Chapter 3: Storage and Retrieval

DDIA Chapter 3 — The internals that every backend engineer needs to understand. This is the chapter that transforms you from someone who USES databases to someone who UNDERSTANDS them. We break down Chapter 3 of "Designing Data-Intensive Applications" by Martin Kleppmann — covering the storage and retrieval internals that power every database you have ever used. What you will learn: - The world's simplest database — and why it reveals the fundamental storage trade-off - Hash Indexes and Bitcask — the fastest possible disk-backed key-value store and its fatal limitation - SSTables — how sorting the log solves the "keys must fit in RAM" problem - LSM-Trees — the architecture behind Cassandra, RocksDB, HBase, and Elasticsearch - Bloom Filters — how LSM-trees avoid unnecessary disk reads for missing keys - B-Trees — why they have dominated relational databases for 50 years - Write-Ahead Logs (WAL) — how databases survive crashes - B-Trees vs. LSM-Trees — the complete engineering trade-off analysis - Write Amplification — what it is, why it matters, and how it limits throughput - Clustered, Covering, and Multi-Column Indexes — the index types most engineers get wrong - In-Memory Databases — why Redis is fast (it is NOT what most engineers think) - OLTP vs. OLAP — two fundamentally different database problems - Data Warehousing, ETL, Star Schema, Snowflake Schema - Column-Oriented Storage — the technology behind BigQuery, Redshift, and Parquet - Bitmap Encoding and Vectorized Processing — how analytics databases achieve petabyte-scale performance - Materialized Views and OLAP Cubes — precomputing answers for sub-second analytics