Harnessing Golang, PostgreSQL, and AI with Security in Mind
Multi-tenancy applications present unique challenges in isolating tenants while ensuring scalability, performance, and security. This project combines Golang's concurrency, PostgreSQL's robust data management with a database-per-tenant strategy, and AI-driven features like text extraction from images and PDFs using PaddleOCR via gRPC.
Leveraging goroutines and channels for concurrent task handling. Each request carries a tenant ID extracted from JWT tokens to route to the correct database.
Database-per-tenant strategy with connection pooling using pgx driver for optimal performance and isolation.
Python gRPC server for PaddleOCR text extraction integrated with Golang via gRPC for high-performance OCR processing.
JWT tokens with tenant ID claims ensure users only access their own tenant's data.
Credentials stored in HashiCorp Vault, fetched at runtime with no hardcoding.
Prepared statements with pgx and input sanitization prevent SQL injection.
Mutual TLS for gRPC communication between Golang and Python services.
Token bucket algorithm implementation for tenant-specific rate limiting.
Comprehensive logging of all requests with tenant ID, user ID, and actions.
Initial underestimation of connection needs required fine-tuning pool sizes and implementing connection timeouts.
PaddleOCR's resource intensity necessitated multiple gRPC servers behind a load balancer.
mTLS and audit logging introduced slight latency but provided essential protection.
Building this multi-tenancy app with Golang, PostgreSQL, and AI integrations was a challenging but rewarding journey. The combination of Golang's concurrency, PostgreSQL's reliability, and PaddleOCR's capabilities through gRPC created a powerful system. A strong focus on security at every layer ensured the solution remained robust while handling complex multi-tenant requirements.