Ritesh Roushan

System Design Tip #5 Always start with non-functional requirements (scale, latency, availability). Prevents building something that won’t survive real traffic. Saved us from multiple complete redesigns. Do you clarify NFRs first?

Because sessions are stored in one server’s memory. With load balancing, the next request may hit a different server → no session → user appears logged out. Fix: • Use shared session store (Redis/DB) • Or enable sticky sessions (temporary fix) • Or move to stateless auth (JWT) Root issue: session state isn’t shared across instances.

Your API uses server-side sessions stored in memory. Works great until you add load balancing - users get logged out randomly. Why and how do you fix it?

@ChillxxLife Bro fr 😅 Best idea to get job faster.

Sacrificing myself for my homies 🤧😭😂

@Inosukeei_coder Poor guidance with weak projects.

What’s stopping more people from getting software jobs? - too much competition. - poor guidance. - inconsistency. - weak projects. Choose one.

@Inosukeei_coder These types of DSA problems asked in Big tech. Just need practice and get the job .

@devXritesh Amazing breakdown bro 🔥, & Here i understood they used trie data structure & also top k frequent concept we see this type of problems on many platforms.

🧠 Google autocomplete is straight-up sorcery. You type just "how to..." And it instantly drops: • how to make money • how to cook pasta • how to tie a tie This happens across 16.4 BILLION searches every single day in 2026. How the hell does it return perfect suggestions in under 100ms at this scale? Deep technical breakdown 👇 1. Trie (Prefix Tree) - The Magic Data Structure Every character is a node in a tree. Typing “h-o-w- -t-o” walks straight to the exact prefix node in O(K) time (K = length of input). No scanning billions of queries just instant pointer to all completions. 2. Precomputed + Smart Ranking Offline pipelines crunch trillions of past searches daily. Each trie node stores top-K suggestions pre-ranked by: • Frequency & trends • Click-through rate • Personalization (your history) + location + freshness ML models keep everything updated in real-time. 3. Production-Grade Scaling • Sharded & replicated across global data centers • In-memory layers (custom + Redis-style caching) for hottest prefixes • Edge/CDN caching + microservices load balancing • Handles 100,000+ queries per second with p99 latency <100ms This exact pattern powers autocomplete on LinkedIn, Amazon, YouTube anywhere you see instant suggestions. Backend & system design devs: this is the gold standard. Saved this thread? Drop 🔥 below. What Google feature should I break down next?

Here is the solution 👇 x.com/i/status/20348…

As a developer, Have you ever wondered : You type just "how to" in Google search and it instantly shows full suggestions like "how to make money", "how to cook pasta" etc... There are 8.5+ billion searches globally every day. How is this autocomplete so fast?

@knowRowan Nothing bro. being in tech it's eye opening post.

@devXritesh 🤷

@LuckyGoldx They have used Trie data structures and edge caching to serve this massive results.

..autocomplete looks up your keystrokes in a massive prefix tree stored in memory across data centers. edge servers cache the hottest prefixes, rank them by popularity/context, and return top results in milliseconds, no full web search needed. with 8.5 billion daily queries, they pre-compute and shard the index for speed.

@shub0414 It's trie DS and edge cache is real game in this massive result.

@devXritesh CDN maybe

@pravinemani Trie+ edge cache both

@devXritesh I assumed it was tries as well, the comments confirmed that:)

@knowRowan Correct 💯

@devXritesh Google's autocomplete runs on tries + massive caching

@shub0414 Great share subh

html ↓ css ↓ javascript ↓ git & github ↓ react ↓ node.js ↓ express ↓ mongodb ↓ rest apis ↓ authentication (jwt / oauth) ↓ typescript ↓ next.js ↓ docker ↓ ci/cd congrats you are a full-stack developer now

@0xlelouch_ Throughput is limited by partition parallelism. Adding consumers beyond partition count doesn’t help. Also check processing latency per message could be the real bottleneck.

