Implement API Throttling

Implement sophisticated API throttling with dynamic rate limits, quota management, tiered pricing, and advanced traffic control strategies to ensure fair usage and optimal performance.

When to Use This Command

Use /implement-throttling when you need to:

• Protect APIs from abuse and overload
• Implement usage-based billing and quotas
• Provide differentiated service tiers (free/premium)
• Ensure fair resource allocation among users
• Prevent cascade failures from traffic spikes
• Comply with third-party API rate limits

DON'T use this when:

• Building internal-only APIs with trusted clients (may be overkill)
• Prototype or MVP phase (premature optimization)
• Already using API gateway with throttling (avoid duplication)

Design Decisions

This command implements Token Bucket + Sliding Window as the primary approach because:

• Allows burst traffic while maintaining overall limits
• Provides smooth rate limiting without hard cutoffs
• Memory-efficient for high-traffic scenarios
• Supports dynamic rate adjustment
• Works well with distributed systems
• Industry-proven algorithm combination

Alternative considered: Fixed Window

• Simpler implementation
• Susceptible to thundering herd at window boundaries
• Less smooth traffic distribution
• Recommended for simple use cases

Alternative considered: Leaky Bucket

• Constant output rate
• Better for streaming scenarios
• Less flexible for burst traffic
• Recommended for bandwidth limiting

Prerequisites

Before running this command:

1. Define rate limit tiers and quotas
2. Choose storage backend (Redis recommended)
3. Determine billing/pricing model if applicable
4. Plan graceful degradation strategy
5. Set up monitoring and alerting

Implementation Process

Step 1: Configure Rate Limit Storage

Set up Redis or similar for distributed rate limit tracking.

Step 2: Implement Throttling Algorithms

Deploy token bucket and sliding window algorithms with configurable parameters.

Step 3: Create Middleware

Build middleware for automatic rate limit enforcement.

Step 4: Add Usage Tracking

Implement detailed usage tracking for analytics and billing.

Step 5: Set Up Management API

Create API for managing rate limits, quotas, and user tiers.

Output Format

The command generates:

• middleware/rate-limiter.js - Core throttling middleware
• services/throttling-manager.js - Rate limit management service
• models/usage-tracking.js - Usage data models
• config/rate-limits.json - Tier configurations
• api/rate-limit-api.js - Management endpoints
• monitoring/throttling-metrics.js - Prometheus metrics

Code Examples

Example 1: Advanced Token Bucket + Sliding Window Implementation

// services/throttling-manager.js
const Redis = require('ioredis');
const crypto = require('crypto');

class ThrottlingManager {
  constructor(redisClient = new Redis()) {
    this.redis = redisClient;
    this.tiers = {
      free: {
        rateLimit: 100,        // requests per hour
        burst: 10,             // burst allowance
        dailyQuota: 1000,      // daily limit
        monthlyQuota: 10000,   // monthly limit
        priority: 1            // queue priority (lower = higher priority)
      },
      basic: {
        rateLimit: 1000,
        burst: 50,
        dailyQuota: 10000,
        monthlyQuota: 250000,
        priority: 2
      },
      premium: {
        rateLimit: 10000,
        burst: 200,
        dailyQuota: 100000,
        monthlyQuota: 3000000,
        priority: 3
      },
      enterprise: {
        rateLimit: -1,         // unlimited rate
        burst: 1000,
        dailyQuota: -1,        // unlimited daily
        monthlyQuota: -1,      // unlimited monthly
        priority: 4
      }
    };
  }

  async checkRateLimit(userId, tier = 'free', weight = 1) {
    const config = this.tiers[tier];
    if (!config) {
      throw new Error(`Unknown tier: ${tier}`);
    }

    // Skip rate limiting for unlimited tiers
    if (config.rateLimit === -1) {
      return {
        allowed: true,
        limit: -1,
        remaining: -1,
        resetAt: null
      };
    }

    // Token bucket algorithm
    const tokenBucket = await this.checkTokenBucket(
      userId,
      config.rateLimit,
      config.burst,
      weight
    );

    if (!tokenBucket.allowed) {
      return tokenBucket;
    }

    // Check quotas
    const quotaCheck = await this.checkQuotas(userId, config, weight);

    return quotaCheck.allowed ? tokenBucket : quotaCheck;
  }

  async checkTokenBucket(userId, limit, burst, weight) {
    const now = Date.now();
    const window = 3600000; // 1 hour in ms
    const key = `throttle:bucket:${userId}`;

