Thus far, nobody seems to have addressed the memory footprint of each of these lists besides the general consensus that a
LinkedList is "lots more" than an
ArrayList so I did some number crunching to demonstrate exactly how much both lists take up for N null references.
Since references are either 32 or 64 bits (even when null) on their relative systems, I have included 4 sets of data for 32 and 64 bit
Note: The sizes shown for the
ArrayList lines are for trimmed lists - In practice, the capacity of the backing array in an
ArrayList is generally larger than its current element count.
Note 2: (thanks BeeOnRope) As CompressedOops is default now from mid JDK6 and up, the values below for 64-bit machines will basically match their 32-bit counterparts, unless of course you specifically turn it off.
The result clearly shows that
LinkedList is a whole lot more than
ArrayList, especially with a very high element count. If memory is a factor, steer clear of
The formulas I used follow, let me know if I have done anything wrong and I will fix it up. 'b' is either 4 or 8 for 32 or 64 bit systems, and 'n' is the number of elements. Note the reason for the mods is because all objects in java will take up a multiple of 8 bytes space regardless of whether it is all used or not.
ArrayList object header + size integer + modCount integer + array reference + (array oject header + b * n) + MOD(array oject, 8) + MOD(ArrayList object, 8) == 8 + 4 + 4 + b + (12 + b * n) + MOD(12 + b * n, 8) + MOD(8 + 4 + 4 + b + (12 + b * n) + MOD(12 + b * n, 8), 8)
LinkedList object header + size integer + modCount integer + reference to header + reference to footer + (node object overhead + reference to previous element + reference to next element + reference to element) * n) + MOD(node object, 8) * n + MOD(LinkedList object, 8) == 8 + 4 + 4 + 2 * b + (8 + 3 * b) * n + MOD(8 + 3 * b, 8) * n + MOD(8 + 4 + 4 + 2 * b + (8 + 3 * b) * n + MOD(8 + 3 * b, 8) * n, 8)
ArrayList is what you want.
LinkedList is almost always a (performance) bug.
- It uses lots of small memory objects, and therefore impacts performance across the process.
- Lots of small objects are bad for cache-locality.
- Any indexed operation requires a traversal, i.e. has O(n) performance. This is not obvious in the source code, leading to algorithms O(n) slower than if
- Getting good performance is tricky.
- Even when big-O performance is the same as
ArrayList, it is probably going to be significantly slower anyway.
- It's jarring to see
LinkedListin source because it is probably the wrong choice.
As someone who has been doing operational performance engineering on very large scale SOA web services for about a decade, I would prefer the behavior of LinkedList over ArrayList. While the steady-state throughput of LinkedList is worse and therefore might lead to buying more hardware -- the behavior of ArrayList under pressure could lead to apps in a cluster expanding their arrays in near synchronicity and for large array sizes could lead to lack of responsiveness in the app and an outage, while under pressure, which is catastrophic behavior.
Similarly, you can get better throughput in an app from the default throughput tenured garbage collector, but once you get java apps with 10GB heaps you can wind up locking up the app for 25 seconds during a Full GCs which causes timeouts and failures in SOA apps and blows your SLAs if it occurs too often. Even though the CMS collector takes more resources and does not achieve the same raw throughput, it is a much better choice because it has more predictable and smaller latency.
ArrayList is only a better choice for performance if all you mean by performance is throughput and you can ignore latency. In my experience at my job I cannot ignore worst-case latency.
Update (Aug 27, 2021 -- 10 years later): This answer (my most historically upvoted answer on SO as well) is very likely wrong (for reasons outlined in the comments below). I'd like to add that ArrayList will optimize for sequential reading of memory and minimize cache-line and TLB misses, etc. The copying overhead when the array grows past the bounds is likely inconsequential by comparison (and can be done by efficient CPU operations). This answer is also probably getting worse over time given hardware trends. The only situations where a LinkedList might make sense would be something highly contrived where you had thousands of Lists any one of which might grow to be GB-sized, but where no good guess could be made at allocation-time of the List and setting them all to GB-sized would blow up the heap. And if you found some problem like that, then it really does call for reengineering whatever your solution is (and I don't like to lightly suggest reengineering old code because I myself maintain piles and piles of old code, but that'd be a very good case of where the original design has simply run out of runway and does need to be chucked). I'll still leave my decades-old poor opinion up there for you to read though. Simple, logical and pretty wrong.
I know this is an old post, but I honestly can't believe nobody mentioned that
Deque. Just look at the methods in
Queue); if you want a fair comparison, try running
ArrayDeque and do a feature-for-feature comparison.
Let's compare LinkedList and ArrayList w.r.t. below parameters:
ArrayList is the resizable array implementation of list interface , while
LinkedList is the Doubly-linked list implementation of the list interface.
get(int index) or search operation
ArrayList get(int index) operation runs in constant time i.e O(1) while
LinkedList get(int index) operation run time is O(n) .
The reason behind ArrayList being faster than LinkedList is that ArrayList uses an index based system for its elements as it internally uses an array data structure, on the other hand,
LinkedList does not provide index-based access for its elements as it iterates either from the beginning or end (whichever is closer) to retrieve the node at the specified element index.
insert() or add(Object) operation
Insertions in LinkedList are generally fast as compare to ArrayList. In LinkedList adding or insertion is O(1) operation .
While in ArrayList, if the array is the full i.e worst case, there is an extra cost of resizing array and copying elements to the new array, which makes runtime of add operation in ArrayList O(n), otherwise it is O(1).
Remove operation in LinkedList is generally the same as ArrayList i.e. O(n).
In LinkedList, there are two overloaded remove methods. one is remove() without any parameter which removes the head of the list and runs in constant time O(1). The other overloaded remove method in LinkedList is remove(int) or remove(Object) which removes the Object or int passed as a parameter. This method traverses the LinkedList until it found the Object and unlink it from the original list. Hence this method runtime is O(n).
While in ArrayList remove(int) method involves copying elements from the old array to new updated array, hence its runtime is O(n).
3. Reverse Iterator
LinkedList can be iterated in reverse direction using descendingIterator() while
there is no descendingIterator() in ArrayList , so we need to write our own code to iterate over the ArrayList in reverse direction.
4. Initial Capacity
If the constructor is not overloaded, then ArrayList creates an empty list of initial capacity 10, while
LinkedList only constructs the empty list without any initial capacity.
5. Memory Overhead
Memory overhead in LinkedList is more as compared to ArrayList as a node in LinkedList needs to maintain the addresses of the next and previous node. While
In ArrayList each index only holds the actual object(data).
In addition to the other good arguments above, you should notice
RandomAccess interface, while
So, somehow they address slightly different problems, with difference of efficiency and behavior (see their list of methods).
It depends upon what operations you will be doing more on the List.
ArrayList is faster to access an indexed value. It is much worse when inserting or deleting objects.
To find out more, read any article that talks about the difference between arrays and linked lists.