Database Issues

This guide covers common database-related issues that may arise when working with the Definable backend.

Connection Issues

Database connection failures

Symptoms:

Error: Could not connect to server: Connection refused
Error: OperationalError: (psycopg2.OperationalError) connection to server at "localhost" (::1), port 5432 failed

Solutions:

Verify PostgreSQL is running:

# For Linux
sudo systemctl status postgresql

# For macOS
brew services list | grep postgres

# For Windows
# Check Services application (services.msc)

Check connection parameters:
- Verify hostname, port, username, password, and database name
- Make sure database exists: psql -U postgres -c "SELECT datname FROM pg_database;"

Test connection with psql:

psql -U postgres -h localhost -p 5432 -d postgres

Check PostgreSQL logs:

# Location varies by system
sudo tail -f /var/log/postgresql/postgresql-14-main.log  # Debian/Ubuntu
sudo tail -f /var/lib/pgsql/data/log/  # RHEL/CentOS

asyncpg connection issues

Symptoms:

Error: asyncpg.exceptions.PostgresConnectionError: connection to server was closed
Error: asyncpg.exceptions.InvalidAuthorizationSpecificationError: password authentication failed

Solutions:

Check your connection string format:

# Correct format for asyncpg
"postgresql+asyncpg://username:password@hostname:port/database"

URL encode special characters in password:

# If your password contains special characters like @, %, etc.
from urllib.parse import quote_plus
password = quote_plus("your@complex!password")
connection_string = f"postgresql+asyncpg://username:{password}@hostname:port/database"

Check PostgreSQL authentication settings (pg_hba.conf):
- Ensure it allows password authentication (md5 or scram-sha-256)
- For development, temporarily set local connections to ‘trust’

Test with a minimal example:

import asyncio
import asyncpg

async def test_connection():
    conn = await asyncpg.connect("postgresql://username:password@localhost/postgres")
    version = await conn.fetchval("SELECT version();")
    print(version)
    await conn.close()

asyncio.run(test_connection())

SSL connection issues

Symptoms:

Error: ssl_error_want_read or ssl_error_want_write
Error: SSL SYSCALL error: EOF detected

Solutions:

Disable SSL for local development (if needed):

# In your connection string
postgresql+asyncpg://username:password@hostname:port/database?ssl=false

For production, configure SSL properly:

# Using SSL
postgresql+asyncpg://username:password@hostname:port/database?ssl=true

# Verify SSL certificate
postgresql+asyncpg://username:password@hostname:port/database?ssl=true&sslmode=verify-full

For Supabase with SSL issues:
- Use the connection pooler URL instead of direct connection
- Go to Supabase dashboard > Project Settings > Database > Connection Pooling

Migration Issues

Alembic migration failures

Symptoms:

Error: FAILED: Multiple head revisions are present
Error: Can't locate revision identified by '...'

Solutions:

For multiple heads:

# List heads
alembic heads

# Create a merge migration
alembic merge -m "merge heads" head1 head2

# Then upgrade to the merged head
alembic upgrade head

For missing revisions:

# Fix the down_revision in the problematic migration file
# Make sure all migration files are in the versions directory

# Or start fresh (development only)
alembic stamp base  # Reset alembic version table
alembic upgrade head  # Apply all migrations

Check alembic configuration:
- Verify alembic.ini has correct database URL
- Check env.py imports your SQLAlchemy models correctly

Migration conflicts

Symptoms:

Error: Error: Target database is not up to date.
Error: Can't locate revision identified by '...'

Solutions:

Synchronize with the team:

# Pull latest migrations from version control
git pull

# Check current database revision
alembic current

# Upgrade to latest
alembic upgrade head

For development, reset migration state:

# WARNING: This will destroy data in the development database
# DROP DATABASE and recreate it

# Then reset alembic
alembic stamp base
alembic upgrade head

Fix revision chain manually:
- Edit the down_revision in migration files to fix the chain
- Use alembic history to understand the current chain

Autogenerate issues

Symptoms:

alembic revision --autogenerate doesn’t detect model changes
Generated migration has unexpected changes

Solutions:

Ensure models are imported in env.py:

# In alembic/env.py
from src.models import *  # Make sure your models are imported here

Check model metadata:

# Make sure your models use the correct metadata
from src.database import Base

class YourModel(Base):
    # model definition...

