Mastering Garbage Collection: A Deep Dive into JVM Memory Management and Custom Algorithms

Introduction to Automatic Memory Management

In the landscape of modern software engineering, memory management remains one of the most critical aspects of system stability and performance. For decades, developers wrestling with languages like C and C++ had to manually allocate and deallocate memory, a process prone to fatal errors such as dangling pointers and memory leaks. The advent of Java Programming and the Java Virtual Machine (JVM) popularized the concept of automatic Garbage Collection (GC), fundamentally changing how we approach Java Development.

Garbage Collection is the daemon process that looks at heap memory, identifies which objects are in use and which are not, and deletes the unused objects. An in-use object, or a referenced object, means that some part of your program still maintains a pointer to that object. An unused object, or unreferenced object, is no longer referenced by any part of your program. So the memory used by an unreferenced object can be reclaimed.

For developers working on Java Enterprise applications, Spring Boot microservices, or high-throughput Java Backend systems, understanding the mechanics of GC is not just academic—it is a necessity for JVM Tuning and ensuring Java Scalability. Whether you are using Java 17, migrating to Java 21, or maintaining legacy systems, the efficiency of your Garbage Collector directly impacts latency and throughput. This article explores the core algorithms behind GC, provides a simulation of a Mark-Sweep collector, and offers Java Best Practices for optimization.

Section 1: Core Concepts and The Mark-Sweep Algorithm

To understand how Java Frameworks like Hibernate or Jakarta EE manage data, we must first understand the underlying algorithms. The most fundamental of these is the “Mark and Sweep” algorithm. While modern JVMs use complex generational collectors, the basic logic remains similar.

The Two Phases of Garbage Collection

The Mark-Sweep algorithm operates in two distinct phases:

1. Mark Phase: The collector traverses the object graph starting from the “GC Roots” (local variables, static variables, active threads). Every object encountered is flagged as “alive.”
2. Sweep Phase: The collector scans the heap memory. Any object that was not marked in the previous phase is considered garbage and its memory is reclaimed.

Understanding GC Roots

In Java Architecture, a GC Root is an object that is accessible from outside the heap. The following are typically GC Roots:

• Local variables in the Java Stack (active methods).
• Active Java Threads.
• Static variables defined in classes.
• JNI References.

Let’s look at a conceptual Java example demonstrating how objects become eligible for garbage collection. This is fundamental knowledge for Java Basics and Clean Code Java.

public class GCEligibilityDemo {

    // A simple object representing data in a Java Database application
    static class DataNode {
        String name;
        DataNode next;

        public DataNode(String name) {
            this.name = name;
        }
        
        @Override
        public void finalize() {
            // NOTE: finalize is deprecated in newer Java versions, 
            // but useful here to demonstrate when GC happens.
            System.out.println("Garbage Collecting: " + this.name);
        }
    }

    public static void main(String[] args) {
        // Step 1: Create objects
        DataNode node1 = new DataNode("Node 1");
        DataNode node2 = new DataNode("Node 2");
        
        // Step 2: Create a relationship
        node1.next = node2; // Node 1 references Node 2
        
        // Step 3: Making objects eligible for GC
        
        // node2 variable is set to null, BUT the object "Node 2" 
        // is still referenced by node1.next. It is NOT eligible for GC yet.
        node2 = null; 
        
        // Now node1 is set to null. 
        // The object "Node 1" is no longer reachable from GC Roots.
        // Consequently, "Node 2" (referenced only by Node 1) is also unreachable.
        // Both are now eligible for the Mark-Sweep process.
        node1 = null;
        
        // Requesting JVM to run GC (Not guaranteed, but likely)
        System.gc();
        
        try {
            Thread.sleep(1000); // Wait to see the output
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
}

In the example above, even though we nullified node2, the object itself remained reachable via node1. This highlights a common pitfall in Java Collections and linked structures where unintentional references prevent memory reclamation.

Section 2: Implementing a Custom Garbage Collection Simulator

To truly grasp Java Advanced concepts, it is helpful to simulate how the JVM works internally. While we cannot easily modify the HotSpot JVM source code, we can write a simulator in Java that mimics the Mark-Sweep logic. This is a great exercise for understanding Java Data Structures and recursion.

