Backend Project Implementation Guides

Deep-dive guides for backend projects that demonstrate production-level thinking. Each project includes what to build, why it matters, the key technical decisions to make, and how to discuss it in interviews. These complement the ideas listed in project-ideas.md by providing architectural depth and implementation roadmaps.

1. REST API with Authentication, Rate Limiting, and Caching

What to Build

A production-grade REST API for a resource management service (e.g., a bookmark manager, note-taking API, or task tracker) with JWT authentication, role-based access control, per-user rate limiting, and Redis-backed caching.

Why It Matters

This project demonstrates that you understand the middleware stack every production backend needs — auth, rate limiting, and caching are non-negotiable in real services.

Suggested Tech Stack

• Language: Go (for performance) or Node.js/Python (for speed of development)
• Database: PostgreSQL with connection pooling (pgxpool / Prisma)
• Cache: Redis
• Auth: JWT with RS256 signing, refresh token rotation
• Rate Limiting: Redis-based sliding window algorithm

Key Architecture Decisions to Implement

Authentication Flow:

POST /auth/register → hash password (bcrypt) → store in DB → return tokens
POST /auth/login    → verify credentials → issue access + refresh tokens
POST /auth/refresh  → validate refresh token → rotate → issue new pair

Rate Limiting Middleware:

Request → Rate Limiter → Auth Middleware → Handler → Cache → Response
            │                            │
         Redis ZSET              Check cache first
         sliding window          → cache miss → DB query

Caching Strategy:

• Cache-aside pattern: check Redis before DB, populate on miss
• Cache invalidation: on write, delete the corresponding cache key
• TTL-based expiry for less-critical data

What to Discuss in Interviews

• “Why did I use RS256 (asymmetric) instead of HS256 (symmetric) for JWT?”
• “How does refresh token rotation prevent token theft?”
• “Why sliding window over fixed window for rate limiting?”
• “Cache stampede prevention with singleflight/mutex”

Difficulty: Intermediate | Estimated Time: 2–3 weeks

2. Real-Time Notification Service

What to Build

A multi-channel notification service that accepts events via API, stores them, and delivers through WebSocket (for online clients), email, or webhooks. Support user preferences (opt-in/opt-out per channel), and notification batching.

Why It Matters

Notifications are a core backend concern for any product with active users. This project shows you understand event-driven architecture and real-time communication.

Suggested Tech Stack

• API: Node.js/Go with WebSocket support (ws / socket.io)
• Queue: Redis Streams or RabbitMQ for event buffering
• Storage: PostgreSQL for notification logs and user preferences
• Email: SMTP integration (Resend/SendGrid SDK)
• Deployment: Docker Compose for local, Kubernetes for production

Key Architecture Decisions

Producer ──► API ──► Message Queue ──► Dispatcher
                 │                     ├──► WebSocket (real-time)
                 │                     ├──► Email Worker
                 │                     └──► Webhook Worker
                 ▼
              PostgreSQL

Notification Flow:

1. API receives notification request → validates → pushes to queue
2. Dispatcher reads from queue → checks user preferences → routes to appropriate channel
3. WebSocket: delivers immediately to connected clients; queues for offline delivery
4. Email: batch similar notifications to reduce email volume
5. Mark as read/delivered through API

What to Discuss in Interviews

• “Why a message queue instead of direct delivery?” → decoupling, retry, backpressure
• “How do you handle WebSocket reconnection?” → heartbeat, message queue for offline state
• “How do you prevent notification spam?” → user preferences, rate limits, digest mode
• “At-least-once vs. exactly-once delivery trade-offs”

Difficulty: Hard | Estimated Time: 3–4 weeks

3. URL Shortener with Analytics

What to Build

While the basic URL shortener is listed in project-ideas.md, this guide focuses on the analytics engine: click tracking, geographic distribution, referrer analysis, device breakdown, and time-series aggregation for traffic patterns.

Why It Matters

A URL shortener is read-heavy with a high QPS requirement. The analytics layer adds data engineering depth — aggregation, time-series queries, and dashboard serving.

Suggested Tech Stack

• Core API: Go (for redirect performance)
• Storage: PostgreSQL for URL metadata, ClickHouse for analytics events
• Cache: Redis for hot URL lookups and rate limiting
• Analytics Ingestion: Kafka → ClickHouse
• Dashboard: Grafana or simple React frontend

Key Architecture Decisions

Short URL Request → Redis Cache → Hit? → 301 Redirect
                                  → Miss? → PostgreSQL → Cache → 301

Click Event → Kafka Topic → ClickHouse (batch insert)
                                  ↓
                           Grafana Dashboard

Analytics Schema Design:

CREATE TABLE click_events (
    short_code String,
    click_time DateTime,
    ip_address String,
    country LowCardinality(String),
    referrer String,
    user_agent String,
    device_type Enum8('mobile'=1, 'desktop'=2, 'tablet'=3)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(click_time)
ORDER BY (short_code, click_time);

What to Discuss in Interviews

• “Why ClickHouse over PostgreSQL for analytics?” → columnar storage, compression, fast aggregations
• “How do you handle click attribution when users block cookies?”
• “301 vs 302 redirect — which and why?” → 301 for caching, 302 for analytics accuracy
• “How do you prevent abuse (link farms, spam)?”

Difficulty: Intermediate-Hard | Estimated Time: 3 weeks

4. Job Queue with Priority Scheduling

What to Build

A distributed job queue that accepts tasks via API, schedules them based on priority (CRITICAL > HIGH > NORMAL > LOW), supports retries with exponential backoff, dead-letter queues, and worker health monitoring.