Run with verbose output:

alembic revision --autogenerate -m "your message" --verbose

Check for unsupported model features:
- Some SQLAlchemy constructs aren’t detected by Alembic
- Add manual migrations for: Constraints, Indexes, some Column types

Migration execution errors

Symptoms:

Error like: ProgrammingError: column X does not exist
Error when executing migration’s upgrade() function

Solutions:

Edit the migration file:
- Fix the SQL or Alembic operations causing errors

For data corruption, clean approach (development only):

# Reset to previous working migration
alembic downgrade your_last_working_revision

# Fix the problematic migration file

# Try upgrading again
alembic upgrade head

For schema issues where tables already exist:

# In your migration file, check if table exists before creating
if not op.has_table('table_name'):
    op.create_table(...)

SQLAlchemy Issues

SQLAlchemy engine connection issues

Symptoms:

Error: TimeoutError: Connection attempt timed out
Application hangs when connecting to database

Solutions:

Configure connection pooling correctly:

# Adjust pool settings
engine = create_async_engine(
    DATABASE_URL,
    pool_size=5,  # Adjust based on your needs
    max_overflow=10,
    pool_timeout=30,  # Seconds
    pool_recycle=1800,  # Recycle connections after 30 minutes
)

Implement retry logic for transient failures:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=1, max=10))
async def get_connection():
    async with engine.connect() as conn:
        return conn

Check for database resource limitations:
- Maximum connections (max_connections in postgresql.conf)
- Check current connections: SELECT count(*) FROM pg_stat_activity;

SQLAlchemy async session issues

Symptoms:

Error: AssertionError: A sync operation occurred within an async transaction.
Error: InterfaceError: connection is closed

Solutions:

Ensure you’re using async operations throughout:

# Correct async pattern
async with async_session() as session:
    async with session.begin():
        result = await session.execute(query)
        # Don't mix sync and async operations

Check for instances of sync operations:
- Replace .all() with await session.execute(query).all()
- Replace .first() with await session.execute(query).first()
- Use await session.commit() instead of session.commit()

Fix connection closing issues:

# Create a fresh session for each request
async def get_db():
    async with async_session() as session:
        try:
            yield session
        finally:
            await session.close()

SQLAlchemy query problems

Symptoms:

Unexpected query results
Error: AttributeError: 'Query' object has no attribute 'xxx'

Solutions:

Debug queries by logging SQL:

# Add to your settings or session creation
engine = create_async_engine(
    DATABASE_URL,
    echo=True,  # This will log all SQL
)

Review SQLAlchemy 2.0-style execution:

# 2.0 style querying
from sqlalchemy import select

# Query
query = select(User).where(User.email == email)
result = await session.execute(query)
user = result.scalar_one_or_none()

Check for ORM vs. Core confusion:
- Result objects differ between ORM queries and Core queries
- For ORM: Use .scalars() to get model instances
- For Core: Use .mappings() to get dictionaries

PostgreSQL Issues

pgvector-related errors

Symptoms:

Error: ERROR: relation "vector" does not exist
Error: ERROR: function vector_l2_squared_distance(vector, vector) does not exist

Solutions:

Install and enable the pgvector extension:

-- Connect as superuser
CREATE EXTENSION IF NOT EXISTS vector;

-- Verify it's installed
SELECT * FROM pg_extension WHERE extname = 'vector';

Check if extension is installed in the database:

# List installed extensions
psql -U postgres -d your_database -c "SELECT * FROM pg_extension;"

For Docker, use a container with pgvector:

docker pull ankane/pgvector
docker run --name postgres -e POSTGRES_PASSWORD=password -p 5432:5432 -d ankane/pgvector

For hosted databases (Supabase), enable via dashboard:
- Dashboard > Database > Extensions > Enable vector

Schema permission errors

Symptoms:

Error: permission denied for schema public
Error: permission denied for relation your_table

Solutions:

Grant permissions to your database user:

-- Connect as superuser
GRANT ALL PRIVILEGES ON DATABASE your_database TO your_user;
GRANT ALL PRIVILEGES ON SCHEMA public TO your_user;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO your_user;
GRANT ALL PRIVILEGES ON ALL SEQUENCES IN SCHEMA public TO your_user;

