Creating a Dynamic Array in Java

Creating a dynamic array in Java involves using a resizable data structure that adjusts its storage capacity during runtime. Unlike static arrays, dynamic arrays optimize memory usage and reduce reallocation overhead within the Java Virtual Machine.

This flexibility ensures efficient heap memory utilization, making it ideal for scalable Java applications. Understanding how the array expands during execution helps developers build more performant, resource-efficient programs. 

This guide covers the process of creating and implementing a dynamic array in Java, with examples to demonstrate its functionality.

What is Dynamic Array in Java?

A dynamic array in Java offers a flexible, resizable data structure, overcoming fixed-size array limitations. It automatically expands its capacity on the heap as new elements are added, ensuring efficient memory handling. 

This adaptive nature is crucial for managing variable datasets, as seen in machine learning and AI applications. It provides fundamental data management flexibility needed for developing scalable solutions.

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Let's examine the intricate operational mechanics of a dynamic array in Java to understand its adaptive behavior.

How Dynamic Array in Java Works?

A dynamic array in Java offers adaptive storage, managing data additions and internal re-allocations dynamically. Its real-time behavior impacts system performance, especially concerning memory allocation and CPU utilization for algorithms.

• Step 1: Initially, elements occupy contiguous memory slots within the allocated capacity, ensuring optimal data locality for rapid access. Remaining capacity acts as a buffer against immediate re-allocation, minimizing overhead.
• Step 2: When a new element exceeds current capacity, a high-performance re-allocation process begins. The JVM requests a larger memory segment, often double the existing size, on the heap.
• Step 3: All previously stored elements are then efficiently migrated to this new memory location using System.arraycopy(). This operation is highly optimized at the system level.
• Step 4: While an individual re-allocation is O(n) (linear time complexity), the amortized cost for a sequence of additions averages O(1). This makes the dynamic array in Java highly efficient for most growth patterns.

Code Example:

import java.util.ArrayList;
import java.util.List;

public class RealTimeDynamicArray {

    public static void main(String[] args) {
        // Assume initial capacity is 10 for ArrayList by default in most JVMs.
        // We'll simulate adding beyond that to observe growth.
        List<String> dailyTasks = new ArrayList<>(3); // Starting with a smaller explicit capacity for demonstration
        System.out.println("Initial capacity: " + 3 + ", Current tasks: " + dailyTasks.size()); // Simulate capacity for clarity

        System.out.println("\n--- Adding elements beyond initial capacity ---");
        // Add tasks relevant to Indian daily life
        dailyTasks.add("Pay Electricity Bill"); // Task 1
        System.out.println("Added: " + dailyTasks.get(dailyTasks.size() - 1) + ", Size: " + dailyTasks.size());

        dailyTasks.add("Buy Vegetables"); // Task 2
        System.out.println("Added: " + dailyTasks.get(dailyTasks.size() - 1) + ", Size: " + dailyTasks.size());

        dailyTasks.add("Prepare Dinner"); // Task 3 (Capacity reached)
        System.out.println("Added: " + dailyTasks.get(dailyTasks.size() - 1) + ", Size: " + dailyTasks.size());

        // This next add operation will trigger a re-allocation (doubling capacity to ~6)
        dailyTasks.add("Call Relatives in Delhi"); // Task 4 (Triggers resize)
        System.out.println("Added: " + dailyTasks.get(dailyTasks.size() - 1) + ", Size: " + dailyTasks.size() + " (Capacity likely expanded)");

        dailyTasks.add("Check Cricket Scores"); // Task 5
        System.out.println("Added: " + dailyTasks.get(dailyTasks.size() - 1) + ", Size: " + dailyTasks.size());

        System.out.println("\nAll Daily Tasks: " + dailyTasks);
        System.out.println("Final Task Count: " + dailyTasks.size());
    }
}

Output:
Initial capacity: 3, Current tasks: 0 
--- Adding elements beyond initial capacity --- 
Added: Pay Electricity Bill, Size: 1 
Added: Buy Vegetables, Size: 2 
Added: Prepare Dinner, Size: 3 
Added: Call Relatives in Delhi, Size: 4 (Capacity likely expanded) 
Added: Check Cricket Scores, Size: 5 
All Daily Tasks: [Pay Electricity Bill, Buy Vegetables, Prepare Dinner, Call Relatives in Delhi, Check Cricket Scores] 
Final Task Count: 5

Output Explanation:

This code demonstrates a dynamic array in Java expanding its capacity in real-time as dailyTasks are added, seamlessly handling underlying memory re-allocations. It highlights how the array automatically adapts, showcasing runtime behavior for varying data loads.

