Practice of Cloud-Native PostgreSQL Migration Solution Based on Multi-Agent Architecture

This article introduces a cloud-native PostgreSQL migration solution based on a multi-agent architecture, using Microsoft Agent Framework and Azure AI Foundry to build an agent collaboration system. It addresses pain points in traditional migration processes such as high complexity, high labor costs, and difficult verification, enabling automated migration, verification, and execution workflows.

Database migration is a common challenge for enterprises during cloud adoption. Traditional methods rely on manual scripts and verification, leading to issues like high process complexity (requiring fine-grained dependency management), high labor costs (professional staff on duty throughout), difficult verification (sampling checks can't guarantee 100% accuracy), and poor scalability (hard to coordinate parallel migrations). Multi-agent systems provide a new approach to solving these problems by decomposing tasks into independent agents and coordinating them.

This project adopts a cloud-native architecture, with core components including:

1. Microsoft Agent Framework: Responsible for task scheduling, state management, and inter-agent communication, supporting sequential execution, error handling, and retry mechanisms;
2. Azure AI Foundry: Provides GenAI capabilities to assist in schema difference analysis, migration script generation, and diagnosis of migration failure causes;
3. Azure Kubernetes Service (AKS): Serves as the execution environment, running migration tasks as Jobs and supporting auto-scaling;
4. Azure DevOps CI/CD: Integrates the migration process into pipelines, automatically triggers migration tests, and ensures script quality.

The system's migration process is completed through collaboration among multiple agents:

1. Data Reading and Backup: Connect to the local PostgreSQL, export schemas and data, and handle compatibility issues like character encoding and time zones;
2. Schema Migration: Compare metadata between the source and target databases, and automatically generate compatibility conversion scripts;
3. Data Loading: Supports batch import and resumable transfer; parallel import strategies are used for large tables;
4. Row Count Verification: Compare the number of rows in each table between the source and target databases to quickly identify obvious data loss issues.

The project is developed using Python 3.11 with a modular code structure (including Kubernetes configurations, Jobs, Dockerfile, CI/CD configurations, etc.);

• Dockerized Deployment: The image includes all dependencies and PostgreSQL client tools to ensure environment consistency;
• Kubernetes Job Scheduling: Each agent corresponds to a Job, supporting failure retries and parallel execution;
• Configuration Management: Sensitive information is managed via ConfigMaps and Secrets, and configuration templates are provided for quick deployment.

Advantages: Task decoupling (easy to maintain and test), flexible orchestration (adjust order/parallel execution), fault tolerance and recovery (automatic retries), observability (status records for easy monitoring); Applicable Scenarios: Local to Azure PostgreSQL migration, hybrid cloud periodic synchronization, automated repeatable processes, systems with basic consistency requirements; Limitations: Row count verification cannot detect subtle content differences; strict checks like hash comparison are needed for high-demand scenarios.

Summary: This solution combines AI intelligent decision-making with containerized elastic execution, providing a modern solution for enterprise data migration; Outlook: In the future, agents can handle more complex requirements (schema conflicts, performance optimization, risk prediction). It is recommended that enterprises try such technologies in advance to facilitate digital transformation.