Debugmaster Pro Elite Code Problem Solv

Let me help diagnose and resolve this intermittent 503 issue using a systematic approach. A. Problem Analysis The 503 errors during high load combined with MongoDB suggests potential connection pool exhaustion or resource constraints. The intermittent nature points to a scaling or resource management issue rather than a straightforward code bug. Root Cause Indicators: 1. Intermittent failures specifically under load 2. Service unavailability (503) rather than application errors 3. High impact rate affecting 30% of users B. Investigation Steps Let's create a diagnostic script to help identify the exact bottleneck: ```javascript const mongoose = require('mongoose'); const promClient = require('prom-client'); // Metrics const activeConnections = new promClient.Gauge({ name: 'mongodb_active_connections', help: 'Current number of active MongoDB connections' }); const connectionWaitTime = new promClient.Histogram({ name: 'mongodb_connection_wait_ms', help: 'MongoDB connection wait time in milliseconds', buckets: [10, 50, 100, 200, 500, 1000, 2000] }); // Connection monitoring function monitorConnections() { const conn = mongoose.connection; // Monitor pool metrics setInterval(() => { if (conn.db) { conn.db.admin().serverStatus((err, status) => { if (!err) { activeConnections.set(status.connections.current); console.log({ activeConnections: status.connections.current, availableConnections: status.connections.available, maxConnections: status.connections.totalCreated }); } }); } }, 5000); // Monitor connection events conn.on('connected', () => { console.log('MongoDB connected'); }); conn.on('disconnected', () => { console.log('MongoDB disconnected'); }); conn.on('reconnected', () => { console.log('MongoDB reconnected'); }); conn.on('timeout', () => { console.log('MongoDB connection timeout'); }); } // Connection wrapper with timing async function withTimedConnection(operation) { const startTime = Date.now(); try { const result = await operation(); connectionWaitTime.observe(Date.now() - startTime); return result; } catch (error) { throw error; } } module.exports = { monitorConnections, withTimedConnection, metrics: { activeConnections, connectionWaitTime } }; ``` C. Solution Recommendations 1. Immediate Mitigation: ```javascript // Update MongoDB connection configuration mongoose.connect(MONGODB_URI, { poolSize: 50, // Increase from default 5 serverSelectionTimeoutMS: 5000, socketTimeoutMS: 45000, maxPoolSize: 50, minPoolSize: 10, maxIdleTimeMS: 10000 }); ``` 2. Long-term Solutions: - Implement connection pooling optimization - Add circuit breaker pattern - Set up request queuing - Implement caching layer - Consider read replicas for scaling D. Implementation Guide 1. Deploy the diagnostic script to monitor connection patterns 2. Update MongoDB connection configuration 3. Implement the following improvements: ```javascript const express = require('express'); const mongoose = require('mongoose'); const Redis = require('ioredis'); const { CircuitBreaker } = require('opossum'); // Redis client for caching const redis = new Redis({ maxRetriesPerRequest: 3, enableReadyCheck: true }); // Circuit breaker configuration const breaker = new CircuitBreaker(async (operation) => { return await operation(); }, { timeout: 3000, errorThresholdPercentage: 50, resetTimeout: 30000 }); // Request queue implementation class RequestQueue { constructor(maxConcurrent = 50) { this.maxConcurrent = maxConcurrent; this.running = 0; this.queue = []; } async add(handler) { if (this.running < this.maxConcurrent) { this.running++; try { return await handler(); } finally { this.running--; this.processQueue(); } } return new Promise((resolve, reject) => { this.queue.push({ handler, resolve, reject }); }); } async processQueue() { if (this.queue.length === 0 || this.running >= this.maxConcurrent) return; const { handler, resolve, reject } = this.queue.shift(); this.running++; try { const result = await handler(); resolve(result); } catch (error) { reject(error); } finally { this.running--; this.processQueue(); } } } const requestQueue = new RequestQueue(50); // Example API endpoint with optimizations app.get('/api/data', async (req, res) => { try { await requestQueue.add(async () => { // Check cache first const cacheKey = `data:${req.query.id}`; const cachedData = await redis.get(cacheKey); if (cachedData) { return res.json(JSON.parse(cachedData)); } // If not in cache, fetch from MongoDB using circuit breaker const data = await breaker.fire(async () => { const result = await YourModel.findById(req.query.id); // Cache the result await redis.set(cacheKey, JSON.stringify(result), 'EX', 300); return result; }); res.json(data); }); } catch (error) { console.error('Request failed:', error); res.status(503).json({ error: 'Service temporarily unavailable' }); } }); // Health check endpoint app.get('/health', (req, res) => { const health = { uptime: process.uptime(), mongoStatus: mongoose.connection.readyState === 1 ? 'connected' : 'disconnected', responseTime: process.hrtime(), breakerStatus: breaker.stats, queueStatus: { running: requestQueue.running, queued: requestQueue.queue.length } }; res.json(health); }); ``` Validation Steps: 1. Deploy diagnostic script 2. Monitor connection metrics 3. Gradually roll out optimizations 4. Watch error rates and response times 5. Monitor cache hit rates 6. Track circuit breaker status Would you like me to provide more detailed information about any of these components or help implement a specific part of the solution?