Building a Production-Ready Rate Limiter with Redis in NestJS (Part 1: Core Redis Implementation)
Rate limiting is critical for any production API - it protects your infrastructure from abuse, prevents DDoS attacks, and ensures fair resource allocation across users. After building several SaaS applications, I've learned that a good rate limiter needs to be more than just "X requests per minute."
In this two-part series, I'll walk you through building a production-grade rate limiter that I'm currently using in production. This isn't a basic tutorial - it's a battle-tested implementation that handles Redis cluster support, atomic operations with Lua scripts, and graceful degradation.
Part 1 (this article) covers the core Redis implementation with Lua scripts for atomic operations.
Part 2 will cover workspace-aware business logic, plan-based limits, and multi-tenancy considerations.
Why Redis for Rate Limiting?
Before diving into code, let's address the fundamental question: why Redis?
In-memory solutions (like storing counters in your Node.js process) work fine for single-server setups, but they break in distributed systems. If you're running multiple instances of your API (which you should be), each instance would have its own counter, making your rate limits ineffective.
Redis solves this by providing a centralized, extremely fast storage layer that all your API instances can share. It's perfect for rate limiting because:
- Sub-millisecond read/write operations
- Atomic operations via Lua scripts (no race conditions)
- Built-in TTL (automatic key expiration)
- Supports both single-instance and cluster deployments
The TypeScript Interfaces
First, let's define the contracts our rate limiter will work with:
export interface RateLimitConfig {
windowMs: number; // Time window in milliseconds
maxRequests: number; // Max requests allowed in window
blockDurationMs?: number; // How long to block after exceeding limit
}
export interface RateLimitResult {
totalHits: number; // Current request count
resetTime: Date; // When the window resets
remaining: number; // Requests remaining
isBlocked: boolean; // Whether the user is blocked
retryAfter?: number; // Seconds until unblocked
}
Setting Up the Redis Connection
Our RedisRateLimiter service needs to support multiple deployment scenarios - local development with a single Redis instance, production with Redis clusters, and cloud providers like Upstash or Railway.
import {
Injectable,
Logger,
OnModuleInit,
OnModuleDestroy,
} from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import Redis, { Cluster } from 'ioredis';
import { RateLimitResult, RateLimitConfig } from '../interfaces/security.interface';
@Injectable()
export class RedisRateLimiter implements OnModuleInit, OnModuleDestroy {
private readonly logger = new Logger(RedisRateLimiter.name);
private redis!: Redis | Cluster;
private readonly keyPrefix = 'rate_limit:';
private readonly blockPrefix = 'rate_limit_block:';
constructor(private readonly configService: ConfigService) {}
async onModuleInit() {
await this.initializeRedis();
this.startPeriodicCleanup();
}
async onModuleDestroy() {
if (this.redis) {
await this.redis.disconnect?.();
}
}
private async initializeRedis(): Promise<void> {
const redisConfig = this.getRedisConfig();
try {
if (redisConfig.cluster && redisConfig.cluster.length > 0) {
// Redis Cluster for high availability
this.redis = new Redis.Cluster(redisConfig.cluster, {
redisOptions: {
password: redisConfig.password,
db: redisConfig.db,
keyPrefix: this.keyPrefix,
maxRetriesPerRequest: 3,
lazyConnect: true,
keepAlive: 30000,
connectTimeout: 10000,
commandTimeout: 5000,
},
enableOfflineQueue: false,
retryDelayOnFailover: 100,
scaleReads: 'slave',
});
} else if (redisConfig.url) {
// Single instance via URL (Render, Upstash, Railway)
this.redis = new Redis(redisConfig.url, {
keyPrefix: this.keyPrefix,
lazyConnect: true,
maxRetriesPerRequest: 3,
keepAlive: 30000,
connectTimeout: 10000,
commandTimeout: 5000,
});
} else {
// Single instance via host/port (local development)
this.redis = new Redis({
