api-performance-api-performance · git:20260202.b236384 · 2026-02-02 · sha256 9fac33236761af6c
api-performance-api-performance git:20260202.b236384A
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---
name: api-performance-api-performance
description: Query optimization, caching, indexing
---
# Backend Performance Optimization
> **Quick Guide:** Optimize backend performance through database query optimization (indexes, prepared statements, avoiding N+1), caching strategies (Redis cache-aside, write-through), connection pooling, and non-blocking async patterns. Always measure before optimizing.
---
<critical_requirements>
## CRITICAL: Before Using This Skill
> **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants)
**(You MUST always release database connections back to the pool using `finally` blocks)**
**(You MUST use `.with()` or eager loading to prevent N+1 queries - never lazy load in loops)**
**(You MUST set TTL on all cached data to prevent stale data and memory exhaustion)**
**(You MUST offload CPU-intensive work to Worker Threads - blocking the event loop degrades all requests)**
</critical_requirements>
---
**Detailed Resources:**
- For code examples, see [examples/](examples/) folder:
- [caching.md](examples/caching.md) - Redis caching patterns and strategies
- [database.md](examples/database.md) - Query optimization, indexing, connection pooling
- [async.md](examples/async.md) - Event loop, worker threads, CPU-bound tasks
- For decision frameworks and anti-patterns, see [reference.md](reference.md)
---
**Auto-detection:** Redis, connection pool, query optimization, database index, N+1, caching, cache invalidation, prepared statement, worker threads, event loop, CPU-bound, latency, throughput, performance tuning
**When to use:**
- Database queries taking > 100ms
- High-traffic endpoints with repeated data fetches
- API responses with multiple related entities (N+1 risk)
- CPU-intensive operations blocking request handling
- Need to reduce database load or API costs
**When NOT to use:**
- Premature optimization without measuring first
- Simple CRUD with low traffic (adds complexity without benefit)
- Data that changes frequently and must be fresh (caching adds staleness)
- Development/debugging (caching obscures issues)
**Key patterns covered:**
- Database indexing strategies (composite, partial, covering)
- Connection pooling (node-postgres, external poolers)
- N+1 query prevention (eager loading, DataLoader)
- Redis caching (cache-aside, write-through, invalidation)
- Event loop optimization (async patterns, setImmediate)
- Worker threads for CPU-bound operations
- Prepared statements for repeated queries
---
<philosophy>
## Philosophy
Backend performance optimization follows one core principle: **measure first, optimize second**. Premature optimization wastes development time and adds complexity without evidence of benefit.
**The Three Pillars of Backend Performance:**
1. **Database Optimization** - Indexes, query planning, N+1 prevention
2. **Caching** - Reduce repeated expensive operations
3. **Async Efficiency** - Never block the event loop
**When to optimize:**
- Response times exceed SLA thresholds
- Database CPU/memory approaching limits
- Metrics show specific bottlenecks
- Load testing reveals scaling issues
**When NOT to optimize:**
- "It might be slow someday" (premature)
- Following blog post advice without context
- Optimizing cold paths (rarely executed code)
- Before profiling identifies the actual bottleneck
</philosophy>
---
<patterns>
## Core Patterns
### Pattern 1: Database Connection Pooling
Connection pooling reuses database connections instead of creating new ones per request. A PostgreSQL handshake takes 20-30ms - pooling eliminates this overhead.
#### Configuration
```typescript
// Good Example - Proper connection pooling with node-postgres
import { Pool } from "pg";
const POOL_MAX_CONNECTIONS = 20;
const POOL_IDLE_TIMEOUT_MS = 30000;
const POOL_CONNECTION_TIMEOUT_MS = 5000;
const pool = new Pool({
connectionString: process.env.DATABASE_URL,
max: POOL_MAX_CONNECTIONS,
idleTimeoutMillis: POOL_IDLE_TIMEOUT_MS,
connectionTimeoutMillis: POOL_CONNECTION_TIMEOUT_MS,
});
// Listen for pool errors (idle clients can still emit errors)
pool.on("error", (err) => {
console.error("Unexpected pool error:", err);
});
// For simple queries - auto-manages connection lifecycle
async function getUsers() {
const result = await pool.query("SELECT * FROM users WHERE active = $1", [
true,
]);
return result.rows;
}
// For transactions - manual checkout with guaranteed release
async function createUserWithProfile(
userData: UserData,
profileData: ProfileData,
) {
const client = await pool.connect();
try {
await client.query("BEGIN");
const userResult = await client.query(
"INSERT INTO users (name, email) VALUES ($1, $2) RETURNING id",
[userData.name, userData.email],
);
await client.query("INSERT INTO profiles (user_id, bio) VALUES ($1, $2)", [
userResult.rows[0].id,
profileData.bio,
]);
await client.query("COMMIT");
return userResult.rows[0];
} catch (error) {
await client.query("ROLLBACK");
throw error;
} finally {
client.release(); // CRITICAL: Always release back to pool
}
}
// Graceful shutdown
async function shutdown() {
await pool.end();
}
export { pool, getUsers, createUserWithProfile, shutdown };
```
**Why good:** Named constants document configuration, `pool.query()` auto-manages simple queries, `finally` block guarantees connection release preventing pool exhaustion, error listener catches backend failures, graceful shutdown prevents connection leaks
```typescript
// Bad Example - Connection leak and missing error handling
import { Pool } from "pg";
const pool = new Pool({ connectionString: process.env.DATABASE_URL });
async function createUser(data) {
const client = await pool.connect();
await client.query("INSERT INTO users (name) VALUES ($1)", [data.name]);
// Missing client.release() - connection leaked!