Also Read: Stack vs Heap: What's the difference?

Let's understand the crucial distinction between logical size and allocated capacity in a dynamic array in Java.

Understanding Size and Capacity in Dynamic Arrays

A dynamic array in Java maintains distinct concepts of size and capacity for efficient data handling. Understanding this difference is crucial for effective memory utilization and predicting performance characteristics. It highlights the internal mechanisms of resource allocation and management within the JVM.

• Capacity: This represents the total memory pre-allocated for the underlying contiguous array structure. It defines the maximum elements a dynamic array in Java can hold without triggering a costly re-allocation process.
• Size: This indicates the actual number of elements currently present and logically accessible within the dynamic array in Java. It reflects the current logical length of the stored data, not the total physical memory reserved.
• Performance Implications: A larger capacity than size means unused memory, yet it minimizes the overhead of frequent data copying. This balance optimizes the amortized time complexity for successive element additions, thereby impacting the overall algorithm efficiency.
• Resource Management: Developers can influence initial capacity, impacting startup memory footprint and re-allocation frequency. Fine-tuned capacity management through methods like trimToSize() ensures optimal resource utilization for the dynamic array in Java.

Use Case:

Consider a microservice that processes live data streams for millions of users across India, possibly deployed via Docker containers and orchestrated by Kubernetes. Efficiently managing incoming data within a dynamic array in Java is critical. Understanding its size versus capacity helps optimize resource allocation within these containerized environments, ensuring high throughput and scalable operations.

Let's now understand the process of developing and instantiating a dynamic array in Java with optimal configurations.

Developing a Dynamic Array in Java

Developing a dynamic array in Java involves instantiating a class that internally manages a traditional fixed-size array, offering runtime flexibility. This abstraction allows developers to focus on data manipulation rather than low-level memory re-allocation logic.

To effectively implement or utilize a dynamic array in Java, consider these technical aspects:

• Class Instantiation: A dynamic array in Java typically originates from a class, like java.util.ArrayList, rather than a direct array declaration. This class encapsulates resizing algorithms and element management within its object structure.
• Initial Capacity Provisioning: During instantiation, an optional initial capacity can be specified, pre-allocating contiguous memory on the heap. This proactive provisioning helps mitigate early re-allocation overhead for expected data volumes.
• Type Parameterization: Generic type parameters (<E>) ensure compile-time type safety for elements stored in the dynamic array in Java. This design prevents runtime ClassCastException issues, adhering to modern JVM practices.
• Internal Array Management: The ArrayList maintains a private Object[] reference, which transparently handles data storage and growth. All add() and remove() operations are managed by this internal mechanism, abstracting complexity.

Code Example:

import java.util.ArrayList;
import java.util.List; // Using List interface for good practice

public class DynamicArrayDevelopment {

    public static void main(String[] args) {
        // Professional Example 1: Managing concurrent user sessions for a web application
        // Initial capacity chosen based on anticipated average concurrent users from India.
        List<String> activeSessionIDs = new ArrayList<>(500); // Expecting up to 500 initial sessions
        System.out.println("Initial active session list size: " + activeSessionIDs.size());
        activeSessionIDs.add("user_session_Delhi_001");
        activeSessionIDs.add("user_session_Mumbai_002");
        System.out.println("Current active sessions: " + activeSessionIDs.size());

        System.out.println("\n---");

