Thanks for the answer. It is not really necessary for me to read the documents. That's what you get if you find code searching the net and using it without really thinking or understanding it. I will just step through the terms and set the bits as you said. I will add some maximum number of terms since we deal with a few million documents and that can be kind of expansive in time and memory consumption, if too many terms match, as i experienced. Unfortunately, as you said, it is hard to predict, what users will actually search for, so it is kind of hard to really cache any wildcard filters. But filters really seem to be quite fast.
Thanks again, Thomas -----Ursprüngliche Nachricht----- Von: Erick Erickson [mailto:[EMAIL PROTECTED] Gesendet: Tuesday, September 19, 2006 3:59 PM An: java-user@lucene.apache.org Betreff: Re: Question about termDocs.read(docs, freqs) I'll side-step the explanations part of your mail since I don't know how to answer.. But a few observations, see below. On 9/19/06, Kroehling, Thomas <[EMAIL PROTECTED]> wrote: > > Hi, > I am trying to write a WildcardFilter in order to prevent > TooManyBooleanClauses and high memory usage. I wrap a Filter in a > ConstantScoreQuery. I enumerate over the WildcardTerms for a query. > This way I can set a maximum number of terms which i will evaluate. If > too many terms match, I throw an exception. I also have a maximum > number of documents which are allowed to match using BitsSets > cardinality. I am not sure, if that is necessary, but I thought, if > only a few terms, but a few million documents might match, that could > also considerably slow down a search. This seems like a prime candidate for generating "unexpected" results, so I'd start by taking it out and seeing if your search and wildcard enumerations agree better. I thought, I could get the TermDocs for each WildcardTerm and use: > > int count = termDocs.read(docs, freqs); > > In order to have an optimized way to read not more than a maximum > number of documents which match this term. You shouldn't be reading documents at all, just enumerating the terms that are indexed and setting bits. It's expensive to read the docs, and the javadocs warn against this (of course I could just not understand what you're doing...). I would then step through docs and > set the bits for these documents. Sometimes this works, but sometimes > this returns a different number search results. > When I search for "content:test" in my index, I find 66 documents, but > when I search for "t*st" with my WildcardFilter, I only find 23. There > is only one term "test" matching this query and searching for this > term in Luke also returns 66 documents. I found out that the > SegmentTermDocs sets a variable df to "23", which leads to stop searching any > further. > Unfortunately I do not quite understand, where this variable really > comes from and what it is for. > > I probably could just step through the TermDocs for each WildcardTerm. Start here. Unless and until you have some idea that the simple way of doing things isn't too slow for your problem, don't try anything fancy. Filters were *built* for this type of thing, and I've been pleasantly surprised at how fast they can be built. Admittedly, mine are on about 1M documents..... Here's some sample code that works for me, field and value are fields set in the constructor...... public BitSet bits(IndexReader reader) throws IOException { bits = new BitSet(reader.maxDoc()); TermDocs termDocs = reader.termDocs(); WildcardTermEnum wildEnum = new WildcardTermEnum(reader, new Term(field, value)); for (Term term = null; (term = wildEnum.term()) != null; wildEnum.next()) { termDocs.seek(new Term( field, term.text())); while (termDocs.next()) { bits.set(termDocs.doc()); } } return bits; } Note a few things... CachingWrapperFilter will cache these filters for future use. If you have an idea of the kinds of wildcards you will need ahead of time, you could always generate filters and store them away if it turns out that performance is a problem (although I've rarely seen this be practical since the silly users type things unpredictably<G>). I'd really recommend that you try the simple thing first and try a couple of timings on really ugly filter creation, something like a* before trying anything more complex.... Best Erick Is that should a correct (and not dramatically slow) way to find all > documents? But I would like to understand, the difference in search > results and what the method TermDocs.read(docs, freqs) method does and > if my kind of filter does really make sense. I periodically rebuild my > index and I wonder why my WildcardFilter sometimes returns the correct > search results and sometimes not. What is the difference between > steping through the term docs with termDocs.next() and using the read-method. > Can anybodey explain that? > > Thanks in advance, > Thomas > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] > > --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
