Folding the Universe, Part III: Java 8 List and Stream

Datetime:2016-08-23 00:36:13          Topic: Java8           Share

This is the third article in a series about applying functional programming techniques like folding to Java programs. This series is a complement to my book Functional Programming in Java published by Manning.

The first two articles are available here:

In the two previous articles of this series, I showed how nearly every problem could be modeled as folds. This does not in any way mean that solutions to all problems should be implemented using folds. Folding a list with a single element might be a good idea when this is a special case of a list that could have an arbitrary number of elements. On the other hand, using the same implementation to fold lists that never have more than one element makes little sense beside understanding the common parts of two different problems.

List Persistence and Element Mutability

The word "fold" generally refers to some technical manipulation that is applied to a collection. In other words, it mostly describes a computation, rather than the result of this computation. There are other terms that are more or less synonymous, the most often used being "reduction". The concept of reduction is present in the more general concept of "map/reduce". In the previous articles, I showed that "mapping" could be implemented through a fold, which implies that mapping would, in such case, be an operation implying folding. This is not true. Mapping may be implemented using a fold, but it may also be implemented without it, depending of the implementation of the collection.

For this discussion, we have to consider collection implementations under a specific criteria: persistence. There are basically two ways to implement collections. The first way is the way Java uses for ArrayList . The list is backed by an array, and you can modify it by adding, removing or replacing array elements. Of course, the implementation takes care of consistency. You can only add elements at some places, typically before the first, after the last or between two elements. You generally can’t have "holes" in the array. And when inserting, the implementation takes care of moving the subsequent elements. But this is an implementation detail, as is the fact that when the array is full, the implementation will copy the elements in a larger array in order to allow adding more elements.

Such an implementation has many benefits, the main being that it is fast, allowing constant time access to any element by its index. It also has a number of drawbacks. When growing beside the size of the original array, copying the elements to a new larger array takes additional time. Replacing an element with a new one is possible, which is a benefit, but this is generally incompatible with some iteration based access techniques. This makes this kind of collection good for storing mutable elements, that can be modified in place, but makes it difficult to use with immutable elements since such elements may not be mutated and have to be replaced by a new ones when they change. To understand the problem, think about a List<String> of names that you want to switch to upper case. You can’t do the following:

List<String> names = new ArrayList<>(Arrays.asList("Mickey", "Donald", "Pluto"));
for(String name : names) {
  name.toUpperCase();
}

This does not work because String is immutable, so the toUpperCase method does not change a string in any way, but it returns a new string with the modification applied. The following won’t work neither because we are updating the name variable, and not the list:

List<String> names = new ArrayList<>(Arrays.asList("mickey", "donald", "pluto"));
for(String name : names) {
  name = name.toUpperCase();
}

Whether immutable objects are better or worse than mutable ones is a different debate. What appears here is that immutable objects does not fit well with mutable lists. This is a marginal problem since Java objects are more than often implemented with mutable properties. Let’s say we have defined a Name class such as:

public class Name {
  String value;

  public Name(String value) {
    this.value = value;
  }

  public void toUpperCase() {
    value = value.toUpperCase();
  }

  public String toString() {
    return value;
  }
}

(OK, this class makes little sense, but it is just meant to be an example of a mutable object.)

With such a class, using Java lists is straightforward:

List<Name> names = new ArrayList<>(Arrays.asList(new Name("mickey"),
                                                 new Name("donald"),
                                                 new Name("pluto")));
for(Name name : names) {
  name.toUpperCase();
}

Starting from Java 8, this even makes more sense since you can write:

List<Name> names = new ArrayList<>(Arrays.asList(new Name("mickey"),
                                                 new Name("donald"),
                                                 new Name("pluto")));
names.forEach(Name::toUpperCase);

But with immutable objects like strings, we have to resort to folding, or reduction, to achieve the same goal:

List<String> names = new ArrayList<>(Arrays.asList("mickey", "donald", "pluto"));
List<String> namesUpper = new ArrayList<>();
for(String name : names) {
  namesUpper.add(name.toUpperCase());
}

(Yes, I know that there are much better ways to solve this problem. This is just for demonstration.)

