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From "Michael McCandless (JIRA)" <j...@apache.org>
Subject [jira] [Created] (LUCENE-3030) Block tree terms dict & index
Date Thu, 14 Apr 2011 22:49:05 GMT
Block tree terms dict & index
-----------------------------

                 Key: LUCENE-3030
                 URL: https://issues.apache.org/jira/browse/LUCENE-3030
             Project: Lucene - Java
          Issue Type: Improvement
            Reporter: Michael McCandless
            Assignee: Michael McCandless
             Fix For: 4.0
         Attachments: LUCENE-3030.patch

Our default terms index today breaks terms into blocks of fixed size
(ie, every 32 terms is a new block), and then we build an index on top
of that (holding the start term for each block).

But, it should be better to instead break terms according to how they
share prefixes.  This results in variable sized blocks, but means
within each block we maximize the shared prefix and minimize the
resulting terms index.  It should also be a speedup for terms dict
intensive queries because the terms index becomes a "true" prefix
trie, and can be used to fast-fail on term lookup (ie returning
NOT_FOUND without having to seek/scan a terms block).

Having a true prefix trie should also enable much faster intersection
with automaton (but this will be a new issue).

I've made an initial impl for this (called
BlockTreeTermsWriter/Reader).  It's still a work in progress... lots
of nocommits, and hairy code, but tests pass (at least once!).

I made two new codecs, temporarily called StandardTree, PulsingTree,
that are just like their counterparts but use this new terms dict.

I added a new "exactOnly" boolean to TermsEnum.seek.  If that's true
and the term is NOT_FOUND, we will (quickly) return NOT_FOUND and the
enum is unpositioned (ie you should not call next(), docs(), etc.).

In this approach the index and dict are tightly connected, so it does
not support a pluggable index impl like BlockTermsWriter/Reader.
Blocks are stored on certain nodes of the prefix trie, and can contain
both terms and pointers to sub-blocks (ie, if the block is not a leaf
block).  So there are two trees, tied to one another -- the index
trie, and the blocks.  Only certain nodes in the trie map to a block
in the block tree.

I think this algorithm is similar to burst tries
(http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.3499),
except it allows terms to be stored on inner blocks (not just leaf
blocks).  This is important for Lucene because an [accidental]
"adversary" could produce a terms dict with way too many blocks (way
too much RAM used by the terms index).  Still, with my current patch,
an adversary can produce too-big blocks... which we may need to fix,
by letting the terms index not be a true prefix trie on it's leaf
edges.

Exactly how the blocks are picked can be factored out as its own
policy (but I haven't done that yet).  Then, burst trie is one policy,
my current approach is another, etc.  The policy can be tuned to
the terms' expected distribution, eg if it's a primary key field and
you only use base 10 for each character then you want block sizes of
size 10.  This can make a sizable difference on lookup cost.

I modified the FST Builder to allow for a "plugin" that freezes the
"tail" (changed suffix) of each added term, because I use this to find
the blocks.


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