High-Level Design

18 posts

Distributed systems, scalability, and architecture.

Load Balancing: One Front Door for a Fleet That Keeps Changing

A single server melts under Black-Friday traffic. Load balancing is how you put ten servers behind one address — and the L4-vs-L7 choice, the routing algorithm, and the health checks are where systems live or die.

Asked at Amazon, Google, Netflix +3

Aug 8, 2026·16 min read

Mediumhigh level design distributed systems caching

Caching Strategies: The Bet That Reads Repeat

A cache is a wager that the same data gets read again before it changes. Get the read/write pattern, the eviction policy, and — the hard part — invalidation right, and one box absorbs the load that would have flattened your database.

Asked at Meta, Amazon, Netflix +3

Aug 8, 2026·13 min read

Mediumhigh level design distributed systems networking

CDNs: Move the Bytes to the User, Not the User to the Bytes

A user in Sydney pays ~200ms to reach a Virginia origin before any work happens — physics you can't code around. This is how CDNs cheat the speed of light with edge PoPs, anycast routing, and HTTP caching.

Asked at Cloudflare, Akamai, Fastly +3

Aug 8, 2026·16 min read

Mediumhigh level design distributed systems microservices

The API Gateway: One Front Door for a Hundred Services

When a mobile screen fires a dozen calls to a dozen services, each doing its own auth and its own CORS, the answer isn't a smarter client — it's a single entry point that owns the cross-cutting concerns once.

Asked at Netflix, Amazon, Uber +2

Aug 8, 2026·15 min read

Mediumhigh level design distributed systems databases

SQL vs NoSQL: Model the Access Pattern, Then Pick the Store

The choice isn't a religion — it's a consequence. Design how your data is read and written first, and the datastore that fits falls out of it. Pick the store first and you'll hand-join in application code for the next three years.

Asked at Amazon, Google, Netflix +3

Aug 8, 2026·15 min read

Mediumhigh level design distributed systems databases

Database Indexing: Why B-Trees Read Fast and LSM-Trees Write Fast

A missing index turns a one-user lookup into a scan of fifty million rows. Here's how the two great index families — B+ trees and LSM-trees — buy that lookup back, and what each one charges you for it.

Asked at Amazon, Google, Meta +3

Aug 8, 2026·13 min read

Mediumhigh level design distributed systems databases

Database Replication: Copies That Disagree With Each Other

Why the same query returns different answers on two servers a second apart — and the failover, lag, and conflict traps that turn 'just add a read replica' into a 3am incident.

Asked at Amazon, Google, Meta +3

Aug 8, 2026·15 min read

Hardhigh level design distributed systems databases

Database Sharding: When One Machine Stops Being Enough

Your 4 TB users table no longer fits — or no longer keeps up with writes — on a single node. Sharding is the answer, and the shard key you pick quietly decides everything that follows.

Asked at Amazon, Google, Meta +3

Aug 8, 2026·16 min read

Hardhigh level design distributed systems databases storage

Storage Engines: LSM-Trees vs B-Trees

Every database makes one foundational choice — how to lay bytes on disk. B-trees update in place; LSM-trees never overwrite, they append and compact. That single decision sets your write throughput, read latency, and space usage. Drive an LSM write path — memtable, flush, compaction — and see the tradeoff for yourself.

Asked at Google, Meta, Amazon +3

Aug 9, 2026·14 min read

Mediumhigh level design distributed systems messaging

Message Queues & Event Streaming: Stop Making Users Wait for Work They Don't Care About

A checkout that emails, bills, and updates inventory before it answers is one flaky partner away from a timeout. How queues and logs let producers and consumers stop holding each other hostage.

Asked at Amazon, LinkedIn, Netflix +3

Aug 8, 2026·17 min read

Mediumhigh level design distributed systems networking

Rate Limiting: How a Bucket of Tokens Keeps Your API Alive

One misbehaving client with a retry loop can take down a service faster than any DDoS. Rate limiting is the cheap, boring control that decides who gets served — here's how the five classic algorithms actually work, and how to make one correct across a fleet.

Asked at Stripe, Cloudflare, GitHub +2

Aug 8, 2026·16 min read

Mediumhigh level design distributed systems networking

Real-Time Updates: Polling, Long Polling, SSE, and WebSockets

HTTP can't push — the server only answers when asked. Here's the ladder of techniques that fake or fix that, each one solving the last one's flaw, and the fan-out problem that breaks all of them at scale.

Asked at Slack, Discord, WhatsApp +3

Aug 8, 2026·16 min read

Mediumhigh level design distributed systems consistency

CAP Theorem: The Choice You Only Make When the Network Breaks

Everyone quotes 'pick 2 of 3' and everyone is wrong. CAP is a decision forced only during a partition — and PACELC is the sharper lens for the 99.9% of the time when the network is fine.

Asked at Amazon, Google, Microsoft +3

Aug 8, 2026·12 min read

Hardhigh level design distributed systems consensus

Consensus & Leader Election: How a Cluster Agrees on One Truth

Split-brain corrupts data quietly. This is how Raft gets N crash-prone nodes to agree on a leader, a log, and a lock — and why a majority is the only quorum that can't lie to itself.

Asked at Google, Amazon, Microsoft +3

Aug 8, 2026·17 min read

Hardhigh level design distributed systems consensus

Raft, Driven: Watch a Cluster Elect, Replicate, and Heal

Everyone can recite 'Raft elects a leader by majority.' The hard part is the log — how a follower that missed writes during a partition gets repaired, and the one subtle rule that stops a committed entry from ever being lost. Drive a live 5-node cluster, then take it apart.

Asked at Google, Amazon, Microsoft +4

Aug 9, 2026·20 min read

Hardhigh level design distributed systems transactions

Distributed Transactions: When One Business Action Spans Many Services

The payment succeeded but the inventory reservation failed — and there's no ROLLBACK that spans both. How 2PC, Sagas, and the Outbox pattern give you atomicity when a single ACID transaction is no longer on the table.

Asked at Amazon, Uber, DoorDash +3

Aug 8, 2026·17 min read

Mediumhigh level design distributed systems reliability

Idempotency: How to Retry a Payment Without Charging Twice

The network fails after your server does the work but before the answer gets home, so the client retries — and a naive charge endpoint bills the card twice. Idempotency is the discipline that makes every retry safe.

Asked at Stripe, Amazon, PayPal +3

Aug 8, 2026·15 min read

Mediumhigh level design distributed systems data structures

Bloom Filters: The Yes/No That Saves a Disk Seek

A tiny bit-array that answers 'definitely not here' or 'maybe here' in constant time — how Cassandra, HBase, and Chrome skip millions of expensive lookups for keys that were never there.

Asked at Google, Amazon, Meta +3

Aug 8, 2026·12 min read