Below is a simplified implementation of a custom memory system. This simulation helps visualize how Java Memory Management handles object graphs, which is relevant when debugging Java Memory Leaks.

import java.util.ArrayList;
import java.util.List;
import java.util.HashSet;
import java.util.Set;

/**
 * A simulator demonstrating the Mark-Sweep Algorithm logic
 * typically found in JVM internals.
 */
public class SimpleGCSimulator {

    // Represents an object in our simulated Heap
    static class HeapObject {
        int id;
        boolean marked = false;
        List references = new ArrayList<>();

        public HeapObject(int id) {
            this.id = id;
        }

        public void addReference(HeapObject obj) {
            this.references.add(obj);
        }

        @Override
        public String toString() {
            return "Object-" + id;
        }
    }

    // The simulated Heap
    static List heap = new ArrayList<>();
    
    // The Stack (GC Roots)
    static List stack = new ArrayList<>();

    public static void main(String[] args) {
        // 1. Allocate objects
        HeapObject obj1 = new HeapObject(1);
        HeapObject obj2 = new HeapObject(2);
        HeapObject obj3 = new HeapObject(3); // Will be garbage
        HeapObject obj4 = new HeapObject(4); // Will be garbage

        heap.add(obj1);
        heap.add(obj2);
        heap.add(obj3);
        heap.add(obj4);

        // 2. Create references (The Object Graph)
        // obj1 -> obj2
        obj1.addReference(obj2);

        // 3. Define Roots (What is currently in scope?)
        // Only obj1 is on the stack (Root)
        stack.add(obj1);

        System.out.println("Heap size before GC: " + heap.size());

        // 4. Run Garbage Collection
        runMarkSweepGC();

        System.out.println("Heap size after GC: " + heap.size());
        System.out.println("Remaining objects: " + heap);
    }

    public static void runMarkSweepGC() {
        System.out.println("--- Starting GC ---");
        
        // Phase 1: Mark
        // Start from all roots (Stack)
        for (HeapObject root : stack) {
            mark(root);
        }

        // Phase 2: Sweep
        sweep();
        
        System.out.println("--- GC Completed ---");
    }

    // Recursive DFS marking
    private static void mark(HeapObject obj) {
        if (obj == null || obj.marked) return;

        System.out.println("Marking: " + obj);
        obj.marked = true;

        for (HeapObject child : obj.references) {
            mark(child);
        }
    }

    // Sweep unmarked objects
    private static void sweep() {
        List newHeap = new ArrayList<>();
        for (HeapObject obj : heap) {
            if (obj.marked) {
                obj.marked = false; // Reset for next GC cycle
                newHeap.add(obj);
            } else {
                System.out.println("Reclaiming memory for: " + obj);
            }
        }
        heap = newHeap;
    }
}

This simulation clarifies why circular references (e.g., A references B, B references A) are handled correctly by Mark-Sweep but fail in simple Reference Counting algorithms. Since the reachability is determined from the root, isolated cycles are correctly identified as garbage. This robustness is why Java Web Development platforms can handle complex object graphs without constant memory leaks.

Section 3: Advanced JVM Collectors and Tuning

Moving from simulation to reality, the modern JVM (specifically HotSpot) uses far more sophisticated strategies than simple Mark-Sweep. When deploying Java Microservices on Kubernetes Java clusters or AWS Java environments, selecting the right collector is vital.

Generational Garbage Collection

Most JVMs operate on the “Weak Generational Hypothesis,” which states that most objects die young. The heap is divided into:

• Young Generation: Where new objects are allocated (Eden Space). GC here is frequent and fast (Minor GC).
• Old Generation (Tenured): Objects that survive multiple Minor GCs are moved here. GC here is slower and more expensive (Major GC).

Modern Collectors in Java 17 and Java 21

1. Serial GC: Single-threaded. Good for small Android Java apps or simple CLI tools.
2. Parallel GC: Throughput-oriented. Uses multiple threads for Young Gen. Good for batch processing.
3. G1GC (Garbage First): The default for Java 17+. It splits the heap into regions and prioritizes cleaning regions with the most garbage. Ideal for Spring Boot servers requiring predictable latency.
4. ZGC & Shenandoah: Low-latency collectors. They perform concurrent marking and compaction, ensuring pause times do not exceed a few milliseconds, regardless of heap size. This is a game-changer for Java Cloud applications and real-time trading systems.

