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Most large systems don’t struggle with writes. They struggle with reads. Think about it: Thousands of users browsing products, feeds, or profiles every second. Instead of hitting one database, syste
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In an e-commerce app: Millions of users → browsing products Few users → placing orders So how do systems scale? Simple idea: Write → Master DB Read → Slave Replicas That’s Master–Slave architecture
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User updates profile… Refreshes immediately… Still sees old data 😳 That’s replication lag. Slaves update after master — not instantly. This leads to eventual consistency. 👉 Full explanation in
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Most applications are read-heavy 😳 If all reads hit one database → bottleneck. Solution? Distribute reads across replicas. That’s read scalability. 👉 Full explanation in bio #systemdesign #data
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How does Master-Slave actually work? 🤔 Writes → Master Replication → Slaves Reads → Slaves Simple flow. Powerful scaling. 👉 Full explanation in bio #systemdesign #database #masterslave #replicat
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Too many read requests? 😳 Instead of overloading one database — Distribute reads to replicas. Master writes. Slaves read. That’s Master-Slave architecture. 👉 Full explanation in bio #systemdesig
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High latency? Too many requests? 😳 You don’t scale randomly. You optimize smartly: - Indexing - Partitioning - Replication - Caching That’s how real systems handle traffic. 👉 Full explanation in
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App is slow… but database is overloaded? 😳 Too many read requests can crash a single DB. Master handles writes. Slaves handle reads. That’s how real systems scale. 👉 Full explanation in bio #sys
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Can we build a system that is: - Consistent ✅ - Available ✅ - Partition Tolerant ✅ No 😳 During network failure, you must sacrifice one. That’s the CAP trade-off. 👉 Full explanation in bio #syst
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Would you rather: Always get response… OR always get correct data? 🤯 Banking systems choose correctness. That’s CP. 👉 Full explanation in bio #systemdesign #captheorem #cp #distributedsystems #
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User must always get response — even if data is slightly outdated? 🤯 That’s AP. Availability + Partition Tolerance. Consistency may be delayed. 👉 Full explanation in bio #systemdesign #captheor
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Can we build a system that is Consistent AND Available? 🤯 Yes… but only if the network never fails. And in distributed systems — network failure is guaranteed. That’s why CA isn’t practical. 👉 F
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About @base2coder

Base 2 Coder is a Instagram growing creator. With 18 followers and 27 posts, base2coder's account shows a follower-to-following ratio of 9.0:1. View all content anonymously on Pictame - no login required.

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Out of the last 12 posts, 100% are videos and 0% are photos. New posts appear roughly every 2 days — a steady, reliable rhythm. Captions lean moderate, averaging 373 characters.

9.72% engagement rate — 94% above average for 1K-10K follower accounts.
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@base2coder

Base 2 Coder

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Base2Coder 👨‍💻 From Basics → Interview Ready DSA | System Design | Web Dev Helping you crack tech interviews 🚀

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