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LSM Trees: Why Your Database Writes Are Fast and Your Reads Are Lying to You

DEV Community·우병수·21 days ago
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TL;DR: The thing that broke my comfortable ignorance about storage engines was a pipeline ingesting sensor telemetry — about 50,000 inserts per second into a PostgreSQL 15 cluster. The hardware wasn't cheap: NVMe drives, 32 cores, 128GB RAM. 📖 Reading time: ~42 min What's in this article The Problem That Made Me Actually Care About Storage Engines What an LSM Tree Actually Does (Without the Textbook Nonsense) The Write Path Step by Step The Read Path — Why It's More Expensive Than You Think Compaction: The Thing That Keeps LSM Trees From Falling Apart Setting Up RocksDB and Hitting the Real Rough Edges How Cassandra and ScyllaDB Use LSM Differently Than RocksDB LSM vs B-Tree Storage Engines: When You're Picking the Wrong Tool The Problem That Made Me Actually Care About Storage Engines The thing that broke my comfortable ignorance about storage engines was a pipeline ingesting sensor telemetry — about 50,000 inserts per second into a PostgreSQL 15 cluster.…

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