    // Lua script for atomic token bucket
    const luaScript = `
      local key = KEYS[1]
      local limit = tonumber(ARGV[1])
      local burst = tonumber(ARGV[2])
      local weight = tonumber(ARGV[3])
      local now = tonumber(ARGV[4])
      local window = tonumber(ARGV[5])

      local bucket = redis.call('HMGET', key, 'tokens', 'last_refill')
      local tokens = tonumber(bucket[1]) or limit
      local last_refill = tonumber(bucket[2]) or now

      -- Calculate tokens to add based on time passed
      local time_passed = now - last_refill
      local tokens_to_add = (time_passed / window) * limit
      tokens = math.min(tokens + tokens_to_add, limit + burst)

      -- Check if request can be served
      if tokens >= weight then
        tokens = tokens - weight
        redis.call('HMSET', key, 'tokens', tokens, 'last_refill', now)
        redis.call('EXPIRE', key, window / 1000)
        return {1, math.floor(tokens), now + (window * weight / limit)}
      else
        local wait_time = ((weight - tokens) * window) / limit
        return {0, math.floor(tokens), now + wait_time}
      end
    `;

    const result = await this.redis.eval(
      luaScript,
      1,
      key,
      limit,
      burst,
      weight,
      now,
      window
    );

    return {
      allowed: result[0] === 1,
      limit: limit,
      remaining: result[1],
      resetAt: new Date(result[2])
    };
  }

  async checkQuotas(userId, config, weight) {
    const now = new Date();
    const dailyKey = `quota:daily:${userId}:${this.getDayKey(now)}`;
    const monthlyKey = `quota:monthly:${userId}:${this.getMonthKey(now)}`;

    // Check daily quota
    if (config.dailyQuota > 0) {
      const dailyUsage = await this.redis.incrby(dailyKey, 0);
      if (dailyUsage + weight > config.dailyQuota) {
        return {
          allowed: false,
          limit: config.dailyQuota,
          remaining: Math.max(0, config.dailyQuota - dailyUsage),
          resetAt: this.getNextDay(now),
          reason: 'Daily quota exceeded'
        };
      }
    }

    // Check monthly quota
    if (config.monthlyQuota > 0) {
      const monthlyUsage = await this.redis.incrby(monthlyKey, 0);
      if (monthlyUsage + weight > config.monthlyQuota) {
        return {
          allowed: false,
          limit: config.monthlyQuota,
          remaining: Math.max(0, config.monthlyQuota - monthlyUsage),
          resetAt: this.getNextMonth(now),
          reason: 'Monthly quota exceeded'
        };
      }
    }

    // Increment quotas
    const pipeline = this.redis.pipeline();
    if (config.dailyQuota > 0) {
      pipeline.incrby(dailyKey, weight);
      pipeline.expire(dailyKey, 86400); // 24 hours
    }
    if (config.monthlyQuota > 0) {
      pipeline.incrby(monthlyKey, weight);
      pipeline.expire(monthlyKey, 2592000); // 30 days
    }
    await pipeline.exec();

    return {
      allowed: true,
      limit: config.dailyQuota,
      remaining: Math.max(0, config.dailyQuota - (await this.redis.get(dailyKey) || 0)),
      resetAt: this.getNextDay(now)
    };
  }

  async getUsageStats(userId) {
    const now = new Date();
    const dailyKey = `quota:daily:${userId}:${this.getDayKey(now)}`;
    const monthlyKey = `quota:monthly:${userId}:${this.getMonthKey(now)}`;
    const bucketKey = `throttle:bucket:${userId}`;

    const [dailyUsage, monthlyUsage, bucket] = await Promise.all([
      this.redis.get(dailyKey),
      this.redis.get(monthlyKey),
      this.redis.hgetall(bucketKey)
    ]);

    return {
      daily: {
        used: parseInt(dailyUsage) || 0,
        resetAt: this.getNextDay(now)
      },
      monthly: {
        used: parseInt(monthlyUsage) || 0,
        resetAt: this.getNextMonth(now)
      },
      rateLimit: {
        tokens: parseFloat(bucket.tokens) || 0,
        lastRefill: bucket.last_refill ? new Date(parseInt(bucket.last_refill)) : null
      }
    };
  }

  async resetUserLimits(userId, type = 'all') {
    const keys = [];

