pygcpinventory 0.1.0

GCP Project Inventory Collection Library

pygcpinventory

GCP Project Inventory Collection Library - A lightweight Python library for collecting metadata from Google Cloud Platform objects across multiple services.

Overview

pygcpinventory provides a unified interface to discover and collect metadata from GCP resources including:

• ⏰ Cloud Scheduler jobs (TRIGGER)
• 🔄 Cloud Workflows (WORKFLOW)
• ⚡ Cloud Functions (FUNCTION)
• 📊 BigQuery datasets (DATASET)
• 🪣 Cloud Storage buckets (BUCKET)
• 📨 Pub/Sub topics (TOPIC)
• 📝 Logging sinks (SINK)

Features

• Stable Object IDs: Consistent ID generation across runs (OBJ0001, OBJ0002, ...)
• Type-Safe: Full type hints and enum-based object types
• Minimal Dependencies: Only requires GCP client libraries
• Test-Driven: 91% code coverage with comprehensive tests

Installation

pip install -e .

Quick Start

from gcpinventory import ETLObject, ObjectType, ObjectIDAssigner

# Create GCP objects
objects = [
    ETLObject(
        object_id=None,
        object_type=ObjectType.TRIGGER,
        name="daily-scheduler",
        gcp_resource_name="projects/my-project/locations/us-central1/jobs/daily-scheduler"
    ),
    ETLObject(
        object_id=None,
        object_type=ObjectType.WORKFLOW,
        name="etl-workflow",
        gcp_resource_name="projects/my-project/locations/us-central1/workflows/etl-workflow"
    ),
]

# Assign stable IDs
assigner = ObjectIDAssigner()
assigner.assign_ids(objects)

# Use the objects
for obj in objects:
    print(f"{obj.object_id}: {obj.name} ({obj.object_type.value})")

Output:

OBJ0001: daily-scheduler (TRIGGER)
OBJ0002: etl-workflow (WORKFLOW)

Core Components

ETLObject

Represents a discovered GCP object with metadata:

• object_id: Unique identifier (OBJ0001, OBJ0002, ...)
• object_type: Type of GCP resource (ObjectType enum)
• name: Object name
• gcp_resource_name: Full GCP resource path
• metadata: Additional service-specific metadata (dict)

ObjectType Enum

Seven supported GCP object types:

• TRIGGER - Cloud Scheduler jobs
• WORKFLOW - Cloud Workflows
• FUNCTION - Cloud Functions
• DATASET - BigQuery datasets
• BUCKET - Cloud Storage buckets
• TOPIC - Pub/Sub topics
• SINK - Logging sinks

ObjectIDAssigner

Assigns stable, unique IDs to objects:

• Generates IDs in format OBJ0001, OBJ0002, ...
• Deduplication: same object always gets same ID
• Supports reverse lookup (ID → name)

Development

# Install dev dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Run tests with coverage
pytest --cov=gcpinventory --cov-report=html

# Format code
black gcpinventory tests
isort gcpinventory tests

Testing

Test Coverage: 91% (27/27 tests passing)

# Run all tests (unit + integration)
pytest tests/ -v

# Run unit tests only
pytest tests/test_models.py tests/test_assigner.py -v

# Run integration tests with real GCP credentials
pytest tests/test_integration_gcp.py -v

Integration Tests

Integration tests validate the package with real GCP service accounts:

• Authentication: Verifies service account loading and GCP API connectivity
• BigQuery Collection: Tests fetching real datasets and creating ETLObjects
• Cloud Scheduler: Tests collecting Cloud Scheduler jobs as TRIGGER objects
• ID Assignment: Validates stable ID generation with production data
• Serialization: Tests ETLObject.to_dict() with real GCP metadata

Requirements:

• Service account file at: E:\A\GCP_ETL_Pipeline\hackathon\SyncFlow_GCP_Intelligence\config\service-account.json
• GCP project: prismatic-smoke-463810-c1
• APIs enabled: BigQuery, Cloud Scheduler

Project Structure

gcpinventory/
├── __init__.py          # Public API
├── version.py           # Version info
├── models.py            # Data models (ETLObject, ObjectType, EdgeType)
└── assigner.py          # ID assignment logic

tests/
├── test_models.py            # Model tests (11 tests)
├── test_assigner.py          # Assigner tests (8 tests)
└── test_integration_gcp.py   # Integration tests with real GCP (8 tests)

License

Apache License 2.0