Feature: Intelligent Caching System

[flemzord]

Feature: Intelligent Caching System

Summary

Implement a smart caching layer to minimize GitHub API calls, improve performance, and provide offline capabilities while maintaining data freshness for compliance accuracy.

Problem Statement

The GitHub Compliance CLI makes numerous API calls which can:

• Hit rate limits quickly for large organizations
• Cause slow execution times
• Increase costs for GitHub Enterprise customers with API quotas
• Prevent offline analysis of previously fetched data
• Result in redundant calls for unchanged data

Proposed Solution

Build an intelligent caching system that stores API responses, tracks data freshness, uses ETags for conditional requests, and provides smart invalidation strategies.

Detailed Design

Command Line Interface

# Run with caching enabled (default)
github-compliance-cli --config compliance.yml --token $TOKEN --cache

# Disable cache for fresh data
github-compliance-cli --config compliance.yml --token $TOKEN --no-cache

# Use cache-only mode (offline)
github-compliance-cli --config compliance.yml --cache-only

# Clear cache
github-compliance-cli cache clear

# Show cache statistics
github-compliance-cli cache stats

# Warm up cache
github-compliance-cli cache warm --org my-org

# Export/import cache
github-compliance-cli cache export --output cache-backup.tar.gz
github-compliance-cli cache import --input cache-backup.tar.gz

Configuration Schema

cache:
  enabled: true
  strategy: "smart"  # smart, aggressive, conservative, custom

  storage:
    type: "filesystem"  # filesystem, redis, memory, sqlite
    path: "~/.github-compliance/cache"
    max_size_mb: 500
    compression: true

  ttl:
    # Time-to-live for different resource types (seconds)
    repository_metadata: 3600      # 1 hour
    branch_protection: 1800        # 30 minutes
    team_membership: 7200          # 2 hours
    security_settings: 900         # 15 minutes
    commit_data: 86400            # 24 hours
    organization_settings: 3600   # 1 hour

  invalidation:
    on_webhook_events: true       # Invalidate on webhook events
    on_error_threshold: 3         # Invalidate after N errors
    partial_invalidation: true    # Only invalidate changed items

  optimization:
    use_etags: true              # Use ETags for conditional requests
    parallel_warming: true       # Warm cache in parallel
    predictive_fetching: true    # Pre-fetch likely needed data
    batch_requests: true         # Use GraphQL for batch fetching

Architecture

Cache Manager

interface CacheManager {
  get<T>(key: string): Promise<CacheEntry<T> | null>;
  set<T>(key: string, value: T, options?: CacheOptions): Promise<void>;
  invalidate(pattern: string): Promise<void>;
  clear(): Promise<void>;
  getStats(): CacheStatistics;
  warm(resources: ResourceList): Promise<void>;
}

interface CacheEntry<T> {
  data: T;
  metadata: {
    etag?: string;
    lastModified?: Date;
    fetchedAt: Date;
    expiresAt: Date;
    hitCount: number;
    headers?: Record<string, string>;
  };
}

interface CacheOptions {
  ttl?: number;
  etag?: string;
  invalidationKeys?: string[];
  compression?: boolean;
}

Smart Caching Strategies

1. Adaptive TTL

class AdaptiveTTLStrategy {
  calculateTTL(resource: Resource): number {
    // Adjust TTL based on:
    // - Change frequency history
    // - Resource criticality
    // - Time of day/week
    // - API rate limit status

    const baselineTTL = this.getBaselineTTL(resource.type);
    const changeFrequency = this.getChangeFrequency(resource);
    const criticality = this.getCriticality(resource);

    return baselineTTL * (1 / changeFrequency) * criticality;
  }

  getChangeFrequency(resource: Resource): number {
    // Analyze historical change patterns
    const history = this.cache.getHistory(resource.id);
    return this.calculateFrequency(history);
  }
}

2. Predictive Fetching

class PredictiveFetcher {
  async prefetch(currentOperation: Operation): Promise<void> {
    const likelyNext = this.predictNextResources(currentOperation);

    for (const resource of likelyNext) {
      if (!this.cache.has(resource) || this.cache.isStale(resource)) {
        this.backgroundFetch(resource);
      }
    }
  }

  predictNextResources(operation: Operation): Resource[] {
    // Use patterns from previous runs
    // Analyze check dependencies
    // Consider configuration rules
    return this.ml.predict(operation);
  }
}

