Arrays vs Linked Lists – What You Actually Need to Know for Interviews

In the array example, inserting at the start required shifting the existing elements (hence O(n)). In the linked list, inserting at the start was just a couple of pointer assignments (hence O(1)).

Interview Takeaway*:* You can say “For insertions and deletions, if we have a direct reference to the position, a linked list can do it in O(1) by pointer adjustments, whereas an array might take O(n) due to element shifts”.

However, do acknowledge that finding that position in a linked list might itself be O(n) if you have to search for it.

3. Memory Usage and Overhead

Arrays:

An array’s memory is straightforward – just the memory for the elements themselves (plus maybe some overhead if it’s a dynamic array with extra capacity).

No additional per-element overhead.

This compactness also helps with cache locality as mentioned.

One thing to watch: if you allocate an array of size N and only fill part of it, you’re still reserving that full block of memory.

In dynamic arrays (like Python lists or Java ArrayList), when the array grows, behind the scenes it allocates a new bigger array and copies elements over, which uses more memory during the copy and might allocate more capacity than you need in anticipation of future growth.

But overall, arrays are quite memory-efficient per element.

Linked Lists:

Each node in a linked list uses extra memory for the pointer/reference to the next node (and possibly a prev node too, if it’s a doubly linked list).

That means a list of N elements uses more memory than an array of N elements – you have to store N data items plus N pointers.

For example, if your data is an integer (let’s say 4 bytes), a singly linked list node might also need 8 bytes for a pointer (on a 64-bit system).

That’s extra overhead.

Also, nodes are allocated on the heap one by one, which can lead to scattered memory (not as cache-friendly).

On the flip side, a linked list only allocates as it grows; there’s no concept of unused reserved capacity.

If you don’t know how many items you'll need, you can keep allocating new nodes as needed and you’re not wasting a big contiguous block (aside from the pointer overhead per node).

Interview Takeaway*:* If asked about memory, you can mention: “Arrays store just the data and have better locality, whereas linked lists use extra memory for pointers and have more memory overhead per element”.

You might also note that if you need a very flexible size and don’t mind the overhead, linked lists can grow without having to reallocate or resize contiguous memory, whereas arrays (if not using a dynamic structure) have a fixed size or incur the cost to resize.

4. Flexibility (Size and Resizing)

This one is tied to memory but focuses on managing size changes:

• Arrays: In low-level terms, a static array has a fixed size – you have to declare how many elements you need up front. If you miscalculate, you either waste memory or run out of space. In higher-level languages, the common dynamic array implementations (like list in Python, ArrayList in Java, std::vector in C++) handle resizing for you: when you append beyond the current capacity, the structure will allocate a new array (often double the size) and move all elements over. This resizing operation is O(n), but it doesn’t happen often, so amortized insertion at the end is O(1). So arrays can be flexible with size, but that flexibility comes at the cost of occasional expensive resize operations. Also, inserting or deleting in the middle of a dynamic array still requires shifting elements around.

• Linked Lists: By design, a linked list can grow or shrink one node at a time. There’s no need to allocate a big chunk ahead of time or to reallocate an entire structure at once. This makes linked lists very flexible for dynamic sizes – you can always add one more node. This is why people often cite linked lists as being good when you don’t know the amount of data in advance. That said, remember the warning: each addition still allocates a small chunk for the new node and links it. There’s no large-scale copy, but frequent allocations can be a bit slower (allocation is not free). Still, you’ll never have the scenario of “oops, my array is full, need to resize”.

Interview Takeaway*:* When discussing dynamic size, say something like: “If the number of elements can change a lot, a linked list can grow or shrink easily node by node. An array can also grow (if it’s dynamic), but it might occasionally need to allocate a larger block and copy everything over”.

5. Real-world Usage and Cache Performance

This is a bit beyond basic interview questions, but it’s great for demonstrating deeper understanding:

In real software, arrays (or array-based structures) are used far more often than naive linked lists for most situations.

Why?

Because modern processors are really fast at iterating through arrays due to contiguous memory and caching.

Even though a linked list has O(1) insertion in theory, in practice it might still be slower than an array for many workloads unless you truly need to insert/remove in the middle frequently.

One experienced engineer summed it up like this: “Arrays are almost always better (faster) than linked lists. The only case where linked lists would be better is when you’re inserting at random positions or at the front very frequently”.

This is a bit of an oversimplification, but it highlights that you shouldn’t use a linked list just because someone told you “they have O(1) inserts” – context matters.

The reason arrays often win in practice is due to cache lines and CPU optimizations.

Access patterns that are sequential through memory (like iterating an array) can be heavily optimized by the hardware (prefetching data, using SIMD instructions, etc.), whereas chasing pointers in a linked list is less predictable for the CPU.

That’s why high-performance libraries (e.g., those in scientific computing like NumPy) use arrays under the hood and not linked lists.

Interview Takeaway*:* If you want to score bonus points, you might add: “In practice, due to better cache locality, arrays tend to outperform linked lists for iteration-heavy tasks. But if I needed to insert/remove at the front often, and random access wasn’t needed, a linked list could be more efficient.”

Just be mindful of your audience; if your interviewer seems more theoretical, stick to the standard Big-O differences first, then mention this practical note if appropriate.

Learn about big-O notation.

When to Use Which (and Why)

By now, it’s probably clear that the question “Which is better, array or linked list?” doesn’t have a one-size-fits-all answer.

It depends on what you need to do.

Here are some guidelines that can help you decide (this is great to mention in an interview to show you understand use-cases):

• Use an array (or array-like structure) when: you need fast random access to elements by index, you’re mostly iterating or accessing elements and not inserting/removing a lot in the middle, or you care about memory overhead (want minimal extra memory per element). Also, if you need to sort the data, arrays have the advantage of being index-friendly for algorithms like quicksort. Most general-purpose scenarios in coding interviews (like searching, sorting, dynamic programming tables, etc.) use arrays. In fact, languages like Python default to dynamic arrays (list) for most sequences, and Java’s ArrayList is usually preferred over LinkedList for general use.

• Use a linked list when: you have many insertions and deletions in the middle or at the beginning of the sequence, and you mostly access the data sequentially anyway. A classic scenario is implementing a queue or deque: you can use a linked list to efficiently add/remove from both ends (although one can also use a double-ended queue array). Linked lists are also useful when you don’t know the final size of the dataset and want to avoid the upfront allocation or resizing cost – you can just keep allocating nodes as needed. Also, certain problems are naturally solved with linked lists (for example, modeling a train of connected cars, or merging sorted lists node by node).

To sum it up in a handy phrase: Use arrays for indexing speed and simplicity, and use linked lists for flexibility and frequent insert/delete operations.

However, always consider the specifics.

• If you only ever add to the end of a list and iterate, a dynamic array works great.
• If you need to insert at both ends, a deque (double-ended queue) or a linked list might be better.
• If memory is very tight and you can calculate size up front, an array wastes less space.
• If memory fragmentation is a concern, arrays keep things together.

Pro tip: In many high-level interview problems, you won’t actually be choosing between using a low-level array or writing a linked list from scratch – you might just use whatever the language provides (like Python list or a simple custom class).

The key is understanding the trade-offs so you can choose the right approach or explain why an algorithm is fast or slow.

Interviewers love when you justify your choices (e.g., “I’ll use a list here because I need to frequently index into it, and that’s O(1) vs O(n) if I tried to traverse a linked structure”).

Find out the common array manipulation interview questions.