Pillar 2: High-Level Design (HLD) 45-60 Minute Architecture Loop

High-Level System Design (HLD) for Engineering Leaders

In leadership loops, interviewers are not just checking if you know what a Load Balancer or Redis cache is. They are evaluating your operational maturity: How do squads own these services? What is the blast radius when a network link drops? How do you roll it out with zero customer downtime?

Hands-on Case Studies

3 Classic System Design Prompts in Leadership Loops

Study the exact requirements, component trade-offs, and failure mode defenses interviewers look for.

Design a Real-Time Team Activity Feed (Jira / Slack style)

Atlassian / Slack / Meta

The Prompt: Design an activity notification and audit stream that broadcasts issue updates, mentions, and pull request events to millions of concurrent active users with sub-second latency.

Scale Targets: 50M daily active users • 100k events/sec peak • 500ms P99 fanout latency

Interactive Architecture Simulator • Fan-out on Write vs. Read

Active Trade-Off Visualizer
Select Author Follower Profile:4,500 Followers
100 (Standard)10,000 (Celebrity Cutoff)150,000 (Org Broadcast)
1. Fan-out on Write (Push)RECOMMENDED FOR USER
Redis Write Operations:4,500 writes / post
Fan-out Write Latency:~2 ms
Timeline Read Latency (P99):~2.4 ms (O(1))
2. Fan-out on Read (Pull)SUB-OPTIMAL FOR READS
Outbox Write Operations:1 write / post
Fan-out Write Latency:< 1 ms
Timeline Read Latency (P99):~26 ms (Fan-in merge)
The Senior Engineering Lead Talk Track (Atlassian / Meta Rubric):

Standard Squad Member (< 10k followers): “With 4,500 followers, the write amplification is completely negligible for a Redis cluster (~2ms). We fan-out on write into each follower's timeline sorted set so timeline reads are instantaneous O(1) lookups (~2ms).”

Key SLO Target:High availability (99.99%) with multi-region active-passive failover
Lead Trade-off Focus:Fan-out on Write vs. Fan-out on Read: When to switch based on user follower count (celebrity/org problem)