Check current user and permissions:

-- Who am I?
SELECT current_user;

-- What permissions do I have?
SELECT * FROM information_schema.role_table_grants 
WHERE grantee = current_user;

For hosted databases with restricted permissions:
- Use the admin/owner account instead of a restricted role
- Contact your database provider for assistance

Full-text search issues

Symptoms:

Poor search results
Performance issues with text search

Solutions:

Create proper indexes:

-- Create GIN index for full-text search
CREATE INDEX idx_your_table_search ON your_table USING GIN (to_tsvector('english', your_text_column));

Optimize your search queries:

# Efficient full-text search with SQLAlchemy
from sqlalchemy import text

query = text("""
    SELECT * FROM your_table
    WHERE to_tsvector('english', your_text_column) @@ plainto_tsquery('english', :search_term)
    ORDER BY ts_rank(to_tsvector('english', your_text_column), plainto_tsquery('english', :search_term)) DESC
""")

result = await session.execute(query, {"search_term": search_term})

Consider using a vector database for semantic search:
- Use pgvector for embedding-based search
- Create appropriate indexes for vector columns

Performance Issues

Slow queries

Symptoms:

Database operations taking too long
API response times deteriorating

Solutions:

Identify slow queries:

-- Find slow queries
SELECT query, calls, total_time, mean_time
FROM pg_stat_statements
ORDER BY mean_time DESC
LIMIT 10;

-- If pg_stat_statements is not enabled, enable it in postgresql.conf

Use EXPLAIN ANALYZE to understand query plans:
```
EXPLAIN ANALYZE your_slow_query;
```

Add appropriate indexes:

-- For columns used in WHERE clauses
CREATE INDEX idx_your_table_column ON your_table(your_column);

-- For foreign keys
CREATE INDEX idx_your_table_fk_column ON your_table(foreign_key_column);

Optimize your queries:
- Use LIMIT to restrict result size
- Only select needed columns
- Avoid multiple joins when possible
- Consider pagination for large result sets

Connection pooling issues

Symptoms:

Error: FATAL: too many connections
Applications waiting for database connections

Solutions:

Configure connection pooling properly:

# Adjust pool settings in SQLAlchemy
engine = create_async_engine(
    DATABASE_URL,
    pool_size=5,  # Start with a reasonable value
    max_overflow=10,
    pool_pre_ping=True,  # Check connections before using them
)

Check current connections and limits:

-- Current connections
SELECT count(*) FROM pg_stat_activity;

-- Connection limit
SHOW max_connections;

For production, consider external connection poolers:
- PgBouncer
- AWS RDS Proxy
- Supabase Connection Pooler

Large result sets

Symptoms:

Out of memory errors
Slow API responses when retrieving many records

Solutions:

Implement pagination:

# With SQLAlchemy
query = select(YourModel).offset(offset).limit(limit)

# In API endpoints
@app.get("/items/")
async def get_items(offset: int = 0, limit: int = 100):
    # Apply pagination

Use cursor-based pagination for large datasets:

# Using keyset pagination (more efficient than offset/limit)
# Example: sorting by id
query = select(YourModel).where(YourModel.id > last_id).order_by(YourModel.id).limit(limit)

Stream results for large exports:

# Using SQLAlchemy async streaming
async def stream_results():
    async with async_session() as session:
        stream = await session.stream(select(YourModel))
        async for row in stream:
            yield row

Data Integrity Issues

UUID generation problems

Symptoms:

Error: invalid input syntax for type uuid
Missing UUID values in records

Solutions:

Ensure UUIDs are used consistently:

# In your SQLAlchemy models
from sqlalchemy.dialects.postgresql import UUID
import uuid

class YourModel(Base):
    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)

For migrations involving UUID columns:

# In alembic migrations
from sqlalchemy.dialects import postgresql

op.create_table(
    'your_table',
    sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), primary_key=True),
    # other columns...
)

Check database and python type consistency:

# Convert strings to UUID when needed
from uuid import UUID

def validate_uuid(value):
    if isinstance(value, str):
        return UUID(value)
    return value

Foreign key constraint violations

Symptoms:

