Edit
Can Solr do fuzzy field collapsing? IE collapsing fields that have similar values, rather than identical ones?
I'd assumed that it could, but now I'm not sure, which makes my original question below invalid.
Original Question
For a large given set of values I need to decide which is the most prevalent. The set of all values will change over time, and so I can expect that the output may change over time too.
I gather Solr can do "field collapsing" to group results by a given field, with a tolerance of similarity. Would it be possible, neigh even appropriate, to use Solr solely to collapse fields, to derive the most common value? We use Solr in other parts of the business, and it would be good to leverage existing code rather than home-brewing a custom solution.
No, solr does not support fuzzy collapsing. (at least not based on what is documented on the wiki)
Solr 4.0 supports group.func which allows you to group results based on the result of a FunctionQuery, so it's possible that at some point in time a function could be created to get you approximately what you want, but none of the existing functions will do what you want.
However, Solr does support result clustering, which will maybe work for your use-case. Clustering is done with Carrot2. If you limit the fields used by carrot to a single field, you may get a similar result to "fuzzy clustering", but you have far less control over what carrot does than you do with field collapsing.
For a normal document you might want all your fields analyzed by carrot, e.g.:
carrot.title=my_title&carrot.snippet=my_title,my_description
But if you have, for example, a manufacturer field with slight variations of spelling or punctuation, it might work to only give carrot a single field for both title and snippet:
carrot.title=manufacturer&carrot.snippet=manufacturer
Related
I have a big list of related terms (not synonyms) that I would like my solr engine to take into account when searching. For example:
Database --> PostgreSQL, Oracle, Derby, MySQL, MSSQL, RabbitMQ, MongoDB
For this kind of list, I would like Solr to take into account that if a user is searching for "postgresql configuration" he might also bring results related to "RabbitMQ" or "Oracle", but not as absolute synonyms. Just to boost results that have these keywords/terms.
What is the best approach to implement such connection? Thanks!
You've already discovered that these are synonyms - and that you want to use that metainformation as a boost (which is a good idea).
The key is then to define a field that does what you want - in addition to your regular field. Most of these cases are implemented by having a second field that does the "less accurate" version of the field, and apply a lower boost to matches in that field compared to the accurate version.
You define both fields - one with synonyms (for example content_synonyms) and one without (content), and then add a copyField instruction from the content field (this means that Solr will take anything submitted to the content field and "copy" it as the source text for the content_synonyms field as well.
Using edismax you can then use qf to query both fields and give a higher weight to the exact content field: qf=content^10 content_synonyms will score hits in content 10x higher than hits in content_synonyms, in effect using the synonym field for boosting content.
The exact weights will have to be adjusted to fit your use case, document profile and query profile.
I am experimenting with boosting in Solr and have become confused how my document scores are being affected.
I have a collection of technical documents that contain fields like Title, Symptoms, Resolution, Classification, Tags, etc. All the fields listed are required except Tags which is optional. All fields are copied to _text_ and that field is the default search field.
When I run a default query
http://search:8983/solr/articles-experimental/select?defType=edismax&fl=id,%20tags,%20score&q=virtualization&qf=_text_
The top article (Article 42014) comes back with a score of 4.182179. This document has 6 instances of the word virtualization in multiple fields -- Title, Symptoms, Resolution, and Classification. This particular article does not have any Tags value.
I now want to experiment with boosting so that articles that have Tag values matching the search terms appear closer to the top of the results. To do this, I send the following query
http://search:8983/solr/articles-experimental/select?defType=edismax&fl=id,tags,score&q=virtualization&qf=tags^2%20_text_
which keeps the same Article 42014 at the top of the list but now with a score of 4.269944. However, results 2 through 65 now all have the same score of 4.255975. In the non-boosted query the scores range from 4.056591 down to 2.7029662.
In addition, the collection of document id coming back are not quite the same as before. I certainly expect some differences but not the extent that I am seeing considering that the vast majority of the articles coming back have the search term as a tag.
Ultimately, I am having trouble finding out exactly how boosting changes the score and what is an "appropriate" boost value. Understanding that it is probably subjective, what criteria should I be considering?
well, with all parameters you set for edismax (plus the default values for all the ones you don't set) Solr runs just the algorithm (BM25) nowadays and all scores will be calculated.
The specific boosting values etc you should use for your query are impossible to guess, you must try and retry. It is a known pain, I even built vifun a tool to help me visualize how different parameters affect score with edismax.
I'm using and playing with Lucene to index our data and I've come across some strange behaviors concerning DocValues Fields.
So, Could anyone please just explain the difference between a regular Document field (like StringField, TextField, IntField etc.) and DocValues fields
(like IntDocValuesField, SortedDocValuesField (the types seem to have change in Lucene 5.0) etc.) ?
