To Stream.slice(fromInclusive, toExclusive) or Stream.slice(toSkip, limit) that is the question

Joe Bowbeer joe.bowbeer at
Fri Oct 11 08:48:20 PDT 2013

slice *is* start:end

The comparison with all the other slices can't be avoided.

If we want skipAndLimit, then let's add one of those.

Our not.
On Oct 11, 2013 7:54 AM, "Brian Goetz" <brian.goetz at> wrote:

> Further argument for dropping: while we don't do implicit op fusion now
> (an earlier version had this, but we dropped it as it was getting in the
> way of some more valuable things), now that the implementation has
> stabilized, it looks like the current implementation is pretty amenable to
> pairwise op fusion.  So it seems likely there's a path to having the
> implementation just turn skip+limit into the more efficient version.
> On 10/11/2013 10:31 AM, Doug Lea wrote:
>> On 10/11/2013 10:18 AM, Brian Goetz wrote:
>>  Several have pointed out that it is surprising that .skip(n).limit(k) is
>>> inefficient enough to want a fusing.  Of course, in the sequential
>>> case, its
>>> fine.  But because skip/limit are constrained to operate in encounter
>>> order, in
>>> the worst case (non-SIZED+SUBSIZED, non-UNORDERED), we have to
>>> buffer.  Doing
>>> two rounds of buffering would suck twice -- and this was the primary
>>> motivation
>>> for a fused operation.
>>> So I think there are two sensible choices here:
>>>   - slice(toSkip, toLimit)
>>>   - drop slice entirely
>>> The cost of the latter is that those who need it in parallel in the
>>> unpleasant
>>> cases are even more likely to have to retreat to sequential.
>> In other words: If running sequentially, skip(n).limit(k) costs almost
>> nothing more than slice. And the same nearly holds for cases where
>> people do an intrinsically sequential operation in parallel
>> (i.e., using either skip or limit on ordered data) --
>> you get about twice the terrible cost of either skip or limit.
>> Seems like a fine idea to just drop it.
>> -Doug
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