deleted: database_optimization_plan.md
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SimolZimol
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# Database Connection Management Improvements for Bot
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# Diese Datei zeigt die empfohlenen Änderungen für bot.py um das "Too many connections" Problem zu lösen
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## Problem Analyse:
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# 1. Bot Pool: 30 Verbindungen
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# 2. App direktverbindungen ohne Pool (jetzt mit Pool: 15)
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# 3. Neue Warning-Funktionen verwenden viele DB-Verbindungen
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# 4. get_user_warnings() wird häufig aufgerufen und öffnet jedes Mal neue Connections
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# 5. Context-Archivierung kann große Datenmengen verarbeiten
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## Lösungsansätze implementiert:
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### 1. App.py Connection Pool (✅ Implementiert):
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- Connection Pool mit 15 Verbindungen für Flask App
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- Context Manager für sichere Verbindungsverwaltung
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- Automatische Verbindungsfreigabe
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- Fallback für Pool-Probleme
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### 2. Optimierungen für Bot.py (Empfohlen):
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# Diese Änderungen sollten in bot.py implementiert werden:
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```python
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# Verbesserte get_user_warnings Funktion mit Connection Pooling
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async def get_user_warnings(user_id, guild_id, active_only=True):
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"""Retrieves warning records for a user - OPTIMIZED VERSION"""
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connection = None
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cursor = None
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try:
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connection = connect_to_database() # Nutzt bereits den Pool
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cursor = connection.cursor()
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# Single query statt multiple calls
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select_query = """
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SELECT id, moderator_id, reason, created_at, message_id, message_content,
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message_attachments, message_author_id, message_channel_id, context_messages, aktiv
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FROM user_warnings
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WHERE user_id = %s AND guild_id = %s {}
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ORDER BY created_at DESC
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""".format("AND aktiv = TRUE" if active_only else "")
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cursor.execute(select_query, (user_id, guild_id))
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results = cursor.fetchall()
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warnings = []
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for row in results:
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warnings.append({
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"id": row[0],
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"moderator_id": row[1],
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"reason": row[2],
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"created_at": row[3],
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"message_id": row[4],
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"message_content": row[5],
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"message_attachments": row[6],
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"message_author_id": row[7],
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"message_channel_id": row[8],
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"context_messages": row[9],
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"aktiv": row[10]
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})
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return warnings
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except Exception as e:
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logger.error(f"Error getting user warnings: {e}")
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return []
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finally:
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if cursor:
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cursor.close()
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if connection:
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close_database_connection(connection) # Gibt Connection an Pool zurück
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```
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### 3. Connection Caching für häufige Abfragen:
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# Implementiere Caching für Warning-Abfragen:
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```python
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import asyncio
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from functools import lru_cache
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# Cache für häufige Warning-Abfragen (5 Minuten TTL)
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warning_cache = {}
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cache_ttl = 300 # 5 Minuten
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async def get_user_warnings_cached(user_id, guild_id, active_only=True):
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"""Cached version of get_user_warnings"""
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cache_key = f"{user_id}_{guild_id}_{active_only}"
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current_time = asyncio.get_event_loop().time()
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# Check cache
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if cache_key in warning_cache:
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cached_data, timestamp = warning_cache[cache_key]
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if current_time - timestamp < cache_ttl:
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return cached_data
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# Fetch fresh data
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warnings = await get_user_warnings(user_id, guild_id, active_only)
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warning_cache[cache_key] = (warnings, current_time)
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# Clean old cache entries
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if len(warning_cache) > 1000: # Limit cache size
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old_keys = [k for k, (_, ts) in warning_cache.items()
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if current_time - ts > cache_ttl]
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for k in old_keys:
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del warning_cache[k]
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return warnings
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```
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### 4. Batch Operations für Context Messages:
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# Reduziere DB-Aufrufe bei Context-Archivierung:
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```python
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async def batch_insert_warnings(warning_data_list):
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"""Insert multiple warnings in a single transaction"""
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if not warning_data_list:
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return
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connection = None
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cursor = None
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try:
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connection = connect_to_database()
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cursor = connection.cursor()
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insert_query = """
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INSERT INTO user_warnings (user_id, guild_id, moderator_id, reason, created_at,
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message_id, message_content, message_attachments,
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message_author_id, message_channel_id, context_messages, aktiv)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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"""
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cursor.executemany(insert_query, warning_data_list)
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connection.commit()
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except Exception as e:
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logger.error(f"Error in batch insert warnings: {e}")
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if connection:
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connection.rollback()
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finally:
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if cursor:
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cursor.close()
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if connection:
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close_database_connection(connection)
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```
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### 5. Pool Monitoring:
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# Überwache Pool-Status:
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```python
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def monitor_connection_pool():
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"""Monitor connection pool status"""
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try:
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pool_size = pool.pool_size
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# This is tricky to get exact usage, but we can log pool creation
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logger.info(f"Connection pool status - Size: {pool_size}")
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return pool_size
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except Exception as e:
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logger.error(f"Error monitoring pool: {e}")
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return 0
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```
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## Sofortige Maßnahmen:
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1. ✅ App.py mit Connection Pool ausgestattet (15 Verbindungen)
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2. 🔄 Bot Pool von 30 auf 25 reduzieren (Gesamtlimit: 40 statt 50+)
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3. 🔄 Warning-Cache implementieren
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4. 🔄 Batch-Operations für große Datensätze
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## Connection Limits:
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- MySQL Standard: 151 gleichzeitige Verbindungen
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- Bot Pool: 30 → empfohlen 25
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- App Pool: 15
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- Reserve für andere Clients: 111
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- Sicherheitspuffer: sollte ausreichend sein
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Das Problem tritt auf, weil:
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1. Neue Warning-Funktionen häufige DB-Zugriffe machen
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2. Context-Archivierung große Datenmengen verarbeitet
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3. get_user_warnings() wird oft aufgerufen (account, viewwarn Commands)
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4. App und Bot konkurrieren um Verbindungen
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