Sandeep Giriwww.KnowBigData.com

Session 1 - Introduction to MongoDB

Sandeep GiriMongoDB www.KnowBigData.com

Interact - Ask Questions

Lifetime access of content

Class Recording

Hands on Training

Cluster Access

WELCOME - KNOWBIGDATA24x7 support

Real Life Project

Quizzes & Certification Test

8 x (3hr class - 5min break)

Socio-Pro Visibility

Mock Interviews

Sandeep GiriMongoDB www.KnowBigData.com

ABOUT ME2014 KnowBigData Founded2014

AmazonBuilt High Throughput Systems for Amazon.com site using in-house NoSql.

20122012 InMobi Built Recommender after churning 200 TB2011

tBits GlobalFounded tBits Global Built an enterprise grade Document Management System

2006

D.E.ShawBuilt the big data systems before the term was coined

20022002 IIT Roorkee Finished B.Tech somehow.

Sandeep GiriMongoDB www.KnowBigData.com

NOSQL?

Sandeep GiriMongoDB www.KnowBigData.com

Next Generation Databases

Not Only SQL

Non-Relational,

Distributed Architecture

Open-Source

Horizontally scalable

WHAT IS A NOSQL DATABASE?Schema-Free

Easy Replication

Simple API

Manage Huge Data

Commodity Hardware

~150 are in the market

Sandeep GiriMongoDB www.KnowBigData.com

2. Agile: Agile sprints, quick iteration, and frequent code pushes

3. Object-oriented programming Ease to store objects

4. High Availability Cater to many concurrent users

5. Scale-out architecture Commodity hardware

Instead of expensive, monolithic architecture

WHY NOSQL?

Agile

Storing objects in RDBMSImpedance Mismatch

1. Big Data

Sandeep GiriMongoDB www.KnowBigData.com

Column Oriented / wide-column

Accumulo, Cassandra, HBase

Document

Clusterpoint, Couchbase, MarkLogic, MongoDB

Key-value

Dynamo, MemcacheDB, Project Voldemort, Redis, Riak

Graph

Allegro, Neo4J, OrientDB, Virtuoso

TYPES OF NOSQL STORES

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NOSQL VS SQL SUMMARY

  SQL DATABASES NOSQL DATABASES

Types One type - minor variations Many different types

Development History 1970s 2000s

Examples MySQL, Postgres, Oracle Database MongoDB, Cassandra, HBase, Neo4j

Data Storage Model Relational Varies based on database type.

Schemas Structure and data types are fixed in advance. Typically dynamic.

Scaling Vertically, Horizontally,

Development Model Mix Open-source

Supports Transactions ACID In certain circumstances and at certain levels (e.g., document level vs. database level)

Data Manipulation SQL Through object-oriented APIs

Consistency Can be configured for strong consistency Depends on product.

Sandeep GiriMongoDB www.KnowBigData.com

IMPOSSIBLE TO GET ALL THREE OF

1. Consistency

1. All nodes see the same data at the same time

2. Availability

1. A guarantee that every request receives a response about whether it was successful or failed

3. Partition tolerance

1. The system continues to operate despite arbitrary message loss or failure of part of the system

CAP THEOREM

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Sandeep GiriMongoDB www.KnowBigData.com

1. Basically Available

1. Basically Available indicates that the system does guarantee availability, in terms of the CAP theorem.

2. Soft State

1. Soft State indicates that the state of the system may change over time, even without

3. Eventual Consistency

1. Eventual Consistency indicates that the system will become consistent over time, given that the system doesn't receive input during that time.

BASEIn Simple Words: A BASE system gives up on consistency.

[Contrived Acronym - opposite of ACID]