Performance and Reliability Optimization #7

New Issue

peterwood · 2025-10-29T19:44:55-07:00

peterwood commented

2025-10-29 19:44:55 -07:00

Originally created by @acedanger on GitHub (May 27, 2025).

Performance Optimization and Enhancement

Issue Summary

Optimize the Telegram bot performance, enhance user experience, and implement advanced features including caching, performance monitoring, auto-scaling, and intelligent response optimization.

Description

Develop performance optimizations and advanced features to ensure the Telegram bot operates efficiently under various load conditions, provides responsive user interactions, and includes intelligent features like auto-completion, command suggestions, and performance analytics.

Requirements

Performance Optimization

Implement response caching for frequently requested data
Optimize JSON log parsing with streaming and indexing
Add connection pooling for webhook and API calls
Implement lazy loading for large datasets
Add response compression for large messages
Optimize database/file I/O operations

User Experience Enhancements

Command auto-completion and suggestions
Interactive inline keyboards for complex operations
Progress indicators for long-running operations
Smart command aliases and shortcuts
Context-aware help system
Message formatting improvements

Advanced Features

Multi-language support framework
Voice message support for status reports
Image/chart generation for performance visualizations
Backup system health scoring with visual indicators
Smart notifications based on user activity patterns
Integration with external monitoring tools

Performance Monitoring

Bot performance metrics tracking
Response time monitoring
Memory and CPU usage tracking
Error rate monitoring
User engagement analytics
Command usage statistics

Technical Implementation

Caching System

import asyncio
from functools import lru_cache
import redis

class SmartCache:
    def __init__(self):
        self.redis_client = redis.Redis()
        self.local_cache = {}
        self.cache_ttl = {
            'status': 60,      # 1 minute
            'logs': 300,       # 5 minutes
            'performance': 600, # 10 minutes
            'trends': 1800     # 30 minutes
        }

    async def get_cached_status(self, system):
        """Get cached system status with automatic refresh"""
        cache_key = f"status_{system}"
        cached = self.redis_client.get(cache_key)
        if cached:
            return json.loads(cached)

        # Cache miss - fetch fresh data
        status = await self.fetch_fresh_status(system)
        self.redis_client.setex(
            cache_key,
            self.cache_ttl['status'],
            json.dumps(status)
        )
        return status

Intelligent Response System

class IntelligentResponse:
    def __init__(self):
        self.user_patterns = {}
        self.command_suggestions = {}

    def analyze_user_patterns(self, user_id):
        """Analyze user command patterns for personalization"""
        # Track frequently used commands
        # Identify user preferences
        # Generate personalized suggestions
        # Return optimization recommendations

    def generate_smart_suggestions(self, user_id, context):
        """Generate context-aware command suggestions"""
        # Analyze current context
        # Consider user history
        # Suggest relevant commands
        # Return suggestion list

Performance Analytics

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'response_times': [],
            'command_counts': {},
            'error_rates': {},
            'user_activity': {}
        }

    async def track_command_performance(self, command, duration):
        """Track command execution performance"""
        # Record execution time
        # Update performance metrics
        # Detect performance degradation
        # Generate optimization alerts

    def generate_performance_report(self):
        """Generate bot performance analytics"""
        # Aggregate performance data
        # Calculate key metrics
        # Identify optimization opportunities
        # Return performance insights

Enhanced User Interface

Interactive Keyboards

def create_system_selection_keyboard():
    """Create interactive keyboard for system selection"""
    keyboard = [
        [
            InlineKeyboardButton("🎬 Plex", callback_data="system_plex"),
            InlineKeyboardButton("📸 Immich", callback_data="system_immich")
        ],
        [
            InlineKeyboardButton("🎭 Media", callback_data="system_media"),
            InlineKeyboardButton("📊 All", callback_data="system_all")
        ],
        [
            InlineKeyboardButton("❌ Cancel", callback_data="cancel")
        ]
    ]
    return InlineKeyboardMarkup(keyboard)

