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https://issues.apache.org/jira/browse/LUCENE-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Matt Ericson updated LUCENE-855:
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Attachment: FieldCacheRangeFilter_Lucene_2.3.0.patch
I have changed the FiledCacheRangeFilter so that it now works with Lucene 2.3.0
IT does not have to change the FieldCache since It can just use Extended Field
Cache
Here are the performance numbers again
[junit] ------------- Standard Output ---------------
[junit] Start interval: Tue Feb 04 22:34:22 PST 2003
[junit] End interval: Sun Feb 03 22:34:22 PST 2008
[junit] Creating RAMDirectory index...
[junit] Reader opened with 100000 documents. Creating RangeFilters...
[junit] Standard RangeFilter finished in 41111ms
[junit] FieldCacheRangeFilter finished in 112ms
[junit] ------------- ---------------- ---------------
I hope this helps and I hope this gets added to Lucene
> MemoryCachedRangeFilter to boost performance of Range queries
> -------------------------------------------------------------
>
> Key: LUCENE-855
> URL: https://issues.apache.org/jira/browse/LUCENE-855
> Project: Lucene - Java
> Issue Type: Improvement
> Components: Search
> Affects Versions: 2.1
> Reporter: Andy Liu
> Assignee: Otis Gospodnetic
> Attachments: contrib-filters.tar.gz, FieldCacheRangeFilter.patch,
> FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch,
> FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch,
> FieldCacheRangeFilter.patch, FieldCacheRangeFilter_Lucene_2.3.0.patch,
> MemoryCachedRangeFilter.patch, MemoryCachedRangeFilter_1.4.patch,
> TestRangeFilterPerformanceComparison.java,
> TestRangeFilterPerformanceComparison.java
>
>
> 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 field.
> 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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