Handling References: Soft, Weak, and Phantom

Standard references are “Strong References.” However, Java Best Practices for caching often involve WeakReference or SoftReference. This allows the GC to reclaim memory if the system is under pressure, preventing OutOfMemoryError.

import java.lang.ref.WeakReference;
import java.util.HashMap;
import java.util.Map;
import java.util.WeakHashMap;

public class ReferenceTypesDemo {

    public static void main(String[] args) {
        // Scenario: Building a Cache for a Java REST API
        
        // BAD PRACTICE for Caching: Strong References
        // This map will grow indefinitely unless manually cleared
        Map strongCache = new HashMap<>();
        
        // GOOD PRACTICE for Caching: WeakHashMap
        // Entries are removed when the Key is no longer referenced elsewhere
        Map weakCache = new WeakHashMap<>();
        
        Object key = new Object();
        String value = "Cached Data";
        
        weakCache.put(key, value);
        
        System.out.println("Cache contains key? " + weakCache.containsKey(key));
        
        // Simulate the key going out of scope (e.g., request finished)
        key = null;
        
        // Trigger GC
        System.gc();
        
        // Wait for GC to happen
        try { Thread.sleep(1000); } catch (Exception e) {}
        
        // The entry should be gone because the key was only held by the WeakHashMap
        System.out.println("Cache contains key after GC? " + weakCache.size()); 
        // Output will likely be 0 or false
    }
}

Section 4: Best Practices, Optimization, and Common Pitfalls

Even with advanced collectors, developers can write code that defeats the Garbage Collector. Issues often arise in Java Concurrency, improper use of Java Threads, or static collections.

1. The Static Collection Trap

Static fields have the same lifecycle as the ClassLoader (usually the life of the app). Adding objects to a static List or static Map without removing them creates a memory leak. This is common in Java Web Development when tracking active user sessions globally.

2. Unclosed Resources

Failing to close connections (DB, Network, IO) can lead to memory leaks outside the Heap (native memory). Always use try-with-resources, a staple of Clean Code Java.

3. Tuning for Containers (Docker/Kubernetes)

When running Docker Java images, the JVM must be aware of the container’s memory limits. In older Java versions, the JVM looked at the host OS memory, not the container limit, causing crashes. Ensure you use:

• -XX:+UseContainerSupport (Default in newer versions)
• -XX:MaxRAMPercentage=75.0 to allow overhead for non-heap memory.

4. Monitoring Tools

To optimize Java Performance, you cannot fly blind. Use tools like:

• VisualVM: For visualizing heap dumps.
• JConsole: For monitoring JMX beans.
• Eclipse MAT (Memory Analyzer Tool): For analyzing memory leaks in large heaps.

Practical Example: Preventing Leaks in Asynchronous Tasks

When using CompletableFuture or Java Async processing, ensure that thread pools are managed correctly. Unbounded thread pools can hold references to tasks and objects indefinitely.

import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class AsyncCleanupDemo {

    public static void main(String[] args) {
        // Best Practice: Use a bounded custom executor
        // Avoid Executors.newCachedThreadPool() for high-load services
        ExecutorService executor = Executors.newFixedThreadPool(10);

        CompletableFuture.supplyAsync(() -> {
            // Simulate processing
            return processHeavyData();
        }, executor).thenAccept(result -> {
            System.out.println("Processed: " + result);
        }).exceptionally(ex -> {
            // Always handle exceptions to prevent thread leakage or stuck states
            System.err.println("Error: " + ex.getMessage());
            return null;
        });
        
        // Graceful shutdown is essential for Java DevOps pipelines
        executor.shutdown();
    }

    private static String processHeavyData() {
        // Simulating data processing
        byte[] data = new byte[1024 * 1024]; // 1MB allocation
        return "Data Size: " + data.length;
    }
}

Conclusion

Garbage Collection is the unsung hero of the Java Ecosystem. From the basic Mark-Sweep algorithms simulated in this article to the sophisticated ZGC used in high-performance Java Cloud deployments, GC allows developers to focus on business logic rather than memory arithmetic.

However, “automatic” does not mean “magic.” As a developer utilizing Java Spring, Jakarta EE, or building Android Development projects, you must understand how your code interacts with the heap. Avoiding static leaks, choosing the right reference types, and tuning JVM flags for your Java Deployment environment are critical skills.

By mastering these concepts, you ensure your applications are not just functional, but robust, scalable, and ready for the demands of modern Java Enterprise computing. As you move forward, consider experimenting with the GC simulator code provided above, extending it to handle memory compaction or generational copying, to deepen your understanding of the JVM’s inner workings.