Released
from version 2.3.2 MongoDB supports text indexes to support text search of
string content in documents. Text indexes
can include any field whose value is a string or an array of string elements.
In this post I will talk about creating and using text indexes in MongoDB using
pymongo to make a full text search.
- Features of MongoDB Full text Search
- Full text search as an index type when creating new indexes.
- Advanced queries like negation and phrase matching supported.
- Multiple fields indexing with weighting to give different fields higher priority.
- Avoid stop words.
- Stemming, to deal with plurals.
- Multiple language support with initially Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish.
Creating
MongoDB text Indexes
First
step to create MongoDB text index is to enable text indexing. You can enable
the text indexing using following command in mongo console.
Enabling
Index Support
use <DB_NAME>
db.adminCommand ({
setParameter : "*", textSearchEnabled : true });
After this you we can insert data into the database and start using the text index for search. Here I use a question database to create index on questions text and then make a question search. The structure of the “question” collection is as follows:-
{
"_id" :
ObjectId("53a71fb3421aa9422f49ac8c"),
"answer" : {
"a" : {
"text"
: "$ sin1^{0} > sin1 $"
"image"
: "",
},
"b" : {
"image"
: "",
"text"
: "$ sin1^{0} < sin1 $"
},
"c" : {
"image"
: "",
"text"
: "$ sin1^{0} = sin1 $"
},
"correct" :
"b",
"d" : {
"image"
: "",
"text"
: "$ sin1^{0} = \\frac {\\pi}{180} sin1 $"
}
},
"exam_code" :
201,
"exam_type" :
"ENGINEERING",
"marks" : 1,
"question" : {
"html" :
"Which one of the following is correct?",
"image" :
"",
"text" :
"Which one of the following is correct?"
},
"question_number" : 1,
"subject" :
"mathematics"
}
Creating Indexing
We will create index on text field
of question in the question collection and then use the index from mongo
console using following command:-
db.questions.ensureIndex( { "question.text": "text" } );
The
index created above is the single index we can also create compound index in
the collection.
Using
the index
Now our
index is ready to be used. For pymongo
with MongoDB version 2.4
client = MongoClient(<host>, <port>)
db = client[‘db name’]
result = db.command ("text",
question , search = ‘query text’, project = fields, limit = limit)
Command execution
in Mongo Console:
result = db.question.runCommand("text", search = ‘query text’, project = fields,
limit = limit)
For
pymongo with MongoDB version 2.6
client = MongoClient(<host>, <port>)
db = client[db_name]
result = db.question.find ({"$text”: {“$search”: ‘query text’ }},
project={‘_id’:0}).limit(10)
Command execution
in Mongo Console:
result = db.question.find ({"$text”: {“$search”: ‘query text’ }},
{‘_id’:0}).limit(10)
Using
limit we can find the top documents that matches the search queries.
