+1 to test on real vector data -- if you test on synthetic data you
draw synthetic conclusions.
Can someone post the theoretical performance (CPU and RAM required) of
HNSW construction? Do we know/believe our HNSW implementation has
achieved that theoretical big-O performance? Maybe we have some silly
performance bug that's causing it not to?
As I understand it, HNSW makes the tradeoff of costly construction for
faster searching, which is typically the right tradeoff for search use
cases. We do this in other parts of the Lucene index too.
Lucene will do a logarithmic number of merges over time, i.e. each doc
will be merged O(log(N)) times in its lifetime in the index. We need
to multiply that by the cost of re-building the whole HNSW graph on
each merge. BTW, other things in Lucene, like BKD/dimensional points,
also rebuild the whole data structure on each merge, I think? But, as
Rob pointed out, stored fields merging do indeed do some sneaky tricks
to avoid excessive block decompress/recompress on each merge.
> As I understand it, vetoes must have technical merit. I'm not sure
that this veto rises to "technical merit" on 2 counts:
Actually I think Robert's veto stands on its technical merit already.
Robert's take on technical matters very much resonate with me, even if
he is sometimes prickly in how he expresses them ;)
His point is that we, as a dev community, are not paying enough
attention to the indexing performance of our KNN algo (HNSW) and
implementation, and that it is reckless to increase / remove limits in
that state. It is indeed a one-way door decision and one must
confront such decisions with caution, especially for such a widely
used base infrastructure as Lucene. We don't even advertise today in
our javadocs that you need XXX heap if you index vectors with
dimension Y, fanout X, levels Z, etc.
RAM used during merging is unaffected by dimensionality, but is
affected by fanout, because the HNSW graph (not the raw vectors) is
memory resident, I think? Maybe we could move it off-heap and let the
OS manage the memory (and still document the RAM requirements)? Maybe
merge RAM costs should be accounted for in IW's RAM buffer
accounting? It is not today, and there are some other things that use
non-trivial RAM, e.g. the doc mapping (to compress docid space when
deletions are reclaimed).
When we added KNN vector testing to Lucene's nightly benchmarks, the
indexing time massively increased -- see annotations DH and DP here:
https://home.apache.org/~mikemccand/lucenebench/indexing.html. Nightly
benchmarks now start at 6 PM and don't finish until ~14.5 hours
later. Of course, that is using a single thread for indexing (on a
box that has 128 cores!) so we produce a deterministic index every
night ...
Stepping out (meta) a bit ... this discussion is precisely one of the
awesome benefits of the (informed) veto. It means risky changes to
the software, as determined by any single informed developer on the
project, can force a healthy discussion about the problem at hand.
Robert is legitimately concerned about a real issue and so we should
use our creative energies to characterize our HNSW implementation's
performance, document it clearly for users, and uncover ways to
improve it.
Mike McCandless
http://blog.mikemccandless.com
On Mon, Apr 10, 2023 at 6:41 PM Alessandro Benedetti
<a.benede...@sease.io> wrote:
I think Gus points are on target.
I recommend we move this forward in this way:
We stop any discussion and everyone interested proposes an option
with a motivation, then we aggregate the options and we create a
Vote maybe?
I am also on the same page on the fact that a veto should come
with a clear and reasonable technical merit, which also in my
opinion has not come yet.
I also apologise if any of my words sounded harsh or personal
attacks, never meant to do so.
My proposed option:
1) remove the limit and potentially make it configurable,
Motivation:
The system administrator can enforce a limit its users need to
respect that it's in line with whatever the admin decided to be
acceptable for them.
Default can stay the current one.
That's my favourite at the moment, but I agree that potentially in
the future this may need to change, as we may optimise the data
structures for certain dimensions. I am a big fan of Yagni (you
aren't going to need it) so I am ok we'll face a different
discussion if that happens in the future.
On Sun, 9 Apr 2023, 18:46 Gus Heck, <gus.h...@gmail.com> wrote:
What I see so far:
1. Much positive support for raising the limit
2. Slightly less support for removing it or making it
configurable
3. A single veto which argues that a (as yet undefined)
performance standard must be met before raising the limit
4. Hot tempers (various) making this discussion difficult
As I understand it, vetoes must have technical merit. I'm not
sure that this veto rises to "technical merit" on 2 counts:
1. No standard for the performance is given so it cannot be
technically met. Without hard criteria it's a moving target.
