Full Text Search
Couchbase Lite database data querying concepts — full text search
Overview
To run a full-text search (FTS) query, you must create a full-text index on the expression being matched. Unlike regular queries, the index is not optional.
You can choose to use SQL++ or QueryBuilder syntaxes to create and use FTS indexes.
The following examples use the data model introduced in Indexing. They create and use an FTS index built
from the hotel’s overview text.
SQL++
Create Index
SQL++ provides a configuration object to define Full Text Search indexes — FullTextIndexConfiguration.
Example 1. Using SQL++'s FullTextIndexConfiguration
Use Index
Full-text search is enabled using the SQL++ match() function.
With the index created, you can construct and run a full-text search (FTS) query using the indexed properties.
The index will omit a set of common words, to avoid words like "I", "the", and "an" from overly influencing your queries. See full list of these stop words.
The following example finds all hotels mentioning Michigan in their overview text.
Example 2. Using SQL++ Full Text Search
QueryBuilder
Create Index
The following example creates an FTS index on the overview property.
Example 3. Using the IndexBuilder method
Use Index
With the index created, you can construct and run a full-text search (FTS) query using the indexed properties.
The following example finds all hotels mentioning Michigan in their overview text.
Example 4. Using QueryBuilder Full Text Search
val ftsQuery = QueryBuilder
.select(
SelectResult.expression(Meta.id),
SelectResult.property("overview")
)
.from(DataSource.collection(collection))
.where(FullTextFunction.match("overviewFTSIndex", "michigan"))
ftsQuery.execute().use { rs ->
rs.allResults().forEach {
println("${it.getString("Meta.id")}: ${it.getString("overview")}")
}
}
Operation
In the examples above, the pattern to match is a word, the full-text search query matches all documents that contain the
word "michigan" in the value of the doc.overview property.
Search is supported for all languages that use whitespace to separate words.
Stemming, which is the process of fuzzy matching parts of speech, like "fast" and "faster", is supported in the following languages: Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish.
Pattern Matching Formats
As well as providing specific words or strings to match against, you can provide the pattern to match in these formats.
Prefix Queries
The query expression used to search for a term prefix is the prefix itself with a "*" character appended to it.
Example 5. Prefix query
Query for all documents containing a term with the prefix "lin".
This will match:
- All documents that contain "linux"
- And … those that contain terms "linear", "linker", "linguistic", and so on.
Overriding the Property Name
Normally, a token or token prefix query is matched against the document property specified as the left-hand side of the
match operator. This may be overridden by specifying a property name followed by a ":" character before a basic term
query. There may be space between the ":" and the term to query for, but not between the property name and the ":"
character.
Example 6. Override indexed property name
Query the database for documents for which the term "linux" appears in the document title, and the term "problems" appears in either the title or body of the document.
Phrase Queries
A phrase query is one that retrieves all documents containing a nominated set of terms or term prefixes in a specified order with no intervening tokens.
Phrase queries are specified by enclosing a space separated sequence of terms or term prefixes in double quotes (").
Example 7. Phrase query
Query for all documents that contain the phrase "linux applications".
NEAR Queries
A NEAR query is a query that returns documents that contain two or more nominated terms or phrases within a specified proximity of each other (by default with 10 or less intervening terms). A NEAR query is specified by putting the keyword "NEAR" between two phrases, tokens or token prefix queries. To specify a proximity other than the default, an operator of the form "NEAR/<number>" may be used, where <number> is the maximum number of intervening terms allowed.
Example 8. Near query
Search for a document that contains the phrase "replication" and the term "database" with not more than 2 terms separating the two.
AND, OR & NOT Query Operators
The enhanced query syntax supports the AND, OR and NOT binary set operators. Each of the two operands to an operator may be a basic FTS query, or the result of another AND, OR or NOT set operation. Operators must be entered using capital letters. Otherwise, they are interpreted as basic term queries instead of set operators.
Example 9. Using And, Or and Not
Return the set of documents that contain the term "couchbase", and the term "database".
Operator Precedence
When using the enhanced query syntax, parenthesis may be used to specify the precedence of the various operators.
Example 10. Operator precedence
Query for the set of documents that contains the term "linux", and at least one of the phrases "couchbase database" and "sqlite library".
Ordering Results
It’s very common to sort full-text results in descending order of relevance. This can be a very difficult heuristic to define, but Couchbase Lite comes with a ranking function you can use.
In the OrderBy array, use a string of the form Rank(X), where X is the property or expression being searched, to
represent the ranking of the result.