host: redisConfig.host,
port: redisConfig.port,
password: redisConfig.password,
db: redisConfig.db,
keyPrefix: this.keyPrefix,
lazyConnect: true,
maxRetriesPerRequest: 3,
keepAlive: 30000,
connectTimeout: 10000,
commandTimeout: 5000,
});
}
// Event handlers for monitoring
this.redis.on('connect', () => this.logger.log('Connected to Redis'));
this.redis.on('error', (error) =>
this.logger.error('Redis connection error:', error),
);
this.redis.on('close', () => this.logger.warn('Redis connection closed'));
this.redis.on('reconnecting', () =>
this.logger.log('Reconnecting to Redis...'),
);
await this.redis.ping();
this.logger.log('Redis rate limiter initialized successfully');
} catch (error) {
this.logger.error('Failed to initialize Redis:', error);
throw error;
}
}
private getRedisConfig() {
const clusterNodes = this.configService.get<string>('REDIS_CLUSTER_NODES');
return {
url: this.configService.get<string>('REDIS_URL'),
host: this.configService.get<string>('REDIS_HOST', 'localhost'),
port: Number(this.configService.get<number>('REDIS_PORT', 6379)),
password: this.configService.get<string>('REDIS_PASSWORD'),
db: Number(this.configService.get<number>('REDIS_DB', 0)),
cluster: clusterNodes
? clusterNodes.split(',').map((node: string) => {
const [host, port] = node.split(':');
return { host, port: parseInt(port || '6379', 10) };
})
: undefined,
};
}
}
Key design decisions here:
- Flexible configuration - Works with Redis URLs, host/port combos, or cluster nodes
- Lazy connection - Doesn't block app startup if Redis is temporarily unavailable
- Comprehensive event handlers - Logs all connection state changes for debugging
- Sensible timeouts - 10s connect timeout, 5s command timeout prevents hanging requests
The Magic: Lua Scripts for Atomic Operations
Here's where things get interesting. Rate limiting requires multiple operations:
- Check if user is currently blocked
- Get current request count
- Check if time window expired
- Increment counter
- Set/update expiry
- Determine if limit exceeded
- Optionally block the user
If you do these as separate Redis commands, you'll have race conditions. Two requests arriving simultaneously could both read the counter before either increments it, allowing both through even if they should exceed the limit.
The solution: Lua scripts. Redis executes Lua scripts atomically - no other operation can run while your script is executing.
private readonly rateLimitScript = `
local key = KEYS[1]
local block_key = KEYS[2]
local window = tonumber(ARGV[1])
local limit = tonumber(ARGV[2])
local now = tonumber(ARGV[3])
local block_duration = tonumber(ARGV[4])
local increment = tonumber(ARGV[5])
-- Check if currently blocked
local blocked_until = redis.call('GET', block_key)
if blocked_until then
blocked_until = tonumber(blocked_until)
if now < blocked_until then
local remaining_block = blocked_until - now
return {0, blocked_until, 0, 1, remaining_block}
else
redis.call('DEL', block_key)
end
end
-- Get current count and expiry
local current = redis.call('HMGET', key, 'count', 'reset_time')
local count = tonumber(current[1]) or 0
local reset_time = tonumber(current[2]) or 0
-- Check if window has expired
if now >= reset_time then
count = 0
reset_time = now + window
end
-- Increment counter
if increment > 0 then
count = count + increment
end
-- Store new values with TTL
redis.call('HMSET', key, 'count', count, 'reset_time', reset_time)
redis.call('EXPIRE', key, math.ceil(window / 1000))
local remaining = math.max(0, limit - count)
local is_blocked = 0
local retry_after = 0
-- Block if limit exceeded
if count > limit then
is_blocked = 1
if block_duration > 0 then
local block_until = now + block_duration
redis.call('SETEX', block_key, math.ceil(block_duration / 1000), block_until)
retry_after = block_duration
else
retry_after = reset_time - now
end
end
return {count, reset_time, remaining, is_blocked, retry_after}
`;
What this script does:
Block checking first - If the user is blocked, we return immediately without even checking the counter. This is a performance optimization.