Of course, we would want to abstract this into a specific method that we could put in a library:

List<String> names = new ArrayList<>(Arrays.asList("mickey", "donald", "pluto"));
  List<String> namesUpper = reduce(names, String::toUpperCase);

...

static List<String> reduce(List<String> names, UnaryOperator<String> operator) {
  List<String> namesUpper = new ArrayList<>();
  for(String name : names) {
    namesUpper.add(name.toUpperCase());
  }
  return namesUpper;
}

And we would also want to make this method generic, so that it could be used with any type and any operation:

static <A, B> List<B> reduce(List<A> as, Function<A, B> f) {
  List<B> bs = new ArrayList<>();
  for(A a : as) {
    bs.add(f.apply(a));
  }
  return bs;
}

Note that the method is called reduce because it reduces a collection of values to a single value. The fact that this single value happens to be a list is misleading. Reducing to collections is generally not called reduction, and this is why "fold" is probably a better name.

Reduction of Fold?

Another common difference between what people call "reduction" and "fold" is the way the "empty" case is treated. Here, if the List<A> to fold is empty, we return an empty List<B> . This shows the limits of this approach. The value to return in case of an empty argument list is known only because the return type is known. We can imagine that this special value to return in case of an empty argument list of A is a an empty list of B . In the same way, if we were to return the sum of a list of integers, we would guess that the value to return for an empty list would be 0. This is because we know that the "identity" element (of "neutral" element) of the sum operation is 0. So if we want to make our method even more generic and work for any operation, we have to make it accept the identity element as an additional parameter.

You may also notice that we are in fact doing to different things. One is reducing the list to a single value that happens to be a new list, the other one is converting the elements to upper case. This last operation is in fact what is called a "mapping". So we may make the whole thing fully generic by adding a separate function:

List<String> identity = new ArrayList<>();
  List<String> namesUpper = mapReduce(names, identity, FoldLibrary::add, String::toUpperCase);

...

private static <A> List<A> add(A a, List<A> list) {
  list.add(a);
  return list;
}

private static <A, B, C> C mapReduce(List<A> as, C identity, BiFunction<B, C, C> accumulator, Function<A, B> mapper) {
  C result = identity;
  for(A a : as) {
    result = accumulator.apply(mapper.apply(a), result);
  }
  return result;
}

But to be exhaustive, we should deal with the fact that Java List is mutable. So we should make a defensive copy of the list before iterating on it, since it could happen that the list be modified by another thread while we are iterating, which would produce an exception. Of course, this copy should be made atomically.

In fact, we do not have to bother with all these details. All has already been made available in the Java 8 Stream class.

Folding (or Reducing) With Streams

Transforming a List into a Stream is just a matter of calling the stream() method on the list:

List<String> names = new ArrayList<>(Arrays.asList("mickey", "donald", "pluto"));
names.stream()...

Then, looking at the Stream interface, we can see three reduce methods:

T reduce(T identity, BinaryOperator<T> accumulator);

Optional<T> reduce(BinaryOperator<T> accumulator);

<U> U reduce(U identity, BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner);

Which one should we use? None of them. The first one is used to reduce a Stream to a value of the same type as its elements, for example to sum a list of integers. This method takes the identity for the given operation as its first argument.

The second one is used for the same thing when no identity is provided. In such case, the first element is taken as the starting element (we can’t call it "identity"). As there might not always be such an element (if the list is empty) the method returns an Optional that may contain the result or be empty.

