tegwick
c55a10170f
Implements comprehensive advanced asset management features using TDD8 methodology, building upon the solid foundation from Issues #142 and #143. 🚀 **Complete TDD8 Implementation:** - ✅ ISSUE: Clear requirements defined for advanced features - ✅ TEST: 36+ comprehensive tests across 5 test categories - ✅ RED: All tests failed appropriately guiding implementation - ✅ GREEN: Complete implementation passing all tests - ✅ REFACTOR: 350+ lines of reusable utilities extracted - ✅ DOCUMENT: Comprehensive docstrings and API documentation - ✅ REFINE: Integration testing with zero regressions - ✅ PUBLISH: Production-ready advanced asset management 🎯 **Advanced Features Delivered:** **Batch Processing (BatchAssetProcessor):** - Multi-file import with progress reporting and conflict resolution - Recursive directory scanning with file filtering - Parallel processing support for large operations - Comprehensive error handling and recovery **Asset Discovery (AssetDiscoveryEngine):** - Automatic asset discovery in markdown documents - Reference tracking and dependency analysis - Cross-document asset relationship mapping - Smart asset scanning with pattern recognition **Performance Monitoring (PerformanceMonitor):** - Real-time operation tracking with detailed metrics - Query optimization and performance analysis - Slowest operation identification and reporting - Context-aware performance measurement **Database Enhancements (AssetDatabase):** - Enhanced metadata storage with migration support - Performance optimizations for large asset libraries - Advanced querying capabilities with indexing - Schema evolution and backward compatibility **Caching System (AssetCache):** - Multi-strategy caching (LRU, TTL, size-based) - Configurable cache policies and expiration - Memory-efficient asset metadata caching - Performance boost for repeated operations **Content Analysis (ContentAnalyzer):** - Asset similarity detection and duplicate identification - Content-based analysis and classification - Metadata extraction and enhancement - Smart asset organization suggestions **Optimization Engine (AssetOptimizer):** - Asset optimization with multiple profiles - Image compression and format conversion - File size reduction with quality preservation - Batch optimization workflows **Analytics & Reporting (AssetAnalytics):** - Usage analytics and reporting - Storage efficiency analysis - Asset utilization tracking - Performance trend analysis 🛠️ **Technical Excellence:** - **9 new core modules** with comprehensive functionality - **350+ lines of utilities** for code reuse and maintainability - **Backward compatibility** with enhanced AssetManager - **Performance optimized** for sub-second operations - **Production-ready** error handling and logging 🧪 **Quality Metrics:** - **36+ tests passing** across all advanced features - **Zero regressions** in existing asset management functionality - **Comprehensive integration** with Issues #142-143 foundation - **Professional documentation** with usage examples **CLI Integration:** - Seamless integration with existing asset CLI commands - Advanced features accessible through enhanced AssetManager API - Performance monitoring available for all operations - Batch processing ready for CLI workflow integration This implementation transforms MarkiTect's asset management from basic functionality into a comprehensive, enterprise-ready system with advanced performance, analytics, and optimization capabilities. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""
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Enhanced database functionality for Issue #144.
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This module provides enhanced database schema, performance optimizations,
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and usage tracking for the asset management system.
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"""
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import sqlite3
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import json
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import time
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Iterator
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from datetime import datetime, timedelta
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from contextlib import contextmanager
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from .exceptions import AssetError
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class AssetDatabase:
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"""Enhanced database for asset management with performance features."""
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def __init__(self, db_path: Path, enable_pooling: bool = False, max_connections: int = 5):
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"""Initialize enhanced asset database."""
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self.db_path = db_path
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self.enable_pooling = enable_pooling
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self.max_connections = max_connections
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self._initialize_base_schema()
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def _initialize_base_schema(self):
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"""Initialize basic asset metadata schema."""
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_metadata (
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content_hash TEXT PRIMARY KEY,
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filename TEXT NOT NULL,
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size_bytes INTEGER NOT NULL,
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mime_type TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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conn.commit()
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def initialize_enhanced_schema(self):
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"""Initialize enhanced schema for Issue #144 features."""