Fixed window algorithm - We use fixed time windows (e.g., "60 seconds starting from first request"). This is simpler than sliding windows but can allow brief bursts at window boundaries.
Progressive blocking - When limit is exceeded, we can temporarily block the user for a specified duration. This prevents repeated violations.
Single round-trip - All operations happen in one Redis call, minimizing latency.
Automatic cleanup - The
EXPIREcommand ensures keys are automatically deleted after the window passes.
The Rate Limit Check Method
Now let's implement the method that calls our Lua script:
async checkRateLimit(
identifier: string,
config: RateLimitConfig,
increment: number = 1,
): Promise<RateLimitResult> {
try {
const key = this.getRateLimitKey(identifier);
const blockKey = this.getBlockKey(identifier);
const now = Date.now();
const result = await this.redis.eval(
this.rateLimitScript,
2, // number of keys
key,
blockKey,
config.windowMs.toString(),
config.maxRequests.toString(),
now.toString(),
(config.blockDurationMs || 0).toString(),
increment.toString(),
);
const [count, resetTime, remaining, isBlocked, retryAfter] = result;
return {
totalHits: count,
resetTime: new Date(resetTime),
remaining: remaining,
isBlocked: Boolean(isBlocked),
retryAfter: retryAfter > 0 ? Math.ceil(retryAfter / 1000) : undefined,
};
} catch (error) {
this.logger.error(
`Rate limit check failed for ${identifier}:`,
error as Error,
);
// FAIL OPEN - allow request if Redis is down
return {
totalHits: 0,
resetTime: new Date(Date.now() + config.windowMs),
remaining: config.maxRequests,
isBlocked: false,
};
}
}
private getRateLimitKey(identifier: string): string {
return `${this.keyPrefix}${identifier}`;
}
private getBlockKey(identifier: string): string {
return `${this.blockPrefix}${identifier}`;
}
Critical decision: Fail open, not closed
Notice the catch block - when Redis fails, we return a permissive result rather than blocking all requests. This is intentional. A rate limiter outage shouldn't take down your entire API. Temporary over-usage is better than a complete service outage.
In production, you should:
- Monitor Redis health closely
- Set up alerts for rate limiter failures
- Have a plan to scale or failover Redis quickly
Utility Methods for Observability
Production systems need visibility. Here are essential utility methods:
// Manual reset (for customer support)
async resetRateLimit(identifier: string): Promise<void> {
try {
const key = this.getRateLimitKey(identifier);
const blockKey = this.getBlockKey(identifier);
await Promise.all([
this.redis.del(key),
this.redis.del(blockKey),
]);
this.logger.log(`Rate limit reset for ${identifier}`);
} catch (error) {
this.logger.error(
`Failed to reset rate limit for ${identifier}:`,
error as Error,
);
}
}
// Get current status (for debugging)
async getRateLimitInfo(identifier: string): Promise<{
count: number;
resetTime: Date;
isBlocked: boolean;
blockedUntil?: Date;
} | null> {
try {
const key = this.getRateLimitKey(identifier);
const blockKey = this.getBlockKey(identifier);
const [rateLimitData, blockedUntil] = await Promise.all([
this.redis.hmget(key, 'count', 'reset_time'),
this.redis.get(blockKey),
]);
if ((!rateLimitData || !rateLimitData[0]) && !blockedUntil) {
return null;
}
return {
count: parseInt(rateLimitData?.[0] || '0', 10),
resetTime: new Date(parseInt(rateLimitData?.[1] || '0', 10)),
isBlocked: Boolean(blockedUntil),
blockedUntil: blockedUntil
? new Date(parseInt(blockedUntil, 10))
: undefined,
};
} catch (error) {
this.logger.error(
`Failed to get rate limit info for ${identifier}:`,
