How to sum a list of integers with java streams?

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);

This will work, but the i -> i is doing some automatic unboxing which is why it "feels" strange. mapToInt converts the stream to an IntStream "of primitive int-valued elements". Either of the following will work and better explain what the compiler is doing under the hood with your original syntax:

integers.values().stream().mapToInt(i -> i.intValue()).sum();
integers.values().stream().mapToInt(Integer::intValue).sum();

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.

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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);