Design for collections upgrades (was: Re: lambda-dev Digest, Vol 15, Issue 20 [reduce method result type])

[Pavel Minaev]

I argued for that as well, originally, but then operations on all
collections would be lazy (as they are all Iterables). On the other hand, if
specific collections override operations and redefine them as eager, we get
an undesired inconsistency in behavior - some Iterables are lazy, while
others are not. The best you can then do is provide some kind of
toIterable() method on any Iterable, which would wrap it into a simple lazy
Iterable with default implementations for map/filter/..., hiding any
overriden behavior - and then you also have to explain to the users the
difference between writing ((Iterable)list).map() and
list.toIterable().map(). In contrast, iterator functions (as in, a function
taking an iterator and returning a new iterator) are always perceived as
lazy, whether the original iterator was obtained from a collection or not.

I understand that this is not a strict argument, and it's all about what the
API client "expects" to see, which is very subjective. But we've already had
a lengthy argument on how to best name lazy operations such that they're not
confused with eager operations provided by the collection itself; and that
is mainly because the existing Java collection API is eager and mutable; and
I see the reasons behind this argument. E.g. list.add() mutates the list
in-place, right there and then; it does not defer the update until the list
is iterated, nor does it create and return a new list - and so it's hard to
argue against the claim that list.filter() should do things in an entirely
different way. Hence the desire for some kind of
list.magicalSomething().filter() that would clearly mark the transition to
laziness.


On Sat, Mar 12, 2011 at 8:37 PM, Colin Decker <cgdecker at gmail.com> wrote:

