[java] How to sum a list of integers with java streams?

I want to sum a list of integers. It works as follows, but the syntax does not feel right. Could the code be optimized?

Map<String, Integer> integers;
integers.values().stream().mapToInt(i -> i).sum();

May this help those who have objects on the list.

If you have a list of objects and wanted to sum specific fields of this object use the below.

List<ResultSom> somList = MyUtil.getResultSom();
BigDecimal result= somList.stream().map(ResultSom::getNetto).reduce(
                                             BigDecimal.ZERO, BigDecimal::add);

This would be the shortest way to sum up int type array (for long array LongStream, for double array DoubleStream and so forth). Not all the primitive integer or floating point types have the Stream implementation though.

IntStream.of(integers).sum();

You can use reduce() to sum a list of integers.

int sum = integers.values().stream().reduce(0, Integer::sum);

You can use reduce method:

long sum = result.stream().map(e -> e.getCreditAmount()).reduce(0L, (x, y) -> x + y);

or

long sum = result.stream().map(e -> e.getCreditAmount()).reduce(0L, Integer::sum);

You can use collect method to add list of integers.

List<Integer> list = Arrays.asList(2, 4, 5, 6);
int sum = list.stream().collect(Collectors.summingInt(Integer::intValue));

class Pojo{
    int num;

    public Pojo(int num) {
        super();
        this.num = num;
    }

    public int getNum() {
        return num;
    }

    public void setNum(int num) {
        this.num = num;
    }
}

List<Pojo> list = new ArrayList<Pojo>();
            list.add(new Pojo(1));
            list.add(new Pojo(5));
            list.add(new Pojo(3));
            list.add(new Pojo(4));
            list.add(new Pojo(5));

            int totalSum = list.stream().mapToInt(pojo -> pojo.getNum()).sum();
            System.out.println(totalSum);

I have declared a list of Integers.

ArrayList<Integer> numberList = new ArrayList<Integer>(Arrays.asList(1, 2, 3, 4, 5));

You can try using these different ways below.

Using mapToInt

int sum = numberList.stream().mapToInt(Integer::intValue).sum();

Using summarizingInt

int sum = numberList.stream().collect(Collectors.summarizingInt(Integer::intValue)).getSum();

Using reduce

int sum = numberList.stream().reduce(Integer::sum).get().intValue();

Most of the aspects are covered. But there could be a requirement to find the aggregation of other data types apart from Integer, Long(for which specialized stream support is already present). For e.g. stram with BigInteger For such a type we can use reduce operation like

list.stream().reduce((bigInteger1, bigInteger2) -> bigInteger1.add(bigInteger2))

I suggest 2 more options:

integers.values().stream().mapToInt(Integer::intValue).sum();
integers.values().stream().collect(Collectors.summingInt(Integer::intValue));

The second one uses Collectors.summingInt() collector, there is also a summingLong() collector which you would use with mapToLong.

And a third option: Java 8 introduces a very effective LongAdder accumulator designed to speed-up summarizing in parallel streams and multi-thread environments. Here, here's an example use:

LongAdder a = new LongAdder();
map.values().parallelStream().forEach(a::add);
sum = a.intValue();

From the docs

Reduction operations A reduction operation (also called a fold) takes a sequence of input elements and combines them into a single summary result by repeated application of a combining operation, such as finding the sum or maximum of a set of numbers, or accumulating elements into a list. The streams classes have multiple forms of general reduction operations, called reduce() and collect(), as well as multiple specialized reduction forms such as sum(), max(), or count().

Of course, such operations can be readily implemented as simple sequential loops, as in:

int sum = 0;
for (int x : numbers) {
   sum += x;
}

However, there are good reasons to prefer a reduce operation over a mutative accumulation such as the above. Not only is a reduction "more abstract" -- it operates on the stream as a whole rather than individual elements -- but a properly constructed reduce operation is inherently parallelizable, so long as the function(s) used to process the elements are associative and stateless. For example, given a stream of numbers for which we want to find the sum, we can write:

int sum = numbers.stream().reduce(0, (x,y) -> x+y);

or:

int sum = numbers.stream().reduce(0, Integer::sum);

These reduction operations can run safely in parallel with almost no modification:

int sum = numbers.parallelStream().reduce(0, Integer::sum);

So, for a map you would use:

integers.values().stream().mapToInt(i -> i).reduce(0, (x,y) -> x+y);

Or:

integers.values().stream().reduce(0, Integer::sum);