How to Choose the Right Database for Your Project

The database decision is one of the most consequential architectural choices you'll make — and one of the hardest to reverse. Getting it wrong means painful migrations, performance ceilings, and the kind of technical debt that gets engineers to update their CV. Here's a framework that's served us well across 40+ projects.

Start With Your Data Shape

Before anything else, answer: Is your data relational (users have orders, orders have line items) or document-like (each record is largely self-contained)? If relational: PostgreSQL. If document-like with wildly variable schemas: MongoDB or DynamoDB. If you're uncertain, choose PostgreSQL — it supports JSON columns, so you get relational integrity plus document flexibility.

The Database Decision Guide

• <strong>PostgreSQL</strong> — The default choice. ACID transactions, JSON support, full-text search, PostGIS for geo, excellent query planner. Use it unless you have a specific reason not to.
• <strong>MongoDB</strong> — Best for: content management, product catalogues, event logging, anything with a highly variable schema. Avoid for financial data (transactions are still less ergonomic than Postgres).
• <strong>Redis</strong> — Never your primary database. Always a layer: caching, sessions, pub/sub, rate limiting, leaderboards, queues.
• <strong>DynamoDB</strong> — Serverless, infinitely scalable, single-digit millisecond latency. The right choice when your access patterns are simple and you need massive scale. Wrong choice for complex queries.
• <strong>ClickHouse / TimescaleDB</strong> — For analytics and time-series data. If you're running complex aggregate queries over millions of rows, these will be 100× faster than PostgreSQL.

The PostgreSQL + Redis Stack

For 80% of web applications, PostgreSQL as the primary database + Redis as the cache layer is the optimal stack. PostgreSQL handles your authoritative data with full ACID guarantees. Redis caches expensive queries, stores sessions, and handles rate limiting. This combination is battle-tested at scale — Instagram ran on PostgreSQL + Redis for its first 5 years, scaling to 30M users.

// Cache-aside pattern with Redis + PostgreSQL
async function getUser(id) {
  const cacheKey = `user:${id}`;

  // 1. Try cache first
  const cached = await redis.get(cacheKey);
  if (cached) return JSON.parse(cached);

  // 2. Cache miss — hit the database
  const user = await db.query('SELECT * FROM users WHERE id = $1', [id]);

  // 3. Populate cache (TTL: 5 minutes)
  await redis.setex(cacheKey, 300, JSON.stringify(user.rows[0]));

  return user.rows[0];
}