I was watching a presentation on Java, and at one point, the lecturer said:
"Mutability is OK, sharing is nice, shared mutability is devil's work."
What he was referring to is the following piece of code, which he considered an "extremely bad habit":
//double the even values and put that into a list.
List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 1, 2, 3, 4, 5);
List<Integer> doubleOfEven = new ArrayList<>();
numbers.stream()
.filter(e -> e % 2 == 0)
.map(e -> e * 2)
.forEach(e -> doubleOfEven.add(e));
He then proceeded writing the code that should be used, which is:
List<Integer> doubleOfEven2 =
numbers.stream()
.filter(e -> e % 2 == 0)
.map(e -> e * 2)
.collect(toList());
I don't understand why the first piece of code is "bad habit". To me, they both achieve the same goal.
Explanation to the first example snippet
The problem comes into play when performing parallel processing.
//double the even values and put that into a list.
List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 1, 2, 3, 4, 5);
List<Integer> doubleOfEven = new ArrayList<>();
numbers.stream()
.filter(e -> e % 2 == 0)
.map(e -> e * 2)
.forEach(e -> doubleOfEven.add(e)); // <--- Unnecessary use of side-effects!
This unnecessarily uses side-effects while not all side effects are bad if used correctly when it comes to using streams one must provide behaviour that is safe to execute concurrently on different pieces of the input. i.e. writing code which doesn’t access shared mutable data to do its work.
The line:
.forEach(e -> doubleOfEven.add(e)); // Unnecessary use of side-effects!
unnecessarily uses side-effects and when executed in parallel, the non-thread-safety of ArrayList would cause incorrect results.
A while back I read a blog by Henrik Eichenhardt answering as to why a shared mutable state is the root of all evil.
This is a short reasoning as to why shared mutability is not good; extracted from the blog.
non-determinism = parallel processing + mutable state
This equation basically means that both parallel processing and mutable state combined result in non-deterministic program behaviour. If you just do parallel processing and have only immutable state everything is fine and it is easy to reason about programs. On the other hand if you want to do parallel processing with mutable data you need to synchronize the access to the mutable variables which essentially renders these sections of the program single threaded. This is not really new but I haven't seen this concept expressed so elegantly. A non-deterministic program is broken.
This blog goes on to derive the inner details as to why parallel programs without proper synchronization are broken, which you can find within the appended link.
Explanation to the second example snippet
List<Integer> doubleOfEven2 =
numbers.stream()
.filter(e -> e % 2 == 0)
.map(e -> e * 2)
.collect(toList()); // No side-effects!
This uses a collect reduction operation on the elements of this stream using a Collector.
This is much safer, more efficient, and more amenable to parallelization.