Error: violates foreign key constraint
Error: insert or update on table violates foreign key constraint

Solutions:

Check referential integrity:

-- Find the constraint that's failing
SELECT conname, conrelid::regclass, confrelid::regclass
FROM pg_constraint
WHERE contype = 'f';

-- Check if referenced id exists
SELECT * FROM parent_table WHERE id = 'referenced_id';

Fix application logic:
- Ensure parent records exist before creating child records
- Implement proper cascading deletes in models
```
# In SQLAlchemy model
parent_id = Column(UUID(as_uuid=True), ForeignKey("parent.id", ondelete="CASCADE"))
```

For migration or data fixing:

-- Temporarily disable constraints (for data fixing only)
SET session_replication_role = 'replica';
-- Fix data...
SET session_replication_role = 'origin';

Unique constraint violations

Symptoms:

Error: duplicate key value violates unique constraint
Insert or update operations failing

Solutions:

Check existing data:

-- Find duplicate records
SELECT column_name, COUNT(*) 
FROM your_table 
GROUP BY column_name 
HAVING COUNT(*) > 1;

Implement upsert logic:

# Using SQLAlchemy 2.0 style
from sqlalchemy.dialects.postgresql import insert

stmt = insert(YourModel).values(
    email="user@example.com",
    name="User"
)

# ON CONFLICT DO UPDATE
stmt = stmt.on_conflict_do_update(
    index_elements=['email'],
    set_=dict(name="User")
)

await session.execute(stmt)

For batch operations, find and filter duplicates before insert:

# Get existing records
existing = await session.execute(
    select(YourModel.email).where(YourModel.email.in_(emails_to_insert))
)
existing_emails = {r[0] for r in existing.fetchall()}

# Filter out duplicates
new_items = [item for item in items if item.email not in existing_emails]

Advanced Issues

JSON/JSONB field issues

Symptoms:

Error: Unexpected JSON type
Problems querying or updating JSON fields

Solutions:

Use appropriate column type:

# In models
from sqlalchemy.dialects.postgresql import JSONB

settings = Column(JSONB, nullable=False, server_default='{}')

Query JSON data efficiently:

# Get records where settings contains specific key
query = select(YourModel).where(YourModel.settings.contains({"key": "value"}))

# Get records based on JSON path
query = select(YourModel).where(YourModel.settings["nested"]["key"].astext == "value")

Update JSON fields:

# Update specific key in JSONB
from sqlalchemy.dialects.postgresql import insert
from sqlalchemy import func

await session.execute(
    update(YourModel)
    .where(YourModel.id == model_id)
    .values(settings=func.jsonb_set(YourModel.settings, '{key}', '"new_value"', True))
)

Transaction management issues

Symptoms:

Deadlocks
Error: current transaction is aborted, commands ignored until end of transaction block

Solutions:

Use proper transaction patterns:

# Context manager approach
async with session.begin():
    # All operations here are in a transaction
    # Auto-commits at the end or rolls back on exception

# Explicit approach
try:
    await session.begin()
    # operations...
    await session.commit()
except:
    await session.rollback()
    raise

Handle nested transactions:

# Using savepoints for nested transactions
async with session.begin_nested() as nested:
    # Create a savepoint
    try:
        # operations that might fail
        await nested.commit()
    except:
        # This rolls back to the savepoint, not the entire transaction
        pass

For deadlocks, implement retry logic:

from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
from sqlalchemy.exc import OperationalError

@retry(
    stop=stop_after_attempt(3), 
    wait=wait_exponential(multiplier=1, min=1, max=10),
    retry=retry_if_exception_type(OperationalError)
)
async def execute_with_retry():
    async with session.begin():
        # Your database operations

pgvector performance issues

Symptoms:

Slow vector similarity searches
High CPU usage during vector operations

Solutions:

Create appropriate indexes:

-- For cosine distance
CREATE INDEX ON your_table USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

-- For L2 distance
CREATE INDEX ON your_table USING ivfflat (embedding vector_l2_ops)
  WITH (lists = 100);

Tune search parameters:

