[External] : Re: Question about Streams, Gatherers, and fetching too many elements

[David Alayachew]

Makes a lot of sense. And I agree -- hopefully we do end up in a place
where all the methods on Stream do execute on the same island.

Until then, I will make do with keeping as much functionality as possible
on the Gatherers and Collectors, so that I can eliminate island hopping
entirely. At the very least, I will avoid using the non-Collector terminal
operations, as it appears that, using a Collector keeps everything on the
same island for the duration of the stream.


On Wed, Nov 13, 2024, 5:00 AM Viktor Klang <viktor.klang at oracle.com> wrote:

> I think the problem is that it depends on the order, and combination, of
> operations to know what executes in the same "island".
>
> My personal preference would try to end up in a place where an entire
> pipeline is executed as a single island, which would mean that
> short-circuit signals would always propagate right back to the source.
>
> Cheers,
> √
>
>
> *Viktor Klang*
> Software Architect, Java Platform Group
> Oracle
> ------------------------------
> *From:* David Alayachew <davidalayachew at gmail.com>
> *Sent:* Wednesday, 13 November 2024 00:37
> *To:* Viktor Klang <viktor.klang at oracle.com>
> *Cc:* core-libs-dev <core-libs-dev at openjdk.org>
> *Subject:* Re: [External] : Re: Question about Streams, Gatherers, and
> fetching too many elements
>
> Oh sure, I expect something like distinct() to pull everything. In order
> to know if something is distinct, you have to do some variant of "check
> against everyone else". Whether that is holding all instances in memory or
> their hashes, it's clear from a glance that you will need to look at
> everything, and therefore, pre-fetching makes intuitive sense to me.
>
> I 100% did not expect terminal operations like findAny() or reduce() to
> pull the whole data set. That was a complete whiplash for me. The method
> findAny() advertises itself as a short-circuiting operation, so to find out
> that it actually pulls the whole data set anyways was shocking.
>
> And that was my biggest pain point -- looking at the documentation, it is
> not clear to me at all that methods like findAny() would pull in all data
> upon becoming parallel().
>
> Do you think it would make sense to add documentation about this to the
> javadocs for Stream/java.util.stream? Or maybe it is already there and I
> misunderstood it (even after reading through it thoroughly over 5 times).
>
>
> On Tue, Nov 12, 2024, 10:06 AM Viktor Klang <viktor.klang at oracle.com>
> wrote:
>
> >We are told how Streams can process unbounded data sets, but when it
> tries to do a findAny() with parallel(), it runs into an OOME because it
> fetched all the data ahead of time. In fact, almost of the terminal
> operations will hit an OOME in the exact same way if they are parallel and
> have a big enough data set. It's definitely not the end of the world, but
> it seems that I have to fit everything into a Collector and/or a Gatherer
> if I want to avoid pre-fetching everything.
>
> Yeah, I think it is important to distinguish "can process unbounded data
> sets" from "always able to process unbounded data sets".
>
> Some operations inherently need the end of the stream, so even something
> somple like: stream.distinct() or stream.sorted() can end up pulling in all
> data (which of course won't terminate).
>
> Fortunately, I think Gatherers can unlock much more situations where
> unbounded streams can be processed.
>
> Cheers,
> √
>
>
> *Viktor Klang*
> Software Architect, Java Platform Group
> Oracle
> ------------------------------
> *From:* David Alayachew <davidalayachew at gmail.com>
> *Sent:* Tuesday, 12 November 2024 15:08
> *To:* Viktor Klang <viktor.klang at oracle.com>
> *Cc:* core-libs-dev <core-libs-dev at openjdk.org>
> *Subject:* Re: [External] : Re: Question about Streams, Gatherers, and
> fetching too many elements
>
>
> Oh woah. I certainly did not. Or rather, I had dismissed the idea as soon
> as I thought of it.
>
>
> I hand-waved away the idea because I thought that the method would turn