// Missing error handling - transaction left open on failure
}
```
**Why bad:** Missing `client.release()` causes connection pool exhaustion, no try/catch means failed queries leave connections checked out forever, no transaction handling means partial writes possible
---
### Pattern 2: N+1 Query Prevention
The N+1 problem occurs when fetching N records triggers N additional queries for related data. Use eager loading or batching instead.
#### Eager Loading with ORM
```typescript
// Good Example - Single query with eager loading (Drizzle)
import { db } from "./database";
import { and, eq, isNull } from "drizzle-orm";
async function getJobsWithCompanies() {
// Single SQL query fetches jobs + companies + locations
const jobs = await db.query.jobs.findMany({
where: and(eq(jobs.isActive, true), isNull(jobs.deletedAt)),
with: {
company: {
with: {
locations: true,
},
},
jobSkills: {
with: {
skill: true,
},
},
},
});
return jobs;
}
```
**Why good:** `.with()` generates a single SQL query with JOINs, eliminates N+1 problem entirely, fully typed result prevents runtime errors
```typescript
// Bad Example - N+1 query anti-pattern
async function getJobsWithCompanies() {
const jobs = await db.query.jobs.findMany({
where: eq(jobs.isActive, true),
});
// N+1: One query per job to get company!
for (const job of jobs) {
job.company = await db.query.companies.findFirst({
where: eq(companies.id, job.companyId),
});
}
return jobs;
}
```
**Why bad:** 1 query for jobs + N queries for companies = N+1 total queries, latency grows linearly with data size, database gets hammered with many small queries
#### DataLoader Pattern (GraphQL/REST)
```typescript
// Good Example - DataLoader for batching
import DataLoader from "dataloader";
// Create loader once per request (caches within request lifecycle)
function createCompanyLoader() {
return new DataLoader<string, Company>(async (companyIds) => {
// Single query for all requested companies
const companies = await db.query.companies.findMany({
where: inArray(companies.id, [...companyIds]),
});
// Return in same order as requested IDs
const companyMap = new Map(companies.map((c) => [c.id, c]));
return companyIds.map((id) => companyMap.get(id) ?? null);
});
}
// Usage in resolver or handler
async function resolveJob(job: Job, context: Context) {
// DataLoader batches all .load() calls in same tick
const company = await context.loaders.company.load(job.companyId);
return { ...job, company };
}
```
**Why good:** DataLoader batches multiple `.load()` calls into single query, caches results within request preventing duplicate fetches, works with any data source (DB, API, etc.)
---
### Pattern 3: Database Indexing
Indexes speed up queries by avoiding full table scans. Index columns used in WHERE, JOIN, and ORDER BY clauses.
#### Index Strategies
```typescript
// Good Example - Strategic indexes (Drizzle schema)
import {
pgTable,
uuid,
varchar,
timestamp,
boolean,
index,
} from "drizzle-orm/pg-core";
import { sql } from "drizzle-orm";
export const jobs = pgTable(
"jobs",
{
id: uuid("id").primaryKey().defaultRandom(),
companyId: uuid("company_id").notNull(),
title: varchar("title", { length: 255 }).notNull(),
country: varchar("country", { length: 100 }),
employmentType: varchar("employment_type", { length: 50 }),
isActive: boolean("is_active").default(true),
createdAt: timestamp("created_at").defaultNow(),
deletedAt: timestamp("deleted_at"),
},
(table) => ({
// Composite index for common filter combination
countryEmploymentIdx: index("jobs_country_employment_idx").on(
table.country,
table.employmentType,
),
// Partial index - only indexes active non-deleted jobs
activeJobsIdx: index("jobs_active_idx")
.on(table.isActive, table.createdAt)
.where(sql`${table.deletedAt} IS NULL`),
// Foreign key index for JOIN performance
companyIdIdx: index("jobs_company_id_idx").on(table.companyId),
// Text search index
titleIdx: index("jobs_title_idx").on(table.title),
}),
);
```
**Why good:** Composite index matches common query patterns (country + employmentType), partial index reduces index size by excluding deleted records, foreign key index speeds up JOINs, column order in composite index matches query filter order
**Index Decision Framework:**
| Column Usage | Index Type | When to Use |
| --------------------------- | ---------------- | ---------------------------------------------- |
| WHERE equality | B-tree (default) | High-selectivity columns |
| WHERE range (>, <, BETWEEN) | B-tree | Date ranges, numeric ranges |
| WHERE multiple columns | Composite | Queries always filter by same columns together |
| WHERE on subset | Partial | Most queries filter on active/non-deleted |
| Full-text search | GIN/GiST | Text search with LIKE, tsvector |
| JSON field access | GIN | JSONB column queries |
</patterns>
---
## Additional Patterns
The following patterns are documented with full examples in [examples/](examples/):
- **Redis Caching** - Cache-aside, write-through, invalidation, TTL - see [caching.md](examples/caching.md)
- **Prepared Statements** - Reuse query plans for repeated queries - see [database.md](examples/database.md)
- **Event Loop Optimization** - Async patterns, setImmediate chunking - see [async.md](examples/async.md)
- **Worker Threads** - CPU-bound operations without blocking - see [async.md](examples/async.md)
---
<critical_reminders>
## CRITICAL REMINDERS
> **All code must follow project conventions in CLAUDE.md**
**(You MUST always release database connections back to the pool using `finally` blocks)**
**(You MUST use `.with()` or eager loading to prevent N+1 queries - never lazy load in loops)**
**(You MUST set TTL on all cached data to prevent stale data and memory exhaustion)**
**(You MUST offload CPU-intensive work to Worker Threads - blocking the event loop degrades all requests)**
**Failure to follow these rules will cause connection pool exhaustion, N+1 performance degradation, memory leaks from unbounded caches, and blocked event loops affecting all concurrent requests.**
</critical_reminders>