Show HN: Samchika – A Java Library for Fast, Multithreaded File Processing

Even in older versions, if the compiler can see that there are no side-effects, it is free to remove the loop and simply return the value from the first iteration.

I'm actually pretty curious to see what this method does on versions that don't have the optimization to treat hashCodes as quasi-final.

A quick test using Java 17 shows it's not being optimized away _completely_, but it's taking...~1 ns per iteration, which is not enough to compute a hash code.

Edit: I'm being silly. It will just compute the hashcode the first time, and then repeatedly check that it's cached and return it. So the JIT doesn't have to do any _real_ work to make this skip the hash code calculation.

So most likely, the effective code is:

    computeHashCode();
    for (int i = 0; i < 10000; i++) {
        if (false) { // pretend this wouldn't have dead code elimination, and the boolean is actually checked
            computeHashCode();
        }
    }

You are write. This code does not recalculate. However, it was written just as a sample. Mainly user will provide his own method to process the file.

It’s been ages since I did Sanskrit last, but wouldn’t sam-cika typically have the m realized as an anusvara rather than ñ?

I will add unit tests next.

Should the tests include some 10 GB files?

System calls aren't context switches. They flip a permission bit in the CPU but don't do the work a context switch involves like modifying the MMU, flushing the TLBs, modifying kernel structures, doing scheduling etc.

Also, modern filing systems are all thread safe. You can have multiple threads reading and even writing in parallel on different CPU cores.

> system call, the context switches

No, there is no separate kernel "executing". When you do a syscall, your thread becomes kernel mode and it executes the function behind the syscall, then when it's done, your thread reverts to user mode.

A context switch is when one thread is being swapped out for another. Now the syscall could internally spawn a thread and context switch to that, but I'm not sure if this happens in read() or any syscall for that matter.

What all other siblings said - syscalls are not context switch, they are called 'mode switch' and it has significantly less impact.

If you open() read-only I don't think it blocks (some other process writing to it might block though).

Since most things in Java are handled by reference, including Strings there should be not that much memory overhead. From a quick look I could not find any actual line duplication.

At least not without an initial scan. You could do post processing (e.g. parsing numbers and dates and things) in parallel after you’ve done correct line break processing.

If you're using a newer JVM you can also map a "MemorySegment", which doesn't have the 2GiB limit that byte buffers have.

Memory mapping is fun, but shouldn't we have some kind of async IO / uring support by now? If you're looking at really high-perf I/O, mmaping isn't really state of the art right now.

Then again, if you're in Java/JVM land you're probably not building bleeding edge DBs ala ScyllaDB. But I'm somewhat surprised at the lack of projects in this space. One would think this would pair well with some of the reactive stream implementations so that you wouldn't have to reimplement things like backpressure, etc.

Better caveat that with, "but watch memory consumption, given the nature of the likes of CopyOnWriteArraylist". GC will be a bitch.

Thanks for this comment. This will be an interesting aspect to explore.

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.

I've literally never seen a linked list be faster than an array list in a real application, so if you're right, this is kinda huge for me.

  @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;
  }

  JDK8
  Benchmark                                      Mode  Cnt    Score    Error  Units
  MyBenchmark.arrayListAddMillionNulls          thrpt    5  229.950 ±  9.994  ops/s
  MyBenchmark.arrayListPreallocAddMillionNulls  thrpt    5  344.116 ±  7.070  ops/s
  MyBenchmark.linkedListAddMillionNulls         thrpt    5  199.446 ± 15.910  ops/s

  JDK17
  Benchmark                                      Mode  Cnt    Score    Error  Units
  MyBenchmark.arrayListAddMillionNulls          thrpt    5   90.462 ± 18.576  ops/s
  MyBenchmark.arrayListPreallocAddMillionNulls  thrpt    5  214.079 ± 15.505  ops/s
  MyBenchmark.linkedListAddMillionNulls         thrpt    5  216.796 ± 19.392  ops/s

Huh? It'll be slower and eat a massive amount of memory too.