A field guide to

Databases

A personal reference for the core ideas that come up again and again — schema design, storage and indexing, scaling, and transactions — each with a diagram you can click through and a concrete example. Not a substitute for depth, but a fast way back to the big picture.

Fundamentals

Relationships

How rows in one table connect to rows in another — one-to-one, one-to-many, and many-to-many — and the foreign keys and junction tables that implement each.

Normalization

Structuring tables so each fact is stored exactly once, eliminating redundancy and the update anomalies it causes.

Storage & Indexing

Indexing

A separate structure that trades extra storage and slower writes for dramatically faster lookups — but only on the columns and query shapes it was built for.

Storage Engines

How a database actually lays data out on disk — the two dominant designs are B-trees and LSM-trees, and they make opposite trade-offs.

Scaling

Partitioning

Splitting a large dataset into pieces — across nodes (sharding) or within one node — so size, write load, and query cost stay manageable.

Replication

Copying the same data to multiple nodes for fault tolerance and read scalability — the central trade-off is durability versus write latency.

Transactions & Consistency

ACID Transactions

A transaction groups several operations so they succeed or fail as one unit — ACID is the set of guarantees that makes that safe.

Isolation Levels

How strictly the database hides concurrent transactions from each other — stricter isolation prevents more anomalies at the cost of throughput.