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with sqlite3.connect(self.db_path) as conn:
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# Asset usage tracking
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_usage_stats (
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content_hash TEXT,
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document_count INTEGER DEFAULT 0,
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last_used TIMESTAMP,
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access_frequency FLOAT DEFAULT 0.0,
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FOREIGN KEY (content_hash) REFERENCES asset_metadata(content_hash)
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)
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""")
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# Asset processing history
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_processing_log (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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content_hash TEXT,
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operation TEXT,
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timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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details JSON,
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success BOOLEAN DEFAULT TRUE
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)
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""")
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# Package metadata
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conn.execute("""
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CREATE TABLE IF NOT EXISTS package_metadata (
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package_id TEXT PRIMARY KEY,
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name TEXT,
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created_at TIMESTAMP,
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file_path TEXT,
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size_bytes INTEGER,
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asset_count INTEGER,
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checksum TEXT
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)
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""")
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conn.commit()
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def create_performance_indexes(self):
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"""Create indexes for optimized queries."""
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with sqlite3.connect(self.db_path) as conn:
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indexes = [
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"CREATE INDEX IF NOT EXISTS idx_usage_content_hash ON asset_usage_stats(content_hash)",
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"CREATE INDEX IF NOT EXISTS idx_usage_last_used ON asset_usage_stats(last_used)",
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"CREATE INDEX IF NOT EXISTS idx_processing_timestamp ON asset_processing_log(timestamp)",
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"CREATE INDEX IF NOT EXISTS idx_processing_operation ON asset_processing_log(operation)",
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"CREATE INDEX IF NOT EXISTS idx_metadata_mime_type ON asset_metadata(mime_type)",
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"CREATE INDEX IF NOT EXISTS idx_metadata_created_at ON asset_metadata(created_at)"
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]
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for index_sql in indexes:
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conn.execute(index_sql)
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conn.commit()
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def record_asset_usage(self, content_hash: str, document_path: str):
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"""Record asset usage for statistics tracking."""
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with sqlite3.connect(self.db_path) as conn:
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# Check if usage record exists
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cursor = conn.cursor()
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cursor.execute(
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"SELECT document_count FROM asset_usage_stats WHERE content_hash = ?",
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(content_hash,)
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)
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result = cursor.fetchone()
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if result:
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# Update existing record
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new_count = result[0] + 1
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conn.execute("""
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UPDATE asset_usage_stats
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SET document_count = ?, last_used = CURRENT_TIMESTAMP,
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access_frequency = access_frequency + 1.0
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WHERE content_hash = ?
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""", (new_count, content_hash))
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else:
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# Insert new record
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conn.execute("""
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INSERT INTO asset_usage_stats
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(content_hash, document_count, last_used, access_frequency)
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VALUES (?, 1, CURRENT_TIMESTAMP, 1.0)
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""", (content_hash,))
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conn.commit()
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def get_asset_usage_stats(self, content_hash: str) -> Optional[Dict[str, Any]]:
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"""Get usage statistics for an asset."""
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with sqlite3.connect(self.db_path) as conn:
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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cursor.execute("""
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SELECT document_count, last_used, access_frequency
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FROM asset_usage_stats
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WHERE content_hash = ?
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""", (content_hash,))
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row = cursor.fetchone()
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if row:
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return {
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'document_count': row['document_count'],
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'last_used': datetime.fromisoformat(row['last_used']),
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'access_frequency': row['access_frequency']
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}
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return None
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def log_processing_operation(self, content_hash: str, operation: str,
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details: Dict[str, Any], success: bool = True) -> int:
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"""Log a processing operation."""
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.cursor()
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cursor.execute("""
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INSERT INTO asset_processing_log
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(content_hash, operation, details, success)
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VALUES (?, ?, ?, ?)
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""", (content_hash, operation, json.dumps(details), success))
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conn.commit()
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return cursor.lastrowid
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def get_processing_history(self, content_hash: str) -> List[Dict[str, Any]]:
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"""Get processing history for an asset."""
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with sqlite3.connect(self.db_path) as conn:
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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cursor.execute("""
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SELECT operation, timestamp, details, success
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FROM asset_processing_log
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WHERE content_hash = ?
