Goodbye rows and tables, hello documents and collections

Lots of pretty pictures to fool you.

Noise

Introduction

MongoDB bridges the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems (which provide rich queries and deep functionality).

MongoDB is document-oriented, schema-free, scalable, high-performance, open source. Written in C++

Mongo is not a relational database like MySQL

Goodbye rows and tables, hello documents and collections

FeaturesDocument-oriented

Documents (objects) map nicely to programming language data types Embedded documents and arrays reduce need for joins No joins and no multi-document transactions for high performance and easy scalability

High performance No joins and embedding makes reads and writes fast Indexes including indexing of keys from embedded documents and arrays

High availability Replicated servers with automatic master failover

Easy scalability Automatic sharding (auto-partitioning of data across servers)

Reads and writes are distributed over shards No joins or multi-document transactions make distributed queries easy and fast

Eventually-consistent reads can be distributed over replicated servers

Why ?

Cost - MongoDB is free MongoDb is easily installable. MongoDb supports various programming languages like C, C++,

Java,Javascript, PHP. MongoDB is blazingly fast MongoDB is schemaless Ease of scale-out

If load increases it can be distributed to other nodes across computer networks. It's trivially easy to add more fields -- even complex fields -- to your objects.

So as requirements change, you can adapt code quickly. Background Indexing MongoDB is a stand-alone server Development time is faster, too, since there are no schemas to manage. It supports Server-side JavaScript execution.

Which allows a developer to use a single programming language for both client and server side code

Limitations

Mongo is limited to a total data size of 2GB for all databases in 32-bit mode.

No referential integrity

Data size in MongoDB is typically higher.

At the moment Map/Reduce (e.g. to do aggregations/data analysis) is OK,

but not blisteringly fast.

Group By : less than 10,000 keys.

For larger grouping operations without limits, please use map/reduce .

Lack of predefined schema is a double-edged sword

No support for Joins & transactions

Benchmarking (MongoDB Vs. MySQL)

Script 1 (Insert) Script 2 (Insert) Script 3 (Select)

0

5000

10000

15000

20000

25000

MySQL

MongoDB

Test Machine configuration:

CPU : Intel Xeon 1.6 GHz - Quad Core, 64 BitMemory : 8 GB RAM

OS : Centos 5.2 - Kernel 2.6.18 64 bit

Record StructureField1 -> String, IndexedField2 -> String, IndexedFiled3 -> Date, Not IndexedFiled4 -> Integer, Indexed

Mongo data model

MySQL Term Mongo Term

database database

table collection

index index

row BSON document

column BSON field

Primary key _id field

A Mongo system (see deployment above) holds a set of databasesA database holds a set of collectionsA collection holds a set of documentsA document is a set of fieldsA field is a key-value pairA key is a name (string)A value is a

basic type like string, integer, float, timestamp, binary, etc., a document, or an array of values

SQL to Mongo Mapping Chart

Continued ...

SQL Statement Mongo Statement

Replication / Sharding

Data Redundancy Automated Failover Distribute read load Simplify maintenance (compared to "normal" master-slave) Disaster recovery from user error

Automatic balancing for changes in load and data distribution Easy addition of new machines Scaling out to one thousand nodes No single points of failure Automatic failover

These slides are online:http://amardeep.in/intro_to_mongodb.ppt