2. It appears to encode a valuation of the user's time, and
that valuation is really up to the user. Some users may
consider 2hours useless and not worth it, and others might
happily wait 2 hours. This is not a technical decision,
it's a business decision regarding the relative value of
the time invested vs the value of the result. If I can
cure cancer by indexing for a year, that might be worth
it... (hyperbole of course).
Things I would consider to have technical merit that I don't hear:
1. Impact on the speed of **other** indexing operations.
(devaluation of other functionality)
2. Actual scenarios that work when the limit is low and fail
when the limit is high (new failure on the same data with
the limit raised).
One thing that might or might not have technical merit
1. If someone feels there is a lack of documentation of the
costs/performance implications of using large vectors,
possibly including reproducible benchmarks establishing
the scaling behavior (there seems to be disagreement on
O(n) vs O(n^2)).
The users *should* know what they are getting into, but if the
cost is worth it to them, they should be able to pay it
without forking the project. If this veto causes a fork that's
not good.
On Sun, Apr 9, 2023 at 7:55 AM Michael Sokolov
<msoko...@gmail.com> wrote:
We do have a dataset built from Wikipedia in luceneutil.
It comes in 100 and 300 dimensional varieties and can
easily enough generate large numbers of vector documents
from the articles data. To go higher we could concatenate
vectors from that and I believe the performance numbers
would be plausible.
On Sun, Apr 9, 2023, 1:32 AM Dawid Weiss
<dawid.we...@gmail.com> wrote:
Can we set up a branch in which the limit is bumped to
2048, then have
a realistic, free data set (wikipedia sample or
something) that has,
say, 5 million docs and vectors created using public
data (glove
pre-trained embeddings or the like)? We then could run
indexing on the
same hardware with 512, 1024 and 2048 and see what the
numbers, limits
and behavior actually are.
I can help in writing this but not until after Easter.
Dawid
On Sat, Apr 8, 2023 at 11:29 PM Adrien Grand
<jpou...@gmail.com> wrote:
>
> As Dawid pointed out earlier on this thread, this is
the rule for
> Apache projects: a single -1 vote on a code change
is a veto and
> cannot be overridden. Furthermore, Robert is one of
the people on this
> project who worked the most on debugging subtle
bugs, making Lucene
> more robust and improving our test framework, so I'm
listening when he
> voices quality concerns.
>
> The argument against removing/raising the limit that
resonates with me
> the most is that it is a one-way door. As MikeS
highlighted earlier on
> this thread, implementations may want to take
advantage of the fact
> that there is a limit at some point too. This is why
I don't want to
> remove the limit and would prefer a slight increase,
such as 2048 as
> suggested in the original issue, which would enable
most of the things
> that users who have been asking about raising the
limit would like to
> do.
>
> I agree that the merge-time memory usage and slow
indexing rate are
> not great. But it's still possible to index
multi-million vector
> datasets with a 4GB heap without hitting OOMEs
regardless of the
> number of dimensions, and the feedback I'm seeing is
that many users
> are still interested in indexing multi-million
vector datasets despite
> the slow indexing rate. I wish we could do better,
and vector indexing
> is certainly more expert than text indexing, but it
still is usable in
> my opinion. I understand how giving Lucene more
information about
> vectors prior to indexing (e.g. clustering
information as Jim pointed
> out) could help make merging faster and more
memory-efficient, but I
> would really like to avoid making it a requirement
for indexing
> vectors as it also makes this feature much harder to
use.
>
> On Sat, Apr 8, 2023 at 9:28 PM Alessandro Benedetti
> <a.benede...@sease.io> wrote:
> >
> > I am very attentive to listen opinions but I am
un-convinced here and I an not sure that a single
person opinion should be allowed to be detrimental for
such an important project.
> >
> > The limit as far as I know is literally just
raising an exception.
> > Removing it won't alter in any way the current
performance for users in low dimensional space.
> > Removing it will just enable more users to use Lucene.
> >
> > If new users in certain situations will be unhappy
with the performance, they may contribute improvements.
> > This is how you make progress.
> >
> > If it's a reputation thing, trust me that not
allowing users to play with high dimensional space
will equally damage it.
> >
> > To me it's really a no brainer.
