@ankitcode99

SWE 2 @ Amazon Prime Video • xWalmart, Xalts, Kutumb • Career Coach @Topmate

 Bengaluru, India
topmate.io/ankitcode99
Joined November 2021
  • Tweets60
  • Following635
  • Followers40
4Media
Most engineers reach for Redis the moment someone says "we need a cache." It works, until data grows, traffic patterns shift, or you need something Redis wasn't designed for. Here are the trade-offs worth knowing before you spin up the instance. 🧵👇
1
17
more replies
4 questions worth answering before picking your cache: 1. How large will this data get? (memory ceiling) 2. Can I tolerate a cache miss? (fallback cost) 3. Do I need persistence across restarts? (durability) 4. Read-heavy or write-heavy? (throughput pattern)
1
7
Redis is excellent at what it was built for. The issue isn't Redis. It's defaulting to any tool before defining constraints. Define requirements first. The right tool is usually obvious once you do. This applies to every infrastructure decision, not just caching.
3
"Design a rate limiter." This question shows up in 80% of big tech system design rounds. Most candidates jump straight to token bucket. That's not wrong but it's also not an answer. Here's how to actually structure a response that signals senior-level thinking. 🧵 @Redisinc
1
20
more replies
How to handle Redis going down: Option A: "Fail open" let all requests through (availability wins) Option B: "Fail closed" block all requests (safety wins) Option C: Local fallback counter with eventual sync (complexity wins)
1
3
Interview answer in order: 1. Clarify requirements 2. Pick algorithm + justify it against requirements 3. Design single-server version 4. Scale to distributed address race conditions 5. Handle failure modes This structure works for almost all system design questions.
5
I run load tests across 200+ services at Prime Video. Here's what breaks first during a gameday. It's never the service you tested last month. It's always the dependency nobody thought to check. 🧵
1
1
173
more replies
What happens after a game day matters more than the game day itself. Every finding gets triaged: severity, owner, fix timeline. But the real value is the institutional knowledge. Your team now knows how the system behaves under pressure before a live event.
1
9
Gamedays aren't about finding bugs. They're about building confidence that when traffic spikes, a dependency flakes, or a region goes down, your system degrades gracefully instead of collapses completely. That's what makes it fun & challenging to work for PrimeVideo 🚀🚀
9
Netflix made chaos engineering famous. Most engineers took one lesson from it: randomly kill servers and see what breaks. That's not chaos engineering. That's just breaking things. Here's what it actually is and why the difference matters at scale... 🧵👇
1
22
more replies
Where most chaos programs die: - They only test in staging (prod has different load patterns) - No defined steady state, so results are uninterpretable - Teams treat it as a one-time exercise, not a practice - Findings go into a doc nobody reads Process beats tools. Always.
1
4
I run chaos experiments across 200+ services at Prime Video. The goal is never to break things. It's to find out what breaks before a live event does it for you. Next: what a real game day looks like from the inside. Follow so you don't miss it.
3
Load more