The third method is used to reduce the list to a single value of a different type than the elements type. It takes an identity argument and a BiFunction accumulator, as in our example, and no mapper, but a combiner . The absence of a mapper means that we will have to separately map the list prior to reducing, which is not a big deal. The combiner is used when the stream is processed in parallel. In such a case, it is broken in sub streams that are reduced in parallel, producing a number of partial results that must then be combined, hence the need for a combiner function. As we will not process the stream in parallel, we don’t need the combiner, so we may pass whatever we want as the last parameter, provided the code compiles. For example:

List<String> identity = new ArrayList<>();
List<String> namesUpper2 = names.stream().map(String::toUpperCase).reduce(identity, FoldLibrary::add, FoldLibrary::combine);

...

private static <U> List<U> combine(List<U> list1, List<U> list2) {
  list1.addAll(list2);
  return list1;
}

private static <A> List<A> add(List<A> list, A a) {
  list.add(a);
  return list;
}

There are five important things to note here:

  1. This is not the way you should reduce a list to a new list. No need to protest, it is just for demo purpose.
  2. The add method is not the same as in the previous example. Arguments are in reverse order, so that a method reference may be used.
  3. The combine method can do whatever you want, it will not change anything until you activate parallel processing. But the combiner can’t be null .
  4. The identity  list may not be shared. This is a very common source of bugs. Remember that Java lists are not persistent. Once the identity  list will have been used, it will contain the result of the reduction!
  5. The separate map operation does not involve an additional iteration on the collection. Streams are lazy, and map is non terminal, which means it will eventually be applied in the same iteration as the reduction.

Using Collectors

As I said previously, this is not the best way to reduce a list to a new list. For this, we are supposed to use a Collector . Java 8 contains a Collectors (note the terminal "s") class that contains factory methods returning various Collector instances. But we will first look at the most general way. In our example, here is how we would use a collector:

List<String> names = new ArrayList<>(Arrays.asList("mickey", "donald", "pluto"));
List<String> namesUpper3 = names.stream().map(String::toUpperCase).collect(collector);

Here, collector is an instance of a class implementing the Collector interface. If you use an IDE, you may simply declare an anonymous class and let the IDE create the method stubs for you. You must however provide the type parameters. A Collector takes three type parameters:

  • The first one is the type of the stream elements (in our case, String ).
  • The third one is the expected type of the reduced value (in our case List<String> ).
  • The second is an intermediate type that would be necessary in case we would first have to reduce to this type before transforming the result into the expected type. If you have trouble to understand what this mean, maybe the official documentation can help. It states that this type is the mutable accumulation type of the reduction operation (often hidden as an implementation detail) . If this is not clearer, don’t be afraid. We will see an example soon. For the time being, consider that it is the same type as the expected result type, thus List<String> .

Now you can declare an anonymous class implementing the Collector interface and let the IDE provide empty implementations:

Collector<String, List<String>, List<String>> collector = new Collector<String, List<String>, List<String>>() {
  @Override
  public Supplier<List<String>> supplier() {
    return null;
  }

  @Override
  public BiConsumer<List<String>, String> accumulator() {
    return null;
  }

  @Override
  public BinaryOperator<List<String>> combiner() {
    return null;
  }

  @Override
  public Function<List<String>, List<String>> finisher() {
    return null;
  }

  @Override
  public Set<Characteristics> characteristics() {
    return null;
  }
};

The supplier method is used to provide the identity element, so the implementation is obvious:

public Supplier<List<String>> supplier() {
  return ArrayList::new;
}

The accumulator method is the main difference between the use of a Collector and the reduce method. In the reduce method, the accumulator was a BiFunction which forced us to create a functional method for adding an element to a list (returning the modified list). The Collector interface uses a BiConsumer , allowing direct use of the List.add method:

public BiConsumer<List<String>, String> accumulator() {
  return List::add;
}

Note that using our previous Library.add method would work too, since this was simply a wrapper around a list mutation. Our BiFunction had a side effect, and this side effect would allow us using it here. Although the method is supposed to return a BiConsumer , the same implementation as our BiFunction is ok because type inference will provide the correct type (although probably not the one we thought). It will then work as long as we do not specify the type explicitly. This can be the source of very nasty bugs, because one will rarely search for bugs in a program that is actually working as expected!

