Install
openclaw skills install database-query-best-practicesPrevent connection pool exhaustion when querying Railway PostgreSQL database. Use when (1) Running database queries from local environment, (2) Diagnosing "too many clients" errors, (3) Writing Python scripts that query production DB, (4) Checking connection pool status. CRITICAL - Always close connections immediately after use, use context managers, check pool status before queries.
openclaw skills install database-query-best-practicesCRITICAL SKILL - READ THIS BEFORE QUERYING RAILWAY DATABASE
When querying Railway production database from local development environment, you may encounter:
psycopg2.OperationalError: sorry, too many clients already
This happens when:
npm run dev, npm run develop)Before ANY database query, check active connections:
python3 << 'EOF'
import psycopg2
DATABASE_URL = "postgresql://postgres:password@host:port/railway"
try:
conn = psycopg2.connect(DATABASE_URL, connect_timeout=10)
cur = conn.cursor()
# Check active connections
cur.execute("""
SELECT count(*)
FROM pg_stat_activity
WHERE datname = 'railway'
""")
active = cur.fetchone()[0]
# Check pool limit
cur.execute("SHOW max_connections")
max_conn = cur.fetchone()[0]
print(f"Active connections: {active}/{max_conn}")
if active > int(max_conn) * 0.9:
print(f"WARNING: Connection pool at {(active/int(max_conn))*100:.1f}% capacity")
print("Consider closing dev servers before querying")
else:
print("Connection pool healthy - safe to query")
cur.close()
conn.close()
except Exception as e:
print(f"Cannot connect: {e}")
print("\nSOLUTION:")
print("1. Kill local dev servers: pkill -9 -f 'npm run dev'")
print("2. Wait 30 seconds for connections to close")
print("3. Try again")
EOF
NEVER do this:
conn = psycopg2.connect(DATABASE_URL)
cur = conn.cursor()
cur.execute("SELECT * FROM users")
# Forgot to close! Connection leak!
ALWAYS do this:
import psycopg2
DATABASE_URL = "postgresql://..."
try:
conn = psycopg2.connect(DATABASE_URL, connect_timeout=30)
cur = conn.cursor()
# Do your queries
cur.execute("SELECT * FROM users")
results = cur.fetchall()
# Process results...
finally:
# ALWAYS close in finally block
if cur:
cur.close()
if conn:
conn.close()
For one-off queries, open connection, query, close immediately:
def get_user_count():
"""Get user count - connection opened and closed in function"""
conn = None
try:
conn = psycopg2.connect(DATABASE_URL, connect_timeout=30)
cur = conn.cursor()
cur.execute("SELECT COUNT(*) FROM users")
count = cur.fetchone()[0]
cur.close()
return count
finally:
if conn:
conn.close() # Connection immediately released
# Good: Connection closed after function returns
user_count = get_user_count()
If you need to run database queries, stop dev servers first:
# Stop all dev servers
pkill -9 -f "npm run dev"
pkill -9 -f "npm run develop"
# Wait for connections to close
sleep 10
# Now safe to query
python3 scripts/your_query_script.py
# Restart dev servers after
cd src/backend && npm run dev &
cd AINative-website && npm run dev &
Instead of Python scripts, use Railway CLI when possible:
# Login to Railway
railway login
# Link to project
railway link
# Run SQL query directly
railway run psql -c "SELECT COUNT(*) FROM users WHERE created_at >= NOW() - INTERVAL '30 days'"
This uses Railway's managed connections and doesn't consume local pool.
If you encounter "too many clients already":
pkill -9 -f "npm run dev"
pkill -9 -f "npm run develop"
pkill -9 node
sleep 30 # Wait for DB connections to close
railway run psql -c "
SELECT
pid,
usename,
application_name,
client_addr,
state,
query_start
FROM pg_stat_activity
WHERE datname = 'railway'
ORDER BY query_start DESC
LIMIT 20"
railway run psql -c "
SELECT pg_terminate_backend(pid)
FROM pg_stat_activity
WHERE datname = 'railway'
AND state = 'idle'
AND query_start < NOW() - INTERVAL '10 minutes'"
Before running ANY database query, verify:
The Golden Rule:
ALWAYS close database connections immediately after use. NEVER leave connections open in dev servers during queries.
Best Approach:
Even Better: Use Railway CLI for ad-hoc queries instead of Python scripts.