RFC Status: This document is part of the OpenDocs RFC and subject to change based on community feedback.

Performance Optimization

Optimize your OpenDocs implementation to handle large codebases efficiently. This guide covers memory management, streaming, caching, and performance best practices.

Memory Management

Streaming Large Datasets with JSON $ref

For large codebases, avoid loading all DocItems into memory at once by using JSON $ref to external files:

class StreamingDocItemProcessor {
  private batchSize = 1000;
  private maxMemoryUsage = 50 * 1024 * 1024; // 50MB

  async* processLargeDataset(filename: string): AsyncGenerator<DocItem[]> {
    let batch: DocItem[] = [];
    let currentMemory = 0;

    // Use JSON $ref to load items from external files on demand
    const mainDoc = await this.loadMainDocument(filename);

    for (const ref of mainDoc.items) {
      const item = await this.loadReferencedItem(ref.$ref);
      batch.push(item);
      currentMemory += this.estimateItemSize(item);

      if (batch.length >= this.batchSize || currentMemory >= this.maxMemoryUsage) {
        yield batch;
        batch = [];
        currentMemory = 0;
      }
    }

    if (batch.length > 0) {
      yield batch;
    }
  }

  private estimateItemSize(item: DocItem): number {
    return JSON.stringify(item).length * 2; // Rough estimate
  }

  private async loadMainDocument(filename: string): Promise<any> {
    // Main document contains references to external files
    const content = await fs.readFile(filename, 'utf-8');
    return JSON.parse(content);
  }

  private async loadReferencedItem(refPath: string): Promise<DocItem> {
    // Load individual items from external files as needed
    const content = await fs.readFile(refPath, 'utf-8');
    return JSON.parse(content) as DocItem;
  }
}

Memory-Efficient Extraction

Process files one at a time instead of loading the entire project:

class MemoryEfficientExtractor {
  async* extractFromProject(projectPath: string): AsyncGenerator<DocItem> {
    for await (const filePath of this.getSourceFiles(projectPath)) {
      const items = await this.extractFromFile(filePath);

      for (const item of items) {
        yield item;
      }

      // File processed, AST can be garbage collected
    }
  }

  private async* getSourceFiles(projectPath: string): AsyncGenerator<string> {
    const files = await glob('**/*.ts', { cwd: projectPath });

    for (const file of files) {
      yield path.join(projectPath, file);
    }
  }

  private async extractFromFile(filePath: string): Promise<DocItem[]> {
    const program = ts.createProgram([filePath], {});
    const extractor = new TypeScriptExtractor(program);

    return extractor.extractFromFile(filePath);
  }
}

Batch Processing

Process items in batches for better performance:

async function processInBatches<T>(
  items: AsyncIterable<T>,
  batchSize: number,
  processor: (batch: T[]) => Promise<void>
): Promise<void> {
  let batch: T[] = [];

  for await (const item of items) {
    batch.push(item);

    if (batch.length >= batchSize) {
      await processor(batch);
      batch = [];
    }
  }

  // Process remaining items
  if (batch.length > 0) {
    await processor(batch);
  }
}

// Usage
const extractor = new MemoryEfficientExtractor();

await processInBatches(
  extractor.extractFromProject('./src'),
  100,
  async (batch) => {
    await writeDocItems(batch);
  }
);

Caching Strategies

LRU Cache for DocItems

class DocItemCache {
  private cache = new Map<string, DocItem>();
  private maxSize = 10000;
  private accessCount = new Map<string, number>();

  get(id: string): DocItem | undefined {
    const item = this.cache.get(id);
    if (item) {
      this.accessCount.set(id, (this.accessCount.get(id) || 0) + 1);
    }
    return item;
  }

  set(id: string, item: DocItem): void {
    if (this.cache.size >= this.maxSize) {
      this.evictLeastUsed();
    }
    this.cache.set(id, item);
    this.accessCount.set(id, 1);
  }

  private evictLeastUsed(): void {
    let minAccesses = Infinity;
    let evictId: string | undefined;

    for (const [id, count] of this.accessCount) {
      if (count < minAccesses) {
        minAccesses = count;
        evictId = id;
      }
    }

    if (evictId) {
      this.cache.delete(evictId);
      this.accessCount.delete(evictId);
    }
  }

  clear(): void {
    this.cache.clear();
    this.accessCount.clear();
  }

  size(): number {
    return this.cache.size;
  }
}

File-Based Cache

For very large projects, use file-based caching:

class FileBasedCache {
  constructor(private cacheDir: string) {}

  async get(key: string): Promise<DocItem | undefined> {
    const filePath = this.getFilePath(key);

    try {
      const data = await fs.readFile(filePath, 'utf-8');
      return JSON.parse(data) as DocItem;
    } catch {
      return undefined;
    }
  }

  async set(key: string, item: DocItem): Promise<void> {
    const filePath = this.getFilePath(key);
    await fs.mkdir(path.dirname(filePath), { recursive: true });
    await fs.writeFile(filePath, JSON.stringify(item));
  }

  private getFilePath(key: string): string {
    const hash = crypto.createHash('md5').update(key).digest('hex');
    const dir = hash.substring(0, 2);
    const file = hash.substring(2);
    return path.join(this.cacheDir, dir, `${file}.json`);
  }

  async clear(): Promise<void> {
    await fs.rm(this.cacheDir, { recursive: true, force: true });
  }
}