> Because there's no need to and because Iterables (being reusable) are far
> more flexible than Iterators. The Iterable interface is just as lazy as
> Iterator. The Stream interface should be Iterable.
>
> --
> Colin
>
>
>
> On Sat, Mar 12, 2011 at 11:08 PM, Pavel Minaev <int19h at gmail.com> wrote:
>
>> I'm curious as to why you think that limiting lazy operations to iterators
>> (or streams - the name is immaterial) is undesirable? In terms of code, it
>> is exactly the same as the proposal which started the thread, with a need
>> for one extra method call -  iterator() rather than asStream() - on the
>> collection, before you are in the lazy land. In terms of semantics, it
>> avoids introduction of new abstractions where existing ones are sufficient
>> to capture the intent (iterators are inherently lazy).
>>
>> On Sat, Mar 12, 2011 at 7:53 PM, Colin Decker <cgdecker at gmail.com> wrote:
>>
>>> I don't feel like a comparison to InputStream etc. makes much sense
>>> considering "Stream" is an arbitrary name that can easily be changed to
>>> something more appropriate ("Streamable", say). I also think it's a weakness
>>> of the IO streams that they don't have an Iterable equivalent (like
>>> InputSupplier from Guava). Regardless, limiting lazy filter/map to Iterator
>>> would be... undesirable. I do think Iterator should have such methods as
>>> well, though.
>>>
>>> --
>>> Colin
>>>
>>>
>>>
>>> On Sat, Mar 12, 2011 at 8:30 PM, Pavel Minaev <int19h at gmail.com> wrote:
>>>
>>>> I've been re-reading the "Design for collection upgrades" thread, and
>>>> had
>>>> some thoughts on the nature of the Stream abstraction (for lazy/serial
>>>> scenario) as originally outlined in Brian's post that kicked off the
>>>> thread,
>>>> and users expectations of what comes with that name.
>>>>
>>>> Per the proposal, it sounds like Stream is more or less a marker
>>>> interface
>>>> to indicate lazy operations, and not otherwise any different from
>>>> Iterable.
>>>> However, this isn't what "stream" has historically meant in imperative
>>>> PLs
>>>> and their frameworks. For some examples of what I mean, let me point at
>>>> java.io.InputStream, or at C++ std::basic_istream. In both cases, the
>>>> fundamental property of the streams - the one that is guaranteed to be
>>>> supported by _any_ stream - is that you can read elements. InputStream
>>>> also
>>>> offers mark()/reset(), but those are conditionally supported, and so are
>>>> not
>>>> part of the basic contract of the abstraction; they could (one could
>>>> even
>>>> argue that, from a pure OOD approach, they should) just as well be moved
>>>> to
>>>> a more concrete interface that could be queried by clients instead of
>>>> using
>>>> markSupported().
>>>>
>>>> The important thing with that basic contract is that once a value is
>>>> read
>>>> from a stream, it stays read: a stream by itself is a read-once
>>>> sequence.
>>>> There may be ways to obtain another stream that would re-read the same
>>>> exact
>>>> elements, but that would still be a new stream object. For streams that
>>>> wrap
>>>> some data store (e.g. FileInputStream or ByteArrayInputStream), the
>>>> stream
>>>> object is essentially a cursor into the store - it has a current
>>>> position,
>>>> which every read advances towards the end of the store. Furthermore, the
>>>> stream is not _the_ store - you can have several FileInputStreams over
>>>> the
>>>> same file, or several ByteArrayInputStreams sharing the same byte array.
>>>>
>>>> Now if we take the above and see how it applies to collections, it
>>>> actually
>>>> is a very familiar concept: something that is not a collection itself,
>>>> but
>>>> is a forward-only cursor for a collection, and each collection may have
>>>> more
>>>> than one such cursor - why, that is Iterator. Its next() and hasNext()
>>>> methods match exactly the basic input stream contract; remove() does
>>>> not,
>>>> but it is conditionally-supported anyway. An analogy to highlight this
>>>> point: Iterator is to Iterable/Collection what ByteArrayInputStream is
>>>> to
>>>> byte array.
>>>>
>>>> Iterators, being cursors, are also naturally expected to be lazy by API
>>>> clients - if I provide a method that takes an iterator, applies a filter
>>>> to
>>>> it, and returns another iterator as a result, no-one would expect
>>>> filtering
>>>> to occur over the entire collection right there and then. It's clear
>>>> that
>>>> the result of such operation is a "filtered iterator", that would apply
>>>> the
>>>> filter as it is being iterated.
>>>>
>>>> So, then, why not put lazy map/filter/reduce/... on Iterator? Thus,
>>>> Brian's
>>>> original serial/lazy example would become:
>>>>
>>>>    maxFooWeight = things.iterator()
>>>>                               .filter(#Thing.isFoo)
>>>>                              .map(#Thing.getWeight)
>>>>                              .max();
>>>>
>>>> or maybe with some less concise but more descriptive (and - purely
>>>> subjectively - "Java-like") names:
>>>>
>>>>    maxFooWeight = things.iterator()
>>>>                              .withFilter(#Thing.isFoo)
>>>>                              .withTransform(#Thing.getWeight)
>>>>                              .getLargestValue();
>>>>
>>>> This has a nice property of working with a more fundamental abstraction
>>>> than
>>>> collections - I can write an Iterator that wraps a non-rewindable I/O
>>>> InputStream, but I cannot write such an Iterable (well, I can - if it
>>>> throws
>>>> after the first call to iterator() - but it will only be usable with
>>>> APIs
>>>> which specify that they only call iterator() on the provided Iterable
>>>> once
>>>> as part of their public contract; otherwise, I'm relying on an
>>>> implementation detail).
>>>>
>>>> The only major annoyance I can see with this approach is that
>>>> enhanced-for-loop only supports Iterables (and arrays) but not
>>>> Iterators,
>>>> and so you'd have to write a manual loop with next()/hasNext() to
>>>> iterate
>>>> over the result. But is there any reason why enhanced-for cannot be made
>>>> to
>>>> support Iterators directly? The only thing it does to the provided
>>>> Iterable
>>>> is to call iterator() on it, and it does it exactly once; it would just
>>>> need
