Description

What this gets into:
Database performance problems that become production incidents are almost always the result of schema design decisions, indexing choices, or query patterns that were established months or years earlier. This module develops the database design reasoning that prevents those problems from being designed in — treating database selection and query performance as architectural decisions with long-term consequences rather than implementation details to be optimized after the fact.

Technical territory covered:
– Database selection trade-offs by workload: the specific performance, consistency, operational, and query capability characteristics of relational, document, columnar, and graph database systems — how to evaluate which model fits a specific workload’s actual access patterns rather than its data structure, and how to identify the cases where a workload’s requirements don’t map cleanly onto any single database model
– Indexing strategy and its trade-offs: how database indexes work at the storage level, what different index types optimize for, how index design affects write performance and storage overhead alongside read performance, and how to design an indexing strategy that serves the full query workload rather than the most common query pattern
– Query execution plan analysis: how to read database query execution plans, what execution plan characteristics indicate performance problems, how to use execution plan analysis to diagnose slow queries, and how schema and query design decisions can be adjusted to produce more efficient execution plans without requiring schema migration

Estimated hours: +/- 6

Engineering outcome:
A database design and query performance reasoning capability that prevents the most common categories of database-induced production performance problems — grounded in understanding what databases are actually doing with the schemas and queries they receive rather than what documentation suggests they should do.