google-cloud-alloydb-connector 0.1.3

A Python client library for connecting securely to your Google Cloud AlloyDB instances.

AlloyDB Python Connector

The AlloyDB Python Connector is an AlloyDB Connector library designed for use with the Python language.

Using an AlloyDB Connector provides the following benefits:

• IAM Authorization: uses IAM permissions to control who/what can connect to your AlloyDB instances

• Improved Security: uses robust, updated TLS 1.3 encryption and identity verification between the client connector and the server-side proxy, independent of the database protocol.

• Convenience: removes the requirement to use and distribute SSL certificates, as well as manage firewalls or source/destination IP addresses.

The AlloyDB Python Connector is a package to be used alongside a database driver. Currently supported drivers are:

• pg8000

Installation

You can install this library with pip install:

pip install "google-cloud-alloydb-connector[pg8000]"

Usage

This package provides several functions for authorizing and encrypting connections. These functions are used with your database driver to connect to your AlloyDB instance.

AlloyDB supports network connectivity through private, internal IP addresses only. This package must be run in an environment that is connected to the VPC Network that hosts your AlloyDB private IP address.

Please see Configuring AlloyDB Connectivity for more details.

APIs and Services

This package requires the following to connect successfully:

• IAM principal (user, service account, etc.) with the AlloyDB Client role or equivalent. Credentials for the IAM principal are used to authorize connections to an AlloyDB instance.

• The AlloyDB Admin API to be enabled within your Google Cloud Project. By default, the API will be called in the project associated with the IAM principal.

Credentials

This library uses the Application Default Credentials (ADC) strategy for resolving credentials. Please see these instructions for how to set your ADC (Google Cloud Application vs Local Development, IAM user vs service account credentials), or consult the google.auth package.

How to use this Connector

To connect to AlloyDB using the connector, inititalize a Connector object and call it's connect method with the proper input parameters.

The Connector itself creates database connection objects by calling its connect method but does not manage database connection pooling. For this reason, it is recommended to use the connector alongside a library that can create connection pools, such as SQLAlchemy. This will allow for connections to remain open and be reused, reducing connection overhead and the number of connections needed.

In the Connector's connect method below, input your AlloyDB instance URI as the first positional argument and the name of the database driver for the second positional argument. Insert the rest of your connection keyword arguments like user, password and db etc.

To use this connector with SQLAlchemy, use the creator argument for sqlalchemy.create_engine:

from google.cloud.alloydb.connector import Connector
import sqlalchemy

# initialize Connector object
connector = Connector()

# function to return the database connection
def getconn():
    conn = connector.connect(
        "projects/<YOUR_PROJECT>/locations/<YOUR_REGION>/clusters/<YOUR_CLUSTER>/instances/<YOUR_INSTANCE>",
        "pg8000",
        user="my-user",
        password="my-password",
        db="my-db-name"
    )
    return conn

# create connection pool
pool = sqlalchemy.create_engine(
    "postgresql+pg8000://",
    creator=getconn,
)

The returned connection pool engine can then be used to query and modify the database.

# insert statement
insert_stmt = sqlalchemy.text(
    "INSERT INTO my_table (id, title) VALUES (:id, :title)",
)

with pool.connect() as db_conn:
    # insert into database
    db_conn.execute(insert_stmt, parameters={"id": "book1", "title": "Book One"})

    # query database
    result = db_conn.execute(sqlalchemy.text("SELECT * from my_table")).fetchall()

    # commit transaction (SQLAlchemy v2.X.X is commit as you go)
    db_conn.commit()

    # Do something with the results
    for row in result:
        print(row)

To close the Connector object's background resources, call it's close() method as follows:

connector.close()

Using Connector as a Context Manager

The Connector object can also be used as a context manager in order to automatically close and cleanup resources, removing the need for explicit calls to connector.close().

Connector as a context manager:

from google.cloud.alloydb.connector import Connector
import sqlalchemy

# build connection
def getconn():
    with Connector() as connector:
        conn = connector.connect(
            "projects/<YOUR_PROJECT>/locations/<YOUR_REGION>/clusters/<YOUR_CLUSTER>/instances/<YOUR_INSTANCE>",
            "pg8000",
            user="my-user",
            password="my-password",
            db="my-db-name"
        )
    return conn

# create connection pool
pool = sqlalchemy.create_engine(
    "postgresql+pg8000://",
    creator=getconn,
)

# insert statement
insert_stmt = sqlalchemy.text(
    "INSERT INTO my_table (id, title) VALUES (:id, :title)",
)

# interact with AlloyDB database using connection pool
with pool.connect() as db_conn:
    # insert into database
    db_conn.execute(insert_stmt, parameters={"id": "book1", "title": "Book One"})

    # commit transaction (SQLAlchemy v2.X.X is commit as you go)
    db_conn.commit()

    # query database
    result = db_conn.execute(sqlalchemy.text("SELECT * from my_table")).fetchall()

    # Do something with the results
    for row in result:
        print(row)

Support policy

Major version lifecycle

This project uses semantic versioning, and uses the following lifecycle regarding support for a major version:

Active - Active versions get all new features and security fixes (that wouldn’t otherwise introduce a breaking change). New major versions are guaranteed to be "active" for a minimum of 1 year. Deprecated - Deprecated versions continue to receive security and critical bug fixes, but do not receive new features. Deprecated versions will be publicly supported for 1 year. Unsupported - Any major version that has been deprecated for >=1 year is considered publicly unsupported.

Supported Python Versions

We follow the Python Version Support Policy used by Google Cloud Libraries for Python. Changes in supported Python versions will be considered a minor change, and will be listed in the release notes.

Release cadence

This project aims for a minimum monthly release cadence. If no new features or fixes have been added, a new PATCH version with the latest dependencies is released.

Contributing

We welcome outside contributions. Please see our Contributing Guide for details on how best to contribute.