> > Removing the limit and enable people to use high
dimensional vectors will take minutes.
> > Improving the hnsw implementation can take months.
> > Pick one to begin with...
> >
> > And there's no-one paying me here, no company
interest whatsoever, actually I pay people to
contribute, I am just convinced it's a good idea.
> >
> >
> > On Sat, 8 Apr 2023, 18:57 Robert Muir,
<rcm...@gmail.com> wrote:
> >>
> >> I disagree with your categorization. I put in
plenty of work and
> >> experienced plenty of pain myself, writing tests
and fighting these
> >> issues, after i saw that, two releases in a row,
vector indexing fell
> >> over and hit integer overflows etc on small datasets:
> >>
> >> https://github.com/apache/lucene/pull/11905
> >>
> >> Attacking me isn't helping the situation.
> >>
> >> PS: when i said the "one guy who wrote the code"
I didn't mean it in
> >> any kind of demeaning fashion really. I meant to
describe the current
> >> state of usability with respect to indexing a few
million docs with
> >> high dimensions. You can scroll up the thread and
see that at least
> >> one other committer on the project experienced
similar pain as me.
> >> Then, think about users who aren't committers
trying to use the
> >> functionality!
> >>
> >> On Sat, Apr 8, 2023 at 12:51 PM Michael Sokolov
<msoko...@gmail.com> wrote:
> >> >
> >> > What you said about increasing dimensions
requiring a bigger ram buffer on merge is wrong.
That's the point I was trying to make. Your concerns
about merge costs are not wrong, but your conclusion
that we need to limit dimensions is not justified.
> >> >
> >> > You complain that hnsw sucks it doesn't scale,
but when I show it scales linearly with dimension you
just ignore that and complain about something entirely
different.
> >> >
> >> > You demand that people run all kinds of tests
to prove you wrong but when they do, you don't listen
and you won't put in the work yourself or complain
that it's too hard.
> >> >
> >> > Then you complain about people not meeting you
half way. Wow
> >> >
> >> > On Sat, Apr 8, 2023, 12:40 PM Robert Muir
<rcm...@gmail.com> wrote:
> >> >>
> >> >> On Sat, Apr 8, 2023 at 8:33 AM Michael Wechner
> >> >> <michael.wech...@wyona.com> wrote:
> >> >> >
> >> >> > What exactly do you consider reasonable?
> >> >>
> >> >> Let's begin a real discussion by being HONEST
about the current
> >> >> status. Please put politically correct or your
own company's wishes
> >> >> aside, we know it's not in a good state.
> >> >>
> >> >> Current status is the one guy who wrote the
code can set a
> >> >> multi-gigabyte ram buffer and index a small
dataset with 1024
> >> >> dimensions in HOURS (i didn't ask what hardware).
> >> >>
> >> >> My concerns are everyone else except the one
guy, I want it to be
> >> >> usable. Increasing dimensions just means even
bigger multi-gigabyte
> >> >> ram buffer and bigger heap to avoid OOM on merge.
> >> >> It is also a permanent backwards compatibility
decision, we have to
> >> >> support it once we do this and we can't just
say "oops" and flip it
> >> >> back.
> >> >>
> >> >> It is unclear to me, if the multi-gigabyte ram
buffer is really to
> >> >> avoid merges because they are so slow and it
would be DAYS otherwise,
> >> >> or if its to avoid merges so it doesn't hit OOM.
> >> >> Also from personal experience, it takes trial
and error (means
> >> >> experiencing OOM on merge!!!) before you get
those heap values correct
> >> >> for your dataset. This usually means starting
over which is
> >> >> frustrating and wastes more time.
> >> >>
> >> >> Jim mentioned some ideas about the memory
usage in IndexWriter, seems
> >> >> to me like its a good idea. maybe the
multigigabyte ram buffer can be
> >> >> avoided in this way and performance improved
by writing bigger
> >> >> segments with lucene's defaults. But this
doesn't mean we can simply
> >> >> ignore the horrors of what happens on merge.
merging needs to scale so
> >> >> that indexing really scales.
> >> >>
> >> >> At least it shouldnt spike RAM on trivial data
amounts and cause OOM,
> >> >> and definitely it shouldnt burn hours and
hours of CPU in O(n^2)
> >> >> fashion when indexing.
> >> >>
> >> >>
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>
> --
> Adrien
>
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