A Lucene Query is generated as so:
Query luceneQuery = builder.all().createQuery();
Then facets are applied.
I'm not sure if when facets are applied the luceneQuery is ANDed and ORed with other Querys resulting in a new Lucene Query. Alternatively, perhaps a bunch of BitSets's are applied to the original Query to refine the results. (I don't know).
If a new query is generated I'd like to retrieve it. If not, I need a rethink. That's the crux of the question.
Why:
I'm applying a faceted search on a field with multiple possible values.
E.g. TMovie.class many-to-many TTag.class (multiple-value-facet)
I'm filtering on TMovie where TTag is some value.
Anyway, the filtering works but there is a known problem whereby the Facet-counts returned are incorrect.
Detailed here: Add faceting over multivalued to application using Hibernate Search and https://forum.hibernate.org/viewtopic.php?f=9&t=1010472
I'm using this solution:
http://sujitpal.blogspot.ie/2007/04/lucene-search-within-search-with.html (see comment on new API under article)
The BitSet solution (in this example at least) generates counts based on the original Lucene Query. This works perfectly. However.....
If alternate (different, not TTags) facets are applied to the original query some complications arise.
The Bitset solution calculates on the original Lucene query. It does not calculate on the lucene query now reduced by the application of alternate Facets (a different FacetSelection) (or even TTag Facets themselves for that matter). I.e. the count calculations are irrespective of any other FacetSelection Facets applied.
So...
A. can I get the new Lucene query after facets are applied? The BitSet solution applied to this would be correct.
B. Any other alternative suggestions?
Thanks so much.. All comments welcome.
John
Regarding your first question, applying a facet is not modifying the original query, it uses a custom Collector called FacetCollector - see https://github.com/hibernate/hibernate-search/blob/master/engine/src/main/java/org/hibernate/search/query/collector/impl/FacetCollector.java. Under the hood the collector uses a Lucene FieldCache for doing the facet count. There is also the root of the limitation for multi-value faceting. FieldCache does not support multiple values per field.
Anyways, no additional queries are applied during faceting and the original query is unmodified. The benefit of course is performance. The solution you are pointing to probably works as well, but relies on running multiple queries. However, it might be a valid work around for your use case.
Related
I've seen two solutions mentioned, and was wondering what most people do.
Use logstash
Code your application to make writes to Elasticsearch alongside SQL. For example,
public saveRecord() {
saveToElasticsearch();
saveToSQL();
}
Another question is how to handle actually searching the entity? Do you ONLY use Elasticsearch?
If not, I would assume you fetch from Elasticsearch based on keywords and use the IDs returned to filter your SQL query. My question then, is how do you handle pagination? For example let's say you only want results 50 to 100. First you query Elasticsearch which returns 50-100. Then the SQL query reduces that to 20 results - the other 30 results are in what would've been the next Elasticsearch query (100 - 150 for example). Do you keep going back and forth?
As for your first question check here
As for the second question, if you plan to use elasticsearch as your search layer then better do it for all the searchable/filterable fields. As you've described, the alternative will get very messy very soon. Use elasticsearch for all your searches/filters and even aggregations if it suits your needs. Use the sql database as your point of truth and just get the full payload from there.
In general, if you will need to paginate then your search should better be in one place otherwise it will get ugly.
I have a system where the search queries multiple fields with different boost values. It is running on Lucene.NET 2.9.4 because it's an Umbraco (6.x) site, and that's what version of Lucene.NET the CMS uses.
My client asked me if I could add stemming, so I wrote a custom analyzer that does Standard / Lowercase / Stop / PorterStemmer. The stemming filter seems to work fine.
But now, when I try to use my new analyzer with the MultiFieldQueryParser, it's not finding anything.
The MultiFieldQueryParser is returning a query containing stemmed words - e.g. if I search for "the figure", what I get as part of the query it returns is:
keywords:figur^4.0 Title:figur^3.0 Collection:figur^2.0
i.e. it's searching the correct fields and applying the correct boosts, but trying to do an exact search on stemmed terms on indexes that contained unstemmed words.
I think what's actually needed is for the MultiFieldQueryParser to return a list of clauses which are of type PrefixQuery. so it'll output a query like
keywords:figur*^4.0 Title:figur*^3.0 Collection:figur*^2.0
If I try to just add a wildcard to the end of the term, and feed that into the parser, the stemmer doesn't kick in. i.e. it builds a query to look for "figure*".
Is there any way to combine MultiFieldQueryParser boosting and prefix queries?
You need to reindex using your custom analyzer. Applying a stemmer only at query time is useless. You might kludge together something using wildcards, but it would remain an ugly, unreliable kludge.
