SQLAlchemy Vertica Dialect (sqlalchemy-vertica-python)

Common errors

• NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:python.vertica

  cause This error typically indicates that the `sqlalchemy-vertica-python` package is either not installed correctly, or the dialect name in the connection string is incorrect, preventing SQLAlchemy from finding the registered dialect.
  fix Ensure `sqlalchemy-vertica-python` is installed (`pip install sqlalchemy-vertica-python`) and that your connection string correctly uses the `vertica+vertica_python` prefix, e.g., `create_engine('vertica+vertica_python://...')`.

• sqlalchemy.exc.OperationalError: (vertica_python.errors.ConnectionError) Connection failed: [Errno 111] Connection refused

  cause This error indicates that the Python application could not establish a TCP/IP connection to the Vertica database server. Common causes include incorrect host, port, firewall blocking, or the Vertica server not running or listening on the specified port.
  fix Verify the `host` and `port` in your connection string are correct. Check network connectivity, firewall rules on both client and server, and ensure the Vertica database is running and accessible from the client machine.

• sqlalchemy.exc.ProgrammingError: (vertica_python.errors.QueryError) Password authentication failed for user 'your_user'

  cause The username or password provided in the connection string is incorrect for the specified Vertica user.
  fix Double-check the `user` and `password` in your connection string. Ensure they match a valid Vertica user account and its credentials.

• ValueError: Invalid SQL: SELECT name FROM sqlite_master WHERE type IN ('table', 'view') AND name=?; HINT: When argument 'parameters' is a tuple/list, variables in SQL should be specified with positional format (%s) placeholders. Question mark (?) placeholders have to be used with use_prepared_statements=True setting.

  cause This specific error often arises when `pandas.DataFrame.to_sql` attempts to introspect the schema using an SQLite-specific query, indicating a mismatch or misconfiguration in how the dialect interacts with `pandas`, or a misunderstanding of prepared statement placeholders by the underlying driver.
  fix Ensure the `con` parameter for `df.to_sql` is an SQLAlchemy `Engine` or `Connection` object, not a raw DBAPI connection. If the issue persists, explicitly define `schema` and `name` arguments for `to_sql` and specify `dtype` for columns.

Warnings

• breaking Version 0.6.0 introduced a significant update to support SQLAlchemy 2.0 syntax. Projects using older versions of `sqlalchemy-vertica-python` (pre-0.6.0) with SQLAlchemy 1.x will need to migrate their code to be compatible with SQLAlchemy 2.0 practices if upgrading the dialect.
  Fix: Review the SQLAlchemy 2.0 migration guide and update application code accordingly. For connection string usage, ensure `create_engine` is used. Consider pinning `sqlalchemy` to a 1.x version if immediate migration to 2.0 is not feasible with older dialect versions.
• gotcha It is crucial to explicitly close database connections. After using `engine.connect()`, ensure `connection.close()` is called, followed by `engine.dispose()`. Failing to do so can lead to resource leaks as Python's garbage collector might remove resources before the Vertica connector properly closes the session.
  Fix: Always use `with engine.connect() as connection:` to ensure connections are properly closed, or manually call `connection.close()` and `engine.dispose()` when managing connections manually.
• gotcha The PyPI project classifies its development status as '3 - Alpha'. This indicates that the library might still be evolving, features could be limited, and support may vary.
  Fix: Be aware of potential limitations and ensure thorough testing in production environments. Consider contributing to the project for missing features.
• gotcha When using `pandas.DataFrame.to_sql()` with `sqlalchemy-vertica-python`, there might be issues with `VARCHAR` columns if string variables are not explicitly cast. The dialect might default to `TEXT` which can cause problems.
  Fix: Explicitly define the `dtype` mapping for string columns to `sqlalchemy.types.VARCHAR` when using `df.to_sql` to ensure correct type inference and prevent issues.

Install

• pip install sqlalchemy-vertica-python Install latest version

Imports

• create_engine

  from sqlalchemy import create_engine

  The dialect is loaded implicitly via the connection string prefix.

Quickstart

This quickstart demonstrates how to establish a connection to a Vertica database using `sqlalchemy-vertica-python` and execute a simple query to retrieve the database version. Ensure your Vertica instance is accessible and replace placeholder credentials with actual environment variables or direct values.

import os
from sqlalchemy import create_engine, text

# Environment variables for connection details (replace with your Vertica credentials)
VERTICA_USER = os.environ.get('VERTICA_USER', 'dbadmin')
VERTICA_PASSWORD = os.environ.get('VERTICA_PASSWORD', 'password')
VERTICA_HOST = os.environ.get('VERTICA_HOST', 'localhost')
VERTICA_PORT = os.environ.get('VERTICA_PORT', '5433')
VERTICA_DB = os.environ.get('VERTICA_DB', 'VMart')

# Construct the connection string
connection_string = (
    f"vertica+vertica_python://{VERTICA_USER}:{VERTICA_PASSWORD}"
    f"@{VERTICA_HOST}:{VERTICA_PORT}/{VERTICA_DB}"
)

try:
    # Create an engine instance
    engine = create_engine(connection_string)

    # Establish a connection
    with engine.connect() as connection:
        # Execute a simple query
        result = connection.execute(text("SELECT version();"))
        
        # Fetch and print the result
        for row in result:
            print(f"Connected to Vertica. Version: {row[0]}")

except Exception as e:
    print(f"Error connecting to Vertica or executing query: {e}")