Large Codebase Strategies: Performance at Scale

Working with large codebases (100K+ lines of code) in Cursor requires specific strategies to maintain performance and productivity. This guide covers proven techniques from teams managing millions of lines of code.

Understanding Scale Challenges

Indexing Performance

Initial indexing can take 15+ minutes for very large projects

Context Window Limits

Even with 200K tokens, you can’t fit everything

Memory Usage

Large projects can consume 8GB+ RAM during heavy operations

Search Accuracy

Finding relevant code becomes harder as codebases grow

Optimization Strategies

1. Smart Indexing Configuration

Configure Cursor to index only what matters:

.cursorignore
Indexing Settings

# .cursorignore - Exclude from indexing

# Build artifacts
dist/
build/
out/
.next/
target/

# Dependencies
node_modules/
vendor/
packages/*/node_modules/

# Generated files
*.generated.ts
*.pb.go
schema.graphql

# Large data files
*.csv
*.json > 1MB
fixtures/

# Documentation
docs/api/
*.pdf

# Test snapshots
__snapshots__/
*.snap

{
  "cursor.indexing.enabled": true,
  "cursor.indexing.exclude": [
    "**/node_modules/**",
    "**/dist/**",
    "**/*.min.js",
    "**/coverage/**"
  ],
  "cursor.indexing.include": [
    "src/**",
    "lib/**",
    "packages/*/src/**"
  ],
  "cursor.indexing.incrementalUpdate": true,
  "cursor.indexing.updateInterval": "5m"
}

2. Multi-Root Workspace Strategy

For monorepos or multi-service architectures:

// workspace.code-workspace
{
  "folders": [
    {
      "name": "Frontend",
      "path": "./packages/frontend"
    },
    {
      "name": "Backend API",
      "path": "./packages/api"
    },
    {
      "name": "Shared Libraries",
      "path": "./packages/shared"
    },
    {
      "name": "Infrastructure",
      "path": "./infrastructure"
    }
  ],
  "settings": {
    "cursor.workspacePriority": {
      "Frontend": 1,
      "Backend API": 2,
      "Shared Libraries": 3,
      "Infrastructure": 4
    }
  }
}

3. Context Window Management

Strategies for working within token limits:

The Layered Context Approach

// Level 1: Immediate context (current file + direct dependencies)
"@current @imports Update this authentication logic"

// Level 2: Module context (related files in same module)
"@folder:src/auth Refactor the entire auth module"

// Level 3: Subsystem context (broader architectural view)
"@folder:src/auth @folder:src/users @docs:architecture.md
Implement SSO across auth and user systems"

// Level 4: Full system (use sparingly)
"Analyze the entire codebase for security vulnerabilities"

Context Optimization Patterns

Start Narrow Begin with the most specific context possible
Expand Gradually Add context only when the AI needs more information
Use Semantic Boundaries Include whole modules rather than random files
Leverage Ask Mode First Use Ask mode to understand before including context
Clean Up After Tasks Start fresh chats for new tasks to avoid context pollution

4. Performance Tuning

Memory Configuration

// Cursor performance settings
{
  "cursor.performance.memoryLimit": "8GB",
  "cursor.performance.maxWorkers": 4,
  "cursor.performance.cacheSize": "2GB",
  "cursor.performance.enableLazyLoading": true,
  "cursor.performance.garbageCollection": "aggressive"
}

Model Selection for Scale

Task Type	Recommended Model	Why
Quick edits	Claude 4 Sonnet	Fast, efficient for small changes
Large refactoring	Claude 4 Sonnet	Good balance of capability and speed
Complex analysis	Gemini 2.5 Pro	Handles massive context windows
Architecture planning	Claude 4 Opus	Best reasoning for complex decisions
Bug hunting	o3	Deep reasoning for tricky issues

5. Search Strategies

Effective Search Patterns

// Semantic search for concepts
"Where is user authentication implemented?"

// Combined with regex for precision
"@search('function.*Auth') Show all auth functions"

// Scoped searches for performance
"@folder:src/payments Find payment processing logic"

// Historical search for context
"@git What changes were made to auth in the last month?"