        // Professional Example 2: Storing parsed log entries from a distributed system
        // Leveraging default capacity for dynamic growth of unpredictable log volumes.
        ArrayList<String> serverLogEntries = new ArrayList<>(); // Log entries arrive dynamically
        System.out.println("Initial server log entries list size: " + serverLogEntries.size());
        serverLogEntries.add("2025-06-10T23:00:00Z INFO: Service Startup Complete");
        serverLogEntries.add("2025-06-10T23:01:15Z WARN: High CPU usage on Node-Bengaluru");
        serverLogEntries.add("2025-06-10T23:02:30Z ERROR: Database connection failed");
        System.out.println("Processed log entries count: " + serverLogEntries.size());
    }
}

Output:
Initial active session list size: 0
Current active sessions: 2

---
Initial server log entries list size: 0
Processed log entries count: 3

Output Explanation:

This code illustrates how you can initialize a dynamic array in Java using ArrayList for professional software engineering tasks. It showcases fundamental steps for creating flexible data structures that adapt to varying data volumes, crucial for scalable applications.

Beyond basic instantiation, let's analyze the internal algorithmic management and time complexity implications of a dynamic array in Java.

Key Features and Operations of Dynamic Arrays in Java

A dynamic array in Java offers core functionalities crucial for mutable data collections. These operations, including addition, deletion, and resizing, exhibit distinct performance characteristics. Understanding their time complexities is essential for efficient system design. 

Here are some of the key features for creating a dynamic array in Java:

• Adding Elements: Appending an element typically completes in O(1) amortized time, making it highly efficient for sequential additions. However, exceeding capacity triggers an O(n) memory re-allocation, copying all existing object references.
• Deleting Elements: Removing an element from the middle incurs an O(n) time complexity due to the necessary data shifting required to maintain contiguous memory. Deleting from the end is conceptually faster, often O(1), but Java's remove() methods handle shifts.
• Resizing the Array: Automatic expansion occurs upon capacity overflow, typically doubling the underlying array for future growth. Explicit shrinking, like ArrayList.trimToSize(), reclaims unused capacity, optimizing memory footprint and reducing garbage collector load for the dynamic array in Java.

Code Example:

import java.util.ArrayList;
import java.util.List;

public class DynamicArrayOperations {

    public static void main(String[] args) {
        // Professional scenario: Managing a queue of pending customer support tickets
        // from various Indian states.
        List<String> supportTickets = new ArrayList<>(3); // Small initial capacity for demo
        System.out.println("Initial tickets count: " + supportTickets.size());

        System.out.println("\n--- Adding New Tickets ---");
        supportTickets.add("Ticket_Maharashtra_001"); // O(1) amortized
        supportTickets.add("Ticket_Karnataka_002");
        supportTickets.add("Ticket_TamilNadu_003"); // Capacity reached
        System.out.println("Tickets after initial additions: " + supportTickets + ", Size: " + supportTickets.size());

        // Adding more tickets, triggering re-allocation
        supportTickets.add("Ticket_Gujarat_004"); // O(n) for resize
        supportTickets.add("Ticket_Rajasthan_005");
        System.out.println("Tickets after more additions: " + supportTickets + ", Size: " + supportTickets.size());

        System.out.println("\n--- Processing and Deleting Tickets ---");
        // Simulate resolving a ticket from the middle of the queue
        supportTickets.remove("Ticket_Karnataka_002"); // O(n) for value removal and shift
        System.out.println("Tickets after removing Karnataka ticket: " + supportTickets + ", Size: " + supportTickets.size());

        // Simulate a system-generated error ticket at a specific index
        if (supportTickets.size() > 1) { // Ensure there are enough elements
            supportTickets.remove(1); // Removing "Ticket_TamilNadu_003" - O(n) for index removal and shift
            System.out.println("Tickets after removing index 1: " + supportTickets + ", Size: " + supportTickets.size());
        }

        System.out.println("\n--- Optimizing Capacity ---");
        // Imagine many tickets were processed, and we want to reclaim unused memory
        // This is important for memory-constrained environments or long-running services.
        System.out.println("Before trimToSize, current size: " + supportTickets.size());
        ((ArrayList<String>) supportTickets).trimToSize(); // Explicitly cast to ArrayList to access trimToSize()
        System.out.println("After trimToSize, current size: " + supportTickets.size() + ". Capacity reduced (if applicable).");