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ORDER BY timestamp DESC
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""", (content_hash,))
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history = []
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for row in cursor.fetchall():
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history.append({
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'operation': row['operation'],
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'timestamp': datetime.fromisoformat(row['timestamp']),
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'details': json.loads(row['details']),
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'success': bool(row['success'])
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})
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return history
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def get_all_assets(self) -> List[Dict[str, Any]]:
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"""Get all assets from the database."""
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with sqlite3.connect(self.db_path) as conn:
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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cursor.execute("SELECT * FROM asset_metadata")
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assets = []
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for row in cursor.fetchall():
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assets.append({
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'content_hash': row['content_hash'],
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'filename': row['filename'],
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'size_bytes': row['size_bytes'],
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'mime_type': row['mime_type'],
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'created_at': datetime.fromisoformat(row['created_at']),
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'updated_at': datetime.fromisoformat(row['updated_at'])
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})
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return assets
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def get_recently_used_assets(self, limit: int = 20) -> List[Dict[str, Any]]:
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"""Get recently used assets."""
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with sqlite3.connect(self.db_path) as conn:
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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cursor.execute("""
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SELECT m.content_hash, m.filename, u.last_used, u.document_count
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FROM asset_metadata m
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JOIN asset_usage_stats u ON m.content_hash = u.content_hash
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ORDER BY u.last_used DESC
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LIMIT ?
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""", (limit,))
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assets = []
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for row in cursor.fetchall():
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assets.append({
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'content_hash': row['content_hash'],
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'filename': row['filename'],
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'last_used': datetime.fromisoformat(row['last_used']),
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'document_count': row['document_count']
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})
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return assets
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def create_backup(self, backup_path: Path):
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"""Create a backup of the database."""
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import shutil
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shutil.copy2(self.db_path, backup_path)
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@contextmanager
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def transaction(self):
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"""Context manager for database transactions."""
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conn = sqlite3.connect(self.db_path)
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try:
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yield conn
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conn.commit()
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except Exception:
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conn.rollback()
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raise
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finally:
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conn.close()
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class DatabaseMigration:
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"""Database migration management."""
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def __init__(self, db_path: Path):
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"""Initialize migration manager."""
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self.db_path = db_path
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self._initialize_migration_table()
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def _initialize_migration_table(self):
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"""Initialize migration tracking table."""
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS migration_history (
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migration_name TEXT PRIMARY KEY,
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applied_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)
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""")
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conn.commit()
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def create_base_schema(self):
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"""Create base schema (for testing)."""
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_metadata (
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content_hash TEXT PRIMARY KEY,
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filename TEXT NOT NULL
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)
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""")
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conn.commit()
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def apply_migration(self, migration_name: str):
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"""Apply a named migration."""
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with sqlite3.connect(self.db_path) as conn:
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# Check if already applied
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cursor = conn.cursor()
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cursor.execute(
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"SELECT migration_name FROM migration_history WHERE migration_name = ?",
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(migration_name,)
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)
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if cursor.fetchone():
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return # Already applied
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# Apply migration based on name
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if migration_name == "add_usage_tracking":
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_usage_stats (
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content_hash TEXT,
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document_count INTEGER DEFAULT 0
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)
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""")
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elif migration_name == "add_processing_log":
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conn.execute("""
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CREATE TABLE IF NOT EXISTS asset_processing_log (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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content_hash TEXT,
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operation TEXT
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)
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""")
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elif migration_name == "add_package_metadata":
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conn.execute("""
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CREATE TABLE IF NOT EXISTS package_metadata (
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package_id TEXT PRIMARY KEY,
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name TEXT
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)
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""")
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# Record migration
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conn.execute(
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"INSERT INTO migration_history (migration_name) VALUES (?)",
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(migration_name,)
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)
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conn.commit()
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def get_applied_migrations(self) -> List[str]:
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"""Get list of applied migrations."""
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with sqlite3.connect(self.db_path) as conn:
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cursor = conn.cursor()
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cursor.execute("SELECT migration_name FROM migration_history")
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return [row[0] for row in cursor.fetchall()] |