A Python framework for building and deploying serverless REST APIs on Google Cloud Platform.
Goblet is a Python framework that enables developers to quickly build and deploy serverless REST APIs on Google Cloud Platform. It provides a decorator-based API to integrate with GCP services like Cloud Functions, Cloud Run, API Gateway, Pub/Sub, and Scheduler, automating infrastructure provisioning and deployment. The framework solves the problem of managing complex serverless configurations by offering a simple, code-first approach.
Python developers and teams building serverless applications on Google Cloud Platform who want to streamline API development and deployment. It's ideal for those who need to integrate multiple GCP services without manual infrastructure management.
Developers choose Goblet because it dramatically reduces the boilerplate and configuration needed for GCP serverless projects. Its tight integration with GCP services, local testing environment, and single-command deployment make it faster and easier to go from code to production compared to manual setups.
Goblet is an easy-to-use framework that enables developers to quickly spin up fully featured REST APIs with python on GCP
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Uses Python decorators to define routes, scheduled jobs, and GCP service integrations, drastically reducing boilerplate configuration code as shown in the README examples.
Supports a wide range of GCP services like Cloud Functions, Cloud Run, Pub/Sub, and Redis from a single framework, automating deployment and resource provisioning directly from code.
Includes a built-in local server for testing API endpoints without cloud deployment, enabling faster iteration and debugging, as demonstrated with the 'goblet local' command.
Automatically provisions related GCP resources such as Redis, VPC connectors, and alerts from code annotations, minimizing manual infrastructure management overhead.
Tightly coupled to Google Cloud Platform, making migration to other clouds or hybrid environments difficult without significant code rewrites or alternative frameworks.
The README notes issues like #88 where Cloudfunctions updates require code adjustments, adding maintenance overhead and potential deployment disruptions.
Deploying with private packages involves mounting credentials and custom Dockerfile steps, which can be error-prone and adds complexity beyond basic serverless workflows.