psycopg2.InterfaceError: connection already closed / pgr_astar

Resolving psycopg2.InterfaceError: connection already closed with pgr_astar

Learn to diagnose and fix the 'connection already closed' error when using psycopg2 with pgr_astar in PostgreSQL, ensuring robust database interactions for routing applications.

Encountering a psycopg2.InterfaceError: connection already closed can be a frustrating experience, especially when working with complex database operations like pathfinding using pgr_astar in PostGIS/pgRouting. This error typically indicates that your Python application attempted to use a database connection that was no longer active or had been explicitly closed. This article will delve into the common causes of this issue in the context of psycopg2 and pgr_astar, and provide practical solutions to ensure your database connections remain stable and reliable.

Understanding the 'Connection Already Closed' Error

The psycopg2.InterfaceError: connection already closed error is a clear signal that the psycopg2 driver tried to execute an operation on a database connection object that is no longer valid. This can happen for several reasons, including:

• Explicit Closure: The connection was manually closed using conn.close() earlier in the code.
• Connection Pooling Issues: If using a connection pool, a 'stale' connection might have been returned, or the pool might have closed connections due to inactivity or resource limits.
• Server-Side Closure: The PostgreSQL server itself might have terminated the connection due to timeouts, restarts, or other server-side issues.
• Application Logic Errors: The application might be attempting to use a connection object after it has gone out of scope or after a function that closed it has completed.

When pgr_astar is involved, these issues can be exacerbated by long-running queries or complex transaction management, making it crucial to manage connections carefully.

flowchart TD
    A[Python Application] --> B{Request pgr_astar Path}
    B --> C{Acquire DB Connection}
    C --> D{Execute SQL Query (pgr_astar)}
    D -- Connection Closed Unexpectedly --> E[psycopg2.InterfaceError]
    D -- Query Success --> F{Process Results}
    F --> G{Release/Close Connection}
    E --> H[Error Handling/Retry Logic]
    H --> C

Flowchart illustrating the typical process and potential failure point leading to 'connection already closed' during a pgr_astar query.

Common Scenarios and Solutions

Let's explore specific scenarios where this error commonly arises and how to address them effectively.

Scenario 1: Connection Closed Prematurely

This is perhaps the most straightforward cause. A connection might be closed in one part of your code, and then another part attempts to use the same connection object. This often happens with global connection objects or when connections are passed between functions without proper lifecycle management.

import psycopg2

def get_connection():
    return psycopg2.connect("dbname=routing user=postgres password=your_password")

def run_query(conn):
    try:
        with conn.cursor() as cur:
            cur.execute("SELECT 1")
            print("Query executed successfully.")
    except psycopg2.InterfaceError as e:
        print(f"Error: {e}")

# Problematic usage:
conn = get_connection()
conn.close() # Connection closed here
run_query(conn) # Attempting to use a closed connection

Example of prematurely closing a psycopg2 connection.

Solution 1: Context Managers and Local Connections

The most Pythonic and robust way to manage psycopg2 connections is by using them as context managers (with statement). This ensures that the connection is properly closed (or returned to a pool) even if errors occur. For pgr_astar queries, it's often best to acquire a fresh connection (or from a pool) for each significant operation.

import psycopg2

def execute_pgr_astar(start_node, end_node):
    try:
        # Acquire a new connection for this operation
        with psycopg2.connect("dbname=routing user=postgres password=your_password") as conn:
            with conn.cursor() as cur:
                query = f"""
                    SELECT seq, node, edge, cost, agg_cost
                    FROM pgr_astar(
                        'SELECT id, source, target, cost, reverse_cost FROM public.ways',
                        {start_node}, {end_node},
                        directed := true
                    );
                """
                cur.execute(query)
                results = cur.fetchall()
                print(f"pgr_astar path found: {results}")
                return results
    except psycopg2.Error as e:
        print(f"Database error during pgr_astar: {e}")
        return None

# Example usage:
execute_pgr_astar(1, 100)

Using psycopg2 as a context manager for pgr_astar queries.