The combiner is identical to what we used for the reduce method:

public BinaryOperator<List<String>> combiner() {
  return FoldLibrary::combine;
}

One important difference (not visible here) is that the parameter type of the BinaryOperator returned by the combine method, as well as the first parameter type of the BiConsumer returned by the accumulator method, are not the expected result type but the the mutable accumulation type of the reduction , meaning an intermediate type that could be used to simplify the implementation (or make it more efficient). For example, we could work on arrays of String and eventually transform the result into a List<String> . In such a case, this ultimate transformation would be made by the finisher method. The finisher method may also be used to "decorate" the result (we will see an example soon). For now, the implementation does nothing beside returning its argument:

public Function<List<String>, List<String>> finisher() {
   return Function.identity();
}

Note that we are talking of the implementation of the function returned by the finisher method, not the method itself. A function returning its argument unchanged could be written as a lambda: a → a , but it is cleaner to use the one returned by the Function.identity() method (which by the way uses a lambda for its implementation).

The last method is meant to provide additional information about the reduction. Characteristic is an enum with three possible values:

  • CONCURRENT indicates that the reduction can be done in parallel (meaning that the combiner method would be used to assemble the partial results).
  • UNORDERED indicates that the order of the elements is meaningless regarding the reduction. This, for example, would be true for the sum of a list of integers, but not for our example where the order of the strings should be preserved.
  • IDENTITY_FINISH means that the finisher methods returns the identity function and thus can be ignored. If this value is selected, the finisher function will not be called and the result will simply be casted to the expected result type. In such case, the finisher function may be made to return null , although this is certainly not a good idea.

In our case, we only need to return the IDENTITY_FINISH value, which should be done as:

public Set<Characteristics> characteristics() {
  return Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
}

The EnumSet.of method takes a vararg argument, so you can add the other enum values as necessary, in a comma separated list.

As you can see, the two methods used to reduce (or fold) a list to a new list are pretty equivalent, excepted that the Collector leverage the fact that java lists are not persistent and use "in place" modification.

Also note that unlike the folds that we saw in the two previous articles, you have no choice here about doing the operation from left to right or from right to left. Java 8 reduction is at best equivalent to a left fold (if the UNORDERED characteristic is not selected).

Folding a list into a new list is so common that Java 8 provides a factory method returning the necessary collector:

List<String> namesUpper = names.stream().map(String::toUpperCase).collect(Collectors.toList());

Does this mean that it is useless to know how collectors work? Not at all. Here is an example of folding the same list of strings into a comma separated list included between brackets. Once again, this might not be the simplest way to "join" a list of elements, and is only for demonstration purpose:

Collector<Integer, StringBuilder, String> stringCollector = new Collector<Integer, StringBuilder, String>() {
  @Override
  public Supplier<StringBuilder> supplier() {
    return StringBuilder::new;
  }

  @Override
  public BiConsumer<StringBuilder, Integer> accumulator() {
    return (sb, i) -> sb.append(sb.length() == 0 ? "" : ", ").append(i);
  }

  @Override
  public BinaryOperator<StringBuilder> combiner() {
    return StringBuilder::append;
  }

  @Override
  public Function<StringBuilder, String> finisher() {
    return sb -> sb.insert(0, '[').append(']').toString();
  }

  @Override
  public Set<Characteristics> characteristics() {
    return Collections.emptySet();
  }
};

List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6);
System.out.println(list.stream().collect(stringCollector));

This prints:

[1, 2, 3, 4, 5, 6]

Of course, you will want to push abstraction a bit farther. Creating such delimited strings from a list can be parameterized by the prefix, the separator and the postfix. This can be obtained by adding a factory method to create the comparator:

