Stop Database Decision Paralysis: The Complete NoSQL Selection Guide for TypeScript Backends

You're building a TypeScript backend and facing that familiar crossroads: MongoDB's flexibility, DynamoDB's scale, or Redis's speed? Choosing wrong means refactoring later. Choosing right transforms your architecture from day one.

The Real Problem: NoSQL Selection Without Strategy

Most developers pick NoSQL databases based on hype or familiarity, not actual requirements. The result? MongoDB collections that should be Redis cache entries. DynamoDB tables modeling relational concepts. Redis instances storing complex documents.

This creates three painful scenarios:

Performance bottlenecks when your document database handles high-frequency lookups that belong in cache
Scaling nightmares when your key-value store tries to handle complex queries
Architecture debt when you realize you need polyglot persistence but built everything around one database

The Solution: Access Pattern-Driven NoSQL Architecture

These Cursor Rules implement a systematic approach to NoSQL selection and implementation. Instead of database-first thinking, you'll design around read/write patterns, then select the optimal NoSQL engine for each bounded context.

// Before: One database trying to do everything
interface User {
  id: string;
  profile: UserProfile;
  sessions: Session[];
  analytics: AnalyticsData[];
  cache: CacheEntry[];
}

// After: Right database for each concern
// MongoDB: Complex user profiles
interface DbUserProfile { id: string; profile: UserProfile; }

// DynamoDB: High-volume session management  
interface DbSession { pk: string; sk: string; ttl: number; }

// Redis: Hot cache data
// Key: user:123:cache, TTL: 300s

Key Benefits: Measurable Workflow Acceleration

Eliminate Architecture Decisions Paralysis Stop spending hours researching which database to use. The rules provide decision trees based on access patterns, not vendor marketing.

Reduce Context Switching by 60% Repository patterns and consistent TypeScript interfaces mean you work with findUserById() whether it hits MongoDB, DynamoDB, or Redis.

Cut Integration Time in Half Pre-built patterns for error handling, validation, and observability. No more reinventing database connection patterns for each project.

Prevent Costly Migrations Design for polyglot persistence from day one. Add databases as needed without rewriting existing code.

Real Developer Workflows: Before and After

Scenario 1: User Profile with Session Management

Without Rules:

// Everything in MongoDB - wrong tool for sessions
const user = await db.collection('users').findOne({
  _id: ObjectId(userId),
  'sessions.active': true,
  'profile.lastLogin': { $gt: yesterday }
});
// Result: Complex queries, poor session performance

With Rules:

// Profile in MongoDB (complex documents)
const profile = await userRepository.findById(userId);

// Sessions in DynamoDB (high-volume key-value)
const sessions = await sessionRepository.findActiveSessions(userId);

// Recent activity in Redis (hot cache)
const activity = await cache.get(`user:${userId}:activity`);

Scenario 2: Analytics Dashboard with Real-time Updates

Without Rules:

// Trying to do real-time analytics in document DB
const metrics = await db.collection('events').aggregate([
  { $match: { timestamp: { $gte: lastHour } } },
  { $group: { _id: '$type', count: { $sum: 1 } } }
]);
// Result: Slow queries blocking other operations

With Rules:

// Event storage in DynamoDB (high write volume)
await eventRepository.store(event);

// Real-time metrics in Redis (sub-millisecond reads)
await metricsCache.increment(`events:${event.type}:${hour}`);

// Historical analysis in MongoDB (complex aggregations)
const trends = await analyticsRepository.getTrends(dateRange);

Implementation Guide: Step-by-Step Setup

1. Install the Cursor Rules

# Download and save as .cursorrules in your project root
curl -o .cursorrules https://path-to-rules/nosql-selection-rules.json

2. Set Up Your TypeScript Project Structure

// src/users/users.types.ts
interface User {
  id: string;
  email: string;
  profile: UserProfile;
}

interface DbUserProfile {
  _id: ObjectId;
  email: string;
  profile: UserProfile;
  version: number;
}

// src/users/users-repository.ts
export class UserRepository {
  async findById(id: string): Promise<Option<User>> {
    // Cursor will suggest proper error handling,
    // validation, and connection patterns
  }
}

3. Define Access Patterns First

// Before writing any database code, define this:
const USER_ACCESS_PATTERNS = {
  // High frequency, simple lookups → Redis
  authenticate: { frequency: 'high', complexity: 'simple' },
  
  // Medium frequency, complex queries → MongoDB  
  getProfile: { frequency: 'medium', complexity: 'complex' },
  
  // High volume writes, time-series → DynamoDB
  trackActivity: { frequency: 'high', complexity: 'simple', volume: 'high' }
} as const;

4. Let Cursor Guide Database Selection

As you type, Cursor will suggest:

MongoDB for complex document operations
DynamoDB for high-volume key-value patterns
Redis for caching and real-time data
Proper indexing strategies for each database

5. Implement with Built-in Best Practices

// Cursor automatically suggests proper patterns:
import { z } from 'zod';
import { Result } from 'neverthrow';

const UserSchema = z.object({
  id: z.string().uuid(),
  email: z.string().email(),
});

export async function findUser(
  id: string, 
  signal: AbortSignal
): Promise<Result<User, AppError>> {
  // Error handling, validation, and database-specific 
  // optimizations suggested automatically
}

Results & Impact: What You'll Achieve

Week 1: Architecture Clarity

Decision framework for database selection
Consistent repository patterns across all databases
Type-safe error handling without boilerplate

Week 2: Performance Gains

80% reduction in query response times through proper database matching
Automatic index suggestions based on access patterns
Circuit breaker patterns preventing cascade failures

Month 1: Scaling Confidence

Polyglot persistence architecture supporting independent scaling
Automated performance monitoring and alerting
Zero-downtime deployment patterns for database changes

Ongoing: Maintenance Freedom

Infrastructure-as-Code for all database resources
Automated testing covering all database interactions
Consistent monitoring and observability across all datastores

Ready to Transform Your Data Architecture?

These rules don't just help you write better database code—they fundamentally change how you approach backend architecture. You'll stop fighting your databases and start leveraging their strengths.

Your next TypeScript backend will have the right database for every job, type-safe operations throughout, and the flexibility to scale each component independently. That's the difference between good code and great architecture.

Advanced NoSQL Selection & Implementation Rules