> the stream pipeline parallel, thus, recreating the same problem I currently
> have of parallelism causing all of the elements to be fetched ahead of
> time, causing an OOME.
>
>
> It did NOT occur to me that the pipeline would stay sequential, and just
> kick these off sequentially, but have them executing in parallel. I can't
> see why I came to that incorrect conclusion. I have read the javadocs of
> this method several times. Though, to be fair, I came to the same,
> incorrect conclusion about Collectors.groupingByConcurrent(), and it wasn't
> until someone pointed out what the documentation was actually saying that I
> realized it's true properties.
>
> Thanks. That definitely solves at least part of my problem. Obviously, I
> would prefer to write to S3 in parallel too, but at the very least, the
> calculation part is being done in parallel. And worst case scenario, I can
> be really bad and just do the write to S3 in the mapConcurrent, and then
> just return the metadata of each write, and just bundle that up with
> collect.
>
>
> And that's ignoring the fact that I can just use the workaround too.
>
>
> Yeah, the whole "pre-fetch all the data ahead of time" makes sense to me
> from a performance perspective, but is rather unintuitive to me from a
> usability perspective. We are told how Streams can process unbounded data
> sets, but when it tries to do a findAny() with parallel(), it runs into an
> OOME because it fetched all the data ahead of time. In fact, almost of the
> terminal operations will hit an OOME in the exact same way if they are
> parallel and have a big enough data set. It's definitely not the end of the
> world, but it seems that I have to fit everything into a Collector and/or a
> Gatherer if I want to avoid pre-fetching everything.
>
> On Tue, Nov 12, 2024, 6:36 AM Viktor Klang <viktor.klang at oracle.com>
> wrote:
>
> Have you considered Gatherers.mapConcurrent(…)?
>
>
> Cheers,
> √
>
>
> *Viktor Klang*
> Software Architect, Java Platform Group
> Oracle
> ------------------------------
> *From:* David Alayachew <davidalayachew at gmail.com>
> *Sent:* Tuesday, 12 November 2024 01:53
> *To:* Viktor Klang <viktor.klang at oracle.com>
> *Cc:* core-libs-dev <core-libs-dev at openjdk.org>
> *Subject:* Re: [External] : Re: Question about Streams, Gatherers, and
> fetching too many elements
>
> Good to know, ty vm.
>
> At the very least, I have this workaround. This will meet my needs for now.
>
> I guess my final question would be -- is this type of problem better
> suited to something besides parallel streams? Maybe an ExecutorService?
>
> Really, all I am doing is taking a jumbo file, splitting it into batches,
> and then doing some work on those batches. My IO speeds are pretty fast,
> and the compute work is non-trivial, so there is performance being left on
> the table if I give up parallelism. And I am in a position where completion
> time is very important to us.
>
> I just naturally assumed parallel streams were the right choice because
> the compute work is simple. A pure function that I can break out, and then
> call in a map. Once I do that, I just call forEach to write the batches
> back out to S3. Maybe I should look into a different part of the std lib
> instead because I am using the wrong tool for the job? My nose says
> ExecutorService, but I figure I should ask before I dive too deep in.
>
>
> On Mon, Nov 11, 2024, 2:34 PM Viktor Klang <viktor.klang at oracle.com>
> wrote:
>
> You're most welcome!
>
> In a potential future where all intermediate operations are
> Gatherer-based, and all terminal operations are Collector-based, it would
> just work as expected. But with that said, I'm not sure it is practically
> achievable because some operations might not have the same
> performance-characteristics as before.
>
> Cheers,
> √
>
>
> *Viktor Klang*
> Software Architect, Java Platform Group
> Oracle
> ------------------------------
> *From:* David Alayachew <davidalayachew at gmail.com>
> *Sent:* Monday, 11 November 2024 18:32
> *To:* Viktor Klang <viktor.klang at oracle.com>
