Backend Performance Optimization

Quick Guide: Optimize backend performance through database query optimization (indexes, prepared statements, avoiding N+1), caching strategies (cache-aside, write-through), connection pooling, and non-blocking async patterns. Always measure before optimizing -- run EXPLAIN ANALYZE, check event loop lag, and track cache hit rates before adding complexity.

---

<critical_requirements>

CRITICAL: Before Using This Skill

(You MUST always release database connections back to the pool using finally blocks)

(You MUST use eager loading or batching (DataLoader) 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:

• examples/core.md - Database patterns: connection pooling, N+1 prevention, indexing, prepared statements, pagination
• examples/caching.md - Cache-aside, write-through, invalidation, key strategies, TTL guidance
• examples/async.md - Event loop optimization, worker threads, chunked processing, concurrency control
• reference.md - Decision frameworks, performance monitoring

---

Auto-detection: connection pool, query optimization, database index, N+1, caching, cache invalidation, prepared statement, worker threads, event loop, CPU-bound, latency, throughput, performance tuning, EXPLAIN ANALYZE, keyset pagination, cache-aside, write-through

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 via caching

When NOT to use:

• Premature optimization without measuring first
• Simple CRUD with low traffic (adds complexity without benefit)
• Data that changes frequently and must always be fresh (caching adds staleness)
• Development/debugging (caching obscures issues)

Key patterns covered:

• Database indexing strategies (composite, partial, covering)
• Connection pooling with guaranteed release
• N+1 query prevention (eager loading, DataLoader)
• Caching strategies (cache-aside, write-through, invalidation)
• Event loop optimization (async I/O, setImmediate chunking)
• Worker threads for CPU-bound operations
• Keyset pagination for large datasets

---

<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, pagination
2. Caching - Reduce repeated expensive operations with TTL-bounded cache
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 (EXPLAIN ANALYZE, event loop lag)
• Load testing reveals scaling issues

When NOT to optimize:

• "It might be slow someday" (premature)
• Optimizing cold paths (rarely executed code)
• Before profiling identifies the actual bottleneck

</philosophy>

---

<patterns>

Core Patterns

Pattern 1: Connection Pooling with Guaranteed Release

Connection pooling reuses database connections instead of creating new ones per request. A PostgreSQL handshake takes 20-30ms -- pooling eliminates this overhead.

Key rules:

• Use pool.query() for simple queries (auto-manages connection lifecycle)
• For transactions, manually checkout with pool.connect() and always release in finally
• Listen for pool errors (idle clients can still emit errors)

// Transaction with guaranteed connection 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
  }
}

Why good: finally guarantees connection release even on error, preventing pool exhaustion

See examples/core.md for full pool configuration, sizing formula, and external pooler guidance.

---

Pattern 2: N+1 Query Prevention

The N+1 problem occurs when fetching N records triggers N additional queries for related data. With 100 records, that's 101 database round-trips.

Two solutions:

1. Eager loading (ORM .with()) -- single query with JOINs for known relationships
2. DataLoader -- batches .load() calls into single query per tick, ideal for GraphQL

// Eager loading: single query fetches jobs + companies + skills
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 } },
  },
});

// BAD: N+1 anti-pattern -- one query per job
for (const job of jobs) {
  job.company = await db.query.companies.findFirst({
    where: eq(companies.id, job.companyId),
  });
}

Why bad: 1 query for jobs + N queries for companies, latency grows linearly with data size

See examples/core.md for DataLoader batching pattern.

---

Pattern 3: Database Indexing

Indexes speed up queries by avoiding full table scans. Index columns used in WHERE, JOIN, and ORDER BY clauses.

// Strategic indexes on a table definition
export const jobs = pgTable(
  "jobs",
  {
    id: uuid("id").primaryKey().defaultRandom(),
    companyId: uuid("company_id").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
    index("jobs_country_employment_idx").on(
      table.country,
      table.employmentType,
    ),
    // Partial index -- only indexes active non-deleted jobs
    index("jobs_active_idx")
      .on(table.isActive, table.createdAt)
      .where(sql`${table.deletedAt} IS NULL`),
    // Foreign key index for JOIN performance
    index("jobs_company_id_idx").on(table.companyId),
  ],
);

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

Composite index column order: Equality conditions first, range conditions last, high selectivity first.

See examples/core.md for EXPLAIN ANALYZE examples, index monitoring queries, and unused index detection.

