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System Design Fundamentals — Four Pillars

Data modeling, API design, caching, and load balancing — the four reasoning disciplines behind scalable systems, with interactive diagrams and interview-ready mental models.

Jul 8, 20264 min read

Introduction

System design is not art — it is reasoning discipline. The diagram should emerge from the use case, not the other way around. When you start with boxes before constraints, you optimize for aesthetics instead of reliability.

This series breaks system design into four pillars every engineer should reason about before talking about scale:

  1. Data Modeling — how entities are stored, related, and indexed
  2. API Design — the communication contract between services and clients
  3. Caching Strategy — where to cache, when to invalidate, and what consistency costs
  4. Load Balancing — how traffic is distributed for scale and failover

Each guide includes an interactive explorer you can click through during interview prep.

Quick index

#TopicDescription
1Data ModelingEntities, relations, indexes — without this, scale is just noise.
2API DesignEndpoints, versioning, request-response contracts that teams can integrate.
3Caching StrategyCache hit ratio, invalidation, and consistency trade-offs at the right layer.
4Load BalancingTraffic distribution, stickiness, health checks, and failover.

The four pillars

1. Data Modeling

Quote

If you cannot explain how entities are stored, how relations work, and where indexes belong — then scale is just noise.

If you cannot explain what you store, how records relate, and which queries need indexes, adding replicas or caches only moves the bottleneck.

Note

Start with read/write patterns: profile lookups, order history, inventory checks. The schema follows the access pattern — not the other way around.

Read the full guide →

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2. API Design

Endpoint naming, versioning, and request-response structure form the communication contract. A poor API means broken integrations, duplicated client logic, and painful migrations.

Tip

Design APIs for the consumer: predictable naming, explicit errors, pagination cursors, and version headers — not just database tables exposed over HTTP.

Read the full guide →

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3. Caching Strategy

Quote

Do not cache everywhere — cache in the right place.

Not every layer needs a cache. The difference between a fast system and a confusing one is hit ratio, invalidation logic, and consistency trade-offs at the correct boundary.

Performance

Cache public catalog reads at the CDN. Cache computed dashboards in Redis. Never cache auth tokens or financial balances without a strict TTL and invalidation plan.

Read the full guide →

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4. Load Balancing

Quote

When you know how to distribute traffic, the system scales.

Session stickiness, failover, and health checks are the backbone of reliability. A load balancer is not decoration — it is how you survive traffic spikes and unhealthy instances.

Warning

Sticky sessions simplify stateful apps but reduce failover flexibility. Know when to use them and when to externalize session state to Redis instead.

Read the full guide →

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Interview discipline

System design interviews test structured reasoning, not memorized architectures. Clarify requirements, estimate scale, identify bottlenecks, then propose components with trade-offs.

Interview Reflection

Which approach do you use in design rounds?

Interview Answer

What concept caught you in your last design interview? My observation: those who start from the use case — constraints, QPS, consistency — reach the diagram confidently. Those who draw a generic diagram first and then try to fit requirements get stuck on trade-offs.

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Kazi Rahamatullah

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Kazi Rahamatullah

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