MongoDB defines indexes at the collection level and supports indexes on any field or sub-field of the documents in a MongoDB collection.

1. MongoDBMongoDB –– IndexingIndexing
IndexesIndexes are special data structure that store subset of your data in an efficient way forare special data structure that store subset of your data in an efficient way for
easy & faster access to theeasy & faster access to the data.data.
 MongoDB store Index in a b-tree format which allows efficient traversal to the index content
 Proper Index selection is important in MongoDB and makes DB run optimally, improper Indexing
may bring system to a lot of issues in read-write operations and data distribution across sharded
cluster)
 IndexesTypes:
 -id
 Simple
 Compound
 Multi key
 FullText
 Geo-spatial
 Hashed

2. Index continued..Index continued..
 The –id index : It is automatically created, immutable and can’t be removed.
 This is same as primary key in RDBMS.
 Default value is a 12 byte Object ID
 4-Byte timestamp, 3-byte machine id, 2-byte process id,3-byte counter
 Simple Index: A simple Index is an Index on a single key
 Compound Index:A compound Index is created over two or more fields in a document
 Multi-key Index:A multi-key Index is an Index created on a field that contains an array
 Full-text search Index:This is an Index over a text based field, similar to how google indexes web
pages. e.g Find all tweets that mention auto insurance within 30 days. Search Big Data in a blogpost
or all the tweets in last 30 days.
Geo-spatial Index: This Index is to support efficient queries of geospatial coordinate data .It is Geo-spatial Index: This Index is to support efficient queries of geospatial coordinate data .It is
used when you need to query location based spatial data.This Index is really a great feature
because location based data is one of the valuable data being collected today for targeted location
based customer, location based product analysis . e.g Find all customers that live within 50 miles of
NY.
 Hashed Index: Used mainly in Hash based sharding, and allows for more randomized data
distribution across shards
 Create Index syntax:
db.employee.ensureIndex({“email”:1},{“unique”:true})
db.employee.ensureIndex({“age”;1}, {“sparse”: true})
db.employee.find({age: {$gte :25}})

3. Index Continue..Index Continue..
 Index Properties:
 TTL Index-TTL indexes are special indexes that MongoDB can use to automatically remove documents from a
collection after a certain amount of time
 Sparse Index-The sparse property of an index ensures that the index only contain entries for documents that have the
indexed field.The index skips documents that do not have the indexed field.
 Unique Index- To enable the uniqueness of the field.
 Text Search Index:
MongoDB provides text indexes to support text search queries on text content.To perform text search queries, you
must have a text index on your collection.A collection can only have one text search index, but that index can cover
multiple fields.
Creating text search Index over the ”title” and “content” fields :
db.blogpost.ensureIndex( { title: "text", content: "text" } )db.blogpost.ensureIndex( { title: "text", content: "text" } )
Use the $text query operator to perform text searches
on a collection with a text index.
$text perform a logical OR of all such on the intended search string.
For example, we can use the following query to find term MongoDB and BigData in the blogpost.
db.blogpost.find( { $text: { $search:“MongoDB" } } )
db.blogpost.find({$text:{$search:”BigData”}})
DeletingText Index: To delete an existing text index, first find the name of index using the following query,
to get the name of the index >db.blogpost.getIndexes()
Now you can drop the text Index: >db.blogpost.dropIndex(“title_text_content_text")

4. TTextext indexesindexes to support text searchto support text search analyticsanalytics--By exampleBy example

5. That’s it
Thank you !
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