Oh, yes, they are unique within a document. I was also thinking about
something like this. But I would be replacing a large number of fields within
a document by a large number of documents. Let me see if I can work that out.
On May 5, 2011, at 3:01 PM, Mike Sokolov wrote:
> Are the tokens unique within a document? If so, why not store a document for
> every doc/token pair with fields:
>
> id (doc#/token#)
> doc-id (doc#)
> token
> weight1
> weight2
> frequency
>
> Then search for token, sort by weight1, weight2 or frequency.
>
> If the token matches are unique within a document you will only get each
> document listed once. If they aren't unique, it's not clear what you want to
> sort by anyway....
>
> -Mike
>
> On 05/05/2011 04:12 PM, Chris Schilling wrote:
>> Hi,
>>
>> I am trying to figure out how to solve this problem:
>>
>> I have about 500,000 files that I would like to index, but the files are
>> structured. So, each file has the following layout:
>>
>> doc1
>> token1, weight11, frequency1, weight21
>> token2, weight12, frequency2, weight22
>> .
>> .
>> .
>>
>> etc for 500,000 docs.
>>
>> Basically, I would like to index the tokens for each doc. When I search for
>> a token, I would like to be able to return the top docs sorted by weight1,
>> frequency, or weight2.
>>
>> So, in my naive setup, I loop through the files in the directory, then I
>> loop through the lines of the file. In side of the loop through each file,
>> I call this function:
>>
>> public Document processKeywords(Document doc, String keyword, Float
>> weight1, Float weight2, Integer frequency) throws Exception {
>> Document doc = new Document();
>> doc.add(new Field("keywords", keyword, Field.Store.NO,
>> Field.Index.ANALYZED));
>> doc.add(new NumericField(keyword+"weight1",
>> Field.Store.YES, true).setFloatValue(weight1));
>> doc.add(new NumericField(keyword+"weight2",
>> Field.Store.YES, true).setFloatValue(weight2));
>> doc.add(new NumericField(keyword+"frequency",
>> Field.Store.YES, true).setFloatValue(frequency));
>> return doc;
>> }
>>
>> So, for each token, I create 3 new fields each time. Notice how I am trying
>> to index the keyword in the "keywords" field. For the weights and
>> frequency, I create a new field with a name based on the keyword. On
>> average, I have 100 tokens per document, so each document will have about
>> 300 distinct fields.
>>
>> When running my program, the lucene portion eats up tons of memory and when
>> it gets to the max alloted by the JVM (I have tried allowing up to 4 Gb),
>> the program slows to a crawl. I assume it is spending all of its time in
>> garbage collection due to all these fields.
>>
>> My code above seems like a very hacky way of accomplishing what I want
>> (sorting documents based on keyword search using different numeric fields
>> associated with that keyword).
>>
>> FYI, here is the main search code, where q is the token I am searching for
>> and sortby is the field I want to use to sort. I setup a QP to search for
>> the keyword in the "keywords" field. Then, I can extract the stats that I
>> indexed for the given query keyword.
>>
>> private static final QueryParser parser = new
>> QueryParser(Version.LUCENE_30, "keywords", new
>> StandardAnalyzer(Version.LUCENE_30));
>>
>> public void search(String q, String sortby) throws IOException,
>> ParseException {
>> Query query = parser.parse(q);
>> long start = System.currentTimeMillis();
>> TopDocs hits = this.is.search(query, null, 10, new Sort(new
>> SortField(q+"sortby", SortField.FLOAT, true)));
>> long end = System.currentTimeMillis();
>> System.out.println("Found " + hits.totalHits +
>> " document(s) (in " + (end - start) +
>> " milliseconds) that matched query '" +
>> q + "':");
>> for(ScoreDoc scoreDoc : hits.scoreDocs) {
>> Document doc = this.is.doc(scoreDoc.doc);
>> String hash = doc.get("hash");
>> System.out.println(hash + " " + doc.get(q+"sortby") + "
>> " + hash);
>> }
>> }
>>
>> I am pretty new to Lucene, so I hope this makes sense. I tried to pare my
>> problem down as much as possible. Like I said, the main problem I am
>> running into is that after processing about 30000 documents, the indexing
>> slows to a crawl and seems to spend all of its time in the garbage
>> collector. I am looking for a more efficient/effective way of solving this
>> problem. Code tidbits would help, but are not necessary :)
>>
>> Thanks for your help,
>> Chris S.
>>
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