Inosuke
5.4K posts

Inosuke
@Inosukeei_coder
• Tech • shitpost • profession backend • passion frontend • Learning & building. • Java fullstack dev
infinity castle انضم Kasım 2025
568 يتبع1K المتابعون

@Inosukeei_coder These types of DSA problems asked in Big tech. Just need practice and get the job .
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🧠 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?
Ritesh Roushan@devXritesh
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?
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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.
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@ravikiran_dev7 Efficient algorithms, needs.
To fetch & send data efficiently.
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@Inosukeei_coder Learning to code is easy.
Shipping reliable apps while staying sane is brutal. 😭
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@harsh567_8 Seriously my whole course undergraduate fees arr less.
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@Inosukeei_coder Exactly, not caching every possible query, but prefix-based tries + heavily cached popular prefixes + real-time query logs for the long tail.
Billions of daily searches = insane hit rate on the top 0.1% prefixes.
Speed comes from smart precomputation, not magic.
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