    if (type === 'all' || type === 'rate') {
      keys.push(`throttle:bucket:${userId}`);
    }
    if (type === 'all' || type === 'daily') {
      keys.push(`quota:daily:${userId}:${this.getDayKey(new Date())}`);
    }
    if (type === 'all' || type === 'monthly') {
      keys.push(`quota:monthly:${userId}:${this.getMonthKey(new Date())}`);
    }

    if (keys.length > 0) {
      await this.redis.del(...keys);
    }
  }

  getDayKey(date) {
    return `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, '0')}-${String(date.getDate()).padStart(2, '0')}`;
  }

  getMonthKey(date) {
    return `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, '0')}`;
  }

  getNextDay(date) {
    const tomorrow = new Date(date);
    tomorrow.setDate(tomorrow.getDate() + 1);
    tomorrow.setHours(0, 0, 0, 0);
    return tomorrow;
  }

  getNextMonth(date) {
    const nextMonth = new Date(date);
    nextMonth.setMonth(nextMonth.getMonth() + 1);
    nextMonth.setDate(1);
    nextMonth.setHours(0, 0, 0, 0);
    return nextMonth;
  }
}

// middleware/rate-limiter.js
const ThrottlingManager = require('../services/throttling-manager');

function createRateLimitMiddleware(options = {}) {
  const throttling = new ThrottlingManager(options.redis);
  const {
    keyGenerator = (req) => req.user?.id || req.ip,
    tierResolver = (req) => req.user?.tier || 'free',
    weightResolver = (req) => 1,
    skipRoutes = [],
    onLimitExceeded = null
  } = options;

  return async function rateLimitMiddleware(req, res, next) {
    // Skip rate limiting for excluded routes
    if (skipRoutes.includes(req.path)) {
      return next();
    }

    const userId = keyGenerator(req);
    const tier = await tierResolver(req);
    const weight = weightResolver(req);

    try {
      const result = await throttling.checkRateLimit(userId, tier, weight);

      // Set rate limit headers
      res.set({
        'X-RateLimit-Limit': result.limit,
        'X-RateLimit-Remaining': result.remaining,
        'X-RateLimit-Reset': result.resetAt ? result.resetAt.toISOString() : ''
      });

      if (!result.allowed) {
        // Custom handler for rate limit exceeded
        if (onLimitExceeded) {
          return onLimitExceeded(req, res, result);
        }

        // Default response
        return res.status(429).json({
          error: 'Rate limit exceeded',
          message: result.reason || 'Too many requests',
          retryAfter: result.resetAt ? Math.ceil((result.resetAt - Date.now()) / 1000) : 60
        });
      }

      // Track usage for analytics
      req.rateLimitInfo = result;
      next();
    } catch (error) {
      console.error('Rate limiting error:', error);
      // Fail open - allow request if rate limiting fails
      next();
    }
  };
}

// Usage
const express = require('express');
const app = express();

app.use(createRateLimitMiddleware({
  redis: new Redis({
    host: 'localhost',
    port: 6379
  }),
  keyGenerator: (req) => {
    // Use API key if available, otherwise IP
    return req.headers['x-api-key'] || req.ip;
  },
  tierResolver: async (req) => {
    // Look up user tier from database
    if (req.headers['x-api-key']) {
      const user = await getUserByApiKey(req.headers['x-api-key']);
      return user?.tier || 'free';
    }
    return 'free';
  },
  weightResolver: (req) => {
    // Different weights for different operations
    const weights = {
      'GET': 1,
      'POST': 2,
      'PUT': 2,
      'DELETE': 3
    };
    return weights[req.method] || 1;
  },
  onLimitExceeded: (req, res, result) => {
    // Custom handling - maybe queue the request
    console.log(`Rate limit exceeded for ${req.ip}: ${result.reason}`);
    res.status(429).json({
      error: 'Rate limit exceeded',
      upgrade: 'Consider upgrading to premium for higher limits',
      resetAt: result.resetAt
    });
  }
}));

Example 2: Distributed Rate Limiting with Priority Queues

// services/priority-queue-throttler.js
const Bull = require('bull');
const Redis = require('ioredis');

class PriorityQueueThrottler {
  constructor(options = {}) {
    this.redis = options.redis || new Redis();
    this.queues = new Map();
    this.processors = new Map();
    this.config = {
      maxConcurrent: options.maxConcurrent || 100,
      processingTimeout: options.processingTimeout || 30000,
      retryAttempts: options.retryAttempts || 3
    };