        System.out.println("\nFinal Tickets: " + supportTickets);
    }
}

Output:
Initial tickets count: 0

--- Adding New Tickets ---
Tickets after initial additions: [Ticket_Maharashtra_001, Ticket_Karnataka_002, Ticket_TamilNadu_003], Size: 3
Tickets after more additions: [Ticket_Maharashtra_001, Ticket_Karnataka_002, Ticket_TamilNadu_003, Ticket_Gujarat_004, Ticket_Rajasthan_005], Size: 5

--- Processing and Deleting Tickets ---
Tickets after removing Karnataka ticket: [Ticket_Maharashtra_001, Ticket_TamilNadu_003, Ticket_Gujarat_004, Ticket_Rajasthan_005], Size: 4
Tickets after removing index 1: [Ticket_Maharashtra_001, Ticket_Gujarat_004, Ticket_Rajasthan_005], Size: 3

--- Optimizing Capacity ---
Before trimToSize, current size: 3
After trimToSize, current size: 3. Capacity reduced (if applicable).

Final Tickets: [Ticket_Maharashtra_001, Ticket_Gujarat_004, Ticket_Rajasthan_005]

Output Explanation:

This code demonstrates operations for adding, deleting, and managing capacity on a dynamic array in Java. It illustrates how elements are inserted and removed, highlighting the performance implications for processing data that often originates from or is destined for TypeScript-based frontends.

To optimize application performance, let's differentiate the distinct operational characteristics of Java's built-in dynamic array in Java implementations.

Built-in Dynamic Array Implementations in Java

Java's extensive API provides several built-in implementations of a dynamic array in Java. These structures efficiently manage memory and automatically resize, catering to various application needs. Each offers distinct performance trade-offs, making selection critical for optimized software design.

1. ArrayList

• ArrayList internally uses a transient Object[] for storage, offering O(1) random access via index arithmetic. Its contiguous memory layout optimizes CPU cache utilization for sequential operations.
• Expansion involves System.arraycopy() to a new, larger array, typically currentCapacity * 1.5, ensuring amortized O(1) insertion. Capacity reduction is manual via trimToSize().
• As a non-synchronized class, it requires external synchronization mechanisms for thread-safe access in concurrent environments. This design prioritizes single-threaded performance.

Code Example:

import java.util.ArrayList;
import java.util.List;

public class ArrayListExample {
    public static void main(String[] args) {
        // Professional example: Managing cloud instance IDs for a resource dashboard
        List<String> instanceIDs = new ArrayList<>();
        instanceIDs.add("ec2-instance-ap-south-1a-001");
        instanceIDs.add("aks-cluster-mumbai-node-002");
        System.out.println("Active Instance IDs: " + instanceIDs);
        System.out.println("Total Instances: " + instanceIDs.size());
    }
}

Output:

Active Instance IDs: [ec2-instance-ap-south-1a-001, aks-cluster-mumbai-node-002]
Total Instances: 2

Output Explanation:

This code demonstrates basic ArrayList operations for managing cloud resource identifiers. Such lists are vital in monitoring and controlling large-scale infrastructure across AWS or Azure regions.

2. LinkedList

• LinkedList employs a doubly-linked node structure, where each element holds references to its predecessor and successor. This non-contiguous allocation allows O(1) insertions/deletions at either end.
• Random element access or deletion by index requires O(n) traversal from either end, impacting performance for large lists. Memory overhead per element is higher due to node object references.
• Like ArrayList, it is not thread-safe by default, necessitating explicit synchronization for multi-threaded scenarios. Its strength lies in queue-like operations.