Building Mental Models

Create and maintain high-level documentation:

# System Architecture Map

## Core Services
- **Auth Service** - `/src/auth` - Handles all authentication
- **User Service** - `/src/users` - User management and profiles
- **Payment Service** - `/src/payments` - Stripe integration
- **Analytics Service** - `/src/analytics` - Event tracking

## Key Patterns
- All services use dependency injection
- Authentication via JWT tokens
- Database access through repositories
- Events published via message queue

Real-World Large Codebase Scenarios

Scenario 1: Enterprise Monolith (2M+ LOC)

A fintech company successfully manages their massive Java monolith:

banking-system/
├── core/              (500K LOC)
├── modules/           (800K LOC)
│   ├── accounts/
│   ├── transactions/
│   ├── compliance/
│   └── reporting/
├── integrations/      (400K LOC)
├── shared/            (200K LOC)
└── tests/             (300K LOC)

{
  "workspaces": [
    // Only open modules being worked on
    {"path": "./core", "active": false},
    {"path": "./modules/accounts", "active": true},
    {"path": "./shared", "active": true}
  ],
  "rules": [
    "Focus on module boundaries",
    "Use interface contracts",
    "Minimize cross-module deps"
  ]
}

Scenario 2: Microservices Constellation (50+ services)

Managing a distributed system with Cursor:

// Custom workspace manager script
class WorkspaceManager {
  async switchContext(feature: string) {
    const requiredServices = await this.getFeatureDependencies(feature);

    // Close unneeded workspaces
    await this.closeWorkspaces(
      this.openWorkspaces.filter(w => !requiredServices.includes(w))
    );

    // Open required services
    await this.openWorkspaces(requiredServices);

    // Prime Cursor context
    await this.primeContext(feature);
  }

  async primeContext(feature: string) {
    // Create a context document
    const context = `
    Working on: ${feature}
    Services involved: ${this.openWorkspaces.join(', ')}
    Key files: ${await this.getKeyFiles(feature)}
    Recent changes: ${await this.getRecentChanges(feature)}
    `;

    // Save to .cursor/context.md
    await fs.writeFile('.cursor/context.md', context);
  }
}

Scenario 3: Open Source Project (Kubernetes - 1.5M+ LOC)

Strategies used by contributors:

Component Focus

Work on one component at a time (e.g., just the scheduler)

Interface Contracts

Understand interfaces before implementations

Test-Driven Exploration

Use tests to understand behavior

Documentation Integration

Keep design docs in Cursor context

Advanced Techniques

1. Lazy Loading Pattern

// Don't load everything at once
class LazyContextLoader {
  private loaded = new Set<string>();

  async getContext(query: string) {
    const relevant = await this.findRelevantModules(query);

    for (const module of relevant) {
      if (!this.loaded.has(module)) {
        await this.loadModule(module);
        this.loaded.add(module);
      }
    }

    return this.buildContext(relevant);
  }
}

2. Codebase Partitioning

Divide your codebase into logical partitions:

partitions:
  frontend:
    paths: [src/ui, src/components, src/styles]
    rules: frontend-rules.md

  backend:
    paths: [src/api, src/services, src/db]
    rules: backend-rules.md

  infrastructure:
    paths: [deploy, terraform, k8s]
    rules: infra-rules.md

  shared:
    paths: [src/shared, src/utils]
    rules: shared-rules.md

# Use partitions in prompts:
# "@partition:frontend Update the user dashboard"

3. Incremental Understanding

Build understanding incrementally:

Map the Territory

"Create a high-level map of this codebase's architecture"

Identify Entry Points

"What are the main entry points for this application?"

Trace Key Flows

"Trace the user login flow from start to finish"

Understand Patterns

"What patterns are used consistently across this codebase?"

Document Findings

"Create an architecture guide based on our exploration"

Performance Monitoring

Metrics to Track

// Monitor Cursor performance
interface PerformanceMetrics {
  indexingTime: number;        // Initial indexing
  searchLatency: number;       // Average search time
  tokenUsage: number;         // Tokens per task
  memoryUsage: number;        // RAM consumption
  responseTime: number;       // AI response latency
}

// Log and analyze
class PerformanceMonitor {
  logMetric(metric: keyof PerformanceMetrics, value: number) {
    console.log(`[Cursor Perf] ${metric}: ${value}`);

    // Alert on degradation
    if (this.isDegraded(metric, value)) {
      this.alert(`Performance degradation in ${metric}`);
    }
  }
}

Optimization Checklist

Configure .cursorignore for your project
Set up multi-root workspaces if applicable
Tune memory settings based on machine specs
Create partition definitions for large codebases
Document key architectural patterns
Set up performance monitoring
Train team on context management
Regular cleanup of unused workspaces

Troubleshooting Large Codebase Issues

Slow Indexing

Solution: Check .cursorignore, reduce indexed paths, restart Cursor

Out of Memory

Solution: Increase memory limit, close unused workspaces, use exclusions

Inaccurate Suggestions

Solution: Improve context selection, update documentation, use specific modes

Token Limit Exceeded

Solution: Break into smaller tasks, use targeted context, leverage Ask mode

Best Practices Summary

Next Steps

Master these large codebase techniques:

Audit Your Current Setup - Check indexing and performance
Implement Partitioning - Define logical boundaries
Optimize Context Usage - Practice targeted context selection
Explore Multi-Repo Workflows - Continue to the next section

Remember: The goal isn’t to load everything into Cursor, but to load the right things at the right time. Master this, and even million-line codebases become manageable.