Optimize App Performance: Expert Techniques for Speed & Efficiency

Case study: Spring Boot API — eliminate blocking DB calls

Context: a monolithic Spring Boot 2.x/3.x service exposing REST endpoints experienced high tail latencies under load. Symptoms included thread pool saturation and long GC pauses.

Actions taken:

• Introduced HikariCP (recommended production pool; HikariCP 5.x+ in modern JVMs) and tuned maximumPoolSize to match CPU and expected concurrency.
• Rewrote expensive JOIN-heavy queries into read-side denormalized views and added selective indexes after analyzing slow query plans.
• Added an in-process Caffeine 3.x cache for low-cardinality lookups and Redis 6.x for larger cached objects behind short TTLs.
• Instrumented distributed traces with New Relic (8.x+) and produced flame graphs via YourKit (2023.4+) to identify CPU hotspots.

Measured outcome: P95 latency improved substantially — for this system P95 dropped from around ~700ms to ~220ms after query tuning + correct pooling; end-to-end responsiveness became more predictable. Key lesson: combine query tuning and correct pooling first; caching is additive but not a substitute for inefficient queries.

Case study: Node.js API — reduce event-loop blocking

Context: a Node.js 16.x + Express 4.x API showed spiky response times during bulk import jobs because CPU-bound image-processing code ran on the main event loop.

Actions taken:

• Moved image processing to a worker pool using Node's worker_threads and added backpressure by queuing tasks with a bounded in-memory queue.
• Offloaded long-running work to an async message queue (RabbitMQ 3.9+ or Kafka 3.x+) and tracked progress via lightweight status endpoints.
• Used PM2 (5.x+) to manage processes and capture heap snapshots during staging to root-cause memory leaks.

Measured outcome: median response times improved from ~450ms to ~120ms for user-facing endpoints; event-loop blocking spikes reduced from ~200ms to under ~20ms. Key lesson: never run CPU-intensive tasks on the event loop; prefer worker threads or separate services.

Practical configuration snippets

Spring Boot + Redis cache (application.yml):

spring:
  cache:
    type: redis
  redis:
    host: redis.example.internal
    port: 6379

Java method-level caching (Spring):

@Service
public class ProductService {
  @Cacheable("products")
  public Product getProduct(String id) {
    // expensive DB call
  }
}

Node.js worker_threads example (very small):

// main.js
const { Worker } = require('worker_threads');
function runImageTask(payload) {
  return new Promise((resolve, reject) => {
    const w = new Worker('./image-worker.js', { workerData: payload });
    w.on('message', resolve);
    w.on('error', reject);
  });
}

Troubleshooting tip: when introducing caches, add cache-miss metrics, implement a cache-warming strategy and an invalidation path to avoid thundering herd effects. Use circuit breakers and request coalescing for hot keys to prevent overload.