First, why can't I access DocValues using document.get(fieldname)? if so, how can I access them?
Second, I've seen that in Lucene 5.0 some features are changed, for example sorting can only be done on DocValues... why is that?
Third, DocValues can be updated but regular fields cannot (you have to delete and add the whole document)...
Also, and perhaps most important, when should I use DocValues and when regular fields?
Joseph
Most of these questions are quickly answered by either referring to the Solr Wiki or to a web search, but to get the gist of DocValues: they're useful for all the other stuff associated with a modern Search service except for the actual searching. From the Solr Community Wiki:
DocValues are a way of recording field values internally that is more efficient for some purposes, such as sorting and faceting, than traditional indexing.
...
DocValue fields are now column-oriented fields with a document-to-value mapping built at index time. This approach promises to relieve some of the memory requirements of the fieldCache and make lookups for faceting, sorting, and grouping much faster.
This should also answer why Lucene 5 requires DocValues for sorting - it's a lot more efficient than the previous approach.
The reason for this is that the storage format is turned around from the standard format when gathering data for these operations, where the application previously have to go through each document to find the values, it can now look up the values and find the corresponding documents instead. Which is very useful when you already have a list of documents that you need to perform an intersection on.
If I remember correctly, updating a DocValue-based field involves yanking the document out from the previous token list, and then re-inserting it into the new location, compared to the previous approach where it would change loads of dependencies (and reindexing was the only viable strategy).
Use DocValues for fields that need any of the properties mentioned above, such as sorting / faceting / etc.
We have an application for tagging user selections over a large corpus of MS Word documents. We tag these selections with one or more keyword tags, and usually a title tag. We want to add a feature where the selected text is instantly analyzed, and the tagger is presented with a list of most-likely keyword and title tags (based on the existing tagged text selections)
We are using a SOLR index. I have been told that we can simply issue the selected text as the query itself to return similar selections. However, the selected text could be anywhere between 200 and 6000 words long. A 6000 word query may be a problem in terms of memory usage!
I thought we could do some very aggressive stopword removal to significantly reduce the number of words in the queries, leaving only the very meaningful words. We have been working with this corpus for the last 10 years and we are very familiar with the subject matter and the vocabulary used, so this would be easy for us to do. But the problem is that we also use the same index for allowing the normal users to search the index, and if we remove too many common words, then their normal queries may not work properly (especially phrase queries).
We would also like to boost the results that contain the text of the query within a smaller range, rather than just spread arbitrarily throughout the document.
Another issue is that we allow nested selections. The outer selection may be more general in nature and be around 5000 words long, and the inner selections will be shorter and topically more specific. However, since both selections contain the same text, SOLR ranks them both highly, when the outer selection may not be so relevant
I have spent the last few days going through the SOLR query parser documentation, and it looks like this should be doable, but I'm still not sure exactly what I need to do to make this work. Any suggestions would be much appreciated.
Solr have multi-core facility. So if you can have one core for your internal work and you can reveal the other core for public domain, it may solve your issue.
You can refer this section
http://wiki.apache.org/solr/Solr.xml%20(supported%20through%204.x)
or you can refer Solr cores and solr.xml section in solr reference manual.
We are planning on using Solr to show the users the "n" most frequent terms from a field and we want to apply stemming so that similar terms get grouped.
Now, we need to show the terms to the users but the stemmed terms are not always human readable. Is there any way to get an example of the original terms that got stemmed so that those could be shown to the user?
The only solution we can think of is quering two different fields, one with stemming and one without and then do the matching ourselves. But we think that is going to be expensive (two queries) and may be error prone (the matching may produce errors).
Is there any other way to implement this on Solr? Thanks in advance.
Stemming is applied at both query time and index time so I don't think there is an easy way to accomplish what you're trying to do. However, it may be possible, depending on the number of results in your database, to do this by employing a combination of faceting and highlighting. The highlighted term will be the entire matching term rather than the stemmed term (so, for example, the stemmed term might be "associ" but the highlighted terms will be "associated", "association", "associations", etc.). Perhaps what you could do is the following:
?q=keyword&facet=true&facet.field=myfield&&facet.limit=20hl=true&hl.fl=myfield&hl.fragsize=0&rows=10
Getting 10 rows and examining the highlighted results (by default, these are highlighted using <em> </em> tags but you can change this by using hl.simple.pre and hl.simple.post -- for example, using &hl.simple.pre=[&hl.simple.post=] would wrap the matching terms in square brackets) should at least give a sample of the "original" matching terms. hl.fragsize=0 returns the entire field along with highlighting.
Hope this helps. You can read more about highlighting parameters here:
http://wiki.apache.org/solr/HighlightingParameters