error as Error,
);
return null;
}
}
// Find abusive users (for monitoring)
async getTopConsumers(limit: number = 10): Promise<
Array<{
identifier: string;
count: number;
resetTime: Date;
}>
> {
try {
const consumers = [];
let cursor = '0';
const batchSize = 100;
// Use SCAN instead of KEYS to avoid blocking Redis
do {
const [nextCursor, keys] = await this.redis.scan(
cursor,
'MATCH',
`${this.keyPrefix}*`,
'COUNT',
batchSize,
);
cursor = nextCursor;
if (keys.length > 0) {
const pipeline = this.redis.pipeline();
keys.forEach((key) => {
pipeline.hmget(key, 'count', 'reset_time');
});
const results = await pipeline.exec();
results?.forEach((result, index) => {
const data = result?.[1];
if (data) {
const [count, resetTime] = data;
consumers.push({
identifier: keys[index].replace(this.keyPrefix, ''),
count: parseInt(count || '0', 10),
resetTime: new Date(parseInt(resetTime || '0', 10)),
});
}
});
}
} while (cursor !== '0');
return consumers.sort((a, b) => b.count - a.count).slice(0, limit);
} catch (error) {
this.logger.error('Failed to get top consumers:', error as Error);
return [];
}
}
// Health check for monitoring systems
async healthCheck(): Promise<{
status: 'healthy' | 'unhealthy';
latency?: number;
error?: string;
}> {
try {
const start = Date.now();
await this.redis.ping();
const latency = Date.now() - start;
return { status: 'healthy', latency };
} catch (error) {
return {
status: 'unhealthy',
error: error instanceof Error ? error.message : 'Unknown error',
};
}
}
// Get metrics for monitoring dashboards
async getMetrics(): Promise<{
totalKeys: number;
memoryUsage: string;
topConsumers: Array<{
identifier: string;
count: number;
resetTime: Date;
}>;
health: {
status: 'healthy' | 'unhealthy';
latency?: number;
error?: string;
};
}> {
try {
const [topConsumers, health] = await Promise.all([
this.getTopConsumers(10),
this.healthCheck(),
]);
// Count total keys using SCAN (non-blocking)
let totalKeys = 0;
let cursor = '0';
do {
const [nextCursor, keys] = await this.redis.scan(
cursor,
'MATCH',
`${this.keyPrefix}*`,
'COUNT',
1000,
);
cursor = nextCursor;
totalKeys += keys.length;
} while (cursor !== '0');
// Get memory info if available
let memoryUsage = 'N/A';
try {
const info = await this.redis.info('memory');
const usedMemoryMatch = info.match(/used_memory:(\d+)/);
if (usedMemoryMatch) {
const bytes = parseInt(usedMemoryMatch[1], 10);
memoryUsage = `${(bytes / 1024 / 1024).toFixed(2)} MB`;
}
} catch {
// Memory info not available in all Redis configurations
}
return {
totalKeys,
memoryUsage,
topConsumers,
health,
};
} catch (error) {
this.logger.error('Failed to get metrics:', error as Error);
return {
totalKeys: 0,
memoryUsage: 'Error',
topConsumers: [],
health: {
status: 'unhealthy',
error: error instanceof Error ? error.message : 'Unknown error',
},
};
}
}
Memory Management: Automated Cleanup
Rate limiting can create thousands of keys. Even with TTL, you want proactive cleanup:
private readonly cleanupScript = `
local pattern = ARGV[1]
local batch_size = tonumber(ARGV[2]) or 100
local cursor = "0"
local deleted = 0
repeat
local scan_result = redis.call('SCAN', cursor, 'MATCH', pattern, 'COUNT', batch_size)
cursor = scan_result[1]
local keys = scan_result[2]
if #keys > 0 then
-- Use table.unpack for Lua 5.2+, fallback to unpack for 5.1
local unpack_func = table.unpack or unpack
deleted = deleted + redis.call('DEL', unpack_func(keys))
end
until cursor == "0"
return deleted
`;
async cleanupExpiredKeys(): Promise<number> {
try {
const deleted = await this.redis.eval(
this.cleanupScript,
0,
`${this.keyPrefix}*`,
