Projects

SUSTech Merch Store Microservices

Distributed and Cloud Computing course project building a containerized merch store with REST API, gRPC data layer, and Kafka-based logging.

Background

  • Time: Oct 2025
  • Context: Assignment 2 for SUSTech “Distributed and Cloud Computing” (module: Services & API Architectures).
  • Repository: Tsurumalu/SUSTech-Merch-Store

Goal

Build a microservice-based online merch store for SUSTech-branded products. The system exposes RESTful APIs to clients, isolates database access behind a gRPC service, and streams operational logs to Kafka through a dedicated logging service—all orchestrated with Docker Compose.

Architecture

Three application services run alongside shared infrastructure:

ServiceRoleProtocol / Stack
API ServiceREST gateway, JWT auth, business orchestrationFlask, OpenAPI 3.0
DB ServiceUser, product, and order persistencegRPC + psycopg2
Logging ServiceCentralized log ingestiongRPC → Kafka

Infrastructure containers: PostgreSQL 17 (persistent storage), Zookeeper + Kafka (log channel log-channel).

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Client ──HTTP──► API Service ──gRPC──► DB Service ──► PostgreSQL
                    │
                    └──gRPC──► Logging Service ──► Kafka

REST API

Defined in openapi.yaml and implemented in Flask:

  • Dialog: GET / greeting endpoint
  • UserManager: register, deactivate, get user, update profile, login
  • ProductManager: list products, get product by ID
  • OrderManager: place order, cancel order, get order details

Protected routes use JWT (Bearer token, HS256, 24-hour expiration). Authentication failures and key operations are forwarded to the logging service.

Data Model

PostgreSQL schema (db-init/init.sql):

  • products: SUSTech Hoodie, Water Bottle, Notebook (pre-seeded with stock)
  • users: sid, username, email, password hash
  • orders: user–product linkage with quantity cap (1–3 per order) and computed total_price

Implementation Highlights

  • Service decomposition: API layer never talks to PostgreSQL directly; all DB operations go through the gRPC AssistantService.
  • Connection pooling: DB service uses psycopg2.pool.ThreadedConnectionPool for concurrent gRPC requests.
  • Structured logging: API service streams LogMessage events (register, login, order, auth failures) to the logging service, which publishes JSON payloads to Kafka via confluent_kafka.
  • Containerized deployment: compose.yaml wires api-server, db-server, and logging-server with health-checked Kafka/Zookeeper and volume-backed Postgres init scripts.
  • Dev utilities: Makefile helpers for local psql, DB reset, and Kafka log streaming.

Tech Stack

  • Python (Flask, gRPC, PyJWT)
  • PostgreSQL 17
  • Apache Kafka (Confluent Platform 7.7)
  • Docker & Docker Compose
  • OpenAPI 3.0
  • Protocol Buffers (gRPC service definitions)
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