9
Learning Objectives & Key Takeaways
0 / 4 Completed- Understand System Design core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
Architecture Ready
100%
What you will learn
- Cache-aside stores only what readers actually request and bounds staleness with a TTL.
- Write-through keeps the cache in sync by updating it right after the primary database write.
- Redis Pub/Sub broadcasts invalidation events but delivers at-most-once, so it is not a durable feed.
- Top-K gives approximate trending rankings cheaply but requires the RedisBloom module loaded on the server.
- Pick cache-aside or write-through based on your read and write patterns, not by default habit.
The Feed Page That Would Not Load Fast
At 9 AM on a Monday, the home feed for a mid-size social app started taking four seconds to render. The on-call engineer pulled up the database dashboard and saw the primary instance pinned at 95 percent CPU, serving the same feed queries over and over for the same popular users.
Every feed request ran a fresh read against Postgres: fetch posts, fetch author details, fetch like counts. Nothing was cached, so a thousand users looking at the same trending post triggered a thousand identical database round trips.
In the rest of this story
- Cache-Aside for Feed Reads
- Write-Through for Posts and Counters
- Broadcasting Invalidation With Pub/Sub
- Ranking the Feed With Top-K
- Choosing the Right Pattern for Each Piece of Data
Member-only story
Checking your membership…
Members read every Tech Buzz story in full, with code examples, audio and the discussion, plus every course, quiz and interview set in the Learning Dashboard.
