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sqlalchemy-vertica-python

library0.6.3pypypiunverified

sqlalchemy-vertica-python is a Vertica dialect for SQLAlchemy that utilizes the pure-Python DB-API driver `vertica-python` for database connectivity. It is currently at version 0.6.3, actively maintained by BlueLabs, and typically releases updates to align with SQLAlchemy versions or address bugs.

database

pip install sqlalchemy-vertica-python

github ↗package ↗

INSTALL

IMPORT

SIG · SQLALCHEMY-VERTICA

sqlalchemy-vertica-python

databasepythonv0.6.3

Install

1.8s avg

Import

—

Disk

18MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.6.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.000s · 20MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 1.8s · import 0.000s · 21MB

18MB installed

● package 18MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

create_engine

✓ from sqlalchemy import create_engine

✗ from sqlalchemy import create_engine

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}")

Debug

Known issues

breakingVersion 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.

affects: >=0.6.0

gotchaIt 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.

affects: All

gotchaThe 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.

affects: All

gotchaWhen 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.

affects: All

Upgrade

Version history

0.6.3latest on PyPI · released Jun 5, 2023

Audit

Dependencies

sqlalchemyrequiredCore dependency for database abstraction. Version 0.6.0+ is compatible with SQLAlchemy 2.0.

vertica-pythonrequiredThe underlying DB-API driver used by the dialect for Vertica connectivity. The dialect installation typically handles this dependency.

Agent activity

30 hits · last 30 days

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