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From "Andrzej Bialecki (JIRA)" <>
Subject [jira] Commented: (LUCENE-1812) Static index pruning by in-document term frequency (Carmel pruning)
Date Fri, 13 Aug 2010 09:12:17 GMT


Andrzej Bialecki  commented on LUCENE-1812:

Doron, thank you very much for pushing forward this issue! I think your patch looks good,
I'm still reviewing it in the light of 3.1 APIs. It's great that you added a new policy and
test cases - this looks solid now.

In the meantime however I still doubt if the JIRA checkbox is a sufficient counterweight to
a possibility of a patent infringement suit against users of Lucene... I think in cases like
this, where there is a known existing patent that this implementation uses, the ASF requires
an explicit software grant to be made (
which would protect Lucene users from infringing on IBM's IP. I'll forward this to
to see what they say about it - if you can obtain such a grant without too much trouble then
I'm sure we could then close this issue.

> Static index pruning by in-document term frequency (Carmel pruning)
> -------------------------------------------------------------------
>                 Key: LUCENE-1812
>                 URL:
>             Project: Lucene - Java
>          Issue Type: New Feature
>          Components: contrib/*
>    Affects Versions: 2.9, 3.1
>            Reporter: Andrzej Bialecki 
>         Attachments: pruning.patch, pruning.patch, pruning.patch, pruning.patch
> This module provides tools to produce a subset of input indexes by removing postings
data for those terms where their in-document frequency is below a specified threshold. The
net effect of this processing is a much smaller index that for common types of queries returns
nearly identical top-N results as compared with the original index, but with increased performance.

> Optionally, stored values and term vectors can also be removed. This functionality is
largely independent, so it can be used without term pruning (when term freq. threshold is
set to 1).
> As the threshold value increases, the total size of the index decreases, search performance
increases, and recall decreases (i.e. search quality deteriorates). NOTE: especially phrase
recall deteriorates significantly at higher threshold values. 
> Primary purpose of this class is to produce small first-tier indexes that fit completely
in RAM, and store these indexes using IndexWriter.addIndexes(IndexReader[]). Usually the performance
of this class will not be sufficient to use the resulting index view for on-the-fly pruning
and searching. 
> NOTE: If the input index is optimized (i.e. doesn't contain deletions) then the index
produced via IndexWriter.addIndexes(IndexReader[]) will preserve internal document id-s so
that they are in sync with the original index. This means that all other auxiliary information
not necessary for first-tier processing, such as some stored fields, can also be removed,
to be quickly retrieved on-demand from the original index using the same internal document
> Threshold values can be specified globally (for terms in all fields) using defaultThreshold
parameter, and can be overriden using per-field or per-term values supplied in a thresholds
map. Keys in this map are either field names, or terms in field:text format. The precedence
of these values is the following: first a per-term threshold is used if present, then per-field
threshold if present, and finally the default threshold.
> A command-line tool (PruningTool) is provided for convenience. At this moment it doesn't
support all functionality available through API.

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