An ArrayList for huge numbers of add operations is not performant. LinkedList will see your list throughput performance at least double. There are other optimisations you can do but in a brief perusal this stood out like a sore thumb.
Arrays are fast and ArrayList is like a fancy array with bound check and auto grows. Only the grow part can be problematic if it has to grow very often. But that can be avoided by providing an appropriate initial size or reusing the ArrayList by using clear() instead of creating a new one.
Both is used by OP in this project.
Especially since the code copies lists quite often I would expect LinkedList to perform way worse.
Wrong. In fact downvoters are wrong too I'm guessing most are junior devs who don't want to be proven wrong.
LinkedList is much faster for inserts and slow for retrieval. ArrayLists are the opposite.
To the downvoters; I say try it, this is why LinkedList is in the standard library.
When you find I'm right, please consider re-upvoting for the free education.
No, that's not true at all. Adds aren't free. Adding in the middle involves following pointers into the heap all over the disk n/2 times, making them generally as expensive as reads. The only situation I can imagine a linked list making sense is if you only add to the front and only read from/delete the front (or back, if it's doubly linked). So a stack or queue.
But even then, I'm pretty sure Go actually uses an array for it's green stacks nowadays, even while paying the copy penalty for expansion.
Did you count an allocation of LinkedList.Node<E> on every add operation?
You may say it's negligible thanks to TLAB, and I will agree that fast allocation is Java's strength, but in practice I've seen that creating new objects gives order-of-magnitude perf degradation.
I have seen it for millions of add/del operations, an analytics framework actually for a big American games company (first guess and you'll probably say it), which is where I originally did the analysis about 10 years ago.
I've also written a a video processor around that time too that was bottle necked using ArrayLists - typically a decode, store and read once op.
It was at this point I looked at other collections, other list implementations and blocking deques (ArrayList was the wrong collection type to use, but I'd been in a rush
for MVP) and ultimately came across https://github.com/conversant/disruptor and used that instead.
The ArrayList Vs Linkedlist was a real eye opener for me in two different systems this same behaviour was replicated when using ArrayLists like queues or incorrect sizing of the buffer increments as load increases.
Of course, deletion is a whole different story. I was talking about addition in isolation.
Anyway, I felt I had to run the benchmarks myself.
@Benchmark
@Fork(1)
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.SECONDS)
public Object arrayListPreallocAddMillionNulls() {
ArrayList<Object> arrList = new ArrayList<>(1048576);
for (int i = 0; i <= 1_000_000; i++) {
arrList.add(null);
}
return arrList;
}
@Benchmark
@Fork(1)
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.SECONDS)
public Object arrayListAddMillionNulls() {
ArrayList<Object> arrList = new ArrayList<>();
for (int i = 0; i <= 1_000_000; i++) {
arrList.add(null);
}
return arrList;
}
@Benchmark
@Fork(1)
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.SECONDS)
public Object linkedListAddMillionNulls() {
LinkedList<Object> linkList = new LinkedList<>();
for (int i = 0; i <= 1_000_000; i++) {
linkList.add(null);
}
return linkList;
}
And as I expected, on JDK 8 ArrayList with an appropriate initial capacity was faster than LinkedList. Admittedly not an order of magnitude difference, only 1.7x.
This helps prove my point that adds (and deletes) are generally faster by default when not pre sizing, or removing.
Typically (in my experience) ArrayLists are used without thought to sizing, often because initial capacity and amount to resize, cannot be determined sensibly or consistently.
If in your example you were also to resize the lists, (perhaps adding then dropping those in the Fibonacci sequence?), it would help prove my statement further.
Certainly not worth the -2 points I got from making the statement, but hey you can "please some people some of the time..." :D
It's holding a reference on each element, but it no longer has to add large chunks of memory on insert when the current array size is exceeded, just single elements.
So reads are slower and a small amount of reference memory is used per node. Writes however are much faster particularly when the lists are huge (as in this case).
Also I've written video frame processors so I am experienced in this area.