I'm doing some complex querying using Lucene 4.0, and my information-retrieval-theory buddy has told me that anywhere that I can use a filter instead of a query, I should, in order to improve performance. Therefore, I decided to take one particularly hairy component of the query and transform it into a filter. This is relatively straightforward, as there are Filter equivalents of BooleanQuery and NumericRangeQuery, but there doesn't seem to be a TermFilter equivalent of TermQuery. There is a FieldValueFilter, but that seems only to filter on the presence of a given field, not a particular value in that field.
What filter should I use for this?
I believe TermsFilter is what you are looking for.
I need to make a FuzzyQuery using an index that contains around 8 million lines. That kind of query is pretty slow, needing about 20 seconds for every match. The fact is that I can narrow down the results using another field to about 5000 hits before doing the fuzzy search. For this to work, I should be able to make a search by the "narrower" field first, and then use the fuzzy search within those results.
According to the lucene FAQ, the only thing I have to do is a BooleanQuery, where the "narrower" should be required (BooleanClause.Occur.MUST in lucene 3).
Now I have tried two different approaches:
a) Using the Query Parser, with an input like:
narrower:+narrowing_text fuzzy:fuzzy_text~0.9
b) Constructing a BooleanQuery with a TermQuery and a FuzzyQuery
Neither did work, I'm getting about the same times than the ones when the narrower is not used.
Also, just to check that if the narrower was working the times should be much better, I reindexed only the 5000 items that match the narrower, and the search went fast as hell.
In case anyone wonders, I'm using pylucene 3.0.2.
Doppleganger, you can probably use a Filter, specifically a QueryWrapperFilter.
Follow the example from Lucene in Action. You may have to make some modifications for use in python, but otherwise it should be simple:
Create the query that narrows this down to 5000 hits.
Use it to build a QueryWrapperFilter.
Use the filter in a search involving the fuzzy query.
In my tests I suddenly bumped into a Too Many Clauses exception when trying to get the hits from a boolean query that consisted of a termquery and a wildcard query.
I searched around the net and on the found resources they suggest to increase the BooleanQuery.SetMaxClauseCount().
This sounds fishy to me.. To what should I up it? How can I rely that this new magic number will be sufficient for my query? How far can I increment this number before all hell breaks loose?
In general I feel this is not a solution. There must be a deeper problem..
The query was +{+companyName:mercedes +paintCode:a*} and the index has ~2.5M documents.
the paintCode:a* part of the query is a prefix query for any paintCode beginning with an "a". Is that what you're aiming for?
Lucene expands prefix queries into a boolean query containing all the possible terms that match the prefix. In your case, apparently there are more than 1024 possible paintCodes that begin with an "a".
If it sounds to you like prefix queries are useless, you're not far from the truth.
I would suggest you change your indexing scheme to avoid using a Prefix Query. I'm not sure what you're trying to accomplish with your example, but if you want to search for paint codes by first letter, make a paintCodeFirstLetter field and search by that field.
ADDED
If you're desperate, and are willing to accept partial results, you can build your own Lucene version from source. You need to make changes to the files PrefixQuery.java and MultiTermQuery.java, both under org/apache/lucene/search. In the rewrite method of both classes, change the line
query.add(tq, BooleanClause.Occur.SHOULD); // add to query
to
try {
query.add(tq, BooleanClause.Occur.SHOULD); // add to query
} catch (TooManyClauses e) {
break;
}
I did this for my own project and it works.
If you really don't like the idea of changing Lucene, you could write your own PrefixQuery variant and your own QueryParser, but I don't think it's much better.
It seems like you are using this on a field that is sort of a Keyword type (meaning there will not be multiple tokens in your data source field).
There is a suggestion here that seems pretty elegant to me: http://grokbase.com/t/lucene.apache.org/java-user/2007/11/substring-indexing-to-avoid-toomanyclauses-exception/12f7s7kzp2emktbn66tdmfpcxfya
The basic idea is to break down your term into multiple fields with increasing length until you are pretty sure you will not hit the clause limit.
Example:
Imagine a paintCode like this:
"a4c2d3"
When indexing this value, you create the following field values in your document:
[paintCode]: "a4c2d3"
[paintCode1n]: "a"
[paintCode2n]: "a4"
[paintCode3n]: "a4c"
By the time you query, the number of characters in your term decide which field to search on. This means that you will perform a prefix query only for terms with more of 3 characters, which greatly decreases the internal result count, preventing the infamous TooManyBooleanClausesException. Apparently this also speeds up the searching process.
You can easily automate a process that breaks down the terms automatically and fills the documents with values according to a name scheme during indexing.
Some issues may arise if you have multiple tokens for each field. You can find more details in the article