
||||
webgl_info = driver.execute_script("""
|
||||
try {
|
||||
var canvas = document.createElement('canvas');
|
||||
var gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl');
|
||||
if (gl) {
|
||||
var debugInfo = gl.getExtension('WEBGL_debug_renderer_info');
|
||||
if (debugInfo) {
|
||||
return {
|
||||
vendor: gl.getParameter(debugInfo.UNMASKED_VENDOR_WEBGL),
|
||||
renderer: gl.getParameter(debugInfo.UNMASKED_RENDERER_WEBGL)
|
||||
};
|
||||
}
|
||||
}
|
||||
} catch(e) {}
|
||||
return {vendor: null, renderer: null};
|
||||
""")
|
||||
fingerprint["webgl_vendor"] = webgl_info.get("vendor")
|
||||
fingerprint["webgl_renderer"] = webgl_info.get("renderer")
|
||||
except:
|
||||
pass
|
||||
|
||||
# Canvas fingerprint (hash of canvas drawing)
|
||||
try:
|
||||
canvas_hash = driver.execute_script("""
|
||||
try {
|
||||
var canvas = document.createElement('canvas');
|
||||
canvas.width = 200;
|
||||
canvas.height = 50;
|
||||
var ctx = canvas.getContext('2d');
|
||||
ctx.textBaseline = 'top';
|
||||
ctx.font = '14px Arial';
|
||||
ctx.fillStyle = '#f60';
|
||||
ctx.fillRect(125, 1, 62, 20);
|
||||
ctx.fillStyle = '#069';
|
||||
ctx.fillText('Fingerprint', 2, 15);
|
||||
ctx.fillStyle = 'rgba(102, 204, 0, 0.7)';
|
||||
ctx.fillText('Fingerprint', 4, 17);
|
||||
var dataUrl = canvas.toDataURL();
|
||||
// Simple hash
|
||||
var hash = 0;
|
||||
for (var i = 0; i < dataUrl.length; i++) {
|
||||
var char = dataUrl.charCodeAt(i);
|
||||
hash = ((hash << 5) - hash) + char;
|
||||
hash = hash & hash;
|
||||
}
|
||||
return hash.toString(16);
|
||||
} catch(e) {
|
||||
return null;
|
||||
}
|
||||
""")
|
||||
fingerprint["canvas_fingerprint"] = canvas_hash
|
||||
except:
|
||||
pass
|
||||
|
||||
# Hardware info
|
||||
try:
|
||||
fingerprint["hardware_concurrency"] = driver.execute_script(
|
||||
"return navigator.hardwareConcurrency"
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
fingerprint["device_memory"] = driver.execute_script(
|
||||
"return navigator.deviceMemory"
|
||||
)
|
||||
except:
|
||||
pass
|
||||
|
||||
# Bot detection tests
|
||||
try:
|
||||
# Test 1: webdriver property should be hidden/false for undetected Chrome
|
||||
webdriver_hidden = driver.execute_script(
|
||||
"return navigator.webdriver === undefined || navigator.webdriver === false"
|
||||
)
|
||||
fingerprint["bot_detection_tests"]["webdriver_hidden"] = webdriver_hidden
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
# Test 2: chrome runtime should exist in real Chrome
|
||||
chrome_runtime = driver.execute_script(
|
||||
"return typeof window.chrome !== 'undefined'"
|
||||
)
|
||||
fingerprint["bot_detection_tests"]["chrome_runtime"] = chrome_runtime
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
# Test 3: permissions.query should work in real Chrome
|
||||
permissions_query = driver.execute_script("""
|
||||
try {
|
||||
if (navigator.permissions && navigator.permissions.query) {
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
} catch(e) {
|
||||
return false;
|
||||
}
|
||||
""")
|
||||
fingerprint["bot_detection_tests"]["permissions_query"] = permissions_query
|
||||
except:
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
fingerprint["capture_error"] = str(e)
|
||||
|
||||
return fingerprint
|
||||
|
||||
|
||||
def classify_crash(exception: Exception, metrics_history: list) -> str:
|
||||
"""Classify crash type based on exception and metrics."""
|
||||
error_str = str(exception).lower()
|
||||
@@ -519,6 +727,16 @@ def scrape_reviews(driver, url: str, max_reviews: int = 5000, timeout_no_new: in
|
||||
# Use provided log_capture or create a dummy that just prints
|
||||
log = log_capture or LogCapture()
|
||||
|
||||
# Capture session fingerprint early (before navigation) for bot detection analysis
|
||||
session_fingerprint = capture_session_fingerprint(driver)
|
||||
log.info('browser', "Session fingerprint captured", metrics={
|
||||
'user_agent': session_fingerprint.get('user_agent', 'unknown')[:50] + '...' if session_fingerprint.get('user_agent') else 'unknown',
|
||||
'platform': session_fingerprint.get('platform'),
|
||||
'timezone': session_fingerprint.get('timezone'),
|
||||
'webdriver_hidden': session_fingerprint.get('bot_detection_tests', {}).get('webdriver_hidden'),
|
||||
'chrome_runtime': session_fingerprint.get('bot_detection_tests', {}).get('chrome_runtime')
|
||||
})
|
||||
|
||||
# Storage - use review ID as key
|
||||
reviews = {} # review_id -> review
|
||||
seen_ids = set() # Track all IDs we've seen (persists after flush)
|
||||
@@ -946,11 +1164,12 @@ def scrape_reviews(driver, url: str, max_reviews: int = 5000, timeout_no_new: in
|
||||
"category": business_info.get("category"),
|
||||
"address": business_info.get("address"),
|
||||
"total_reviews": total_reviews[0]
|
||||
}
|
||||
},
|
||||
"session_fingerprint": session_fingerprint # Browser fingerprint for bot detection analysis
|
||||
}
|
||||
|
||||
if not scroll_container:
|
||||
return {"reviews": [], "total": 0, "scrolls": 0, "error": "No scroll container found"}
|
||||
return {"reviews": [], "total": 0, "scrolls": 0, "error": "No scroll container found", "session_fingerprint": session_fingerprint}
|
||||
|
||||
# Extract review topics after reviews tab is loaded (before scrolling begins)
|
||||
time.sleep(0.5) # Brief wait for topic filters to render
|
||||
@@ -1408,7 +1627,8 @@ def scrape_reviews(driver, url: str, max_reviews: int = 5000, timeout_no_new: in
|
||||
"logs": log.get_logs(),
|
||||
"review_topics": review_topics, # Topic filters with mention counts
|
||||
"metrics_history": metrics_history, # For crash detection
|
||||
"start_time": start_time # For crash report elapsed time
|
||||
"start_time": start_time, # For crash report elapsed time
|
||||
"session_fingerprint": session_fingerprint # Browser fingerprint for bot detection analysis
|
||||
}
|
||||
|
||||
|
||||
@@ -1544,7 +1764,8 @@ def fast_scrape_reviews(url: str, headless: bool = False, max_scrolls: int = 999
|
||||
"success": True,
|
||||
"error": None,
|
||||
"logs": result.get("logs", []),
|
||||
"review_topics": result.get("review_topics", []) # Topic filters with mention counts
|
||||
"review_topics": result.get("review_topics", []), # Topic filters with mention counts
|
||||
"session_fingerprint": result.get("session_fingerprint") # Browser fingerprint for bot detection
|
||||
}
|
||||
|
||||
# Include validation_info if in validation_only mode
|
||||
|
||||
Reference in New Issue
Block a user