Designing Scalable Backend Systems: Principles I Use Every Day
Scalability is not something you bolt on at the end. Here are the architectural principles and practical patterns I apply when designing backend systems that need to grow.
Designing Scalable Backend Systems: Principles I Use Every Day
Scalability means different things depending on who you ask. To a startup with 100 users it means the system does not fall over when you get on the front page of a news site. To a mid-size company it means the system handles 10x current load without a rewrite. To a large enterprise it means distributed systems with automatic failover across regions.
The principles that get you from 100 to 10,000 users are largely the same as the ones that get you from 10,000 to 1,000,000 — but the implementation complexity increases significantly at each step. Here is what I apply at every scale.
1. Separate What Changes From What Stays Stable
The first design question is always: what changes frequently, and what is stable? High-churn data (logs, events, sessions) needs different storage than stable data (user profiles, product catalogues). Endpoints that are read-heavy need different treatment than write-heavy ones.
In Work Log Pro, work log entries are written constantly and read in aggregated form. I store them in MongoDB with indexes optimised for the query patterns I know will be common — date range queries per user, project, and team. The billing records are written rarely but must be durably consistent, so they get stricter write concern settings.
2. Stateless Services
Every service should be stateless — any instance should be able to handle any request. This is non-negotiable for horizontal scaling. If your application server holds session state in memory, you cannot run two instances without sticky sessions, which limits your scaling options.
Use JWT for authentication (stateless by design), store session data in Redis or a database, and never let application servers hold anything they cannot reconstruct from persistent storage.
3. Database Design First
Most scalability problems are database problems in disguise. An application server is usually cheap to scale horizontally — run more instances. A database is expensive to scale because data has weight.
Design your schema for your access patterns, not your domain model. Know which queries will be common and ensure those queries are served by indexes. Understand the cost of JOINs at scale and consider denormalisation when read performance matters more than write simplicity.