Stream API: Fuse sorted().limit(n) into single operation
Tagir F. Valeev
amaembo at gmail.com
Sat Mar 5 17:35:16 UTC 2016
Hello!
One of the popular bulk data operation is to find given number of
least or greatest elements. Currently Stream API provides no dedicated
operation to do this. Of course, it could be implemented by custom
collector and some third-party libraries already provide it. However
it would be quite natural to use existing API:
stream.sorted().limit(k) - k least elements
stream.sorted(Comparator.reverseOrder()).limit(k) - k greatest elements.
In fact people already doing this. Some samples could be found on
GitHub:
https://github.com/search?l=java&q=%22sorted%28%29.limit%28%22&type=Code&utf8=%E2%9C%93
Unfortunately current implementation of such sequence of operations is
suboptimal: first the whole stream content is dumped into intermediate
array, then sorted fully and after that k least elements is selected.
On the other hand it's possible to provide a special implementation
for this particular case which takes O(k) additional memory and in
many cases works significantly faster.
I wrote proof-of-concept implementation, which could be found here:
http://cr.openjdk.java.net/~tvaleev/patches/sortedLimit/webrev/
The implementation switches to new algorithm if limit is less than
1000 which is quite common for such scenario (supporting bigger values
is also possible, but would require more testing). New algorithm
allocates an array of 2*limit elements. When its size is reached, it
sorts the array (using Arrays.sort) and discards the second half.
After that only those elements are accumulated which are less than the
worst element found so far. When array is filled again, the second
half is sorted and merged with the first half.
Here's JMH test with results which covers several input patterns:
http://cr.openjdk.java.net/~tvaleev/patches/sortedLimit/jmh/
You may check summary first:
http://cr.openjdk.java.net/~tvaleev/patches/sortedLimit/jmh/summary.txt
Speedup values bigger than 1 are good.
The most significant regression in the sequential mode of the new
implementation is the ever decreasing input (especially with the low
limit value). Still, it's not that bad (given the fact that old
implementation processes such input very fast). On the other hand, for
random input new implementation could be in order of magnitude faster.
Even for ever ascending input noteable speedup (like 40%) could be
achieved.
For parallel stream the new implementation is almost always faster,
especially if you ignore the cases when parallel stream is
unprofitable.
What do you think about this improvement? Could it be included into
JDK-9? Are there any issues I'm unaware of? I would be really happy to
complete this work if this is supported by JDK team. Current
implementation has no primitive specialization and does not optimize
the sorting out if the input is known to be sorted, but it's not very
hard to add these features as well if you find my idea useful.
With best regards,
Tagir Valeev.
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