def create_action_keyboard(system):
    """Create action keyboard for selected system"""
    actions = [
        [
            InlineKeyboardButton("📊 Status", callback_data=f"status_{system}"),
            InlineKeyboardButton("📈 Performance", callback_data=f"perf_{system}")
        ],
        [
            InlineKeyboardButton("🔍 Health", callback_data=f"health_{system}"),
            InlineKeyboardButton("📋 Logs", callback_data=f"logs_{system}")
        ],
        [
            InlineKeyboardButton("🚀 Backup Now", callback_data=f"backup_{system}"),
            InlineKeyboardButton("⬅️ Back", callback_data="back_systems")
        ]
    ]
    return InlineKeyboardMarkup(actions)

Progress Indicators

async def show_backup_progress(chat_id, backup_id):
    """Show real-time backup progress"""
    progress_message = await bot.send_message(
        chat_id,
        "🚀 Backup Starting...\n⏳ Initializing..."
    )

    while backup_running(backup_id):
        progress = get_backup_progress(backup_id)
        progress_bar = create_progress_bar(progress.percentage)

        await bot.edit_message_text(
            f"🚀 Backup in Progress\n"
            f"{progress_bar} {progress.percentage}%\n"
            f"📊 Phase: {progress.current_phase}\n"
            f"⏱️ Elapsed: {progress.elapsed_time}\n"
            f"🕐 ETA: {progress.estimated_remaining}",
            chat_id=chat_id,
            message_id=progress_message.message_id
        )

        await asyncio.sleep(5)  # Update every 5 seconds

Smart Command Suggestions

def get_command_suggestions(user_input):
    """Provide intelligent command suggestions"""
    suggestions = []

    # Fuzzy matching for typos
    close_matches = difflib.get_close_matches(
        user_input,
        AVAILABLE_COMMANDS,
        n=3,
        cutoff=0.6
    )

    # Context-aware suggestions
    if 'backup' in user_input.lower():
        suggestions.extend([
            '/backup_now', '/backup_all', '/backup_status'
        ])

    if 'status' in user_input.lower():
        suggestions.extend([
            '/plex_status', '/immich_status', '/media_status'
        ])

    return suggestions

Performance Features

Streaming JSON Parser

class StreamingJSONParser:
    def __init__(self):
        self.buffer = ""
        self.objects = []

    async def parse_large_log_file(self, file_path):
        """Parse large JSON log files efficiently"""
        async with aiofiles.open(file_path, 'r') as file:
            async for line in file:
                self.buffer += line

                # Try to parse complete JSON objects
                while True:
                    try:
                        obj, idx = json.JSONDecoder().raw_decode(self.buffer)
                        self.objects.append(obj)
                        self.buffer = self.buffer[idx:].lstrip()
                    except json.JSONDecodeError:
                        break

        return self.objects

Connection Pooling

import aiohttp

class ConnectionManager:
    def __init__(self):
        self.session = None
        self.connector = aiohttp.TCPConnector(
            limit=100,
            limit_per_host=30,
            ttl_dns_cache=300,
            use_dns_cache=True,
        )

    async def get_session(self):
        """Get or create HTTP session with connection pooling"""
        if self.session is None or self.session.closed:
            self.session = aiohttp.ClientSession(
                connector=self.connector,
                timeout=aiohttp.ClientTimeout(total=30)
            )
        return self.session

Advanced Features Implementation

Visual Performance Charts

import matplotlib.pyplot as plt
import io

async def generate_performance_chart(system, timeframe):
    """Generate performance visualization chart"""
    data = await get_performance_data(system, timeframe)

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

    # Backup duration chart
    ax1.plot(data['dates'], data['durations'], 'b-', linewidth=2)
    ax1.set_title(f'{system.title()} Backup Duration Trend')
    ax1.set_ylabel('Duration (minutes)')
    ax1.grid(True, alpha=0.3)

