lucene-java-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Mark Kristensson <>
Subject Re: IndexWriter.close() performance issue
Date Fri, 05 Nov 2010 17:37:42 GMT
While most of our Lucene indexes are used for more traditional searching, this index in particular
is used more like a reporting repository. Thus, we really do need to have that many fields
indexed and they do need to be broken out into separate fields. There may be another way to
structure the index to reduce the number of fields, but I'm hoping we can optimize the current
design and avoid (yet another) index redesign.

I'll look into the tweaking the merge policy, but I'm more interested in disabling norms because
scoring really doesn't matter for us. Basically, we need nothing more than a binary answer
from Lucene: either a record meets the provided criteria (which can be a rather complex boolean
query with many subqueries) or it doesn't. If the record does match, then we get the IDs from
lucene and run off to get the live data from our primary data store and sort it (in Java)
based upon criteria provided by the user, not by score.

After our initial design mushroomed in size, we redesigned and now (I thought) do not have
norms on any of the fields in this index. So, I'm wondering if there was something in the
results from the CheckIndex that I provided which indicates to you that we may have norms
still enabled? I know that if you have norms on any one document's field, then any other document
with that same field will get "infected" with norms as well.

My understanding is that any field that uses the constants  Index.NOT_ANALYZED_NO_NORMS or
 Index.NO will not  have norms on it, regardless of whether or not the field is stored. Is
that not correct?


On Nov 4, 2010, at 2:56 AM, Michael McCandless wrote:

> Likely what happened is you had a bunch of smaller segments, and then
> suddenly they got merged into that one big segment (_aiaz) in your
> index.
> The representation for norms in particular is not sparse, so this
> means the size of the norms file for a given segment will be
> number-of-unique-indexed-fields X number-of-documents.
> So this count grows quadratically on merge.
> Do these fields really need to be indexed?   If so, it'd be better to
> use a single field for all users for the indexable text if you can.
> Failing that, a simple workaround is to set the maxMergeMB/Docs on the
> merge policy; this'd prevent big segments from being produced.
> Disabling norms should also workaround this, though that will affect
> hit scores...
> Mike
> On Wed, Nov 3, 2010 at 7:37 PM, Mark Kristensson
> <> wrote:
>> Yes, we do have a large number of unique field names in that index, because they
are driven by user named fields in our application (with some cleaning to remove illegal chars).
>> This slowness problem has appeared very suddenly in the last couple of weeks and
the number of unique field names has not spiked in the last few weeks. Have we crept over
some threshold with our linear growth in the number of unique field names? Perhaps there is
a limit driven by the amount of RAM in the machine that we are violating? Are there any guidelines
for the maximum number, or suggested number, of unique fields names in an index or segment?
Any suggestions for potentially mitigating the problem?
>> Thanks,
>> Mark
>> On Nov 3, 2010, at 2:02 PM, Michael McCandless wrote:
>>> On Wed, Nov 3, 2010 at 4:27 PM, Mark Kristensson
>>> <> wrote:
>>>> I've run checkIndex against the index and the results are below. That net
is that it's telling me nothing is wrong with the index.
>>> Thanks.
>>>> I did not have any instrumentation around the opening of the IndexSearcher
(we don't use an IndexReader), just around the actual query execution so I had to add some
additional logging. What I found surprised me, opening a search against this index takes the
same 6 to 8 seconds that closing the indexWriter takes.
>>> IndexWriter opens a SegmentReader for each segment in the index, to
>>> apply deletions, so I think this is the source of the slowness.
>>> From the CheckIndex output, it looks like you have many (296,713)
>>> unique fields names on that one large segment -- does that sound
>>> right?  I suspect such a very high field count is the source of the
>>> slowness...
>>> Mike
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail:
>>> For additional commands, e-mail:
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail:
>> For additional commands, e-mail:
> ---------------------------------------------------------------------
> To unsubscribe, e-mail:
> For additional commands, e-mail:

  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message