crossposting to the user list as I think this issue belongs there. See
my comments inline
On Fri, Feb 5, 2010 at 10:27 AM, lionel duboeuf
<lionel.dubo...@boozter.com> wrote:
> Hi,
>
> Sorry for asking again, **I still have not found a scalable solution to get
> the document frequency of a term t according a set of documents. Lucene only
> store the document frequency for the global corpus, but i would like to be
> able to get the document frequency of a term according only to a subset of
> documents (i.e. a user's collection of documents).
>
> I guess that querying the index to get the number of hits for each term and
> for each field, filtered by a user will be to slow.
> Any idea ?
I have recently developed out-of-the-box faceted navigation exposed
over jcr (hippo repository on top of jackrabbit) where I think you are
looking for efficient faceted navigation as well, right? First of all,
I am also interested if others have something to add to my findings.
First of all, you can approach your issue in two different angles,
where I think depending on the number of results vs number of terms
(unique facets), you can best switch (runtime) between the two
approaches:
Approach (1): The lucene TermEnum is leading: if the lucene field has
*many* (say more then 100.000) unique values, it becomes slow (and
approach two might be better)
You have a BitSet matchingDocs, and you want the count for all the
terms for field 'brand' where of course one of the documents in
matchingDocs should have the term:
Suppose your field is thus 'brand', then you can do:
TermEnum termEnum = indexReader.terms(new Term("brand", ""));
// iterate through all the values of this facet and see
look at number of hits per term
try {
TermDocs termDocs = indexReader.termDocs();
// open termDocs only once, and use seek: this is more efficient
try {
do {
Term term = termEnum.term();
int count = 0;
if (term != null && term.field() ==
internalFacetName) { // interned comparison
termDocs.seek(term);
while (termDocs.next()) {
if (matchingDocs.get(termDocs.doc())) {
count++;
}
}
if (count > 0) {
if (!"".equals(term.text())) {
facetValueCountMap.put(term.text(), new Count(count));
}
}
} else {
break;
}
} while (termEnum.next());
} finally {
termDocs.close();
}
} finally {
termEnum.close();
}
Approach (2): matching docs are leading. All lucene fields that should
be useable for your facet counts, must be indexed with TermVectors.
This approach becomes slow when the matching docs grow > 100.000 hits.
Then, you rather use approach (1)
Create your own HitCollector, and have its hit method something like:
public final void collect(final int docid, final float score) {
try {
if (facetMap != null) {
final TermFreqVector tfv =
reader.getTermFreqVector(docid, internalName);
if (tfv != null) {
for (int i = 0; i < tfv.getTermFrequencies().length; i++) {
addToFacetMap(tfv.getTerms()[i]);
}
Note that the HitCollector's are not advised for large hit sets, also see [1]
This is how i currently have a really performant faceted navigation
exposed as a jcr tree. If somebody has tried more ways, or something
to add, I would be interested
Regards Ard
[1]
http://lucene.apache.org/java/2_3_2/api/org/apache/lucene/search/HitCollector.html
>
>
> regards,
>
> Lionel
>
> *
> *
>
>
>
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