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https://issues.apache.org/jira/browse/LUCENE-2588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12909717#action_12909717
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Simon Willnauer commented on LUCENE-2588:
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bq. Should we really change StandardCodec to support this [non-binary order]?
I'm not sure if we should do, but we should at least document the limitation.
People who work with that level do also read doc strings - if they don't let
them be doomed but if you run into the bug we had in LUCENE-2622 you will have
a super hard time to figure out what is going on without knowing lucene very
very well.
bq. Can't we just fix the test not to use StandardCodec? I mean we aren't
taking any feature away here.
+1 I think we should fix this test ASAP with either using byte sort order or
add some MockCodec (what robert has suggested).
> 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, 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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