    // Initialize priority queues
    this.initializeQueues();
  }

  initializeQueues() {
    const priorities = ['critical', 'high', 'normal', 'low'];

    priorities.forEach(priority => {
      const queue = new Bull(`api-requests-${priority}`, {
        redis: this.redis,
        defaultJobOptions: {
          removeOnComplete: true,
          removeOnFail: false,
          attempts: this.config.retryAttempts,
          backoff: {
            type: 'exponential',
            delay: 2000
          }
        }
      });

      this.queues.set(priority, queue);

      // Set up queue processor
      queue.process(this.config.maxConcurrent, async (job) => {
        return await this.processRequest(job.data);
      });

      // Queue event handlers
      queue.on('completed', (job, result) => {
        console.log(`Request ${job.id} completed with priority ${priority}`);
      });

      queue.on('failed', (job, err) => {
        console.error(`Request ${job.id} failed:`, err);
      });
    });
  }

  async queueRequest(request, priority = 'normal') {
    const queue = this.queues.get(priority);
    if (!queue) {
      throw new Error(`Invalid priority: ${priority}`);
    }

    // Check if user has too many pending requests
    const pendingCount = await this.getPendingCount(request.userId);
    const maxPending = this.getMaxPending(request.tier);

    if (pendingCount >= maxPending) {
      throw new Error('Too many pending requests');
    }

    // Add request to queue with priority
    const job = await queue.add(request, {
      priority: this.getPriorityValue(priority),
      delay: this.calculateDelay(request.tier, pendingCount)
    });

    // Store job info for tracking
    await this.redis.setex(
      `job:${job.id}`,
      3600,
      JSON.stringify({
        userId: request.userId,
        priority,
        createdAt: Date.now()
      })
    );

    return {
      jobId: job.id,
      position: await this.getQueuePosition(job.id, priority),
      estimatedTime: await this.estimateProcessingTime(priority)
    };
  }

  async processRequest(request) {
    const startTime = Date.now();

    try {
      // Simulate API processing
      const result = await this.executeApiRequest(request);

      // Record metrics
      await this.recordMetrics({
        userId: request.userId,
        duration: Date.now() - startTime,
        success: true
      });

      return result;
    } catch (error) {
      await this.recordMetrics({
        userId: request.userId,
        duration: Date.now() - startTime,
        success: false,
        error: error.message
      });

      throw error;
    }
  }

  async getPendingCount(userId) {
    const priorities = ['critical', 'high', 'normal', 'low'];
    let total = 0;

    for (const priority of priorities) {
      const queue = this.queues.get(priority);
      const jobs = await queue.getJobs(['waiting', 'active']);
      total += jobs.filter(job => job.data.userId === userId).length;
    }

    return total;
  }

  getMaxPending(tier) {
    const limits = {
      free: 5,
      basic: 20,
      premium: 50,
      enterprise: 200
    };
    return limits[tier] || 5;
  }

  getPriorityValue(priority) {
    const values = {
      critical: 1,
      high: 2,
      normal: 3,
      low: 4
    };
    return values[priority] || 3;
  }

  calculateDelay(tier, pendingCount) {
    // Add progressive delay based on pending requests
    const baseDelay = {
      free: 1000,
      basic: 500,
      premium: 100,
      enterprise: 0
    };

    const delay = baseDelay[tier] || 1000;
    return delay * Math.max(1, pendingCount / 2);
  }

  async getQueuePosition(jobId, priority) {
    const queue = this.queues.get(priority);
    const jobs = await queue.getJobs(['waiting']);
    const position = jobs.findIndex(job => job.id === jobId);
    return position + 1;
  }

  async estimateProcessingTime(priority) {
    const queue = this.queues.get(priority);
    const [waiting, active] = await Promise.all([
      queue.getWaitingCount(),
      queue.getActiveCount()
    ]);

    const avgProcessingTime = 500; // ms per request
    const totalPending = waiting + active;
    const estimatedMs = (totalPending * avgProcessingTime) / this.config.maxConcurrent;

    return Math.ceil(estimatedMs / 1000); // Return in seconds
  }

  async recordMetrics(metrics) {
    const key = `metrics:${metrics.userId}:${this.getDayKey(new Date())}`;

    await this.redis.hincrby(key, metrics.success ? 'success' : 'failed', 1);
    await this.redis.hincrby(key, 'total_duration', metrics.duration);
    await this.redis.expire(key, 86400); // Keep for 24 hours
  }