    # Backup size chart
    ax2.plot(data['dates'], data['sizes'], 'g-', linewidth=2)
    ax2.set_title(f'{system.title()} Backup Size Trend')
    ax2.set_ylabel('Size (GB)')
    ax2.set_xlabel('Date')
    ax2.grid(True, alpha=0.3)

    plt.tight_layout()

    # Save to bytes buffer
    buffer = io.BytesIO()
    plt.savefig(buffer, format='png', dpi=150, bbox_inches='tight')
    buffer.seek(0)
    plt.close()

    return buffer

Smart Notifications

class SmartNotificationEngine:
    def __init__(self):
        self.user_preferences = {}
        self.activity_patterns = {}

    def should_send_notification(self, user_id, notification_type, urgency):
        """Determine if notification should be sent based on patterns"""
        user_prefs = self.user_preferences.get(user_id, {})
        activity = self.activity_patterns.get(user_id, {})

        # Check user notification preferences
        if not user_prefs.get(notification_type, True):
            return False

        # Check if user is active (don't spam inactive users)
        if urgency == 'low' and not self.is_user_recently_active(user_id):
            return False

        # Check notification frequency limits
        if self.notification_frequency_exceeded(user_id, notification_type):
            return False

        return True

    def optimize_notification_timing(self, user_id):
        """Find optimal time to send notifications"""
        activity = self.activity_patterns.get(user_id, {})

        # Analyze user activity patterns
        # Determine best times for notifications
        # Return optimal timing recommendations

Command Examples with Enhancements

Enhanced Status Command with Interactive UI

📊 System Status Selection

Choose a system to view detailed status:

[🎬 Plex] [📸 Immich] [🎭 Media] [📊 All]

💡 Quick actions:
• Type 'p' for Plex status
• Type 'i' for Immich status
• Type 'm' for Media status
• Type 'a' for All systems

Recent activity: ✅ All systems healthy
Last update: 2 minutes ago

Performance Command with Charts

📈 Performance Analytics

🎬 Plex System Performance (7 days)
├── Avg Duration: 2m 45s (↓8% vs last week)
├── Success Rate: 100%
└── Trend: ✅ Improving

[📊 View Chart] [📋 Detailed Report] [⚙️ Optimize]

💡 Performance Insights:
• Database optimizations effective
• Backup times consistently decreasing
• No performance issues detected

Would you like to see:
• 📈 Duration trends over time
• 💾 Storage usage patterns
• 🔄 Frequency analysis

File Structure

telegram/bot/
├── optimization/
│   ├── __init__.py
│   ├── caching.py          # Response caching system
│   ├── performance.py      # Performance monitoring
│   ├── streaming.py        # Streaming data processing
│   └── connection_pool.py  # Connection management
├── ui/
│   ├── __init__.py
│   ├── keyboards.py        # Interactive keyboards
│   ├── progress.py         # Progress indicators
│   ├── suggestions.py      # Command suggestions
│   └── visualization.py    # Chart generation
├── intelligence/
│   ├── __init__.py
│   ├── pattern_analysis.py # User pattern analysis
│   ├── smart_notifications.py # Intelligent notifications
│   └── recommendations.py  # AI-powered recommendations
└── analytics/
    ├── __init__.py
    ├── metrics.py          # Performance metrics
    ├── user_analytics.py   # User behavior analytics
    └── reporting.py        # Analytics reporting

Success Criteria

Response times under 2 seconds for cached data
Memory usage optimized for large log files
Interactive UI elements functional
Performance monitoring accurate
Smart suggestions relevant and helpful
Visual charts generated successfully

Dependencies

Depends on: All previous issues (#01-07)
aiohttp for async HTTP operations
redis for caching
matplotlib for chart generation
difflib for fuzzy matching

Estimated Effort

Time: 5-6 days
Complexity: High

Testing Requirements

Performance testing under various loads
Cache efficiency validation
UI responsiveness testing
Memory usage profiling
User experience testing
Chart generation validation

Notes

This optimization issue focuses on making the Telegram bot production-ready with enterprise-level performance, user experience, and intelligent features. The enhancements should make the bot not just functional but delightful to use.

