Optimizing Memory Management in Meituan System

As digital platforms grow in complexity, memory management becomes critical for maintaining system performance. Meituan’s management system, serving millions of users daily, requires robust strategies to handle memory allocation and cleanup efficiently. This article explores practical techniques for optimizing memory usage in Meituan’s ecosystem while addressing common challenges faced by developers.

Understanding Memory Usage Patterns
Before implementing cleanup procedures, analyzing memory consumption patterns is essential. Meituan’s backend services, built on Java and Go, often encounter memory leaks due to long-lived objects or improper garbage collection configurations. Tools like JProfiler or Go’s pprof help identify memory hotspots. For instance, a recent analysis revealed that cached restaurant data occupied 40% of heap memory during peak hours, prompting a redesign of the caching layer.

Step-by-Step Cleanup Strategies

1. Garbage Collection Tuning
   Adjusting JVM parameters significantly impacts memory reclamation. For Java-based services, setting -XX:+UseG1GC enables the Garbage-First collector, which prioritizes regions with the most reclaimable space. A Meituan engineering team reduced full GC pauses by 70% after switching from CMS to G1GC with customized -XX:MaxGCPauseMillis=200 settings.

2. Cache Management Optimization
   Implement tiered caching with expiration policies using frameworks like Caffeine or Redis. A hybrid approach stores frequently accessed user session data in-memory while offloading less critical inventory data to distributed caches. The following snippet demonstrates a time-based eviction policy:

   Cache<String, Session> cache = Caffeine.newBuilder()  
    .expireAfterAccess(5, TimeUnit.MINUTES)  
    .maximumSize(10_000)  
    .build();

3. Connection Pool Recycling
   Database connections left idle consume substantial memory. Configuring Apache DBCP with testOnBorrow=true and removeAbandonedTimeout=300 ensures automatic closure of stale connections. Meituan’s order processing system reduced connection-related memory leaks by 90% after implementing these parameters.

4. Thread Pool Adjustments
   Unbounded thread pools cause memory spikes during traffic surges. Using ThreadPoolExecutor with core/max pool sizes and LinkedBlockingQueue capacity limits prevents OOM errors. Monitoring tools like Prometheus track thread allocation trends to inform size adjustments.

Automated Memory Monitoring
Deploying real-time monitoring through Grafana dashboards visualizes memory metrics across services. Alerts trigger at 75% heap utilization, enabling proactive cleanup. Meituan’s DevOps team integrated custom scripts that automatically dump heap snapshots when thresholds are breached, accelerating root cause analysis.

Edge Case Handling
Memory fragmentation in Go services requires special attention. Using runtime.ReadMemStats exposes allocator behavior, while periodic service restarts during low-traffic windows mitigate fragmentation. A/B testing showed 22% better memory utilization after implementing weekly restarts for payment gateway services.

Impact on System Performance
Post-optimization metrics from Meituan’s logistics subsystem show tangible improvements:

• 45% reduction in average GC pause time
• 30% decrease in overall memory footprint
• 15% faster API response times during flash sales

These enhancements directly translate to better user experience and operational stability, particularly during high-traffic events like Singles’ Day promotions.

Future Directions
Emerging technologies like ZGC (Z Garbage Collector) and WebAssembly-based lightweight runtimes present new optimization avenues. Meituan’s R&D team is currently experimenting with ZGC’s sub-millisecond pause targets for real-time recommendation engines.

By combining systematic cleanup protocols with intelligent monitoring, Meituan maintains its position as a leader in scalable platform design. These memory management principles, while specific to Meituan’s architecture, offer valuable insights for any large-scale distributed system aiming to balance performance with resource efficiency.