Performance issues under high load should be handled using a structured approach that includes monitoring, profiling, bottleneck identification, optimization, and continuous performance testing. The goal is not only to fix the current issue but also to improve the application's scalability and stability.
Key Points: • Identify bottlenecks using monitoring and profiling tools before applying optimizations. • Optimize the layer causing the issue, such as database, application code, memory, or network. • Continuously monitor and load test the application after implementing improvements.
Example: Suppose an e-commerce application starts responding slowly during flash sales when thousands of users access product and checkout APIs simultaneously.
Investigation Process:
1. Monitor Application Metrics
Use monitoring tools to collect:
• CPU Usage • Memory Consumption • Thread Count • Request Latency • Error Rates • Database Connections
Common Tools: • Spring Boot Actuator • Prometheus • Grafana • Splunk • ELK Stack
2. Analyze Application Logs
Review:
• Slow API calls • Exception frequency • Timeout errors • Database connection issues
3. Perform Load Testing
Reproduce production traffic using:
• JMeter • Gatling • Locust
This helps identify the exact breaking point of the application.
4. Profile the Application
Use profilers to detect:
• High CPU methods • Memory leaks • Thread contention • Excessive object creation
Common Profiling Tools: • VisualVM • Java Flight Recorder • YourKit • JProfiler
Common Optimization Techniques:
Database Optimization:
• Add indexes on frequently queried columns. • Eliminate N+1 query problems. • Optimize slow SQL queries. • Use pagination for large datasets.
Caching:
• Introduce Redis or EhCache. • Cache frequently accessed data.
Examples: • Product Catalog • User Preferences • Reference Data
Connection Pool Tuning:
• Tune HikariCP configuration. • Increase maximum pool size when necessary.
Asynchronous Processing:
Use @Async for:
• Email Notifications • Report Generation • Audit Logging
This reduces request processing time.
Thread Pool Optimization:
Tune:
• Core Pool Size • Maximum Pool Size • Queue Capacity
JVM Optimization:
• Adjust heap size. • Tune garbage collection settings. • Use G1 GC or ZGC for large applications.
Horizontal Scaling:
• Increase application instances. • Use load balancers to distribute traffic.
Performance Troubleshooting Flow:
High Load ↓ Monitoring ↓ Identify Bottleneck ↓ Optimize Problem Area ↓ Load Testing ↓ Deploy Fix ↓ Continuous Monitoring
Real-World Example:
Issue: • Checkout API response time increased from 300 ms to 5 seconds during peak traffic.
Root Cause: • Database queries causing lock contention.
Solution: • Added indexes. • Introduced Redis caching. • Optimized SQL queries.
Result: • Response time reduced to 400 ms. • Supported three times more concurrent users.
Interview Tip: A concise interview answer is: When a Spring Boot application experiences performance issues under high load, I first monitor metrics using tools such as Spring Boot Actuator, Prometheus, and Grafana. Then I analyze logs, perform load testing, and profile the application to identify bottlenecks. Based on the findings, I optimize database queries, introduce caching, tune thread pools and connection pools, adjust JVM settings, and scale the application if necessary. Finally, I continuously monitor the system to ensure long-term stability and performance.