Backend Performance Optimization with Redis and LRU Cache

Backend Performance Optimization with Redis and LRU Cache — Interactive System Model

Redis & Memory

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In backend systems serving millions of requests, the slowest component is almost always the disk-based relational database. A well-designed caching architecture can bring API response times down from around 300ms to under 15ms.

How LRU (Least Recently Used) Works

Memory is not an infinite resource. An LRU cache automatically evicts the keys that have gone unread the longest and are requested the least, so the hottest data always has room in memory.

Cache-Aside and Write-Through Strategies

The pattern we reach for most often in our applications is Cache-Aside. Data is looked up in the cache first; on a cache miss it is fetched from the database, written to the cache and returned to the client. When the data is updated, the related cache keys are invalidated immediately.

Protecting Against the Cache Thundering Herd

To keep the database from collapsing when thousands of requests query a single expired key at the same moment, mutex locking or stale-while-revalidate approaches should be applied.

Backend Performance Optimization with Redis and LRU Cache · Live Code BlockPerformance & Cache · TS 5.x

Backend Performance Optimization with Redis and LRU Cache · Live Code Block

60 FPS · TS 5.x · Running

1@Injectable()

2export class HttpCacheInterceptor implements NestInterceptor {

3 constructor(@Inject(CACHE_MANAGER) private readonly cacheManager: Cache) {}

5 async intercept(context: ExecutionContext, next: CallHandler): Promise<Observable<any>> {

6 const req = context.switchToHttp().getRequest<Request>();

7 const cacheKey = `http_cache:${req.method}:${req.originalUrl}`;

9 const cachedData = await this.cacheManager.get(cacheKey);

10 if (cachedData) {

11 return of(cachedData);

12 }

14 return next.handle().pipe(

15 tap(async (response) => {

16 await this.cacheManager.set(cacheKey, response, 600); // 10 minutes TTL

17 }),

18 );

19 }

20}

Backend Performance Optimization with Redis and LRU Cache

Backend Performance Optimization with Redis and LRU Cache — Interactive System Model

How LRU (Least Recently Used) Works

Cache-Aside and Write-Through Strategies

Protecting Against the Cache Thundering Herd

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