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How can you efficiently resize a dynamic array?

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Dynamic arrays are data structures that can grow and shrink in size as needed, unlike static arrays that have a fixed capacity. They are useful for storing data that has an unknown or variable length, such as user input, text files, or web pages. However, resizing a dynamic array can be a costly operation that affects the performance and memory usage of your program. In this article, you will learn how to efficiently resize a dynamic array using some common algorithms and techniques.

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1 Why resize a dynamic array?

The main reason to resize a dynamic array is to avoid wasting space or running out of space. If your dynamic array is too large for the amount of data it stores, you are wasting memory that could be used for other purposes. If your dynamic array is too small for the amount of data you want to store, you will encounter errors or exceptions when you try to add more elements. Therefore, you need to resize your dynamic array according to the changes in your data size and your program's needs.

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George N.

Senior ML Engineer, Music Intelligence, SoundCloud
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Space Efficiency: To avoid wasting memory when the array has many unused elements. Accommodate More Elements: To allow more elements to be added when the array reaches its current capacity. Optimal Performance: To balance between over-allocation (wasting space) and frequent resizing (wasting time).

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2 How to resize a dynamic array?

The basic idea of resizing a dynamic array is to create a new array with a different capacity and copy the elements from the old array to the new one. Then, you can delete the old array and replace it with the new one. This process can be done either manually or automatically, depending on the implementation of your dynamic array. For example, in some programming languages, such as Java or Python, the dynamic array is implemented as an ArrayList or a List object, which handles the resizing automatically. In other languages, such as C or C++, you have to manage the memory allocation and deallocation yourself.

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George N.

Senior ML Engineer, Music Intelligence, SoundCloud
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Check Capacity: Determine if the array needs resizing either because it's full (for expansion) or sparsely filled (for reduction). Create a New Array: Allocate a new array with the desired size. Usually, the size is doubled for expansion to maintain a balance between space and time efficiency. Copy Elements: Copy the elements from the old array to the new array. This step is often the most time-consuming part of resizing. Update References: Update any references to the old array to point to the new array. Free Old Array: If using a language with manual memory management, deallocate the memory used by the old array. Update Size Metadata: Adjust the metadata (like current size and capacity) to reflect the changes.

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Maheedhar Puranam

Quality specialist
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The best way is to create a new array with the desired size and copy the contents of the old array into it. Later we can delete the old array by setting it to null.

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3 What are the challenges of resizing a dynamic array?

The main challenge of resizing a dynamic array is to balance between speed and space. If you resize your dynamic array too frequently, you will spend a lot of time copying elements and allocating memory, which will slow down your program. If you resize your dynamic array too infrequently, you will waste a lot of space by having a large array with many empty slots, which will increase your memory usage. Therefore, you need to find a reasonable strategy to resize your dynamic array that minimizes the time and space costs.

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4 What are some common strategies to resize a dynamic array?

When resizing a dynamic array, there are several strategies to choose from, each with its own advantages and disadvantages. Incremental strategy increases or decreases the capacity of the dynamic array by a fixed amount, like 1, 10, or 100. While simple and easy to implement, it can be inefficient if the data size changes rapidly or unpredictably. Doubling/halving strategy doubles or halves the capacity of the dynamic array when it is full or half-empty, respectively. This is more efficient than incremental strategy but can still cause some problems. Geometric strategy multiplies or divides the capacity of the dynamic array by a constant factor when it is full or below a certain threshold. This allows for more flexibility in adjusting the factor according to data size and program's needs, but may require rounding or truncation operations.

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George N.

Senior ML Engineer, Music Intelligence, SoundCloud
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Incremental Increase: Increase the array's size by a fixed amount each time it fills up. This can be more memory-efficient but may lead to more frequent resizing operations. Halving on Deletion: When a significant portion of the array is unused (e.g., only 25% filled), halve its size to save memory. This needs to be balanced to avoid frequent increase and decrease. Shrink to Fit: Some implementations allow explicitly resizing the array to match the number of elements, typically done when the array will no longer be modified extensively.

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5 How to choose the best strategy to resize a dynamic array?

When deciding on the best strategy to resize a dynamic array, several factors should be taken into consideration, such as the expected data size, the frequency of adding and removing elements, the available memory, and the performance requirements of your program. There is no one-size-fits-all solution, so you may need to experiment with different strategies and compare their results. Generally speaking, if your data size is small and stable, you can use a simple incremental strategy or a fixed-size array. For large and unpredictable data sizes, a geometric strategy with a moderate factor (e.g. 1.5 or 2) is recommended in order to avoid frequent resizing or excessive space waste. If your data size is large and predictable, then a doubling/halving strategy or a geometric strategy with a large factor (e.g. 3 or 4) is suitable for fast resizing and optimal space usage. Finally, if your program is sensitive to performance or memory, you can use a hybrid strategy that combines different strategies for different scenarios (e.g. using a doubling/halving strategy for small arrays and a geometric strategy for large arrays).

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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George N.

Senior ML Engineer, Music Intelligence, SoundCloud
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Threshold for Resizing: Define clear rules for when to resize (e.g., resize when 90% full, or 30% empty). Memory Overhead: Each resizing operation requires extra memory temporarily, which might be an issue in memory-constrained environments. Copy Cost: Resizing involves copying elements, which can be costly for large arrays or arrays with complex objects. Example: In a photo editing app, if you have a dynamic array storing image objects, resizing too often (with small increments) can be inefficient due to the large size of each object. Instead, using a larger multiplication factor (like doubling the size) reduces the frequency of expensive copying operations.

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How can you efficiently resize a dynamic array?

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6

1 Why resize a dynamic array?

2 How to resize a dynamic array?

3 What are the challenges of resizing a dynamic array?

4 What are some common strategies to resize a dynamic array?

5 How to choose the best strategy to resize a dynamic array?

6 Here’s what else to consider

Algorithms

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How can you efficiently resize a dynamic array?

1

2

3

4

5

6

1 Why resize a dynamic array?

2 How to resize a dynamic array?

3 What are the challenges of resizing a dynamic array?

4 What are some common strategies to resize a dynamic array?

5 How to choose the best strategy to resize a dynamic array?

6 Here’s what else to consider

Algorithms

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