Pre-release

This release is a pre-release and may not be stable for production use.

Snowflake Connector for Python

[!CAUTION] This version of the driver is in public preview and is not ready for use with production data and may be unstable.

This package includes the Snowflake Connector for Python, which conforms to the Python DB API 2.0 specification.

The Snowflake Connector for Python provides an interface for developing Python applications that can connect to Snowflake and perform all standard operations. It provides a programming alternative to developing applications in Java or C/C++ using the Snowflake JDBC or ODBC drivers.

The connector has no dependencies on JDBC or ODBC. It can be installed using pip on Linux, Mac OSX, and Windows platforms where Python 3.11.0 (or higher) is installed.

Development

Prerequisites

Python 3.11+
uv package manager
Hatch build tool
Rust toolchain (for building core library)
Credentials: ../parameters.json (see main README.md for setup instructions)

Setup

Install uv and Hatch:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install Hatch
uv tool install hatch

Note: The Rust core library is built automatically during the build process via a custom Hatch build hook. You don't need to build it manually.

Environment Variables

IMPORTANT: Set up required environment variables before running tests:

export PARAMETER_PATH="$(pwd)/../parameters.json"

Hatch Environments

This project uses Hatch to manage development environments. There are two primary environments for running tests, each designed for a different use case:

`dev` Environment (for local development)

The dev environment is designed for human developers during active development. It installs the package from sources in editable mode, meaning any code changes are immediately reflected without reinstallation.

Key characteristics:

Installs from sources (skip-install = false)
Editable mode enabled (dev-mode = true)
Changes to source code are immediately available
Supports Python matrix: 3.11, 3.12, 3.13, 3.14

Usage:

# Run all tests with the dev environment
hatch run dev:all

# Run specific test types
hatch run dev:unit              # Unit tests only
hatch run dev:integ             # Integration tests only
hatch run dev:e2e               # End-to-end tests only

# Run with coverage
hatch run dev:all-cov

# Run with specific Python version
hatch run dev.py3.12:all

# Pass additional pytest arguments
hatch run dev:all -k test_connection --maxfail=1

`test` Environment (for CI/CD pipelines)

The test environment is designed for CI systems to test the end-to-end software development lifecycle (SDLC). It does not install from sources - instead, it expects a pre-built wheel to be installed, simulating how end users would install and use the package.

Key characteristics:

Skips source installation (skip-install = true)
No editable mode (dev-mode = false)
Requires explicit wheel installation via install-wheel script
Tests the actual built artifact, not source code

Usage:

# First, build the wheel
hatch build

# Then install and test with the test environment
hatch run test:install-wheel
hatch run test:all

# Or with specific Python version
hatch run test.py3.12:install-wheel
hatch run test.py3.12:all

Environment Comparison

Aspect	`dev`	`test`
Purpose	Local development	CI/CD pipelines
Installs from	Source (editable)	Pre-built wheel
Code changes	Immediately reflected	Requires rebuild
Tests	Source code	Built artifact
Use case	Developer workflow	E2E SDLC validation

Other Environments

# Code quality checks
hatch run precommit:check        # Run all checks (format, lint, type)
hatch run precommit:fix          # Auto-fix formatting and linting issues

# Reference connector tests (for compatibility testing, installs latest v4.x)
hatch run reference:test
# Pin a specific version if needed:
PYTHON_REFERENCE_DRIVER_VERSION='==4.3.0' hatch run reference:test

References

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