Cache with TTL

Add time-to-live to cache entries:

interface CacheEntry<T> {
  value: T;
  expiresAt: number;
}

class TTLCache<T> {
  private cache = new Map<string, CacheEntry<T>>();
  private defaultTTL = 60 * 60 * 1000; // 1 hour

  get(key: string): T | undefined {
    const entry = this.cache.get(key);

    if (!entry) return undefined;

    if (Date.now() > entry.expiresAt) {
      this.cache.delete(key);
      return undefined;
    }

    return entry.value;
  }

  set(key: string, value: T, ttl?: number): void {
    const expiresAt = Date.now() + (ttl || this.defaultTTL);
    this.cache.set(key, { value, expiresAt });
  }

  cleanup(): void {
    const now = Date.now();

    for (const [key, entry] of this.cache) {
      if (now > entry.expiresAt) {
        this.cache.delete(key);
      }
    }
  }
}

Parallel Processing

Worker Threads for Extraction

Use worker threads to parallelize extraction:

import { Worker } from 'worker_threads';

class ParallelExtractor {
  private workerCount = os.cpus().length;

  async extractFromProject(projectPath: string): Promise<DocItem[]> {
    const files = await this.getSourceFiles(projectPath);
    const chunks = this.chunkArray(files, this.workerCount);

    const results = await Promise.all(
      chunks.map(chunk => this.processChunk(chunk))
    );

    return results.flat();
  }

  private async processChunk(files: string[]): Promise<DocItem[]> {
    return new Promise((resolve, reject) => {
      const worker = new Worker('./extractor-worker.js', {
        workerData: { files }
      });

      worker.on('message', (items: DocItem[]) => resolve(items));
      worker.on('error', reject);
      worker.on('exit', (code) => {
        if (code !== 0) {
          reject(new Error(`Worker stopped with exit code ${code}`));
        }
      });
    });
  }

  private chunkArray<T>(array: T[], chunks: number): T[][] {
    const result: T[][] = [];
    const chunkSize = Math.ceil(array.length / chunks);

    for (let i = 0; i < array.length; i += chunkSize) {
      result.push(array.slice(i, i + chunkSize));
    }

    return result;
  }
}

Worker Thread Implementation

// extractor-worker.js
import { parentPort, workerData } from 'worker_threads';
import { TypeScriptExtractor } from './extractor';

async function processFiles(files: string[]): Promise<DocItem[]> {
  const allItems: DocItem[] = [];

  for (const file of files) {
    const program = ts.createProgram([file], {});
    const extractor = new TypeScriptExtractor(program);
    const items = extractor.extractFromFile(file);
    allItems.push(...items);
  }

  return allItems;
}

processFiles(workerData.files).then(items => {
  parentPort?.postMessage(items);
});

Performance Monitoring

Measure Extraction Performance

class PerformanceMonitor {
  private metrics = new Map<string, number[]>();

  measure<T>(name: string, fn: () => T): T {
    const start = performance.now();
    const result = fn();
    const duration = performance.now() - start;

    this.recordMetric(name, duration);

    return result;
  }

  async measureAsync<T>(name: string, fn: () => Promise<T>): Promise<T> {
    const start = performance.now();
    const result = await fn();
    const duration = performance.now() - start;

    this.recordMetric(name, duration);

    return result;
  }

  private recordMetric(name: string, duration: number): void {
    const measurements = this.metrics.get(name) || [];
    measurements.push(duration);
    this.metrics.set(name, measurements);
  }

  getStats(name: string): { avg: number; min: number; max: number; count: number } {
    const measurements = this.metrics.get(name) || [];

    if (measurements.length === 0) {
      return { avg: 0, min: 0, max: 0, count: 0 };
    }

    const sum = measurements.reduce((a, b) => a + b, 0);

    return {
      avg: sum / measurements.length,
      min: Math.min(...measurements),
      max: Math.max(...measurements),
      count: measurements.length
    };
  }

  printStats(): void {
    console.log('\nPerformance Statistics:');
    console.log('='.repeat(80));

    for (const [name, _] of this.metrics) {
      const stats = this.getStats(name);
      console.log(`${name}:`);
      console.log(`  Count: ${stats.count}`);
      console.log(`  Average: ${stats.avg.toFixed(2)}ms`);
      console.log(`  Min: ${stats.min.toFixed(2)}ms`);
      console.log(`  Max: ${stats.max.toFixed(2)}ms`);
    }
  }
}

// Usage
const monitor = new PerformanceMonitor();

const items = await monitor.measureAsync('extract-project', async () => {
  return await extractor.extractFromProject('./src');
});

monitor.printStats();

