[
https://issues.apache.org/jira/browse/LUCENE-855?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#action_12488125
]
Hoss Man commented on LUCENE-855:
---------------------------------
Another thing that occurred to me this morning is that the comparison test
doesn't consider the performance of the various Filter's when cached and reused
(with something like CacheWrappingFilter) ... you may actually see the stock
RangeFilter be faster then either implementation when you can reuse the same
exact Filter over and over on the same IndexReader -- a fairly common use case.
In general the numbers that really need to be conpared are...
1) the time overhead of an implementation when opening a new IndexReader (and
whether that overhead is per field)
2) the time overhead of an implementation the first time a specific Filter is
used on an IndexReader
3) the time on average that it takes to use a Filter
> 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
> Assigned To: Otis Gospodnetic
> Attachments: FieldCacheRangeFilter.patch,
> FieldCacheRangeFilter.patch, FieldCacheRangeFilter.patch,
> FieldCacheRangeFilter.patch, FieldCacheRangeFilter.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
--
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.
---------------------------------------------------------------------
To unsubscribe, e-mail: [EMAIL PROTECTED]
For additional commands, e-mail: [EMAIL PROTECTED]
