Writing Guidelines for Postgres References
This document provides guidelines for creating effective Postgres best practice references that work well with AI agents and LLMs.
Key Principles
1. Concrete Transformation Patterns
Show exact SQL rewrites. Avoid philosophical advice.
Good: "Use WHERE id = ANY(ARRAY[...]) instead of
WHERE id IN (SELECT ...)" Bad: "Design good schemas"
2. Error-First Structure
Always show the problematic pattern first, then the solution. This trains agents to recognize anti-patterns.
**Incorrect (sequential queries):** [bad example]
**Correct (batched query):** [good example]3. Quantified Impact
Include specific metrics. Helps agents prioritize fixes.
Good: "10x faster queries", "50% smaller index", "Eliminates N+1" Bad: "Faster", "Better", "More efficient"
4. Self-Contained Examples
Examples should be complete and runnable (or close to it). Include CREATE TABLE
if context is needed.
-- Include table definition when needed for clarity
CREATE TABLE users (
id bigint PRIMARY KEY,
email text NOT NULL,
deleted_at timestamptz
);
-- Now show the index
CREATE INDEX users_active_email_idx ON users(email) WHERE deleted_at IS NULL;5. Semantic Naming
Use meaningful table/column names. Names carry intent for LLMs.
Good: users, email, created_at, is_active
Bad: table1, col1, field, flag
Code Example Standards
SQL Formatting
-- Use lowercase keywords, clear formatting
CREATE INDEX CONCURRENTLY users_email_idx
ON users(email)
WHERE deleted_at IS NULL;
-- Not cramped or ALL CAPS
CREATE INDEX CONCURRENTLY USERS_EMAIL_IDX ON USERS(EMAIL) WHERE DELETED_AT IS NULL;Comments
- Explain why, not what
- Highlight performance implications
- Point out common pitfalls
Language Tags
sql- Standard SQL queriesplpgsql- Stored procedures/functionstypescript- Application code (when needed)python- Application code (when needed)
When to Include Application Code
Default: SQL Only
Most references should focus on pure SQL patterns. This keeps examples portable.
Include Application Code When:
- Connection pooling configuration
- Transaction management in application context
- ORM anti-patterns (N+1 in Prisma/TypeORM)
- Prepared statement usage
Format for Mixed Examples:
**Incorrect (N+1 in application):**
```typescript
for (const user of users) {
const posts = await db.query("SELECT * FROM posts WHERE user_id = $1", [
user.id,
]);
}
```Correct (batch query):
const posts = await db.query("SELECT * FROM posts WHERE user_id = ANY($1)", [
userIds,
]);Impact Level Guidelines
| Level | Improvement | Use When |
|---|---|---|
| CRITICAL | 10-100x | Missing indexes, connection exhaustion, sequential scans on large tables |
| HIGH | 5-20x | Wrong index types, poor partitioning, missing covering indexes |
| MEDIUM-HIGH | 2-5x | N+1 queries, inefficient pagination, RLS optimization |
| MEDIUM | 1.5-3x | Redundant indexes, query plan instability |
| LOW-MEDIUM | 1.2-2x | VACUUM tuning, configuration tweaks |
| LOW | Incremental | Advanced patterns, edge cases |
Reference Standards
Primary Sources:
- Official Postgres documentation
- Supabase documentation
- Postgres wiki
- Established blogs (2ndQuadrant, Crunchy Data)
Format:
Reference:
[Postgres Indexes](https://www.postgresql.org/docs/current/indexes.html)Review Checklist
Before submitting a reference:
- Title is clear and action-oriented
- Impact level matches the performance gain
- impactDescription includes quantification
- Explanation is concise (1-2 sentences)
- Has at least 1 Incorrect SQL example
- Has at least 1 Correct SQL example
- SQL uses semantic naming
- Comments explain why, not what
- Trade-offs mentioned if applicable
- Reference links included
-
pnpm testpasses