Issue with Java 8 Lambda for effective final while

问题:

I want to use Java 8 Lambda expression in following scenario but I am getting Local variable fooCount defined in an enclosing scope must be final or effectively final. I understand what the error message says, but I need to calculate percentage here so need to increment fooCount and barCount then calculate percentage. So what's the way to achieve it:

        // key is a String with values like "FOO;SomethinElse" and value is Long
        final Map<String, Long> map = null;
    ....
    private int calculateFooPercentage() {
        long fooCount = 0L;
        long barCount = 0L;

        map.forEach((k, v) -> {
            if (k.contains("FOO")) {
                fooCount++;
            } else {
                barCount++;
            }
        });

        final int fooPercentage = 0;
        //Rest of the logic to calculate percentage
        ....
        return fooPercentage;
    }

One option I have is to use AtomicLong here instead of long but I would like to avoid it, so later if possible I want to use parallel stream here.

回答1:

There is a count method in stream to do counts for you.

long fooCount = map.keySet().stream().filter(k -> k.contains("FOO")).count();
long barCount = map.size() - fooCount;

If you want parallelisation, change .stream() to .parallelStream().

Alternatively, if you were trying to increment a variable manually, and use stream parallelisation, then you would want to use something like AtomicLong for thread safety. A simple variable, even if the compiler allowed it, would not be thread-safe.

回答2:

To get both numbers, matching and non-matching elements, you can use

Map<Boolean, Long> result = map.keySet().stream()
    .collect(Collectors.partitioningBy(k -> k.contains("FOO"), Collectors.counting()));
long fooCount = result.get(true);
long barCount = result.get(false);

But since your source is a Map, which knows its total size, and want to calculate a percentage, for which barCount is not needed, this specific task can be solved as

private int calculateFooPercentage() {
    return (int)(map.keySet().stream().filter(k -> k.contains("FOO")).count()
                 *100/map.size());
}

Both variants are thread safe, i.e. changing stream() to parallelStream() will perform the operation in parallel, however, it’s unlikely that this operation will benefit from parallel processing. You would need humongous key strings or maps to get a benefit…

回答3:

I agree with the other answers indicating you should use countor partitioningBy.

Just to explain the atomicity problem with an example, consider the following code:

private static AtomicInteger i1 = new AtomicInteger(0);
private static int i2 = 0;

public static void main(String[] args) {
    IntStream.range(0, 100000).parallel().forEach(n -> i1.incrementAndGet());
    System.out.println(i1);

    IntStream.range(0, 100000).parallel().forEach(n -> i2++);
    System.out.println(i2);
}

This returns the expected result of 100000 for i1 but an indeterminate number less than that (between 50000 and 80000 in my test runs) for i2. The reason should be pretty obvious.