sibr-module 0.2.2

A collection of helper modules for interacting with Google Cloud Platform services, designed to simplify common workflows.

SIBR Module

A collection of helper modules for interacting with Google Cloud Platform services, designed to simplify common workflows. This package provides easy-to-use classes for BigQuery, Google Cloud Storage, Secret Manager, and Cloud Logging.

Features

• BigQuery: Easily upload DataFrames to BigQuery tables with automatic schema detection and support for append, replace, and merge operations.
• CStorage: Upload and download files to and from Google Cloud Storage buckets.
• SecretsManager: Securely access secrets from Google Secret Manager.
• Logger: A flexible logger that supports both local file logging and integration with Google Cloud Logging.

Installation

You can install the package from PyPI:

pip install sibr-module

Quickstart

Ensure you are authenticated with Google Cloud. You can do this by running:

gcloud auth application-default login

If in use locally, make sure to set your credentials available as environment variables using: GOOGLE_APPLICATION_CREDENTIALS=path-to-your-credentials.

import pandas as pd
from sibr_module import BigQuery, Logger

# --- 1. Set up a logger ---
# This will log to a local file and, if enabled, to Google Cloud Logging
logger = Logger(log_name="my_app_logger", enable_cloud_logging=True)
logger.info("Application starting up.")

# --- 2. Prepare your data ---
data = {'name': ['Alice', 'Bob'], 'score': [85, 92]}
my_dataframe = pd.DataFrame(data)

# --- 3. Use the BigQuery helper ---
try:
    # Initialize the client with your Google Cloud Project ID
    bq_client = BigQuery(project_id="your-gcp-project-id", logger=logger)

    # Upload the DataFrame to a BigQuery table
    bq_client.to_bq(
        df=my_dataframe,
        dataset_name="my_dataset",
        table_name="my_table",
        if_exists="append"  # Options: 'append', 'replace', or 'merge'
    )

    logger.info("Successfully uploaded data to BigQuery.")

except Exception as e:
    logger.error(f"An error occurred: {e}")

Usage Details

BigQuery

The BigQuery class handles interactions with Google BigQuery.

• to_bq(df, dataset_name, table_name, if_exists='append', merge_on=None): Uploads a pandas DataFrame.
  • if_exists='append': Adds data to an existing table.
  • if_exists='replace': Deletes the existing table and creates a new one.
  • if_exists='merge': Updates existing rows and inserts new ones. Requires merge_on to be set with a list of key columns.
  • read_bq(query): Executes a query and returns the result as a pandas DataFrame.
  • dtype_map: Optional argument if the user wishes to specify a certain mapping of dtypes to Big Query types

CStorage

The CStorage class simplifies file operations with Google Cloud Storage.

• upload(local_file_path, destination_blob_name): Uploads a local file to the bucket.
• download(source_blob_name, destination_file_path): Downloads a file from the bucket.

SecretsManager

Access your secrets easily.

• get_secret(secret_id): Retrieves the latest version of a secret by its ID.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.