List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6);
    System.out.println(list.stream().collect(toDelimitedString("[", ", ", "]")));

public static <T> Collector<T, StringBuilder, String> toDelimitedString(String prefix, String separator, String postFix) {
  return new Collector<T, StringBuilder, String>() {
    @Override
    public Supplier<StringBuilder> supplier() {
      return StringBuilder::new;
    }

    @Override
    public BiConsumer<StringBuilder, T> accumulator() {
      return (sb, i) -> sb.append(sb.length() == 0 ? "" : separator).append(i);
    }

    @Override
    public BinaryOperator<StringBuilder> combiner() {
      return StringBuilder::append;
    }

    @Override
    public Function<StringBuilder, String> finisher() {
      return sb -> sb.insert(0, prefix).append(postFix).toString();
    }

    @Override
    public Set<Characteristics> characteristics() {
      return Collections.emptySet();
    }
  };
}

Adding Map to Perform Map/Reduce With a Reducing Collector

But "collecting", as Java 8 calls folding and reducing, is generally not used alone, but associated with a map operation (map/reduce). If we where to add tax to prices and format the result with a currency before reducing them to a delimited string, we could write:

list.stream().map(TaxComputer::addTax).map(Formatter::addCurrency).collect(toDelimitedString("[", ", ", "]"))

Any combination of map may be replaced with a single one using function composition:

List<Integer> list = Arrays.asList(1, 2, 3, 4, 5, 6);
Function<Integer, Double> addTax = TaxComputer::addTax;
Function<Double, String> format = Formatter::format;
System.out.println(list.stream().map(format.compose(addTax)).collect(toDelimitedString("[", ", ", "]")));

(Please note that there is no benefits in terms of performance, since several map operations on a stream will only cause one iteration.)

Of course, you can also compose the methods and use a method reference for the mapping. Once you have a single mapping, you can do a map/reduce using one of the reducing collectors provided by the Collector class:

public static <T> Collector<T, ?, T> reducing(T identity, BinaryOperator<T> op)

public static <T> Collector<T, ?, Optional<T>> reducing(BinaryOperator<T> op)

public static <T, U> Collector<T, ?, U> reducing(U identity, Function<? super T, ? extends U> mapper, BinaryOperator<U> op)

The third one is what we need:

list.stream().collect(Collectors.reducing("", format.compose(addTax), (String a, String b) -> a + (a.length() == 0 ? "" : ", ") + b));

but the absence of a finisher method makes it more difficult to add a prefix and a postfix. This can however be done the following way, although it is not very clean:

list.stream().collect(Collectors.reducing("[", format.compose(addTax), (String a, String b) -> a + (a.length() == 1 ? "" : ", ") + b)) + "]";

Note that we do not test the length of the string for 0 to know whether we must add a delimiter, but for 1, which is actually the length of the first delimiter. A cleaner version would be:

String startDelimiter = "[";
String endDelimiter = "]";
Strign result = list.stream().collect(Collectors.reducing(startDelimiter, format.compose(addTax), (String a, String b) -> a + (startDelimiter.equals(a) ? "" : ", ") + b)) + endDelimiter;

Conclusion

We have seen most of the techniques provided by Java 8 to program fold/reduce operations combined with map. So what is the best one? It is difficult to answer this question. Of course, it would seem natural to use the standard Java 8 tools. But these tools are awkward because they are meant to adapt functional techniques that are supposed to be used with persistent data structures to Java 8 data structures that are not persistent. A specific example of this is the use of a BiConsumer instead of a BiFunction for collectors, and the fact that inadvertently using a BiFunction implementation instead of a BiConsumer one could still work if this function has the same side effect as the effect of the expected BiConsumer . The alternative is to use a functional data structure instead of a Java List as we saw in the two previous articles of this series. But how does this compare to the Java 8 Collector way in terms of performance? This is what we will see in the next article.

Remember that this article is a complement to my book Functional Programming in Java . Have a look at it if you are interested by applying functional programming techniques to Java programs.





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