Code Example:

import java.util.LinkedList;
import java.util.Queue; // Using Queue interface for event processing

public class LinkedListExample {
    public static void main(String[] args) {
        // Professional example: Processing a sequence of security audit events
        Queue<String> securityEvents = new LinkedList<>();
        securityEvents.offer("AuthSuccess: User_Delhi_001_Login");
        securityEvents.offer("AuthFailed: User_Chennai_002_Login");
        securityEvents.offer("SystemAlert: Suspicious_Activity_IP_10.0.0.5");
        System.out.println("Pending Security Events: " + securityEvents);
        System.out.println("Processing next event: " + securityEvents.poll());
    }
}

Output:

Pending Security Events: [AuthSuccess: User_Delhi_001_Login, AuthFailed: User_Chennai_002_Login, SystemAlert: Suspicious_Activity_IP_10.0.0.5]
Processing next event: AuthSuccess: User_Delhi_001_Login

Output Explanation:

This example manages a sequence of audit events within a security system. Access to such sensitive data often requires strict Role-Based Access Control (RBAC) implementations.

3. CopyOnWriteArrayList

• This List implementation provides thread safety by performing all mutative operations (add, set, remove) on a new underlying array copy. It utilizes ReentrantLock for write synchronization.
• Read operations proceed on the old, immutable array snapshot, ensuring no concurrent modification exceptions during iteration. This makes reads highly efficient and lock-free.
• It is optimally suited for read-heavy, write-light concurrent environments, as frequent write operations incur significant O(n) overhead for array copying. This is a specialized dynamic array in Java variant.

Code Example:

import java.util.concurrent.CopyOnWriteArrayList;
import java.util.List;

public class CopyOnWriteArrayListExample {
    public static void main(String[] args) {
        // Professional example: Maintaining a list of active subscribers for a real-time data feed
        List<String> activeSubscribers = new CopyOnWriteArrayList<>();
        activeSubscribers.add("subscriber_Pune_A");
        activeSubscribers.add("subscriber_Hyderabad_B");
        System.out.println("Current Subscribers: " + activeSubscribers);
        // Simulate a new subscriber joining
        activeSubscribers.add("subscriber_Bengaluru_C");
        System.out.println("Subscribers after new join: " + activeSubscribers);
    }
}

Output:

Current Subscribers: [subscriber_Pune_A, subscriber_Hyderabad_B]
Subscribers after new join: [subscriber_Pune_A, subscriber_Hyderabad_B, subscriber_Bengaluru_C]

Output Explanation:

This structure effectively manages concurrent data consumers, like active subscribers. Aggregate data from such lists may be processed later for analytical insights or statistical models using tools like R.

4. Vector

• Vector is a legacy List implementation, fundamentally a synchronized dynamic array in Java. Synchronized blocks intrinsically guard all its public methods.
• This built-in synchronization ensures thread safety but introduces performance overhead due to lock contention, even in single-threaded contexts. Its growth factor typically doubles the array size.
• It is generally discouraged in modern Java development in favor of ArrayList or CopyOnWriteArrayList for improved performance and explicit control over concurrency.

Code Example:

import java.util.Vector;

public class VectorExample {
    public static void main(String[] args) {
        // Professional example: Processing historical batch transactions from a legacy system
        Vector<String> legacyTransactions = new Vector<>();
        legacyTransactions.add("TRX_12345_2020_Debit");
        legacyTransactions.add("TRX_67890_2021_Credit");
        System.out.println("Legacy Transactions: " + legacyTransactions);
        System.out.println("First Transaction: " + legacyTransactions.elementAt(0));
    }
}

Output:

Legacy Transactions: [TRX_12345_2020_Debit, TRX_67890_2021_Credit]
First Transaction: TRX_12345_2020_Debit

Output Explanation:

This demonstrates a legacy thread-safe collection from older Java systems. Such data might be migrated to modern cloud platforms like AWS or Azure for scalable processing and enhanced analytics.

Programs: Dynamic Array in Java

This program precisely demonstrates the dynamic array in Java's automatic memory re-allocation behavior within the JVM. It illustrates the underlying mechanism enabling flexible data expansion. Understanding its amortized O(1) performance despite O(n) re-allocations is crucial. This intrinsic dynamism underpins scalable data structures.