# Limit number of probes (trades accuracy for speed)
# In SQLAlchemy
from sqlalchemy import text

# Set session variable for next queries
await session.execute(text("SET ivfflat.probes = 10;"))

# Then perform vector search

Optimize embedding dimension and storage:
- Use the smallest embedding size that maintains accuracy
- Consider dimensionality reduction techniques
- For very large collections, implement clustering or partitioning

Supabase Connection Issues

IPv4 vs IPv6 connection problems

Symptoms:

Error: could not connect to server: Connection timed out (0x0000274C/10060) Is the server running on host "db.abcdefghijkl.supabase.co" and accepting TCP/IP connections on port 5432?
Connection works from one network but not another
Intermittent connection issues

Solutions:

Force IPv4 connections:

# Add host_name=ipv4: prefix to force IPv4
# Original: postgresql+asyncpg://username:password@db.abcdefghijkl.supabase.co:5432/postgres
# Modified:
"postgresql+asyncpg://username:password@ipv4:db.abcdefghijkl.supabase.co:5432/postgres"

Disable IPv6 on your local machine (temporary test):

# For Linux
sudo sysctl -w net.ipv6.conf.all.disable_ipv6=1

# For Windows (run in PowerShell as Administrator)
Disable-NetAdapterBinding -Name "*" -ComponentID ms_tcpip6

# For macOS
networksetup -setv6off Wi-Fi  # or your network interface

Use Supabase connection pooler instead of direct connection:
- Go to Supabase Dashboard > Project Settings > Database > Connection Pooling
- Use the provided connection string which may bypass IP version issues
```
postgresql://[user]:[password]@[host].pooler.supabase.com:6543/postgres
```

Test connection using domain instead of IP address:

psql -h db.abcdefghijkl.supabase.co -p 5432 -U postgres -d postgres

Network and firewall issues with Supabase

Symptoms:

Connections time out after exactly 30-60 seconds
Error: OperationalError: could not connect to server: Connection refused
Error: ssl_error_syscall with Supabase connection

Solutions:

Check if your network allows outbound connections to port 5432:

# Test connectivity with netcat
nc -zv db.abcdefghijkl.supabase.co 5432

# Alternative with telnet
telnet db.abcdefghijkl.supabase.co 5432

Check if your IP is allowlisted in Supabase:
- Go to Supabase Dashboard > Project Settings > Database > Network Restrictions
- Add your current IP address to the allowed list
- If using dynamic IPs, you may need to keep this updated

Use Supabase’s HTTPS connection pooler for restrictive networks:

# Connection pooler over HTTPS (port 443)
# This may bypass firewall restrictions on standard PostgreSQL ports
postgresql://[user]:[password]@[host].pooler.supabase.co:6543/postgres?pgbouncer=true

VPN or proxy solutions:
- If your network has strict firewall rules, consider using a VPN
- Configure a proxy that allows PostgreSQL connections

Supabase connection pool exhaustion

Symptoms:

Error: remaining connection slots are reserved for non-replication superuser connections
Connections work initially but fail under load
Error: sorry, too many clients already when multiple services connect

Solutions:

Configure proper connection pooling in application:

# Properly configured connection pooling for Supabase
engine = create_async_engine(
    DATABASE_URL,
    pool_size=3,  # Keep pool size small for Supabase
    max_overflow=2,
    pool_timeout=30,
    pool_recycle=1800,  # Recycle connections after 30 minutes
    pool_pre_ping=True,  # Verify connections are still alive
)

Use Supabase’s connection pooler:

# Add pgbouncer=true parameter
postgresql://[user]:[password]@[host].pooler.supabase.co:6543/postgres?pgbouncer=true

Implement aggressive connection clean-up:

# Ensure connections are properly closed
async def get_db():
    async with async_session() as session:
        try:
            yield session
        finally:
            await session.close()
            
# For non-FastAPI applications, always use try/finally
try:
    async with async_session() as session:
        # your code
finally:
    await engine.dispose()  # Close all connections when done

Monitor and adjust connection limits in dashboard:
- Go to Supabase Dashboard > Project Settings > Database > Connection Pooling
- Adjust pool mode and connection limits based on your tier
- For Free and Pro tiers, be especially careful with connection counts

Region and latency issues with Supabase

Symptoms:

High latency on database operations
Intermittent timeouts despite successful connections
Connection issues during specific times of day

