Schema Weakness Enumeration

A comprehensive catalog of database schema anti-patterns learned from real-world data integrity failures

26+

SWE Patterns

Real Incidents

Years of Data

15+

Industries

SWE Impact Landscape

Visualizing schema weaknesses by Cantorian magnitude and incident frequency

X-axis: Incident Frequency - How often this pattern causes failures in production systems

Bubble Size: Estimated remediation complexity

Security Integrity Performance Availability

This scale reveals that some problems are not just larger in scale, but entirely different types of problems.

ℵ₀ (Countable) - Known and manageable complexity. Debt you can enumerate and systematically address.

A non-atomic schema migration causing a brief outage during a rolling deployment (e.g., Deno Deploy, 2021).
A single missing index causing performance degradation, which is fixable by adding the index back (e.g., Auth0, 2018).

ℵ₁ (Systemic) - Uncountable complexity emerges. Fixing one problem reveals unknown issues.

For example, a monolithic database cluster becoming a bottleneck for numerous services, requiring functional partitioning to alleviate (e.g., GitHub `mysql1`, 2020).
Outages caused by complex interactions between caching layers, database load, and maintenance activities (e.g., Slack, 2022; Honeycomb, 2023).

2^ℵ₀ (Chaotic) - Explosive chaos. The system becomes an unknown machine where fixes change behavior unpredictably.

For example, decades-old, inadequate crew scheduling systems unable to cope with modern operational scale and disruption complexity, requiring complete replacement (e.g., Southwest Airlines, 2022).
An architecture allowing broad, unconsented friend-of-friend data access via an API, leading to mass data harvesting with significant societal implications (e.g., Facebook/Cambridge Analytica, 2018).