Optimization Tips

1. Use Incremental Extraction

Only re-extract changed files:

class IncrementalExtractor {
  private fileHashes = new Map<string, string>();

  async extractChangedFiles(projectPath: string): Promise<DocItem[]> {
    const files = await this.getSourceFiles(projectPath);
    const changedFiles: string[] = [];

    for (const file of files) {
      const currentHash = await this.hashFile(file);
      const previousHash = this.fileHashes.get(file);

      if (currentHash !== previousHash) {
        changedFiles.push(file);
        this.fileHashes.set(file, currentHash);
      }
    }

    console.log(`Processing ${changedFiles.length} of ${files.length} files`);

    const items: DocItem[] = [];
    for (const file of changedFiles) {
      items.push(...await this.extractFromFile(file));
    }

    return items;
  }

  private async hashFile(filePath: string): Promise<string> {
    const content = await fs.readFile(filePath);
    return crypto.createHash('sha256').update(content).digest('hex');
  }
}

2. Lazy Load Child Items

Don’t extract all members upfront:

class LazyDocItem {
  private _items?: DocItem[];

  get items(): DocItem[] {
    if (!this._items) {
      this._items = this.extractChildItems();
    }
    return this._items;
  }

  private extractChildItems(): DocItem[] {
    // Extract only when accessed
    return [];
  }
}

3. Optimize JSON with External References

Use JSON $ref to external files for large outputs instead of streaming JSONL:

async function writeItemsWithReferences(
  items: AsyncIterable<DocItem>,
  outputDir: string,
  mainFile: string
): Promise<void> {
  const references: any[] = [];
  let counter = 0;

  // Create output directory
  await fs.mkdir(outputDir, { recursive: true });

  for await (const item of items) {
    const refPath = path.join(outputDir, `item-${counter}.json`);

    // Write individual item to external file
    await fs.writeFile(refPath, JSON.stringify(item, null, 2));

    // Create reference entry
    references.push({
      $ref: refPath
    });

    counter++;
  }

  // Write main file with references
  const mainContent = {
    items: references,
    total: counter
  };

  await fs.writeFile(mainFile, JSON.stringify(mainContent, null, 2));
}

Format Evolution: The format property in OpenDocs configurations enables future format support. While JSON $ref is currently recommended for large datasets, JSONL (line-delimited JSON) is being considered as an additional format option. JSONL would provide similar streaming performance benefits but with a single-file approach, making it suitable for different use cases and tooling preferences.

4. Profile Your Code

Use Node.js profiling tools:

# CPU profiling
node --prof index.js

# Process the profile
node --prof-process isolate-*.log > profile.txt

# Memory profiling
node --inspect index.js
# Then connect with Chrome DevTools

Benchmarking

Create Performance Tests

describe('Performance', () => {
  it('should extract 1000 files in under 10 seconds', async () => {
    const start = Date.now();

    const items = await extractor.extractFromProject('./large-project');

    const duration = Date.now() - start;

    expect(duration).toBeLessThan(10000);
    expect(items.length).toBeGreaterThan(0);
  });

  it('should use less than 500MB memory', async () => {
    const before = process.memoryUsage().heapUsed;

    await extractor.extractFromProject('./large-project');

    const after = process.memoryUsage().heapUsed;
    const used = (after - before) / 1024 / 1024;

    expect(used).toBeLessThan(500);
  });
});

Concepts

Guides

OpenDocs Specification

Architecture

Performance Optimization

Performance Optimization

Memory Management

Streaming Large Datasets with JSON $ref

Memory-Efficient Extraction

Batch Processing

Caching Strategies

LRU Cache for DocItems

File-Based Cache

Cache with TTL

Parallel Processing

Worker Threads for Extraction

Worker Thread Implementation

Performance Monitoring

Measure Extraction Performance

Optimization Tips

1. Use Incremental Extraction

2. Lazy Load Child Items

3. Optimize JSON with External References

4. Profile Your Code

Benchmarking

Create Performance Tests

See Also

Concepts

Guides

OpenDocs Specification

Architecture

​Performance Optimization

​Memory Management

​Streaming Large Datasets with JSON $ref

​Memory-Efficient Extraction

​Batch Processing

​Caching Strategies

​LRU Cache for DocItems

​File-Based Cache

​Cache with TTL

​Parallel Processing

​Worker Threads for Extraction

​Worker Thread Implementation

​Performance Monitoring

​Measure Extraction Performance

​Optimization Tips

​1. Use Incremental Extraction

​2. Lazy Load Child Items

​3. Optimize JSON with External References

​4. Profile Your Code

​Benchmarking

​Create Performance Tests

​See Also

Performance Optimization

Memory Management

Streaming Large Datasets with JSON $ref

Memory-Efficient Extraction

Batch Processing

Caching Strategies

LRU Cache for DocItems

File-Based Cache

Cache with TTL

Parallel Processing

Worker Threads for Extraction

Worker Thread Implementation

Performance Monitoring

Measure Extraction Performance

Optimization Tips

1. Use Incremental Extraction

2. Lazy Load Child Items

3. Optimize JSON with External References

4. Profile Your Code

Benchmarking

Create Performance Tests

See Also