TSO (Time Space Organization) Of Web Applications

A comprehensive framework for organizing time and space complexity in modern web applications.

Introduction.

TSO (Time Space Organization) is a methodology that helps developers balance the trade-offs between execution time and memory usage in web applications.

The TSO Framework.

Time Dimension.

The time dimension considers how long operations take to execute.

Key metrics:

• First Contentful Paint (FCP).
• Time to Interactive (TTI).
• Largest Contentful Paint (LCP).
• API response times.
• Database query execution time.

Space Dimension.

The space dimension considers memory and storage utilization.

Key metrics:

• JavaScript heap size.
• DOM node count.
• Network payload size.
• Local storage usage.
• Cache memory allocation.

Organization Dimension.

The organization dimension considers code structure and maintainability.

Key aspects:

• Component architecture.
• State management patterns.
• Module boundaries.
• Service abstractions.

TSO Optimization Strategies.

Strategy 1: Time-Optimized Approach.

Prioritize execution speed over memory usage.

// Cache computed values in memory.
const cache = new Map<string, ComputedResult>();

function getComputedValue(key: string): ComputedResult {
  if (!cache.has(key)) {
    cache.set(key, expensiveComputation(key));
  }
  return cache.get(key)!;
}

Strategy 2: Space-Optimized Approach.

Prioritize memory efficiency over execution speed.

// Compute on demand without caching.
function getComputedValue(key: string): ComputedResult {
  return expensiveComputation(key);
}

Strategy 3: Balanced Approach.

Use bounded caches with eviction policies.

// LRU cache with size limit.
class LRUCache<K, V> {
  private cache: Map<K, V>;
  private maxSize: number;

  constructor(maxSize: number) {
    this.cache = new Map();
    this.maxSize = maxSize;
  }

  get(key: K): V | undefined {
    const value = this.cache.get(key);
    if (value) {
      // Move to end for LRU.
      this.cache.delete(key);
      this.cache.set(key, value);
    }
    return value;
  }

  set(key: K, value: V): void {
    if (this.cache.size >= this.maxSize) {
      // Remove oldest entry.
      const firstKey = this.cache.keys().next().value;
      this.cache.delete(firstKey);
    }
    this.cache.set(key, value);
  }
}

Applying TSO to React Applications.

Component Level.

• Use React.memo for time optimization.
• Implement lazy loading for space optimization.
• Structure components for organizational clarity.

State Level.

• Normalize state for efficient updates.
• Use selectors to minimize re-renders.
• Implement pagination for large datasets.

Network Level.

• Implement request deduplication.
• Use GraphQL for precise data fetching.
• Cache API responses appropriately.

Guardrails for TSO.

1. Set Memory Budgets. Define maximum heap sizes.
2. Define Latency Targets. Set p95 response time goals.
3. Monitor in Production. Use real user monitoring.
4. Review Regularly. Conduct performance audits quarterly.

Conclusion.

TSO provides a structured approach to balancing performance trade-offs. By considering time, space, and organization together, you can build more efficient and maintainable web applications.

Written by the NGEK TECH engineering team.