  async executeApiRequest(request) {
    // Simulate actual API request processing
    return new Promise((resolve) => {
      setTimeout(() => {
        resolve({
          success: true,
          data: request.data,
          processedAt: Date.now()
        });
      }, Math.random() * 1000);
    });
  }

  getDayKey(date) {
    return `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, '0')}-${String(date.getDate()).padStart(2, '0')}`;
  }

  async getQueueStats() {
    const stats = {};

    for (const [priority, queue] of this.queues) {
      const [waiting, active, completed, failed] = await Promise.all([
        queue.getWaitingCount(),
        queue.getActiveCount(),
        queue.getCompletedCount(),
        queue.getFailedCount()
      ]);

      stats[priority] = {
        waiting,
        active,
        completed,
        failed
      };
    }

    return stats;
  }
}

// API for queue management
const express = require('express');
const router = express.Router();
const throttler = new PriorityQueueThrottler();

router.post('/api/queue', async (req, res) => {
  try {
    const priority = req.user?.tier === 'enterprise' ? 'high' : 'normal';

    const result = await throttler.queueRequest({
      userId: req.user?.id || req.ip,
      tier: req.user?.tier || 'free',
      data: req.body
    }, priority);

    res.status(202).json({
      message: 'Request queued',
      ...result
    });
  } catch (error) {
    res.status(429).json({
      error: error.message
    });
  }
});

router.get('/api/queue/:jobId/status', async (req, res) => {
  // Get job status
  const job = await queue.getJob(req.params.jobId);

  if (!job) {
    return res.status(404).json({ error: 'Job not found' });
  }

  res.json({
    id: job.id,
    status: await job.getState(),
    progress: job.progress(),
    result: job.returnvalue,
    failedReason: job.failedReason
  });
});

module.exports = router;

Example 3: Adaptive Rate Limiting with Machine Learning

# adaptive_throttling.py
import time
import numpy as np
from sklearn.linear_model import LinearRegression
from collections import deque
import redis
import json
from datetime import datetime, timedelta

class AdaptiveThrottling:
    """
    Machine learning-based adaptive rate limiting that adjusts
    limits based on system performance and user behavior.
    """

    def __init__(self, redis_client=None):
        self.redis = redis_client or redis.Redis()
        self.performance_history = deque(maxlen=1000)
        self.model = LinearRegression()
        self.base_limits = {
            'free': 100,
            'basic': 500,
            'premium': 2000,
            'enterprise': 10000
        }
        self.initialize_model()

    def initialize_model(self):
        """Initialize ML model with synthetic training data."""
        # Features: [hour_of_day, day_of_week, current_load, user_history]
        X_train = np.random.rand(100, 4) * [24, 7, 1, 100]
        # Target: optimal rate limit multiplier
        y_train = 1 + 0.5 * np.sin(X_train[:, 0] * np.pi / 12) + np.random.rand(100) * 0.2
        self.model.fit(X_train, y_train)

    def calculate_dynamic_limit(self, user_id, tier):
        """Calculate adaptive rate limit based on current conditions."""
        base_limit = self.base_limits.get(tier, 100)

        # Get current system metrics
        features = self.extract_features(user_id)

        # Predict optimal multiplier
        multiplier = self.model.predict([features])[0]
        multiplier = max(0.5, min(2.0, multiplier))  # Bound between 0.5x and 2x

        # Apply multiplier to base limit
        dynamic_limit = int(base_limit * multiplier)

        # Store decision for analysis
        self.redis.setex(
            f"adaptive:decision:{user_id}",
            3600,
            json.dumps({
                'base': base_limit,
                'multiplier': multiplier,
                'dynamic': dynamic_limit,
                'timestamp': time.time()
            })
        )

        return dynamic_limit

    def extract_features(self, user_id):
        """Extract features for ML model."""
        now = datetime.now()

        # Time-based features
        hour_of_day = now.hour
        day_of_week = now.weekday()

        # System load (simplified - would use actual metrics)
        current_load = self.get_system_load()

        # User behavior history
        user_history = self.get_user_history(user_id)

        return [hour_of_day, day_of_week, current_load, user_history]

    def get_system_load(self):
        """Get current system load (0-1 scale)."""
        # Simplified - would use actual CPU/memory metrics
        total_requests = self.redis.get("system:requests:current")
        max_capacity = 10000
        return min(1.0, float(total_requests or 0) / max_capacity)

    def get_user_history(self, user_id):
        """Get user's historical usage pattern."""
        history_key = f"user:history:{user_id}"
        history = self.redis.lrange(history_key, 0, -1)

        if not history:
            return 50  # Default neutral score

        # Calculate average request rate
        rates = [float(r) for r in history]
        return np.mean(rates[-10:])  # Last 10 data points

    def update_model(self):
        """Update ML model based on recent performance data."""
        if len(self.performance_history) < 100:
            return

