If you’ve encountered the dreaded error psycopg2.DatabaseError: error with status PGRES_TUPLES_OK and no message from the libpq, you know how frustrating and confusing it can be. 

This error often shows up in Python applications using psycopg2, SQLAlchemy, or Flask when dealing with PostgreSQL databases. 

It seems like a cryptic message at first glance, but there are a few common reasons for it, each with its own solution. 

In this guide, I’ll share what I’ve learned from through various resources, such as GitHub discussions and Stack Overflow posts, on handling this psycopg2.DatabaseError. Let’s look in and solve this issue step-by-step.

Understanding the PGRES_TUPLES_OK Error

First, let’s break down the error message itself. The PGRES_TUPLES_OK part in the error code generally means the database operation was successful in retrieving rows but somehow ended up in an unexpected state. 

The real frustration comes from the “no message from the libpq” part—this tells us that the PostgreSQL client library (libpq) is unable to give a clear explanation for the failure.

So, why does this happen? Here are a few common scenarios:

  • Multiprocessing Conflicts – This error often arises when using multiprocessing in applications like Flask with SQLAlchemy.
  • Session Issues – In SQLAlchemy, unscoped sessions or session reuse in different threads or processes can lead to this issue.
  • Connection Pooling Misconfiguration – Connection pools that aren’t configured to handle multiple processes may cause deadlocks and trigger this error.

Solution 1: Handle Multiprocessing Correctly

One of the most common reasons for this error is using multiprocessing with psycopg2 in frameworks like Flask. Here’s a reliable fix that worked for me.

  • Close All Sessions Before Forking: In applications where multiprocessing is needed (e.g., with Celery workers), close all active SQLAlchemy sessions before starting any new process. This helps ensure that each process has a unique connection. 
    from sqlalchemy.orm import scoped_session

# Assume 'db_session' is the SQLAlchemy session
db_session.remove()
  • Configure Celery with a Scoped Session: If using Celery, configure the session as scoped, so each worker manages its database connection.
  • Use a Different Library: Some developers have found success with pg8000 instead of psycopg2 as it handles multiprocessing better with fewer issues in some configurations.

Solution 2: Properly Configure Connection Pooling in SQLAlchemy

Incorrect pooling configurations can also lead to the PGRES_TUPLES_OK error. To avoid issues, set up pooling in SQLAlchemy correctly:

  • Use SingletonThreadPool in Development: When testing locally, SingletonThreadPool can help avoid unnecessary connections. However, do not use this pool in production. 
    from sqlalchemy.pool import SingletonThreadPool

engine = create_engine("postgresql+psycopg2://user:password@host/dbname", poolclass=SingletonThreadPool)
  • Set Pool Size and Overflow: In production, you may want a more robust pool class. Adjust the pool_size and max_overflow parameters to match your app’s needs. 
    engine = create_engine("postgresql+psycopg2://user:password@host/dbname", pool_size=5, max_overflow=10)
  • Disable Connection Pooling (as a last resort): If nothing works, disable pooling by setting poolclass=NullPool, although this is not ideal for performance.

Solution 3: Check the Scope of SQLAlchemy Sessions 

If you’re working in Flask with SQLAlchemy, the sessions could be the issue. Scoped sessions prevent errors across multiple threads by ensuring each thread has its own session. If using Celery or other async tasks, make sure to scope your sessions appropriately. 

from sqlalchemy.orm import scoped_session, sessionmaker

# Ensure sessions are scoped
session_factory = sessionmaker(bind=engine)
Session = scoped_session(session_factory)
After implementing scoped sessions, ensure each worker (e.g., Celery worker) begins and closes its session independently. 

Solution 4: Recreate the Database Connection 

If you are facing this issue intermittently, another quick fix is to refresh the connection whenever you encounter the psycopg2.DatabaseError.


try:
    # Your database operation
except psycopg2.DatabaseError as e:
    # Log the error and reconnect
    print("Database error, reconnecting...")
    engine.dispose()  # Dispose of all pooled connections
    session.rollback()
    session.commit()
This can reduce the occurrence of the PGRES_TUPLES_OK error by ensuring a clean slate after an error.

The psycopg2.DatabaseError with status PGRES_TUPLES_OK is a tricky one, but with some adjustments to your session and connection management, you can work around it. Start by addressing any multiprocessing issues, double-check your connection pooling, and make sure each thread or process manages its sessions carefully best of luck!

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