Solutions:

Select appropriate Supabase region:
- When creating a new project, choose the region closest to your users/servers
- For existing projects, consider migrating to a closer region if latency is critical

Configure longer timeouts for high-latency connections:

# Increase connection timeout and command timeout
engine = create_async_engine(
    DATABASE_URL,
    connect_args={
        "command_timeout": 30.0,  # Seconds for each query
        "timeout": 60.0           # Seconds for connection establishment
    }
)

Implement connection caching for read-heavy workloads:

# Use Redis or in-memory caching for frequently accessed data
from functools import lru_cache

@lru_cache(maxsize=100)
async def get_cached_data(id):
    async with async_session() as session:
        result = await session.execute(select(Data).where(Data.id == id))
        return result.scalar_one_or_none()

Consider Supabase Edge Functions for latency-sensitive operations:
- Deploy Edge Functions closer to the database
- Reduce round-trip time for critical operations

SSL and TLS issues with Supabase

Symptoms:

Error: SSL SYSCALL error: EOF detected
Error: ssl_error_want_read or ssl_error_want_write
Error: SSL error: certificate verify failed

Solutions:

Configure SSL properly:

# Properly configure SSL for Supabase connection
engine = create_async_engine(
    DATABASE_URL,
    connect_args={
        "ssl": True,
        "sslmode": "require"  # Or verify-full for stricter validation
    }
)

For development/testing, disable strict certificate verification (not recommended for production):

# Less strict SSL for development only
import ssl

engine = create_async_engine(
    DATABASE_URL,
    connect_args={
        "ssl": ssl.create_default_context(),
        "ssl_context": ssl.create_default_context(ssl.Purpose.SERVER_AUTH, cafile=None)
    }
)

Update CA certificates:

# For Linux
sudo update-ca-certificates

# For macOS
brew install openssl@3

# For Windows
# Check for Windows updates or update browser

Use Supabase connection string with SSL parameters:

postgresql://[user]:[password]@[host].supabase.co:5432/postgres?sslmode=require

Supabase pricing tier limitations

Symptoms:

Hitting connection limits unexpectedly
Database operations becoming slower at certain times
Error: sorry, too many clients already despite proper connection pooling

Solutions:

Be aware of tier limitations:
- Free tier: Limited connections, compute, and may pause after inactivity
- Pro tier: Higher limits but still restricted compared to enterprise
- Check current tier limits at Supabase dashboard

Implement application-level connection management:

# Add retry logic with exponential backoff
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(5), wait=wait_exponential(multiplier=1, min=2, max=60))
async def execute_db_operation():
    async with async_session() as session:
        # Your database operations

Add monitoring to track connection usage:

# Log connection information
import logging

logging.info(f"Engine stats: {engine.pool.status()}")

# Create dashboards to monitor connection count trends

Consider upgrading tier or optimizing application:
- For production workloads, higher tiers provide better guarantees
- Implement aggressive connection pooling
- Add read replicas for read-heavy workloads if available in your tier

Next Steps

If you’ve resolved your database issues, consider reviewing these related guides:

If you’re still experiencing database problems, check PostgreSQL and SQLAlchemy official documentation or contact the development team for assistance.

🎯 Welcome

🚀 Getting Started

💡 Core Concepts

🏗️ Platform Architecture

🔐 Security & Auth

📡 API Reference

🛠️ Development Guides

🚨 Troubleshooting

Connection Issues

Migration Issues

SQLAlchemy Issues

PostgreSQL Issues

Performance Issues

Data Integrity Issues

Advanced Issues

Supabase Connection Issues

Next Steps

🎯 Welcome

🚀 Getting Started

💡 Core Concepts

🏗️ Platform Architecture

🔐 Security & Auth

📡 API Reference

🛠️ Development Guides

🚨 Troubleshooting

​Connection Issues

​Migration Issues

​SQLAlchemy Issues

​PostgreSQL Issues

​Performance Issues

​Data Integrity Issues

​Advanced Issues

​Supabase Connection Issues

​Next Steps

Connection Issues

Migration Issues

SQLAlchemy Issues

PostgreSQL Issues

Performance Issues

Data Integrity Issues

Advanced Issues

Supabase Connection Issues

Next Steps