> *Cc:* core-libs-dev <core-libs-dev at openjdk.org>
> *Subject:* [External] : Re: Question about Streams, Gatherers, and
> fetching too many elements
>
>
> Thanks for the workaround. It's running beautifully.
>
> Is there a future where this island concept is extended to the rest of
> streams? Tbh, I don't fully understand it.
>
> On Mon, Nov 11, 2024, 9:59 AM Viktor Klang <viktor.klang at oracle.com>
> wrote:
>
> Hi David,
>
> This is the effect of how parallel streams are implemented, where
> different stages, which are not representible as a join-less Spliterator
> are executed as a series of "islands" where the next isn't started until
> the former has completed.
>
> If you think about it, parallelization of a Stream works best when the
> entire data set can be split amongst a set of worker threads, and that sort
> of implies that you want eager pre-fetch of data, so if your dataset does
> not fit in memory, that is likely to lead to less desirable outcomes.
>
> What I was able to do for Gatherers is to implement "gather(…) +
> collect(…)"-fusion so any number of consecutive gather(…)-operations
> immediately followed by a collect(…) is run in the same "island".
>
> So with that said, you could try something like the following:
>
> static <T> Collector<T, ?, Void> *forEach*(Consumer<? *super* T> *each*) {
>     *return* Collector.of(() -> null, (*v*, *e*) -> each.accept(e), (*l*,
> *r*) -> l, (*v*) -> null, Collector.Characteristics.IDENTITY_FINISH);
> }
>
>
> stream
> .parallel()
> .unordered()
> .gather(Gatherers.windowFixed(BATCH_SIZE))
> .collect(forEach(eachList -> println(eachList.getFirst())));
>
>
> Cheers,
> √
>
>
> *Viktor Klang*
> Software Architect, Java Platform Group
> Oracle
> ------------------------------
> *From:* core-libs-dev <core-libs-dev-retn at openjdk.org> on behalf of David
> Alayachew <davidalayachew at gmail.com>
> *Sent:* Monday, 11 November 2024 14:52
> *To:* core-libs-dev <core-libs-dev at openjdk.org>
> *Subject:* Re: Question about Streams, Gatherers, and fetching too many
> elements
>
> And just to avoid the obvious question, I can hold about 30 batches in
> memory before the Out of Memory error occurs. So this is not an issue of my
> batch size being too high.
>
> But just to confirm, I set the batch size to 1, and it still ran into an
> out of memory error. So I feel fairly confident saying that the Gatherer is
> trying to grab all available data before sending any of it downstream.
>
> On Mon, Nov 11, 2024, 8:46 AM David Alayachew <davidalayachew at gmail.com>
> wrote:
>
> Hello Core Libs Dev Team,
>
> I was trying out Gatherers for a project at work, and ran into a rather
> sad scenario.
>
> I need to process a large file in batches. Each batch is small enough that
> I can hold it in memory, but I cannot hold the entire file (and thus, all
> of the batches) in memory at once.
>
> Looking at the Gatherers API, I saw windowFixed and thought that it would
> be a great match for my use case.
>
> However, when trying it out, I was disappointed to see that it ran out of
> memory very quickly. Here is my attempt at using it.
>
> stream
> .parallel()
> .unordered()
> .gather(Gatherers.windowFixed(BATCH_SIZE))
> .forEach(eachList -> println(eachList.getFirst()))
> ;
>
> As you can see, I am just splitting the file into batches, and printing
> out the first of each batch. This is purely for example's sake, of course.
> I had planned on building even more functionality on top of this, but I
> couldn't even get past this example.
>
> But anyways, not even a single one of them printed out. Which leads me to
> believe that it's pulling all of them in the Gatherer.
>
> I can get it to run successfully if I go sequentially, but not parallel.
> Parallel gives me that out of memory error.
>
> Is there any way for me to be able to have the Gatherer NOT pull in
> everything while still remaining parallel and unordered?
>
> Thank you for your time and help.
> David Alayachew
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://mail.openjdk.org/pipermail/core-libs-dev/attachments/20241113/750b392c/attachment-0001.htm>