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From Michael McCandless <luc...@mikemccandless.com>
Subject Re: BlockTreeTermsReader consumes crazy amount of memory
Date Thu, 28 Aug 2014 18:28:39 GMT
Can you drill down some more to see what's using those ~46 MB?  Is the
the FSTs in the terms index?

But, we need to decouple the "single segment is opened with multiple
SegmentReaders" from e.g. "single SegmentReader is using too much RAM
to hold terms index".  E.g. from this screen shot it looks like there
are 88 fields totaling ~46 MB so ~0.5 MB per indexed field ...

Mike McCandless

http://blog.mikemccandless.com


On Thu, Aug 28, 2014 at 1:56 PM, Vitaly Funstein <vfunstein@gmail.com> wrote:
> Here's the link:
> https://drive.google.com/file/d/0B5eRTXMELFjjbUhSUW9pd2lVN00/edit?usp=sharing
>
> I'm indexing let's say 11 unique fields per document. Also, the NRT reader
> is opened continually, and "regular" searches use that one. But a special
> kind of feature allows searching a particular point in time (they get
> cleaned out based on some other logic), which requires opening a non-NRT
> reader just to service such search requests - in my understanding no
> segment readers for this reader can be shared with the NRT reader's pool...
> or am I off here? This seems evident from another heap dump fragment that
> shows a full new set of segment readers attached to that "temporary"
> reader:
>
> https://drive.google.com/file/d/0B5eRTXMELFjjSENXZV9kejR3bDA/edit?usp=sharing
>
>
> On Thu, Aug 28, 2014 at 10:13 AM, Michael McCandless <
> lucene@mikemccandless.com> wrote:
>
>> Hmm screen shot didn't make it ... can you post link?
>>
>> If you are using NRT reader then when a new one is opened, it won't
>> open new SegmentReaders for all segments, just for newly
>> flushed/merged segments since the last reader was opened.  So for your
>> N commit points that you have readers open for, they will be sharing
>> SegmentReaders for segments they have in common.
>>
>> How many unique fields are you adding?
>>
>> Mike McCandless
>>
>> http://blog.mikemccandless.com
>>
>>
>> On Wed, Aug 27, 2014 at 7:41 PM, Vitaly Funstein <vfunstein@gmail.com>
>> wrote:
>> > Mike,
>> >
>> > Here's the screenshot; not sure if it will go through as an attachment
>> > though - if not, I'll post it as a link. Please ignore the altered
>> package
>> > names, since Lucene is shaded in as part of our build process.
>> >
>> > Some more context about the use case. Yes, the terms are pretty much
>> unique;
>> > the schema for the data set is actually borrowed from here:
>> > https://amplab.cs.berkeley.edu/benchmark/#workload - it's the UserVisits
>> > set, with a couple of other fields added by us. The values for the fields
>> > are generated almost randomly, though some string fields are picked at
>> > random from a fixed dictionary.
>> >
>> > Also, this type of heap footprint might be tolerable if it stayed
>> relatively
>> > constant throughout the system's life cycle (of course, given the index
>> set
>> > stays more or less static). However, what happens here is that one
>> > IndexReader reference is maintained by ReaderManager as an NRT reader.
>> But
>> > we also would like support an ability to execute searches against
>> specific
>> > index commit points, ideally in parallel. As you might imagine, as soon
>> as a
>> > new DirectoryReader is opened at a given commit, a whole new set of
>> > SegmentReader instances is created and populated, effectively doubling
>> the
>> > already large heap usage... if there was a way to somehow reuse readers
>> for
>> > unchanged segments already pooled by IndexWriter, that would help
>> > tremendously here. But I don't think there's a way to link up the two
>> sets,
>> > at least not in the Lucene version we are using (4.6.1) - is this
>> correct?
>> >
>> >
>> > On Wed, Aug 27, 2014 at 12:56 AM, Michael McCandless
>> > <lucene@mikemccandless.com> wrote:
>> >>
>> >> This is surprising: unless you have an excessive number of unique
>> >> fields, BlockTreeTermReader shouldn't be such a big RAM consumer.
>> >>
>> >> Bu you only have 12 unique fields?
>> >>
>> >> Can you post screen shots of the heap usage?
>> >>
>> >> Mike McCandless
>> >>
>> >> http://blog.mikemccandless.com
>> >>
>> >>
>> >> On Tue, Aug 26, 2014 at 3:53 PM, Vitaly Funstein <vfunstein@gmail.com>
>> >> wrote:
>> >> > This is a follow up to the earlier thread I started to understand
>> memory
>> >> > usage patterns of SegmentReader instances, but I decided to create
a
>> >> > separate post since this issue is much more serious than the heap
>> >> > overhead
>> >> > created by use of stored field compression.
>> >> >
>> >> > Here is the use case, once again. The index totals around 300M
>> >> > documents,
>> >> > with 7 string, 2 long, 1 integer, 1 date and 1 float fields which are
>> >> > both
>> >> > indexed and stored. It is split into 4 shards, which are basically
>> >> > separate
>> >> > indices... if that matters. After the index is populated (but not
>> >> > optimized
>> >> > since we don't do that), the overall heap usage taken up by Lucene
is
>> >> > over
>> >> > 1 GB, much of which is taken up by instances of BlockTreeTermsReader.
>> >> > For
>> >> > instance for the largest segment in one such an index, the retained
>> heap
>> >> > size of the internal tree map is around 50 MB. This is evident from
>> heap
>> >> > dump analysis, which I have screenshots of that I can post here, if
>> that
>> >> > helps. As there are many segments of various sizes in the index, as
>> >> > expected, the total heap usage for one shard stands at around 280 MB.
>> >> >
>> >> > Could someone shed some light on whether this is expected, and if so
-
>> >> > how
>> >> > could I possibly trim down memory usage here? Is there a way to switch
>> >> > to a
>> >> > different terms index implementation, one that doesn't preload all
the
>> >> > terms into RAM, or only does this partially, i.e. as a cache? I'm not
>> >> > sure
>> >> > if I'm framing my questions correctly, as I'm obviously not an expert
>> on
>> >> > Lucene's internals, but this is going to become a critical issue for
>> >> > large
>> >> > scale use cases of our system.
>> >>
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>> >>
>> >
>> >
>> >
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>>
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>>

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