Details
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Improvement
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Status: Closed
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Major
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Resolution: Duplicate
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2.1
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None
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None
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New, Patch Available
Description
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