'1000', // batch size
);
if (deleted > 0) {
this.logger.log(`Cleaned up ${deleted} expired rate limit keys`);
}
return deleted;
} catch (error) {
this.logger.error('Failed to cleanup expired keys:', error as Error);
return 0;
}
}
private startPeriodicCleanup(): void {
// Clean up expired keys every 5 minutes
setInterval(
async () => {
await this.cleanupExpiredKeys();
},
5 * 60 * 1000,
);
}
Why automated cleanup matters:
- Redis's passive expiration might not be aggressive enough under load
- Proactive cleanup prevents memory bloat
- The
SCANcommand (notKEYS) won't block Redis for other operations
Environment Variables Setup
# Single Redis instance via URL (recommended for cloud providers)
REDIS_URL=redis://username:password@host:port/db
# Or separate configuration (for local development)
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=your-password
REDIS_DB=0
# Redis Cluster (for high availability)
REDIS_CLUSTER_NODES=node1:6379,node2:6379,node3:6379
Testing the Rate Limiter
For unit tests, mock the Redis client:
describe('RedisRateLimiter', () => {
let rateLimiter: RedisRateLimiter;
let mockRedis: jest.Mocked<Redis>;
beforeEach(() => {
mockRedis = {
eval: jest.fn(),
ping: jest.fn().mockResolvedValue('PONG'),
on: jest.fn(),
disconnect: jest.fn(),
} as any;
// Inject mock
rateLimiter = new RedisRateLimiter(configService);
(rateLimiter as any).redis = mockRedis;
});
it('should allow requests under limit', async () => {
mockRedis.eval.mockResolvedValue([5, Date.now() + 60000, 95, 0, 0]);
const result = await rateLimiter.checkRateLimit('test-user', {
windowMs: 60000,
maxRequests: 100,
});
expect(result.isBlocked).toBe(false);
expect(result.remaining).toBe(95);
});
it('should block requests over limit', async () => {
mockRedis.eval.mockResolvedValue([101, Date.now() + 60000, 0, 1, 300000]);
const result = await rateLimiter.checkRateLimit('test-user', {
windowMs: 60000,
maxRequests: 100,
blockDurationMs: 300000,
});
expect(result.isBlocked).toBe(true);
expect(result.retryAfter).toBe(300);
});
it('should fail open when Redis is down', async () => {
mockRedis.eval.mockRejectedValue(new Error('Redis connection failed'));
const result = await rateLimiter.checkRateLimit('test-user', {
windowMs: 60000,
maxRequests: 100,
});
expect(result.isBlocked).toBe(false);
expect(result.remaining).toBe(100);
});
});
Performance Characteristics
In production, this implementation handles:
- ~10,000 checks/second on a single Redis instance (t3.small)
- Sub-5ms latency for rate limit checks (including network)
- Millions of unique identifiers without memory issues
- Zero race conditions thanks to Lua scripts
What's Next?
In Part 2, we'll build the business logic layer that uses this Redis rate limiter. We'll cover:
- Workspace-aware rate limiting for multi-tenant SaaS
- Plan-based limits (Free vs Pro vs Enterprise)
- Security-focused limits for authentication endpoints
- Standard rate limit HTTP headers
- Handling different routes with different strategies
- Real production configuration examples
The core Redis implementation we built today provides the foundation - atomic, fast, and reliable. In the next article, we'll add the intelligence that makes it production-ready for a real SaaS application.
Questions about the implementation? Drop a comment below! I'd love to hear about your rate limiting challenges.
Coming up in Part 2: Workspace-aware business logic, plan-based limits, and multi-tenancy strategies.
Tags: #nestjs #redis #ratelimiting #typescript #backend #nodejs #lua #performance
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