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From "Andy Liu (JIRA)" <>
Subject [jira] Commented: (LUCENE-855) MemoryCachedRangeFilter to boost performance of Range queries
Date Mon, 09 Apr 2007 18:37:32 GMT


Andy Liu commented on LUCENE-855:

In your updated benchmark, you're combining the range filter with a term query that matches
one document.  I don't believe that's the typical use case for a range filter.  Usually the
user employs a range to filter a large document set.  

I created a different benchmark to compare standard range filter, MemoryCachedRangeFilter,
and Matt's FieldCacheRangeFilter using MatchAllDocsQuery, ConstantScoreQuery, and TermQuery
(matching one doc like the last benchmark).  Here are the results:

Reader opened with 100000 documents.  Creating RangeFilters...
RangeFilter w/MatchAllDocsQuery:
  * Bits: 4421
  * Search: 5285

RangeFilter w/ConstantScoreQuery:
  * Bits: 4200
  * Search: 8694

RangeFilter w/TermQuery:
  * Bits: 4088
  * Search: 4133

MemoryCachedRangeFilter w/MatchAllDocsQuery:
  * Bits: 80
  * Search: 1142

MemoryCachedRangeFilter w/ConstantScoreQuery:
  * Bits: 79
  * Search: 482

MemoryCachedRangeFilter w/TermQuery:
  * Bits: 73
  * Search: 95

FieldCacheRangeFilter w/MatchAllDocsQuery:
  * Bits: 0
  * Search: 1146

FieldCacheRangeFilter w/ConstantScoreQuery:
  * Bits: 1
  * Search: 356

FieldCacheRangeFilter w/TermQuery:
  * Bits: 0
  * Search: 19

Here's some points:

1. When searching in a filter, bits() is called, so the search time includes bits() time.
2. Matt's FieldCacheRangeFilter is faster for ConstantScoreQuery, although not by much.  Using
MatchAllDocsQuery, they run neck-and-neck.  FCRF is much faster for TermQuery since MCRF has
to create the BItSet for the range before the search is executed.
3. I get less document hits when running FieldCacheRangeFilter with ConstantScoreQuery.  Matt,
there may be a bug in getNextSetBit().  Not sure if this would affect the benchmark.
4. I'd be interested to see performance numbers when FieldCacheRangeFilter is used with ChainedFilter.
 I suspect that MCRF would be faster in this case, since I'm assuming that FCRF has to reconstruct
a standard BitSet during clone().

> 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: 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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