Notre avis
Ce skill fournit des commandes et des conseils pour diagnostiquer et résoudre les problèmes de performance de base de données dans le projet ettametta.
Points forts
- Commandes concrètes pour inspecter les connexions, requêtes actives et tailles de tables
- Couvre la configuration des connexions et les patterns de sessions (async, sync, Celery)
- Identifie les risques N+1 et les index manquants
Limites
- Spécifique au projet ettametta et à son schéma de base de données
- Peut nécessiter des adaptations pour d'autres environnements
- Ne couvre pas les optimisations avancées (requêtes complexes, index GIN/GIST)
Lors de l'analyse de requêtes lentes, d'épuisement du pool de connexions, de conflits de migration ou de patterns N+1.
Pour des problèmes de performance non liés à la base de données, ou pour un réglage général de PostgreSQL sans contexte applicatif.
Analyse de sécurité
SûrThe skill provides read-only diagnostic SQL commands and environment inspection via docker compose, with no destructive operations or data exfiltration.
Aucun point d'attention détecté
Exemples
I'm experiencing slow performance in the application. Please run the database debugging commands from the db-performance skill to check active queries, connection count, and table sizes.Analyze the query patterns in the routes/nexus.py file for potential N+1 issues using the db-performance skill.Review the database schema and suggest missing indexes based on the common issues listed in the db-performance skill.name: db-performance description: Debug and troubleshoot database performance in ettametta. Use when investigating slow queries, connection pool issues, migration conflicts, N+1 patterns, or schema drift.
Database Performance Debugging
Quick Diagnostics
# Connection count
docker compose exec db psql -U postgres -c "SELECT count(*) FROM pg_stat_activity;"
# Active queries
docker compose exec db psql -U postgres -c "SELECT pid, now()-query_start AS duration, query FROM pg_stat_activity WHERE state='active' ORDER BY duration DESC;"
# Table sizes
docker compose exec db psql -U postgres -c "SELECT relname, n_live_tup, pg_size_pretty(pg_total_relation_size(relid)) FROM pg_stat_user_tables ORDER BY n_live_tup DESC;"
# Migration state
alembic heads
alembic current
alembic check
Connection Configuration
Async engine (FastAPI): pool_pre_ping=True, defaults pool_size=5, max_overflow=10
Sync engine (Celery): Default QueuePool, pool_size=5, max_overflow=10
Celery task-scoped: pool_size=2, max_overflow=0 (tight isolation)
NullPool for Celery workers: Fresh engine per call, disposed after use. Expensive but avoids event-loop conflicts.
Default DATABASE_URL: sqlite:///./data/db/ettametta.db. Production uses PostgreSQL.
Session Management
- Pattern A (routes):
get_db()dependency with rollback on exception - Pattern B (services):
async_session_factory()inline - Pattern C (Celery):
get_async_session()with NullPool
All use expire_on_commit=False, autocommit=False, autoflush=False.
Models
35 ORM models. Zero relationship() declarations — all cross-table access via explicit joins.
Key models: UserDB (7 unique indexes), ContentCandidateDB (external_id unique, niche, region), VideoJobDB, PublishedContentDB, ScheduledPostDB, SocialAccount.
Missing indexes (potential)
- video_jobs.user_id
- audit_logs.user_id
- scheduled_posts.user_id
- nexus_jobs.user_id
No composite indexes declared.
Migrations
20 migration files. Current HEAD: d410fb0d40a9. 2 merge migrations exist.
CI/CD bypasses Alembic: Deployment uses create_all() not alembic upgrade head.
Query Patterns
N+1 risks
- Nexus stats: 4 separate COUNT(*) queries (lines 482-506 in routes/nexus.py). Fix: single GROUP BY.
- Settings: 2 separate queries for system + user settings. Fix: join.
In-memory pagination
Discovery routes use paginate_list() — loads all then slices. Use SQL .offset().limit().
DateTime Monkeypatch
database.py lines 12-24: strips timezone info from DateTime. Workaround for SQLite/PG compat.
Common Issues
Connection pool exhaustion
docker compose exec db psql -U postgres -c "SELECT count(*) FROM pg_stat_activity;"
Stale connections
Async engine has pool_pre_ping=True. Sync engine does not.
NullPool performance
get_async_session() creates/disposes full engine per call. Expensive for high-frequency ops.
Missing composite indexes
Queries on multiple columns do sequential scans. Add Index() declarations.
expire_on_commit=False
Correct for async but means stale data if objects reused after commit without refresh.
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