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From "Matt Ericson (JIRA)" <>
Subject [jira] Updated: (LUCENE-855) MemoryCachedRangeFilter to boost performance of Range queries
Date Sun, 15 Apr 2007 17:46:15 GMT


Matt Ericson updated LUCENE-855:

    Attachment: FieldCacheRangeFilter.patch

This version if the Field Cache Range filter has a new AbstractGetOnlyBitSet a base BitSet
that will use all of its functions like nextClearBit using get() 

I have also added a RuntimeChainedFilter this will work just like the normal chained filter
but it does not do the AND or OR or XOR until you call the get() function this will allow
for the BitSets that the FieldCacheRangeFilter create to be chained correctly. 

There are also testes for all of my new code. The FieldCacheRangeFilter still has the nested
If statements to allow for Long, Ints and Floats. I think these are not that complicated and
it allows users to pick the type of filter they want while saving space. In My application
we use Ints for dates even though we know it will only support dates going up to 2038 as right
now we need the memory. 
This code will give the flexibility to the user creating the filters so they can tune their
app just they way they want it.

I hope you all like it. Please let me know what you think 

> MemoryCachedRangeFilter to boost performance of Range queries
> -------------------------------------------------------------
>                 Key: LUCENE-855
>                 URL:
>             Project: Lucene - Java
>          Issue Type: Improvement
>          Components: Search
>    Affects Versions: 2.1
>            Reporter: Andy Liu
>         Assigned To: Otis Gospodnetic
>         Attachments: contrib-filters.tar.gz, FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch,
FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch,
MemoryCachedRangeFilter.patch, MemoryCachedRangeFilter_1.4.patch,,
> Currently RangeFilter uses TermEnum and TermDocs to find documents that fall within the
specified range.  This requires iterating through every single term in the index and can get
rather slow for large document sets.
> MemoryCachedRangeFilter reads all <docId, value> pairs of a given field, sorts
by value, and stores in a SortedFieldCache.  During bits(), binary searches are used to find
the start and end indices of the lower and upper bound values.  The BitSet is populated by
all the docId values that fall in between the start and end indices.
> TestMemoryCachedRangeFilterPerformance creates a 100K RAMDirectory-backed index with
random date values within a 5 year range.  Executing bits() 1000 times on standard RangeQuery
using random date intervals took 63904ms.  Using MemoryCachedRangeFilter, it took 876ms. 
Performance increase is less dramatic when you have less unique terms in a field or using
less number of documents.
> Currently MemoryCachedRangeFilter only works with numeric values (values are stored in
a long[] array) but it can be easily changed to support Strings.  A side "benefit" of storing
the values are stored as longs, is that there's no longer the need to make the values lexographically
comparable, i.e. padding numeric values with zeros.
> The downside of using MemoryCachedRangeFilter is there's a fairly significant memory
requirement.  So it's designed to be used in situations where range filter performance is
critical and memory consumption is not an issue.  The memory requirements are: (sizeof(int)
+ sizeof(long)) * numDocs.  
> MemoryCachedRangeFilter also requires a warmup step which can take a while to run in
large datasets (it took 40s to run on a 3M document corpus).  Warmup can be called explicitly
or is automatically called the first time MemoryCachedRangeFilter is applied using a given
> So in summery, MemoryCachedRangeFilter can be useful when:
> - Performance is critical
> - Memory is not an issue
> - Field contains many unique numeric values
> - Index contains large amount of documents

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