3. Intelligent Invalidation

class SmartInvalidator {
  async handleWebhook(event: GitHubEvent): Promise<void> {
    const affected = this.determineAffectedCache(event);

    for (const entry of affected) {
      if (this.shouldInvalidate(entry, event)) {
        await this.cache.invalidate(entry.key);
      } else {
        // Mark as potentially stale
        await this.cache.markStale(entry.key);
      }
    }
  }

  determineAffectedCache(event: GitHubEvent): CacheEntry[] {
    // Map events to cache entries
    const mapping = {
      'push': ['commits/*', 'branch_protection/*'],
      'repository': ['repo/*', 'settings/*'],
      'team': ['teams/*', 'permissions/*']
    };

    return this.cache.match(mapping[event.type]);
  }
}

Storage Backends

1. Filesystem Storage

class FilesystemCache {
  private basePath: string;
  private index: CacheIndex;

  async set(key: string, value: any): Promise<void> {
    const path = this.keyToPath(key);
    const compressed = await this.compress(value);
    await fs.writeFile(path, compressed);
    await this.updateIndex(key);
  }

  private keyToPath(key: string): string {
    // Convert key to filesystem path
    // Handle special characters
    // Implement sharding for many files
    const hash = crypto.createHash('sha256').update(key).digest('hex');
    return path.join(this.basePath, hash.slice(0, 2), hash.slice(2, 4), hash);
  }
}

2. Redis Backend

class RedisCache {
  private client: Redis;

  async set(key: string, value: any, ttl: number): Promise<void> {
    const serialized = JSON.stringify(value);
    await this.client.setex(key, ttl, serialized);
    await this.updateMetadata(key);
  }

  async invalidatePattern(pattern: string): Promise<void> {
    const keys = await this.client.keys(pattern);
    if (keys.length > 0) {
      await this.client.del(...keys);
    }
  }
}

Cache Warming

class CacheWarmer {
  async warmOrganization(org: string): Promise<WarmingResult> {
    const plan = this.createWarmingPlan(org);
    const results = await this.executeInBatches(plan);

    return {
      cached: results.filter(r => r.success).length,
      failed: results.filter(r => !r.success).length,
      duration: this.calculateDuration(),
      savedApiCalls: this.estimateSavedCalls()
    };
  }

  private createWarmingPlan(org: string): WarmingPlan {
    return {
      repositories: { priority: 1, batch_size: 100 },
      teams: { priority: 2, batch_size: 50 },
      branch_protection: { priority: 3, batch_size: 20 },
      security_settings: { priority: 4, batch_size: 30 }
    };
  }
}

Performance Optimizations

GraphQL Batching

class GraphQLBatcher {
  async batchFetch(resources: Resource[]): Promise<Map<string, any>> {
    const query = this.buildBatchQuery(resources);
    const response = await this.github.graphql(query);
    return this.parseResponse(response);
  }

  private buildBatchQuery(resources: Resource[]): string {
    // Build efficient GraphQL query
    // Minimize over-fetching
    // Handle pagination
  }
}

User Stories

• As a user with slow internet, I want to work with cached data when possible
• As an admin of a large org, I want to avoid rate limits through intelligent caching
• As a CI/CD pipeline, I want fast execution through cache reuse
• As a developer, I want fresh data when investigating issues
• As an auditor, I want to analyze historical cached data

Technical Considerations

Cache Coherency

• Use ETags for conditional requests
• Implement cache versioning
• Handle partial updates correctly
• Manage concurrent access

Performance

• Lazy loading of cache entries
• Background cache updates
• Compression for large payloads
• Sharding for filesystem storage

Monitoring

interface CacheMetrics {
  hitRate: number;
  missRate: number;
  averageAge: number;
  totalSize: number;
  apiCallsSaved: number;
  performanceGain: number;
}

Testing Strategy

• Unit tests for cache operations
• Integration tests with different backends
• Performance benchmarks
• Cache coherency tests
• Concurrent access tests
• TTL and invalidation tests

Documentation Needs

• Cache configuration guide
• Performance tuning guide
• Troubleshooting cache issues
• Best practices for different scenarios
• Migration guide for cache backends

Success Criteria

• 70%+ cache hit rate in typical usage
• 50%+ reduction in API calls
• 3x performance improvement for cached runs
• Cache size remains under configured limits
• ETags reduce 304 responses by 40%
• Zero data inconsistency issues
• Cache warming completes in <5 minutes for 1000 repos

Dependencies

• Storage backend libraries (Redis, SQLite)
• Compression library (zlib, lz4)
• GraphQL client for batching
• File locking for concurrent access

Open Questions

1. Should we support distributed caching for teams?
2. How to handle cache migration between versions?
3. Should we implement cache sharing between users?
4. How to handle sensitive data in cache?
5. Should we support cache plugins for custom backends?

Future Enhancements

• Machine learning for predictive caching
• Distributed cache synchronization
• Cache analytics dashboard
• Differential caching (store only changes)
• P2P cache sharing in organizations
• Cache-as-a-service for enterprises