---

Pattern 4: Cache-Aside with TTL

The most common caching pattern. Check cache first, fetch from database on miss, store with TTL.

const CACHE_TTL_SECONDS = 300;
const CACHE_PREFIX = "app:user";

async function getUserById(userId: string): Promise<User | null> {
  const cacheKey = `${CACHE_PREFIX}:${userId}`;
  const cached = await cacheClient.get(cacheKey);
  if (cached) return JSON.parse(cached) as User;

  const user = await db.query.users.findFirst({ where: eq(users.id, userId) });
  if (!user) return null;

  await cacheClient.set(cacheKey, JSON.stringify(user), {
    EX: CACHE_TTL_SECONDS,
  });
  return user;
}

Why good: TTL prevents stale data accumulation, namespaced keys prevent collisions, early return on cache hit

See examples/caching.md for write-through, tag-based invalidation, key strategies, and TTL guidance.

---

Pattern 5: Worker Threads for CPU-Bound Operations

Node.js uses a single thread for JavaScript. CPU-intensive work blocks ALL concurrent requests.

Rule of thumb:

CPU Duration	Solution	Rationale
< 50ms	Keep on main thread	Worker overhead not worth it
50-500ms	setImmediate chunking	Yields to event loop between chunks
> 500ms	Worker Threads	Offload completely to separate thread

// Chunked processing with setImmediate -- yields to event loop between batches
const CHUNK_SIZE = 100;

async function processLargeArray(items: Item[]): Promise<ProcessedItem[]> {
  const results: ProcessedItem[] = [];
  for (let i = 0; i < items.length; i += CHUNK_SIZE) {
    const chunk = items.slice(i, i + CHUNK_SIZE);
    for (const item of chunk) {
      results.push(expensiveTransform(item));
    }
    if (i + CHUNK_SIZE < items.length) {
      await new Promise((resolve) => setImmediate(resolve));
    }
  }
  return results;
}

See examples/async.md for worker pool implementation, concurrency control with p-limit, and event loop lag monitoring.

---

Pattern 6: Keyset Pagination for Large Datasets

OFFSET pagination scans all previous rows -- at OFFSET 100,000 the database reads and discards 100,000 rows. Keyset pagination uses a cursor for constant-time performance.

-- BAD: OFFSET scans all previous rows
SELECT * FROM products ORDER BY id LIMIT 20 OFFSET 100000;

-- GOOD: Keyset pagination -- constant time regardless of position
SELECT * FROM products
WHERE id > :last_seen_id
ORDER BY id LIMIT 20;

When to use offset: Small datasets (< 100k rows), need total count, random page access required.

When to use keyset: Large datasets, infinite scroll, real-time data where inserts shouldn't cause duplicates.

See examples/core.md for full TypeScript implementations of both patterns.

</patterns>

---

<red_flags>

RED FLAGS

High Priority Issues:

• Missing connection release -- connections never returned to pool cause pool exhaustion and application hangs
• N+1 queries in loops -- fetching related data one-by-one instead of eager loading destroys performance
• Blocking event loop -- synchronous I/O or CPU-intensive work blocks all concurrent requests
• Cache without TTL -- unbounded cache grows until memory exhaustion or serves infinitely stale data
• Full table scans on large tables -- missing indexes on WHERE/JOIN columns

Medium Priority Issues:

• No index on foreign keys -- JOINs and ON DELETE CASCADE become slow
• Over-indexing -- every index slows writes; remove unused indexes with pg_stat_user_indexes
• Cache key collisions -- generic keys cause wrong data returned to wrong users
• Offset pagination on large tables -- OFFSET scans all previous rows; use keyset pagination
• Unbounded parallelism -- Promise.all on 10,000 items overwhelms downstream services

Gotchas & Edge Cases:

• EXPLAIN shows estimates; EXPLAIN ANALYZE shows actual -- always use ANALYZE for real performance data
• Composite index (a, b) does NOT help queries filtering only on b -- column order matters
• Applying functions to indexed columns (e.g., YEAR(created_at)) prevents index use -- rewrite as range conditions
• DataLoader caches within request -- don't reuse across requests or you get stale data
• Worker threads have ~30ms startup overhead -- don't use for fast operations
• Connection pool idleTimeoutMillis can cause "connection terminated unexpectedly" if set too short
• SELECT * fetches unnecessary data and prevents covering index optimization -- select specific columns
• Cache SET with EX option replaces existing TTL -- calling SET again resets expiration timer

</red_flags>

---

<critical_reminders>

CRITICAL REMINDERS

(You MUST always release database connections back to the pool using finally blocks)

(You MUST use eager loading or batching (DataLoader) 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>