Your Kafka consumer is processing messages very slowly. Increasing consumer count from 3 to 10 doesn't help. Why not and what's the bottleneck?

@SumitM_X I’d rebase. Keeps history clean and avoids a messy merge commit. But only if the branch isn’t shared otherwise, merge to avoid rewriting history.

Your feature branch has 50 commits. Main has moved ahead with 300 commits. What will you do: Rebase your branch or merge main into your branch?

@ChillxxLife You got this buddy and also add caching too.

@devXritesh Trie data structure 🤧 Last topic of DSA 💀🤧

@thekevinqi DS using trie + edge cache for faster lookups and result serving

@devXritesh Caching

@Itstheanurag Totally agree! Diversifying from field experts > one creator grind. Unemployed dev wisdom hits different .

See i believe if you want to learn something learn from the experts in that field. Learning about databases from ezsnippet would be stupid if Hussain nasser and arpit bhayani exist. Learning DSA from code with Harry would be stupid if kunal's videos exists. Learning rust from technical thapa would be stupid if let's get rusty exists. The point is stop working one guy, everyone on YouTube has something they are best known for. By not following them all you are hindering your progress. You are learning things from the top layer only but never from the core of it. Thanks for this matter, an unemployed dev and not the president of United States.

@Inosukeei_coder Helpful bro, keep sharing

30 tech companies with massive workforce + strong engineer pay.. - Amazon — 1,576,000 employees — SWE avg pay: ~$190k–$230k . - IBM — ~270,000 employees — SWE avg pay: ~$150k–$190k. - Microsoft — 228,000 employees — SWE avg pay: ~$220k–$250k . - Accenture — ~210,000 tech total company is far bigger — SWE avg pay: ~$120k–$170k. - Alphabet (Google) — 190,820 employees — SWE avg pay: ~$250k–$340k . - Apple — 166,000 employees — SWE avg pay: ~$230k–$280k. - Oracle — 162,000 employees — SWE avg pay: ~$180k–$230k. - Cisco — ~90,000 employees — SWE avg pay: ~$190k–$230k. - Salesforce — 76,453 employees — SWE avg pay: ~$210k–$260k . - Lenovo — 72,000 employees — SWE avg pay: ~$120k–$170k . - HP — 55,000 employees — SWE avg pay: ~$140k–$180k. - Intel — ~124,800 employees — SWE avg pay: ~$180k–$230k. - Dell — ~97,000 employees — SWE avg pay: ~$140k–$180k . - SAP — 109,973 employees — SWE avg pay: ~$160k–$210k . - Meta — ~74,000 employees — SWE avg pay: ~$280k–$380k . - Adobe — 31,360 employees — SWE avg pay: ~$210k–$260k . - NVIDIA — ~36,000 employees — SWE avg pay: ~$240k–$320k. -Uber — ~31,000 employees — SWE avg pay: ~$220k–$300k. LinkedIn — ~18,000 employees — SWE avg pay: ~$316k. - Intuit — 18,200 employees — SWE avg pay: ~$210k–$260k . - Airbnb — ~7,300 employees — SWE avg pay: ~$250k–$330k. - Atlassian — ~14,400 after recent layoffs — SWE avg pay: ~$180k–$240k . - ServiceNow — ~26,000 employees — SWE avg pay: ~$210k–$270k. - Workday — ~20,400 employees — SWE avg pay: ~$190k–$240k. - PayPal — ~27,000 employees — SWE avg pay: ~$170k–$220k. - Shopify — ~8,100 employees — SWE avg pay: ~$170k–$230k . - Expedia — 16,000 employees — SWE avg pay: ~$160k–$210k . - Block — ~12,000 employees — SWE avg pay: ~$200k–$270k. - Pinterest — ~4,200 employees — SWE avg pay: ~$220k–$300k. - Dropbox — ~2,300 employees — SWE avg pay: ~$210k–$280k.