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https://issues.apache.org/jira/browse/LUCENE-2588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12895506#action_12895506
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Robert Muir commented on LUCENE-2588:
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i think this patch as-is is a good improvement (at least as a defensive measure
against "noise" terms and other things). it also seems to buy more savings on
the non-latin data i tested (60kb -> 40kb). +1 to commit
{quote}
In the future we could do crazier things. EG there's no real reason why the
indexed terms must be regular (every N terms), so, we could instead pick terms
more carefully, say "approximately" every N, but favor terms that have a
smaller net prefix
{quote}
I think we should explore this in the future. "randomly" selecting every N
terms isn't optimal when allowing a "fudge" of the interval maybe +/- 5 or 10%
could intentionally select terms that differ very quickly from their previous
term, without wasting a bunch of cpu or unbalancing the terms index...
if additional smarts like this could save enough size on average maybe we could
rethink lowering the default interval of 128?
> terms index should not store useless suffixes
> ---------------------------------------------
>
> Key: LUCENE-2588
> URL: https://issues.apache.org/jira/browse/LUCENE-2588
> Project: Lucene - Java
> Issue Type: Improvement
> Components: Index
> Reporter: Michael McCandless
> Assignee: Michael McCandless
> Fix For: 4.0
>
> Attachments: LUCENE-2588.patch
>
>
> This idea came up when discussing w/ Robert how to improve our terms index...
> The terms dict index today simply grabs whatever term was at a 0 mod 128
> index (by default).
> But this is wasteful because you often don't need the suffix of the term at
> that point.
> EG if the 127th term is aa and the 128th (indexed) term is abcd123456789,
> instead of storing that full term you only need to store ab. The suffix is
> useless, and uses up RAM since we load the terms index into RAM.
> The patch is very simple. The optimization is particularly easy because
> terms are now byte[] and we sort in binary order.
> I tested on first 10M 1KB Wikipedia docs, and this reduces the terms index
> (tii) file from 3.9 MB -> 3.3 MB = 16% smaller (using StandardAnalyzer,
> indexing body field tokenized but title / date fields untokenized). I expect
> on noisier terms dicts, especially ones w/ bad terms accidentally indexed,
> that the savings will be even more.
> In the future we could do crazier things. EG there's no real reason why the
> indexed terms must be regular (every N terms), so, we could instead pick
> terms more carefully, say "approximately" every N, but favor terms that have
> a smaller net prefix. We can also index more sparsely in regions where the
> net docFreq is lowish, since we can afford somewhat higher seek+scan time to
> these terms since enuming their docs will be much faster.
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