JVM Memory Configuration
graph TD
A[JVM Optimization] --> B[Heap Sizing]
A --> C[Garbage Collection]
A --> D[Memory Allocation]
Key JVM Parameters
Parameter |
Description |
Recommended Setting |
-Xms |
Initial Heap Size |
256m |
-Xmx |
Maximum Heap Size |
1024m |
-XX:NewRatio |
Young/Old Generation Ratio |
1-2 |
Garbage Collection Optimization
Garbage Collection Algorithms
## Common GC Algorithms
-XX:+UseG1GC ## G1 Garbage Collector
-XX:+UseParallelGC ## Parallel Collector
-XX:+UseConcMarkSweepGC ## CMS Collector
Garbage Collection Tuning Example
public class GCOptimizationDemo {
public static void main(String[] args) {
// VM Arguments:
// -XX:+PrintGCDetails
// -XX:+UseG1GC
List<byte[]> memoryHog = new ArrayList<>();
for (int i = 0; i < 1000; i++) {
memoryHog.add(new byte[1024 * 1024]); // 1MB allocation
}
}
}
Memory Efficient Coding Practices
Object Allocation Strategies
- Object Pooling
- Lazy Initialization
- Immutable Objects
Code Optimization Example
// Inefficient Approach
public List<String> processData() {
List<String> results = new ArrayList<>();
for (String item : largeDataSet) {
results.add(processItem(item));
}
return results;
}
// Optimized Approach
public List<String> processDataEfficiently() {
return largeDataSet.stream()
.map(this::processItem)
.collect(Collectors.toList());
}
## JVM Monitoring Commands
jstat -gcutil <pid> 1000
top -H -p <pid>
vmstat 1
Memory Profiling Techniques
graph LR
A[Performance Profiling] --> B[Sampling]
A --> C[Instrumentation]
A --> D[Allocation Tracking]
Tool |
Strengths |
Use Case |
VisualVM |
Comprehensive |
Overall Performance |
JProfiler |
Detailed Analysis |
Deep Dive Investigations |
YourKit |
Low Overhead |
Production Monitoring |
Advanced Optimization Techniques
Memory-Efficient Data Structures
- Use primitive arrays instead of object collections
- Prefer
ArrayList
over LinkedList
- Utilize memory-efficient data structures
At LabEx, we provide hands-on environments to practice and master Java performance optimization techniques, helping developers create more efficient applications.
Practical Optimization Workflow
- Profile Application
- Identify Bottlenecks
- Apply Targeted Optimizations
- Measure Impact
- Iterate
Optimization Checklist
- Minimize object creation
- Use appropriate data structures
- Implement caching mechanisms
- Optimize database interactions
- Use lazy loading techniques
Conclusion
Performance optimization is an iterative process requiring continuous monitoring, analysis, and refinement of your Java application's memory management strategies.