How It Works — Scalability

How a system grows from ten users to ten million without rewriting itself. These flows spread traffic across servers, keep data close to readers, and turn synchronous work into asynchronous pipes.

Traffic [Traffic] {1}

Spreading work across many servers so no single one carries the whole load.

Load Balancer — Round-Robin + Health Checks [Load Balancer] {1}

API Gateway [API Gateway] {1}

CDN Edge Lookup [CDN] {1}

Data [Data] {1}

How storage engines keep data durable, fast, and shareable across many readers — writes that survive crashes, caches that absorb reads, and shards that spread the load.

Postgres Write with WAL [Postgres WAL] {1}

Cache-Aside Pattern [Cache-aside] {1}

Leader-Follower Replication [Replication] {1}

Sharding — Routing by Hashed Key [Sharding] {1}

Optimistic vs Pessimistic Locking [Locking] {1}

Isolation Levels [Isolation] {1}

Messaging [Messaging] {1}

Asynchronous pipes — producers write once, consumers read at their own pace. These flows cover queues, streams, change capture, and large blobs.

Kafka Produce / Consume [Kafka] {1}

SQS with Dead-Letter [SQS] {1}

Change Data Capture — Debezium [CDC] {1}

S3 Multipart Upload [S3] {1}