MongoDB week4 Notes

In order to use index in MongoDB, we must give a leftmost set of the indexes. The order of the indexes matter.

db.students.ensureIndex( { student_id : 1 } )     -> create the index on student_id in increasing order

db.students.ensureIndex( { student_id : 1, class : -1 } )  -> create a compound index

db.system.indexes.find( )    -> find all the indexes in the current database, index default on _id field

db.students.getIndexes( )

db.students.dropIndex( { student_id : 1 } )  -> drop the created index

MongoDB allows to create a key on a field which is an array, the index is called multi-key index.

MongoDB allows to create a compound index with an array and a scale, but does not allow to array.

db.stuff.ensureIndex( { thing : 1 }, { unique : true } )  -> create unique index, each key can only appear once

db.stuff.ensureIndex( { thing : 1}, { unique : true, dropDups : true } )  -> drop the duplicates expect for one

sparse index, only create index on the document that has the specific field

In order to find which index to use for a query, MongoDB will experiment different indexes on real data in parallel to test which is optimal and memorize it

db.students.stats( )

Index Cardinality     

• Regular     1 : 1
• Sparse      <= documents
• Multikey    > document ( index on each array elements )

Use hint( ) to manually tell MongoDB what index to use

ensureIndex( {“location” : “2d” } )  -> 2D geospatial index

find( { location : { $near : [x, y] } } )

db.places.find( { location : { $near : {

                                                          $geometry : {

                                                                 type : ‘Point’,

                                                                 coordinates : [x, y] },      

                                                          $maxDistance : 2000

                                              }

                           }

} )

db.sentences.ensureIndex( { ‘words’ : ‘text’ } ) -> support full text search

db.sentences.find( { $text : { $search : ‘dog moss’ } } )

use mongotop to find where does most time have been spent on

mongostat

idx miss -> how many times indexes are not in the memory when they are needed, an import factor

Shard: split up the large data into several mongod client as shards, use a mongos as a sever and let the application talk to mongos. It will use shard_key to issue which shards receive the query. The insert operation must contain the entire shard_key. For update and remove query, if shard_key is not given, mongos will broadcast the query to hall shards.