DPDarshil Parmarinnoobdiaries.hashnode.dev·4d ago · 29 min readDatabase Sharding: Splitting Data Across Machines to Scale WritesIn the replication article, we copied the database onto many machines. That scaled our reads and gave us survival (failover). But we ended on a hard wall: Replication scales reads, not writes. Ten r00
DPDarshil Parmarinnoobdiaries.hashnode.dev·Aug 10 · 20 min readDatabase Replication : How One Database Becomes Many - and Stays Consistent We've spent the series scaling stateless app servers - just add more behind a load balancer. But the database is stateful, and it's still sitting there as a single machine doing two dangerous things a00
DPDarshil Parmarinnoobdiaries.hashnode.dev·Aug 6 · 15 min readInside PostgreSQL and MySQL: Pages, CTID, Clustered Indexes, and MVCCEvery database "saves your data." But where does a row actually sit on your disk, and how does the database find it again in a table of 10 million rows? This article opens up the two most popular rela10
DPDarshil Parmarinnoobdiaries.hashnode.dev·Aug 4 · 10 min readLoad Balancing: How Horizontal Scaling Actually WorksIn the last article, we went through the core vocabulary of System Design — latency, throughput, scalability, availability, and more. While talking about horizontal scaling, one phrase kept coming up:10
DPDarshil Parmarinnoobdiaries.hashnode.dev·Jun 8 · 12 min readSystem Design Fundamentals: The Core Concepts Every Engineer Should KnowIn the previous article, we discussed why System Design matters and how applications face new challenges as they grow from hundreds of users to millions. Before diving into advanced topics like cachin10
