HighLoad Architecture: From Monolith to Microservices
All great projects started as monoliths. But one day you wake up and realize: deployment takes an hour, any fix breaks half the site, and the database is begging for mercy. Welcome to the world of HighLoad.
"Fat Monolith" Syndrome
A monolith is convenient at the start. But as load grows, it becomes a bottleneck.
- Scaling Difficulty: To speed up one module, you need to replicate the entire giant server.
- Single Point of Failure: A bug in the "newsletter" module can bring down "payments".
Step 1: Highload System Development
Transitioning to microservices isn't just changing code, it's changing mindset. Highload system development requires clear separation of concerns.
Monolith: User -> [App (Auth + Billing + Catalog)] -> DB
Microservices:
User -> [API Gateway]
-> [Auth Service] -> DB1
-> [Billing Service] -> DB2
-> [Catalog Service] -> DB3
Highload System Architecture: Main Principles
- Asynchrony: Services shouldn't wait for each other. Use queues (RabbitMQ, Kafka). User clicked "Buy" -> we replied "OK" and queued the task.
- Data Isolation: Each service has its own database. No touching another's DB directly, only via API.
- Fault Tolerance: The system must work even if one service is down (Graceful Degradation).
Risks and Pitfalls
Microservices are complex. You'll need powerful DevOps, Kubernetes (k8s), and distributed tracing (Jaeger). Don't implement them just for the hype.
Conclusion: Splitting a monolith is surgery. Perform it only when the patient has truly outgrown their old clothes.
Related services
FAQ for this topic
Traffic shape and data rarely match prod. You need scenarios, the same metrics as prod, and gradual ramp with rollback.
Often DB/query plans, connection pools, synchronous external calls, and queues are the first suspects for a quick checklist.
Not necessarily: invalidation, cold starts, and key skew can hurt. Cache is designed around read models and SLOs.
When vertical scaling and query tuning hit a ceiling and data growth is predictable along a shard key.
Want to apply this in practice?
Tell us about your system — we’ll propose a work plan and the metrics worth fixing in an SLA/SLO.
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