Originally created by @acedanger on GitHub (May 27, 2025). # Performance Optimization and Enhancement ## Issue Summary Optimize the Telegram bot performance, enhance user experience, and implement advanced features including caching, performance monitoring, auto-scaling, and intelligent response optimization. ## Description Develop performance optimizations and advanced features to ensure the Telegram bot operates efficiently under various load conditions, provides responsive user interactions, and includes intelligent features like auto-completion, command suggestions, and performance analytics. ## Requirements ### Performance Optimization - [ ] Implement response caching for frequently requested data - [ ] Optimize JSON log parsing with streaming and indexing - [ ] Add connection pooling for webhook and API calls - [ ] Implement lazy loading for large datasets - [ ] Add response compression for large messages - [ ] Optimize database/file I/O operations ### User Experience Enhancements - [ ] Command auto-completion and suggestions - [ ] Interactive inline keyboards for complex operations - [ ] Progress indicators for long-running operations - [ ] Smart command aliases and shortcuts - [ ] Context-aware help system - [ ] Message formatting improvements ### Advanced Features - [ ] Multi-language support framework - [ ] Voice message support for status reports - [ ] Image/chart generation for performance visualizations - [ ] Backup system health scoring with visual indicators - [ ] Smart notifications based on user activity patterns - [ ] Integration with external monitoring tools ### Performance Monitoring - [ ] Bot performance metrics tracking - [ ] Response time monitoring - [ ] Memory and CPU usage tracking - [ ] Error rate monitoring - [ ] User engagement analytics - [ ] Command usage statistics ### Technical Implementation #### Caching System ```python import asyncio from functools import lru_cache import redis class SmartCache: def __init__(self): self.redis_client = redis.Redis() self.local_cache = {} self.cache_ttl = { 'status': 60, # 1 minute 'logs': 300, # 5 minutes 'performance': 600, # 10 minutes 'trends': 1800 # 30 minutes } async def get_cached_status(self, system): """Get cached system status with automatic refresh""" cache_key = f"status_{system}" cached = self.redis_client.get(cache_key) if cached: return json.loads(cached) # Cache miss - fetch fresh data status = await self.fetch_fresh_status(system) self.redis_client.setex( cache_key, self.cache_ttl['status'], json.dumps(status) ) return status ``` #### Intelligent Response System ```python class IntelligentResponse: def __init__(self): self.user_patterns = {} self.command_suggestions = {} def analyze_user_patterns(self, user_id): """Analyze user command patterns for personalization""" # Track frequently used commands # Identify user preferences # Generate personalized suggestions # Return optimization recommendations def generate_smart_suggestions(self, user_id, context): """Generate context-aware command suggestions""" # Analyze current context # Consider user history # Suggest relevant commands # Return suggestion list ``` #### Performance Analytics ```python class PerformanceMonitor: def __init__(self): self.metrics = { 'response_times': [], 'command_counts': {}, 'error_rates': {}, 'user_activity': {} } async def track_command_performance(self, command, duration): """Track command execution performance""" # Record execution time # Update performance metrics # Detect performance degradation # Generate optimization alerts def generate_performance_report(self): """Generate bot performance analytics""" # Aggregate performance data # Calculate key metrics # Identify optimization opportunities # Return performance insights ``` ### Enhanced User Interface #### Interactive Keyboards ```python def create_system_selection_keyboard(): """Create interactive keyboard for system selection""" keyboard = [ [ InlineKeyboardButton("🎬 