MongoDB is a document database that stores data in BSON format, which is similar to JSON. It is a non-relational, schema-free database that scales easily and supports massive amounts of data and high availability. MongoDB can replace traditional relational databases for certain applications, as it offers dynamic schemas, horizontal scaling, and high performance. Key features include indexing, replication, MapReduce and rich querying of embedded documents.

1. MongoDB
By
Rony Abraham Gregory
Model Engineering College

2. What is MongoDB?
• Humongous (huge + monstrous)
• Document Database
• Schema free
• C++
• Open Source

3. What is MongoDB?
• GNU AGPL v3.0 Licence
• OSX, Linux, Windows, Solaris | 32
bit, 64 bit
• Development and Support by 10gen
and was first released in February 2009
• NoSQL!

4. Database Paradigms
• Relational (RDBMS)
• NoSQL
oKey-value stores
oDocument databases
oWide column stores (BigTable and
clones)
oGraph databases

5. Relational Databases
• ACID (Atomicity Consistency Isolation
and Durability)
• SQL
• MySQL, PostgreSQL, Oracle, etc.

6. Key-value stores
• “One key, one value, no duplicates and
very fast”
• It’s a Hash!
• The value is a binary object aka “blob” –
the DB doesn’t understand it and
doesn’t want to understand it.
• Amazon Dynamo, MemcacheDB, etc.

7. Key-value stores
Fig: A key-value store

8. Document databases
• Key-value stores, but the value is
(usually) structured and “understood”
by the DB.
• Querying data is possible (by means
other than just a key).
• Amazon
SimpleDB, CouchDB, MongoDB, Riak, e
tc.

9. Document databases
Fig: A document database

10. Why NoSQL?
• Schema-free
• Massive data stores
• Scalability
• Some services simpler to implement
than using RDBMS
• Great fit for many Web 2.0 applications

11. Why NOT NoSQL?
• RDBMSes and its tools are mature
• NoSQL implementations are often in
their “alpha” state
• Data consistency, transactions
• “Don’t scale until you need it”

12. RDBMS vs NoSQL
• Strong consistency vs Eventual
consistency
• Big dataset vs HUGE dataset
• Scaling is possible vs Scaling is easy
• Good availability vs Very high
availability

13. RDBMS vs NoSQL
• Strong consistency vs Eventual
consistency
• Big dataset vs HUGE dataset
• Scaling is possible vs Scaling is easy
• Good availability vs Very high
availability

14. RDBMS vs NoSQL
• Strong consistency vs Eventual
consistency
• Big dataset vs HUGE dataset
• Scaling is possible vs Scaling is easy
• Good availability vs Very high
availability

15. Features
• Standard database stuff
• Indexing
• Replication/failover support

16. Features
• Documents are stored in BSON (JSON)
• BSON is a Binary serialization of JSON-
like objects
• This is extremely powerful, because it
means mongoDB understands JSON
natively
• Any valid JSON can be easily imported
and queried

17. Features
• Schema-less; very flexible
• Auto-sharding (alpha)
• Makes for easy horizontal; scaling
• Map/Reduce

18. Features
• Very, very fast
• Super easy to install
• Strong with major languages

19. Features: Querying
• Rich, JavaScript based query syntax
• Allows us to do deep, nested queries
db.order.find( {shipping: { carrier:”usps” }} );
shipping is an embedded document
(object)

20. Features: Official Drivers
• .NET, Java, JavaScript, Ruby, Node.js, PH
P, Haskell, C/C++, Perl

21. Concepts: Document-Oriented
• Think of “documents” as
objects/database records
• Documents are basically just JSON in
binary
• Ability to store information all together

22. Concepts: Document-Oriented

23. Concepts: Document-Oriented
Concept Mapping

24. Concepts: Cursors
• Queries return “cursors” instead of
collections
• A cursor allows you to iterate through
the result set
• A big reason for this is performance
• Much more efficient than loading all
objects into memory

25. Concepts: Cursors
• The find() function returns a cursor
object

26. Basic Operations
• Insert
• Find
• Update
• Remove
• ensureIndex

27. Basic Operations
• Insert
db.test.insert({hello:’world’});
• Find
oEquivalent of Select * FROM test;

28. Basic Operations:Update
db.test.update(<condition>,<operation>)

29. Basic Operations: Remove
db.test.remove(<condition>)

30. Basic Operations: Indexing
• Its usually a good idea to index
collections
• How and which columns depend on
what is to be done
db.test.ensureIndex({hello:I})

31. MongoDB in action!

32. References
• MongoDB home at http://www.mongodb.org
• MongoDB - The Definitive Guide by Kristina
Chodorow and Dirolf
• NoSQL Paper at
http://wiki.hsr.ch/Datenbanken/files/Weber_NoSQL_
Paper.pdf
• Why MongoDB is awesome at
http://www.slideshare.net/jnunemaker/why-
mongodb-is-awesome
• MongoDB wiki at
http://en.wikipedia.org/wiki/MongoDB