MongoDB

ByRony Abraham Gregory

Model Engineering College

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

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!

Database Paradigms • Relational (RDBMS)• NoSQLoKey-value storesoDocument databasesoWide column stores (BigTable

and clones)oGraph databases

Relational Databases• ACID (Atomicity Consistency

Isolation and Durability)• SQL• MySQL, PostgreSQL, Oracle, etc.

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.

Key-value stores

Fig: A key-value store

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, etc.

Document databases

Fig: A document database

Why NoSQL?• Schema-free• Massive data stores• Scalability• Some services simpler to

implement than using RDBMS• Great fit for many Web 2.0

applications

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”

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

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

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

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

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

Features• Schema-less; very flexible• Auto-sharding (alpha)• Makes for easy horizontal;

scaling• Map/Reduce

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

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)

Features: Official Drivers• .NET, Java, JavaScript, Ruby,

Node.js, PHP, Haskell, C/C++, Perl

Concepts: Document-Oriented• Think of “documents” as

objects/database records• Documents are basically just

JSON in binary• Ability to store information all

together

Concepts: Document-Oriented

Concepts: Document-Oriented

Concept Mapping

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

Concepts: Cursors• The find() function returns a

cursor object

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

Basic Operations• Insertdb.test.insert({hello:’world’});• FindoEquivalent of Select * FROM

test;

Basic Operations:Updatedb.test.update(<condition>,<operation>)

Basic Operations: Removedb.test.remove(<condition>)

Basic Operations: Indexing• Its usually a good idea to index

collections• How and which columns depend

on what is to be donedb.test.ensureIndex({hello:I})

MongoDB in action!

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