Plex", callback_data="system_plex"), InlineKeyboardButton("📸 Immich", callback_data="system_immich") ], [ InlineKeyboardButton("🎭 Media", callback_data="system_media"), InlineKeyboardButton("📊 All", callback_data="system_all") ], [ InlineKeyboardButton("❌ Cancel", callback_data="cancel") ] ] return InlineKeyboardMarkup(keyboard) def create_action_keyboard(system): """Create action keyboard for selected system""" actions = [ [ InlineKeyboardButton("📊 Status", callback_data=f"status_{system}"), InlineKeyboardButton("📈 Performance", callback_data=f"perf_{system}") ], [ InlineKeyboardButton("🔍 Health", callback_data=f"health_{system}"), InlineKeyboardButton("📋 Logs", callback_data=f"logs_{system}") ], [ InlineKeyboardButton("🚀 Backup Now", callback_data=f"backup_{system}"), InlineKeyboardButton("⬅️ Back", callback_data="back_systems") ] ] return InlineKeyboardMarkup(actions) ``` #### Progress Indicators ```python async def show_backup_progress(chat_id, backup_id): """Show real-time backup progress""" progress_message = await bot.send_message( chat_id, "🚀 Backup Starting...\n⏳ Initializing..." ) while backup_running(backup_id): progress = get_backup_progress(backup_id) progress_bar = create_progress_bar(progress.percentage) await bot.edit_message_text( f"🚀 Backup in Progress\n" f"{progress_bar} {progress.percentage}%\n" f"📊 Phase: {progress.current_phase}\n" f"⏱️ Elapsed: {progress.elapsed_time}\n" f"🕐 ETA: {progress.estimated_remaining}", chat_id=chat_id, message_id=progress_message.message_id ) await asyncio.sleep(5) # Update every 5 seconds ``` #### Smart Command Suggestions ```python def get_command_suggestions(user_input): """Provide intelligent command suggestions""" suggestions = [] # Fuzzy matching for typos close_matches = difflib.get_close_matches( user_input, AVAILABLE_COMMANDS, n=3, cutoff=0.6 ) # Context-aware suggestions if 'backup' in user_input.lower(): suggestions.extend([ '/backup_now', '/backup_all', '/backup_status' ]) if 'status' in user_input.lower(): suggestions.extend([ '/plex_status', '/immich_status', '/media_status' ]) return suggestions ``` ### Performance Features #### Streaming JSON Parser ```python class StreamingJSONParser: def __init__(self): self.buffer = "" self.objects = [] async def parse_large_log_file(self, file_path): """Parse large JSON log files efficiently""" async with aiofiles.open(file_path, 'r') as file: async for line in file: self.buffer += line # Try to parse complete JSON objects while True: try: obj, idx = json.JSONDecoder().raw_decode(self.buffer) self.objects.append(obj) self.buffer = self.buffer[idx:].lstrip() except json.JSONDecodeError: break return self.objects ``` #### Connection Pooling ```python import aiohttp class ConnectionManager: def __init__(self): self.session = None self.connector = aiohttp.TCPConnector( limit=100, limit_per_host=30, ttl_dns_cache=300, use_dns_cache=True, ) async def get_session(self): """Get or create HTTP session with connection pooling""" if self.session is None or self.session.closed: self.session = aiohttp.ClientSession( connector=self.connector, timeout=aiohttp.ClientTimeout(total=30) ) return self.session ``` ### Advanced Features Implementation #### Visual Performance Charts ```python import matplotlib.pyplot as plt import io async def generate_performance_chart(system, timeframe): """Generate performance visualization chart""" data = await get_performance_data(system, timeframe) fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8)) # Backup duration chart ax1.plot(data['dates'], data['durations'], 'b-', linewidth=2) ax1.set_title(f'{system.title()} Backup Duration Trend') ax1.set_ylabel('Duration (minutes)') ax1.grid(True, alpha=0.3) # Backup size chart ax2.plot(data['dates'], data['sizes'], 'g-', linewidth=2) ax2.set_title(f'{system.title()} Backup Size Trend') ax2.set_ylabel('Size (GB)') ax2.set_xlabel('Date') ax2.grid(True, alpha=0.3) plt.tight_layout() # Save to bytes buffer buffer = io.BytesIO() plt.savefig(buffer, format='png', dpi=150, bbox_inches='tight') buffer.seek(0) plt.close() return buffer ``` #### Smart Notifications ```python class SmartNotificationEngine: def __init__(self): self.user_preferences = {} self.activity_patterns = {} def should_send_notification(self, user_id, notification_type, urgency): """Determine if notification should be sent based on patterns""" user_prefs = self.user_preferences.get(user_id, {}) activity = self.activity_patterns.get(user_id, {}) # Check user notification preferences if not user_prefs.get(notification_type, True): return False # Check if user is active (don't spam inactive users) if urgency == 'low' and not self.is_user_recently_active(user_id): return False # Check notification frequency limits if self.notification_frequency_exceeded(user_id, notification_type): return False return True def optimize_notification_timing(self, user_id): """Find optimal time to send notifications""" activity = self.activity_patterns.get(user_id, {}) # Analyze user activity patterns # Determine best times for notifications # Return optimal timing recommendations ``` ### Command Examples with Enhancements #### Enhanced Status Command with Interactive UI ``` 📊 System Status Selection Choose a system to view detailed status: [🎬 Plex] [📸 Immich] [🎭 Media] [📊 All] 💡 Quick actions: • Type 'p' for Plex status • Type 'i' for Immich status • Type 'm' for Media status • Type 'a' for All systems Recent activity: ✅ All systems healthy Last update: 2 minutes ago ``` #### Performance Command with Charts ``` 📈 Performance Analytics 🎬 Plex System Performance (7 days) ├── Avg Duration: 2m 45s (↓8% vs last week) ├── Success Rate: 100% └── Trend: ✅ Improving [📊 View Chart] [📋 Detailed Report] [⚙️ Optimize] 💡 Performance Insights: • Database optimizations effective • Backup times consistently decreasing • No performance issues detected Would you like to see: • 📈 Duration trends over time • 💾 Storage usage patterns • 🔄 Frequency analysis ``` ### File Structure ``` telegram/bot/ ├── optimization/ │ ├── __init__.py │ ├── caching.py # Response caching system │ ├── performance.py # Performance monitoring │ ├── streaming.py # Streaming data processing │ └── connection_pool.py # Connection management ├── ui/ │ ├── __init__.py │ ├── keyboards.py # Interactive keyboards │ ├── progress.py # Progress indicators │ ├── suggestions.py # Command suggestions │ └── visualization.py # Chart generation ├── intelligence/ │ ├── __init__.py │ ├── pattern_analysis.py # User pattern analysis │ ├── smart_notifications.py # Intelligent notifications │ └── recommendations.py # AI-powered recommendations └── analytics/ ├── __init__.py ├── metrics.py # Performance metrics ├── user_analytics.py # User behavior analytics └── reporting.py # Analytics reporting ``` ### Success Criteria - [ ] Response times under 2 seconds for cached data - [ ] Memory usage optimized for large log files - [ ] Interactive UI elements functional - [ ] Performance monitoring accurate - [ ] Smart suggestions relevant and helpful - [ ] Visual charts generated successfully ## Dependencies - Depends on: All previous issues (#01-07) - aiohttp for async HTTP operations - redis for caching - matplotlib for chart generation - difflib for fuzzy matching ## Estimated Effort **Time**: 5-6 days **Complexity**: High ## Testing Requirements - [ ] Performance testing under various loads - [ ] Cache efficiency validation - [ ] UI responsiveness testing - [ ] Memory usage profiling - [ ] User experience testing - [ ] Chart generation validation ## Notes This optimization issue focuses on making the Telegram bot production-ready with enterprise-level performance, user experience, and intelligent features. The enhancements should make the bot not just functional but delightful to use.

peterwood added the enhancement label 2025-10-29 19:44:55 -07:00

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Reference: peterwood/shell#7