Code Example:

• Capacity Exceedance: An add() operation triggers a capacity check within the dynamic array in Java. If the current element count equals or exceeds the internal array's limit, a resize process is initiated.
• New Array Allocation: The JVM allocates a new, larger contiguous memory block on the heap, typically 50% larger than the current capacity or doubled. This new array serves as the expanded storage.
• Data Migration: All existing elements from the old, full array are then efficiently transferred to this new, larger array. This bulk copy operation utilizes low-level System.arraycopy() for speed.
• Reference Update: The internal object reference of the dynamic array in Java is atomically updated to point to the newly populated array. The old array then becomes eligible for garbage collection, reclaiming memory.

Code Example:

import java.util.ArrayList;
import java.util.List;

public class DynamicArrayResizingProgram {

    public static void main(String[] args) {
        // Professional example: Tracking a list of product SKUs from a new vendor launch
        // Using a small initial capacity (e.g., 5) to quickly demonstrate resizing
        List<String> newProductSKUs = new ArrayList<>(5);
        System.out.println("Initial SKU List Size: " + newProductSKUs.size());

        System.out.println("\n--- Adding SKUs and observing capacity expansion ---");
        // Add elements until capacity is reached
        for (int i = 1; i <= 5; i++) {
            newProductSKUs.add("PROD_IND_00" + i);
            System.out.println("Added SKU: PROD_IND_00" + i + ", Current Size: " + newProductSKUs.size());
        }

        // This next addition will trigger the dynamic array in Java to resize
        // Capacity will typically grow from 5 to (5 + 5/2) = 7 or 5*2 = 10, depending on JVM/ArrayList version
        newProductSKUs.add("PROD_IND_006_NEW");
        System.out.println("\n--- After adding PROD_IND_006_NEW (resize triggered) ---");
        System.out.println("Added SKU: PROD_IND_006_NEW, Current Size: " + newProductSKUs.size());
        System.out.println("Final SKU List: " + newProductSKUs);
    }
}

Output:
Initial SKU List Size: 0

--- Adding SKUs and observing capacity expansion ---
Added SKU: PROD_IND_001, Current Size: 1
Added SKU: PROD_IND_002, Current Size: 2
Added SKU: PROD_IND_003, Current Size: 3
Added SKU: PROD_IND_004, Current Size: 4
Added SKU: PROD_IND_005, Current Size: 5

--- After adding PROD_IND_006_NEW (resize triggered) ---
Added SKU: PROD_IND_006_NEW, Current Size: 6
Final SKU List: [PROD_IND_001, PROD_IND_002, PROD_IND_003, PROD_IND_004, PROD_IND_005, PROD_IND_006_NEW]

Output Explanation:

This program clearly illustrates how a dynamic array in Java automatically resizes its internal storage when elements exceed capacity. Such data might represent product attributes, potentially rendered using HTML or styled with CSS, on an e-commerce web page.

To fully assess a dynamic array in Java, it is essential to understand both its inherent advantages and operational limitations.

Advantages and Limitations of Dynamic Arrays in Java

The dynamic array in Java presents a compelling trade-off between flexibility and operational nuances. Its design offers significant benefits for scalable data handling, crucial for India's growing digital infrastructure. However, developers must understand its inherent performance characteristics.

Here's a concise overview of their key advantages and inherent limitations:

Dynamic arrays in Java provide an effective solution for scalable applications, offering flexibility and efficient memory management. However, their resizing and reallocation overheads can impact performance. Understanding these trade-offs ensures optimal use in performance-sensitive applications.

Also Read: Top 22 Open Source Java Projects to Enhance Your Development Skills

Conclusion

Successfully creating a dynamic array in Java is crucial for developing adaptable applications, which dynamically manage memory with amortized O(1) growth. While offering fast O(1) access, consider its O(n) resizing overhead and select the appropriate implementation for optimal performance. Strategic capacity planning and operational analysis are crucial for maximizing resource efficiency.

To comprehensively learn scalable data structures and high-performance algorithms, upGrad offers additional programs designed for modern software development.

• Data Structures & Algorithms
• Java Object-oriented Programming

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Reference:
https://www.guvi.in/blog/java-developer-salary/