>>>> to use the provided Iterator directly instead. It sounds like it would
>>>> be
>>>> trivial to add.
>>>>
>>>>
>>>> The above didn't touch on what the design would look like for eager or
>>>> lazy/parallel operations. For parallel, the original design can be
>>>> trivially
>>>> adapted by moving asParallel() to Iterator directly, and producing some
>>>> sort
>>>> of ParallelIterator, which is simply a marker interface to enable
>>>> parallelization for all applied operations, but otherwise is the same as
>>>> Iterator (probably just extending it).
>>>>
>>>> For eager operations, I would prefer in-place mutating methods returning
>>>> the
>>>> receiver (to permit chaining), with distinct but obviously mapped names.
>>>> For
>>>> example, if Iterator has withFilter(), then Collection would have
>>>> filter().
>>>> I don't think there is much utility in having eager ops that do a copy,
>>>> and
>>>> even less so for chaining such. I think a simple addition of something
>>>> like
>>>> clone() to Collection (default implementation could do newInstance()
>>>> assuming a no-arg constructor available, and then addAll(this) on that
>>>> new
>>>> instance) would cover vast majority of all cases where you want to get a
>>>> copy: the usual pattern would then be to do something like:
>>>>
>>>>   Collection<int> getWeights() {
>>>>       return things.clone().filter(#Thing.isFoo).
>>>> transform(#Thing.getWeight);
>>>>   }
>>>>
>>>> where both filter() and map() operate in-place on the cloned collection.
>>>> This also skirts the whole question of the type of the resulting
>>>> collection
>>>> produced by a copying operation - it's clear and unambiguous what it'll
>>>> be
>>>> for clone(), and no other operation makes a copy.
>>>>
>>>>
>>>> On Tue, Mar 8, 2011 at 9:23 AM, Brian Goetz <brian.goetz at oracle.com>
>>>> wrote:
>>>>
>>>> > Since people are already discussing this based on an experimental
>>>> > checkin, let me outline the big picture plan here.
>>>> >
>>>> > The general idea is to add functional-like operations to collections
>>>> --
>>>> > filter, map, reduce, apply.
>>>> >
>>>> > I see three sensible modes, with explicit choices of which you get.
>>>> >
>>>> > 1.  Serial / Eager.  This is the straight
>>>> > collections-with-functional-style mode, and some samples have already
>>>> > been checked in as proof of concept.  Operations on collections yield
>>>> > new collections, and you can chain the calls.  It values ease of use
>>>> > over performance (no new concepts like laziness), but the performance
>>>> > model is still highly predictable.  You get things like
>>>> >
>>>> >      Collection fooAbles = things.filter( #{ t -> t.isFoo() });
>>>> >
>>>> > or, with method references:
>>>> >
>>>> >      Collection fooAbles = things.filter(#Thing.isFoo); // ooh, pretty
>>>> >
>>>> > You can also chain calls together, though you pay a (predictable)
>>>> > performance cost of intermediate collections, which for small
>>>> > collections is unlikely to matter:
>>>> >
>>>> >      maxFooWeight = things.filter(#Thing.isFoo)
>>>> >                           .map(#Thing.getWeight)
>>>> >                           .max();
>>>> >
>>>> > The benefit here is concision and clarity.  The cost is some
>>>> > performance, but maybe not so much that people freak out.  If people
>>>> > care, they move to the next model, which is:
>>>> >
>>>> > 2.  Serial / Lazy.  Here, the primary abstraction is Stream (name to
>>>> be
>>>> > chosen later, Remi used "lazy" in his example.)  To transfer between
>>>> > "eager world" and "lazy world", you use conversion methods (toStream /
>>>> > toCollection).  A typical call chain probably looks like:
>>>> >   collection.toStream / op / op / op / {toCollection,reduce,apply}
>>>> >
>>>> > so the above example becomes
>>>> >
>>>> >      maxFooWeight = things.asStream()
>>>> >                           .filter(#Thing.isFoo)
>>>> >                           .map(#Thing.getWeight)
>>>> >                           .max();
>>>> >
>>>> > The return type of Collection.filter is different from the return type
>>>> > of Stream.filter, so the choice and performance costs are reflected in
>>>> > the static type system.  This avoids the cost of the intermediate
>>>> > collections, but is still serial.  If you care about that, you move up
>>>> > to the next model, which is:
>>>> >
>>>> > 3.  Parallel / Lazy.  Here, the primary abstraction is something like
>>>> > ParallelStream or ParallelIterable.  Let's call it ParallelFoo to
>>>> avoid
>>>> > bikeshedding for the moment.  Now, the code looks like:
>>>> >
>>>> >      maxFooWeight = things.asParallelFoo()
>>>> >                           .filter(#Thing.isFoo)
>>>> >                           .map(#Thing.getWeight)
>>>> >                           .max();
>>>> >
>>>> > Again, the return type of ParallelFoo.filter is different from
>>>> > Stream.filter or Collection.filter, so again the choice is reflected
>>>> in
>>>> > the static type system.  But you don't have to rewrite your code.
>>>> >
>>>> > The beauty here is twofold:
>>>> >
>>>> >  - The base model (serial/eager) is easy to understand and natural to
>>>> > use as a way of expressing what the programmer wants to do, and
>>>> > attractive enough to stand on its own -- just a little slow with big
>>>> > collections.
>>>> >  - Switching between execution models is mostly a matter of adding an
>>>> > explicit conversion or two in the call chain, as the models are
>>>> similar
>>>> > enough that the rest of the code should still work (and even mean the
>>>> > same thing.)
>>>> >
>>>> >
>>>> > On 3/8/2011 8:43 AM, Rémi Forax wrote:
>>>> > >    Le 08/03/2011 14:31, Jim Mayer a écrit :
>>>> > >> // I can tolerate this one
>>>> > >>       long product(List<Integer>   list) {
>>>> > >>         return list.map(#{x ->   (long) x}).reduce(0L, #{sum, x ->
>>>>   sum
>>>> > * x});
>>>> > >>       }
>>>> > >
>>>> > > I prefer this one:
>>>> > >
>>>> > >     long product(List<Integer>   list) {
>>>> > >         return list.lazy().map(#{x ->   (long) x}).reduce(0L, #{sum,
>>>> x ->
>>>> >   sum * x});
>>>> > >     }
>>>> > >
>>>> > > lazy() means, don't do map directly, but wait and do map and reduce
>>>> in
>>>> > > one iteration.
>>>> > >
>>>> > > Rémi
>>>> > >
>>>> > >
>>>> >
>>>> >
>>>>
>>>>
>>>
>>
>