Reference

Notes.

Concise, scannable write-ups on the systems concepts behind every scalable product.

FoundationsPerformanceReliabilityTraffic ManagementAsync PatternsData

Client–Server Model

The request–response cycle that powers nearly every networked application. Understand roles, connections, and how HTTP sits on top.

HTTPTCPArchitecture

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Performance02

Latency, Throughput & Availability

Three numbers that define how a system feels under load. Learn the difference, why they conflict, and how to reason about each.

LatencyThroughputSLA

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Reliability03

Load Balancing

Distributing traffic across servers to prevent hotspots and increase fault tolerance. Covers round-robin, least-connections, and health checks.

Round-RobinHealth ChecksFault Tolerance

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Traffic Management04

Reverse Proxies

An invisible middleman that sits in front of your servers — handling SSL termination, routing strategies, and hiding backend complexity from clients.

SSL TerminationRoutingLoad Shedding

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Performance05

Caching

Store computed results close to the caller to reduce latency and backend pressure. Explore eviction policies, cache invalidation, and CDNs.

LRUTTLInvalidation

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Async Patterns06

Message Queues

Decouple producers from consumers using a durable buffer. Understand at-least-once delivery, ordering guarantees, and dead-letter queues.

KafkaRabbitMQPub/Sub

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Data07

Database Scaling

When a single database can't keep up — vertical limits, read replicas, connection pooling, and when to reach for a distributed store.

ReplicasPoolingOLAP vs OLTP

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Reliability08

Rate Limiting

Protect services from abuse and overload by capping requests per time window. Token bucket, sliding window, and distributed enforcement.

Token BucketSliding WindowDDoS

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Data09

Replication

Copy data across nodes for durability and read scale. Single-leader vs. multi-leader vs. leaderless, and the consistency trade-offs of each.

LeaderConsensusDurability

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Data10

Sharding

Partition a dataset horizontally across multiple nodes. Key-range vs. hash sharding, resharding pain, and cross-shard queries.

PartitioningHashHotspots

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Data11

Databases: SQL vs NoSQL

When to choose structured schemas with JOINs and ACID transactions versus flexible document stores — and how indexing changes everything.

SQLNoSQLIndexingACID

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Foundations12

CAP Theorem

In a distributed system you can guarantee at most two of Consistency, Availability, and Partition tolerance — and why that shapes every design.

ConsistencyAvailabilityPartitions

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Performance13

CDNs

Push static assets to edge servers close to users. Origin pull vs. push, cache-control headers, and when a CDN is the wrong answer.

EdgeCache-ControlLatency

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Interactive

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