Scenario 2: Connection Pooling and Stale Connections

In multi-threaded or long-running applications (like web servers), creating a new connection for every request is inefficient. Connection pooling is the solution, but it introduces its own challenges. A connection returned by the pool might have been closed by the server due to inactivity, or the pool itself might have a bug.

from psycopg2 import pool

# Initialize a connection pool (e.g., at application startup)
conn_pool = pool.SimpleConnectionPool(1, 20, "dbname=routing user=postgres password=your_password")

def get_path_from_pool(start_node, end_node):
    conn = None
    try:
        conn = conn_pool.getconn() # Get connection from pool
        with conn.cursor() as cur:
            query = f"""
                SELECT seq, node, edge, cost, agg_cost
                FROM pgr_astar(
                    'SELECT id, source, target, cost, reverse_cost FROM public.ways',
                    {start_node}, {end_node},
                    directed := true
                );
            """
            cur.execute(query)
            results = cur.fetchall()
            print(f"Path from pool: {results}")
            return results
    except psycopg2.InterfaceError as e:
        print(f"InterfaceError with pooled connection: {e}. Attempting to re-acquire.")
        if conn: # If we got a connection, it might be stale, discard it.
            conn_pool.putconn(conn, close=True) # Close and remove from pool
        # Optionally, retry the operation with a fresh connection
        # return get_path_from_pool(start_node, end_node) # Be careful with infinite loops!
        return None
    except psycopg2.Error as e:
        print(f"Database error with pooled connection: {e}")
        return None
    finally:
        if conn:
            conn_pool.putconn(conn) # Always return connection to pool

# Example usage:
get_path_from_pool(1, 100)

Handling stale connections with psycopg2 connection pooling.

Solution 2: Connection Pool Health Checks and Retries

Implement robust error handling around connection acquisition and usage. If an InterfaceError occurs, it's often best to discard the problematic connection from the pool and try again. psycopg2's connection pools (like SimpleConnectionPool or ThreadedConnectionPool) have mechanisms to manage this, but explicit handling can improve resilience.

1. Configure Connection Pool

Initialize your connection pool with appropriate min/max connections. Consider ThreadedConnectionPool for multi-threaded applications.

2. Wrap Operations in Try-Except-Finally

Always acquire and release connections within try...finally blocks to guarantee they are returned to the pool.

3. Handle InterfaceError

If psycopg2.InterfaceError occurs, call conn_pool.putconn(conn, close=True) to remove the bad connection. Then, you can implement a retry mechanism (with a limit to prevent infinite loops) to acquire a fresh connection and re-attempt the operation.

4. Set Server-Side Timeouts

Configure idle_in_transaction_session_timeout and statement_timeout in PostgreSQL to prevent very long-running or idle connections from consuming resources indefinitely. This can help prevent server-side closures that lead to stale connections.

Scenario 3: Long-Running pgr_astar Queries and Timeouts

pgr_astar can be computationally intensive, especially on large graphs. If a query runs for too long, the PostgreSQL server might terminate it due to statement_timeout settings, or the network connection might drop, leading to a closed connection error when the application tries to fetch results or commit.

Solution 3: Optimize Queries and Adjust Timeouts

Optimizing your pgr_astar queries and adjusting timeouts are key strategies here.

-- Example of setting a statement timeout for a session
SET statement_timeout TO '5min'; -- Set timeout to 5 minutes

SELECT seq, node, edge, cost, agg_cost
FROM pgr_astar(
    'SELECT id, source, target, cost, reverse_cost FROM public.ways',
    1, 100,
    directed := true
);

Setting a statement timeout in PostgreSQL.

import psycopg2

def execute_pgr_astar_with_timeout(start_node, end_node):
    try:
        with psycopg2.connect("dbname=routing user=postgres password=your_password") as conn:
            # Set statement timeout for this specific connection/session
            with conn.cursor() as cur:
                cur.execute("SET statement_timeout TO '2min'") # 2-minute timeout
                query = f"""
                    SELECT seq, node, edge, cost, agg_cost
                    FROM pgr_astar(
                        'SELECT id, source, target, cost, reverse_cost FROM public.ways',
                        {start_node}, {end_node},
                        directed := true
                    );
                """
                cur.execute(query)
                results = cur.fetchall()
                print(f"pgr_astar path found: {results}")
                return results
    except psycopg2.errors.QueryCanceled as e:
        print(f"pgr_astar query timed out: {e}")
        return None
    except psycopg2.Error as e:
        print(f"Database error: {e}")
        return None

execute_pgr_astar_with_timeout(1, 100)

Handling query timeouts in psycopg2 for pgr_astar.

Beyond timeouts, ensure your pgr_astar queries are as efficient as possible:

• Index your graph: Ensure source, target, and id columns in your ways table are indexed.
• Simplify graph: For very large networks, consider simplifying the graph for routing at different zoom levels.
• Use bounding boxes: If possible, filter the graph edges using a bounding box relevant to your start/end nodes before passing them to pgr_astar.