        # Prepare training data from performance history
        X = []
        y = []

        for entry in self.performance_history:
            X.append(entry['features'])
            # Target is based on whether system performed well
            y.append(entry['performance_score'])

        # Retrain model
        self.model.fit(X, y)

        print(f"Model updated with {len(X)} samples")

    def record_performance(self, features, performance_metrics):
        """Record system performance for model updates."""
        performance_score = self.calculate_performance_score(performance_metrics)

        self.performance_history.append({
            'features': features,
            'performance_score': performance_score,
            'timestamp': time.time()
        })

        # Periodically update model
        if len(self.performance_history) % 100 == 0:
            self.update_model()

    def calculate_performance_score(self, metrics):
        """Calculate performance score from metrics."""
        # Weighted scoring based on multiple factors
        score = 0.0
        score += 0.3 * (1 - metrics.get('error_rate', 0))
        score += 0.3 * (1 - min(1, metrics.get('latency', 0) / 1000))
        score += 0.2 * (1 - metrics.get('rejection_rate', 0))
        score += 0.2 * metrics.get('throughput', 0) / 1000

        return max(0, min(1, score))

# Integration with Express.js via subprocess
if __name__ == "__main__":
    import sys

    adaptive = AdaptiveThrottling()

    # Read request from stdin
    request = json.loads(sys.stdin.read())

    # Calculate dynamic limit
    limit = adaptive.calculate_dynamic_limit(
        request['userId'],
        request['tier']
    )

    # Return result
    print(json.dumps({'limit': limit}))

Error Handling

Error	Cause	Solution
"Redis connection failed"	Redis server down	Implement fallback to local memory
"Rate limit exceeded"	Too many requests	Implement retry with backoff
"Invalid tier"	Unknown subscription tier	Use default tier as fallback
"Queue overflow"	Too many pending requests	Increase queue capacity or reject requests
"Quota calculation error"	Time sync issues	Ensure NTP synchronization

Configuration Options

Rate Limiting Algorithms

• token-bucket: Allows burst traffic
• sliding-window: Smooth rate distribution
• fixed-window: Simple time-based limits
• leaky-bucket: Constant output rate

Storage Backends

• redis: Recommended for distributed systems
• memory: For single-server deployments
• dynamodb: For serverless architectures
• postgresql: For persistent quota tracking

Best Practices

DO:

• Use distributed storage for multi-server deployments
• Implement graceful degradation when limits are reached
• Provide clear error messages with retry information
• Monitor rate limit effectiveness
• Adjust limits based on actual usage patterns
• Implement different weights for different operations

DON'T:

• Use only client-side rate limiting
• Ignore time synchronization issues
• Set limits too restrictive initially
• Forget to handle rate limiter failures
• Apply same limits to all operations

Performance Considerations

• Use Lua scripts for atomic Redis operations
• Implement connection pooling for Redis
• Cache tier information to reduce lookups
• Use sliding window for better distribution
• Consider read-heavy vs write-heavy operations

Monitoring and Analytics

// monitoring/throttling-metrics.js
const promClient = require('prom-client');

// Metrics
const rateLimitHits = new promClient.Counter({
  name: 'rate_limit_hits_total',
  help: 'Total number of rate limited requests',
  labelNames: ['tier', 'reason']
});

const quotaUsage = new promClient.Gauge({
  name: 'quota_usage_ratio',
  help: 'Current quota usage ratio',
  labelNames: ['user_id', 'quota_type']
});

const requestsQueued = new promClient.Gauge({
  name: 'requests_queued',
  help: 'Number of requests in queue',
  labelNames: ['priority']
});

Related Commands

• /api-rate-limiter - Basic rate limiting implementation
• /api-monitoring-dashboard - Monitor throttling metrics
• /api-billing-system - Usage-based billing
• /api-gateway-builder - Gateway-level throttling

Version History

• v1.0.0 (2024-10): Initial implementation with token bucket and quotas
• Planned v1.1.0: Add machine learning-based adaptive throttling