Why It Matters

Every backend that does async work (email, report generation, data processing) needs a job queue. Building one shows understanding of producer-consumer patterns, reliability, and scheduling.

Suggested Tech Stack

• API + Scheduler: Go or Python (FastAPI)
• Queue Backend: Redis (LIST + ZSET for priority) or PostgreSQL (SKIP LOCKED)
• Workers: Goroutine workers / Python multiprocessing
• Monitoring: Prometheus metrics + simple dashboard

Key Architecture Decisions

Producer → API → Priority Queue (Redis ZSET)
                    │
                    ▼
              Dispatcher (polls by priority)
                    │
              ┌─────┼─────┐
              ▼     ▼     ▼
           Worker Worker Worker
              │
         ┌────┴────┐
         ▼         ▼
      Success   Failure
         │         │
         ▼         ▼
      Complete   Retry? → Re-enqueue with higher delay
                         │
                         ▼ (max retries exceeded)
                      Dead Letter Queue

Priority Scheduling:

Redis ZSET: key = "queue:pending"
  score = (priority * 10^12) + (MAX_RETRIES - remaining_retries) * 10^8 + timestamp
  → Poll with ZPOPMIN for highest-priority job

What to Discuss in Interviews

• “Why Redis ZSET instead of a simple LIST?” → O(log N) priority dequeue
• “How do you prevent starvation of LOW priority jobs?” → aging: increase effective priority over time
• “What happens when a worker crashes mid-processing?” → heartbeat timeout → re-enqueue
• “Exactly-once vs. at-least-once processing” → idempotency keys

Difficulty: Hard | Estimated Time: 2–3 weeks

5. API Gateway

What to Build

A lightweight API gateway that sits in front of multiple backend services and handles routing, JWT authentication, rate limiting, request/response transformation, circuit breaking, and API versioning.

Why It Matters

API gateways are the front door of microservice architectures. Building one demonstrates systems thinking — you’re building infrastructure that other services depend on.

Suggested Tech Stack

• Language: Go (net/http, reverse proxy) or Rust (actix-web)
• Config: YAML-based route definitions
• Rate Limiting: Redis-backed sliding window
• Discovery: Static config → extend to Consul/etcd
• Metrics: Prometheus client library

Key Architecture Decisions

Client → API Gateway → Route Config → Backend Service A
                          │
                    ┌─────┼─────┐
                    │ Auth Middleware   │
                    │ Rate Limiter     │
                    │ Circuit Breaker  │
                    │ Logger           │
                    └─────────────────┘

Route Configuration Example:

routes:
  - path: /api/v1/users
    upstream: http://user-service:8080
    methods: [GET, POST]
    auth: true
    rate_limit: 100/minute
    timeout: 5s
    circuit_breaker:
      threshold: 5
      timeout: 30s
      half_open_requests: 3
  - path: /api/v1/orders
    upstream: http://order-service:8080
    methods: [GET, POST, PUT]
    auth: true
    rate_limit: 50/minute

What to Discuss in Interviews

• “Why build a gateway instead of using Kong/Envoy?” → understanding the internals, educational
• “How does the circuit breaker state machine work?” → closed → open → half-open
• “How do you handle upstream timeouts?” → context cancellation, configurable timeout per route
• “Path-based vs. header-based routing trade-offs”

Difficulty: Hard | Estimated Time: 3–4 weeks

6. Rate Limiter as a Service

What to Build

A standalone rate limiting microservice that other services call (or deploy as a sidecar). Supports multiple algorithms (token bucket, sliding window, fixed window, leaky bucket), configurable limits per API key/user/IP, and a management API for dynamic rule updates.

Why It Matters

Rate limiting is a cross-cutting concern. Building it as a service shows you understand distributed coordination and can design reusable infrastructure.

Suggested Tech Stack

• Language: Go (for the service) + Lua scripts for Redis atomicity
• State Store: Redis (atomic operations via Lua scripts)
• Protocol: gRPC for inter-service calls, HTTP for management API
• Deployment: Docker, configurable as sidecar or standalone

Key Architecture Decisions

Client → Protected Service → Rate Limiter (gRPC call) → Redis
              │                       │
              ▼                       ▼
         Rate Limited?         Token Bucket Check
         Return 429              via Lua Script (atomic)

Atomic Rate Check (Redis Lua):

local key = KEYS[1]
local limit = tonumber(ARGV[1])
local window = tonumber(ARGV[2])
local now = tonumber(ARGV[3])

redis.call('ZREMRANGEBYSCORE', key, 0, now - window)
local count = redis.call('ZCARD', key)
if count < limit then
    redis.call('ZADD', key, now, now .. ':' .. math.random())
    redis.call('EXPIRE', key, window)
    return {count + 1, limit}
else
    return {-1, limit}
end

What to Discuss in Interviews

• “Why Lua scripts instead of separate Redis commands?” → atomicity, no race condition
• “Token bucket vs. sliding window — when to use which?”
• “How does the sidecar pattern compare to a centralized service?”
• “Handling clock skew in distributed environments”

Difficulty: Intermediate-Hard | Estimated Time: 2 weeks

How to Choose Your Project

Common Mistakes

1. Skipping observability — add logging, metrics, and health checks from day one
2. Not writing tests — even 5 integration tests show engineering maturity
3. Hardcoding configuration — use environment variables and config files
4. Ignoring error handling — every API should have proper error responses with status codes
5. No documentation — README with architecture diagram, API docs, and run instructions