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From eks dev <eks...@yahoo.co.uk>
Subject Re: best practice: 1.4 billions documents
Date Mon, 22 Nov 2010 16:54:45 GMT
Am I the only one who thinks this is not the way to go, MultiReader (or
MulitiSearcher) is not going to fix your problems. Having 1.4B Documents on
one machine is a big number, does not matter how you partition them (or you
have some really expensive hardware at your disposal).  Did I miss the point
somewhere with this recommendation "use MultiReader and you are good for
1.4B Document"?

Imo, you must distribute your index across many machines.

Your best chance is to look at solr cloud and solr replication (solr Wiki is
your friend). Of course, you can do it yourself, but building distributet
setup with what you call "real time updates" is a huge pain.

Alternatively, google for lucene or solr on cassandra (has some very nice
properties about update latency and architectural simplicity).I do not know
if this is somewhere in production.

Good luck,
e.



On Mon, Nov 22, 2010 at 5:18 PM, Uwe Schindler <uwe@thetaphi.de> wrote:

> There is no reason to use MultiSearcher instead the much more consistent
> and effective  MultiReader! We (Robert and me) are already planning to
> deprecate it. MultiSearcher itsself has no benefit over a simple
> IndexSearcher on top of a MultiReader since Lucene 2.9, it has only
> problems.
>
> Use cases for real MultiSearchers are only the subclasses for "remote
> search" or (perhaps) multi-threaded search, but the latter I would not
> recommend (instead let the additional CPUs in your machine be free for other
> users doing searches in parallel). Multithreading a single search should not
> be done, as it slows down multiple users accessing the same index at the
> same time. Spend the additional CPU power for other things like warming
> searchers, indexing additional documents, or filling FieldCache in parallel.
>
> Uwe
>
> -----
> Uwe Schindler
> H.-H.-Meier-Allee 63, D-28213 Bremen
> http://www.thetaphi.de
> eMail: uwe@thetaphi.de
>
>
> > -----Original Message-----
> > From: David Fertig [mailto:dfertig@cymfony.com]
> > Sent: Monday, November 22, 2010 4:54 PM
> > To: java-user@lucene.apache.org
> > Subject: RE: best practice: 1.4 billions documents
> >
> > >> We have a couple billion docs in our archives as well...Breaking them
> > >> up by day worked well for us
> >
> > We do not have 2 billion segments in one index  We have roughly 5-10
> million
> > documents per index. We are currently using a miltisearcher but
> unresolved
> > lucene issues in this will force us to move to a multireader.
> >
> > As far as the parallel searcher goes, read back on the thread with
> subject
> > "Search returning documents matching a NOT range".
> > There is an acknowledged/proven bug with a small unit test, but there is
> some
> > disagreement about the internal reasons it fails. I have been unable to
> > generate further discussion or a resolution. This was supposed to be
> added as a
> > bug to the JIRA for the 3.3 release, but has not been.  I am not which
> class Solr
> > uses, but if it uses MultiSearcher, it will have the same bug.
> >
> > -----Original Message-----
> > From: Luca Rondanini [mailto:luca.rondanini@gmail.com]
> > Sent: Monday, November 22, 2010 1:47 AM
> > To: java-user@lucene.apache.org
> > Subject: Re: best practice: 1.4 billions documents
> >
> > Hi David, thanks for your answer. it really helped a lot! so, you have an
> index
> > with more than 2 billions segments. this is pretty much the answer I was
> > searching for: lucene alone is able to manage such a big index.
> >
> > which kind of problems do you have with the parallel searchers? I'm going
> to
> > build my index in the next couple of weeks if you want we can confront
> our
> > data
> >
> > thanks again
> > Luca
> >
> >
> > On Sun, Nov 21, 2010 at 6:22 PM, David Fertig <dfertig@cymfony.com>
> wrote:
> >
> > > Actually I've been bitten by an still-unresolved issue with the
> > > parallel searchers and recommend a MultiReader instead.
> > > We have a couple billion docs in our archives as well.  Breaking them
> > > up by day worked well for us, but you'll need to do something.
> > >
> > > -----Original Message-----
> > > From: Luca Rondanini [mailto:luca.rondanini@gmail.com]
> > > Sent: Sunday, November 21, 2010 8:13 PM
> > > To: java-user@lucene.apache.org; yonik@lucidimagination.com
> > > Subject: Re: best practice: 1.4 billions documents
> > >
> > > thank you both!
> > >
> > > Johannes, katta seems interesting but I will need to solve the
> > > problems of "hot" updates to the index
> > >
> > > Yonik, I see your point - so your suggestion would be to build an
> > > architecture based on ParallelMultiSearcher?
> > >
> > >
> > > On Sun, Nov 21, 2010 at 3:48 PM, Yonik Seeley
> > > <yonik@lucidimagination.com
> > > >wrote:
> > >
> > > > On Sun, Nov 21, 2010 at 6:33 PM, Luca Rondanini
> > > > <luca.rondanini@gmail.com> wrote:
> > > > > Hi everybody,
> > > > >
> > > > > I really need some good advice! I need to index in lucene
> > > > > something
> > > like
> > > > 1.4
> > > > > billions documents. I had experience in lucene but I've never
> > > > > worked
> > > with
> > > > > such a big number of documents. Also this is just the number of
> > > > > docs at
> > > > > "start-up": they are going to grow and fast.
> > > > >
> > > > > I don't have to tell you that I need the system to be fast and to
> > > support
> > > > > real time updates to the documents
> > > > >
> > > > > The first solution that came to my mind was to use
> > > ParallelMultiSearcher,
> > > > > splitting the index into many "sub-index" (how many docs per index?
> > > > > 100,000?) but I don't have experience with it and I don't know how
> > > > > well
> > > > will
> > > > > scale while the number of documents grows!
> > > > >
> > > > > A more solid solution seems to build some kind of integration with
> > > > hadoop.
> > > > > But I didn't find match about lucene and hadoop integration.
> > > > >
> > > > > Any idea? Which direction should I go (pure lucene or hadoop)?
> > > >
> > > > There seems to be a common misconception about hadoop regarding
> > search.
> > > > Map-reduce as implemented in hadoop is really for batch oriented
> > > > jobs only (or those types of jobs where you don't need a quick
> > > > response time).  It's definitely not for normal queries (unless you
> > > > have unusual requirements).
> > > >
> > > > -Yonik
> > > > http://www.lucidimagination.com
> > > >
> > > > --------------------------------------------------------------------
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> > > > For additional commands, e-mail: java-user-help@lucene.apache.org
> > > >
> > > >
> > >
>
>
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