I'm using RavenDB and I'm having trouble extracting a particular value using the Lucene Query.
Here is the JSON in my document:
{
"customer" : "my customer"
"locations": [
{
"name": "vel arcu. Curabitur",
"settings": {
"enabled": true
}
}
]
}
Here is my query:
var list = session.Advanced.LuceneQuery<ExpandoObject>()
.SelectFields<ExpandoObject>("customer", "locations;settings.enabled", "locations;name")
.ToList();
The list is populated and contains a bunch of ExpandoObjects with customer properties but I can't for the life of me get the location -> name or location -> settings -> enabled to come back.
Is the ";" or "." incorrect usage??
It seems that you have misunderstood the concept of indexes and queries in RavenDB. When you load a document in RavenDB you always load the whole document including all of its contents it contains. So in your case, if you load a customer, you already have the collection and all its children loaded. That means, you can use standard linq-to-objects to extract all these values, no need for anything special like indexes or lucene here.
If you want to do this extraction on the database side, so that you can query on those properties, then you need an index. Indexes are written using linq, but it's important to understand that they run on the server and just extract some data to populate the lucene index from. But here again, in most cases you don't even have to write the indexes yourself because RavenDB can create them automatically for you.
I no case, you need to write lucene queries like the one in your question because in RavenDB lucene queries will always be executed against a pre-built index, and these are generally flat. But again, chances are you don't need to do anything with lucene to get what you want.
I hope that makes sense for you. If not, please update your question and tell us more about what you actually want to do.
Technically, you can use the comma operator "," to nest into collections.
That should work, but it isn't recommended. You can just get your whole object and use it, it is easier and faster.
Related
I'm using sitecore 8 and I'm looking for a way to run a full text search for all my sitecore content. I have a solution in place, but I feel there's got to be a better way to do this.
My approach:
i have a computed field that merges all text fields into a single computed field. Before I execute a search I tokenize my search text and build a ORed predicate to match on the field.
I do not like this approach because it gets really complicated if I need to boost items that match the title vs the body i.e. i loose the field level boosting.
FYI: my code is very similar to this so post.
Thanks
Sitecore already maintains a full text field, _content, that contains all the text fields. You can run your search against that. You can even create computed fields that add to _content (such as the datasource content example here).
So assuming you are building a LINQ query for your full text search, and have already filtered on templates, latest version, location, etc., adding your search terms to the query would look something like this:
var terms = SearchTerm.Split();
var currentExpression = PredicateBuilder.True<SiteSearchResultItem>();
foreach (var term in terms)
{
//Content is mapped to _content
currentExpression = PredicateBuilder.And(currentExpression, x => x.Content.Contains(term));
}
query = query.Where(currentExpression);
Typically you would want to AND search terms rather than ORing them.
You are right that field level boosting is lost in this. In the end, Lucene is not a great solution for creating a quality full-text site search. If this is an important requirement, you may want to look at Coveo or even something like a Google Site Search.
I am using Neo4j embedded as database. I have to store thousands of articles daily and and I need to provide a search functionality where I should return the articles whose content match to the keywords entered by the users. I indexed the content of each and every article and queried on the index like below
val articles = article_content_index.query("article_content", search string)
This works fine. But, its taking lot of time when the search string contains common words like "the", "a" and etc which will be present in each and every article.
How do I solve this problem?
Probably a lucene issue.
You can configure your own analyzer which could leave off those frequent (stop-)words:
http://docs.neo4j.org/chunked/stable/indexing-create-advanced.html
http://lucene.apache.org/core/3_6_2/api/core/org/apache/lucene/analysis/Analyzer.html
http://lucene.apache.org/core/3_6_2/api/core/org/apache/lucene/analysis/standard/StandardAnalyzer.html
You might configure article_content_index as fulltext index, see http://docs.neo4j.org/chunked/stable/indexing-create-advanced.html. To switch to using fulltext index, you first have to remove the index and the first usage of IndexManager.forNodes(String, Map) needs to configure the index on creation properly.
I have an mongo query that looks like the following:
{'schedule': {'$elemMatch': {'time': {'$gt': start, '$lte': end}}}}
Which searches a collection for an item whose "schedule" field (which is a list of objects) that contains an element whose "time" field is between start and end.
I am unclear as to how mongo's indexes handle this situation, and wonder what would be the best practice, if this query were going to be happening regularly?
This is the wrong syntax, but should work. $elemMatch is used when there is more than one field in an object (esp. in an array) that you want to match. With a single field there is no need for $elemMatch.
You should be doing this:
{"schedule.time" : { $gt: <start>, $lte: <end> }}
It is much more clear that you are using a single field, even though it will be in multiple array elements, in this case. An index on "schedule.time" can be used for this query to make it more efficient.
I'm running a simple test--trying to index something and then search for it. I index a simple document, but then when a search for a string in it, I get back what looks to be an empty document (it has no fields). Lucene seems to be doing something, because if I search for a word that's not in the document, it returns 0 results.
Any reason why Lucene would reliably return a document when it finds one that matches the given query, and yet that document has nothing in it?
More details:
I'm actually running Lucandra (Lucene + Cassandra). That certainly may be a relevant detail, but not sure.
The fields are set to Field.Store/YES and Field.Index/ANALYZED
Interestingly, I'm able to get this to work just fine on my local machine, but when we put it on our main server (which is a multi-node cassandra setup), I get the behavior described above. So this seems like probably the relevant detail, but unfortunately, I see no error message to clue me in to what specifically is causing it.
Unsure if this will work with Lucandra, but you have tried opening the index using Luke? Viewing the index contents with Luke might help
It's hard to tell what the problem is since you only provide a very abstract description. However, it sounds a bit like you are not storing the field value in the index. There are different modes for indexing a field. One option determines whether the original value is stored in the index to retrieve it later:
http://lucene.apache.org/java/3_0_0/api/core/org/apache/lucene/document/Field.Store.html
See also the description of the enclosing class Field
Read: http://anismiles.wordpress.com/2010/05/27/lucandra-an-inside-story/
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