I had taken care to make sure that the change was *compatible*. :-(
You've radically changed the range of values that can be represented. Instead of representing numbers from 0 to 7 billion, you represent numbers between -1.75 and 1.5 (at least according to the listings you sent). That's a big change.
What about the change would break lots of folks?
Anyone who uses a document or field boost larger than 1.5 will no longer see what they expect. Also, existing indexes will no longer produce the same scores, or even the same rank ordering.
> My rational was that
if the mapping for positive bytes to postive floats and visa-versa was unchanged the only way to store negative bytes in the index would be to use a negative float as a field or document boost.
But what about all those folks who are using values that were represented by negative bytes? I know I'm one.
I have a highly dynamic index of news headlines where the incoming headlines are often not in cronological order. To make things worse changes must to be made to headlines post-indexing without affecting their chronological order.
I overide the default Similarity instance to disable field normalization and set the date-sorting 'hint' using Document.setBoost(float)
Also using the score I can implementing a forward / back paging as the score is persistent and the document ids are not. I do this my using a org.apache.lucene.search.Filter and accessing the scores through IndexReader.norms(String field) and only setting the BitSet when score is in required range.
A previous solution used the HitFilter and document id solution that you suggested. Alas it did not work 100% correctly.
How about this: when indexing, set the boost to Similarity.decodeNorm(byte). Then, in your HitCollector implementation, use IndexReader.norms() to directly access the byte stored. The divide the score by Similarity.encodeNorm(byte) to remove this factor from the score. Would that do the trick?
Is there a FAQ entry about common date-sorting methods?
Dunno.
Cheers,
Doug
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