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WHY
Shankar MorwalCTO and Founder
Habilelabs.io
CONTENTS
1. Growth of Mongodb
2. Flexible data Model
3. MongoDB features
4. Rich set drivers and connectivity
5. Availability & Uptime
6. Security
LinkedInGoogle
Fastest-Growing Database
RANK DBMS MODEL SCORE GROWTH (20 MO)
1. Oracle Relational DBMS 1,442 -5%
2. MySQL Relational DBMS 1,294 2%
3. Microsoft SQL Server Relational DBMS 1,131 -10%
4. MongoDB Document Store 277 172%
5. PostgreSQL Relational DBMS 273 40%
6. DB2 Relational DBMS 201 11%
7. Microsoft Access Relational DBMS 146 -26%
8. Cassandra Wide Column 107 87%
9. SQLite Relational DBMS 105 19%
Source: DB-engines database popularity rankings; May 2015
Only non-relational in the top 5; 2.5x ahead of nearest NoSQL Competitor
4th Most Popular Database
FLEXIBLE DATA MODEL
DEVELOPER COSTS ON THE RISE
Storage Cost per GB Developer Salary
$0
$20,000
$40,000
$60,000
$80,000
$100,000
1985 2013
$100,000
$0.05$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
1985 2013
OPTIMIZING FOR ENGINEERING PRODUCTIVITY1985 2016
Infrastructure Cost
Engineer Cost
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location: [45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
MongoDB
DOCUMENT MODEL WITH FLEXIBLE SCHEMARDBMS
DOCUMENTS ARE RICH DATA STRUCTURES
{
first_name: ‘Paul’,
surname: ‘Miller’,
cell: 447557505611,
city: ‘London’,
location: [45.123,47.232],
Profession: [‘banking’, ‘finance’, ‘trader’],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
Fields can contain an array
of sub-documents
Fields
Typed field values
Fields can contain arrays
Number
DEVELOPMENT – THE PAST
DEVELOPMENT – WITH MONGODB
MONGODB IS FULL FEATURED
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car between
1970 and 1980
Geospatial• Find all of the car owners within 5km of
Trafalgar Sq.
Text Search• Find all the cars described as having leather
seats
Aggregation• Calculate the average value of Paul’s car
collection
Map Reduce
• What is the ownership pattern of colors by
geography over time (is purple trending in
China?)
DYNAMIC LOOKUP
Combine data from multiple collections with
left outer joins for richer analytics & more
flexibility in data modeling
MODEL OF THE AGGREGATION FRAMEWORK
RICHER IN-DATABASE ANALYTICS & SEARCH
New Aggregation operators extend options
for performing analytics with lower developer
complexity
Array Operators Math Operators Text
• $slice
• $arrayElemAt
• $concatArrays
• $filter
• $min
• $max
• $avg
• $sum
• and more …
• $stdDevSamp
• $stdDevPop
• $sqrt
• $abs
• $trunc
• $ceil
• $floor
• $log
• $pow
• $exp
• and more …
• Case sensitive
text search
• Support for
languages such
as Arabic, Farsi,
Chinese and
more …
RICH SET DRIVERS AND CONNECTIVITY
DRIVERS & FRAMEWORKS
MEAN Stack
Java Python PerlRuby
ANALYTICS AND BI INTEGRATION
MONGODB CONNECTOR FOR BI
Visualize and explore multi-structured data
using SQL-based BI platforms.
Your BI Platform
BI ConnectorProvides Schema
Translates Queries
Translates Response
HIGH AVAILABILITY & UPTIME
REPLICA SETS
• Replica set – 2 to 50 copies
• Makes up a self-healing ‘shard’
• Data center aware
• Addresses:
– High availability
– Data durability, consistency
– Maintenance (e.g., HW swaps)
– Disaster Recovery A Single
Shard
REPLICA SET - INITIALIZE
Node 1
(Primary)
Node 2
(Secondary)
Node 3
(Secondary)
Replication Replication
Heartbeat
REPLICA SET - FAILURE
Node 2
(Secondary)
Node 3
(Secondary)Heartbeat
Primary Election
Node 1
(Primary)
REPLICA SET - FAILOVER
Node 1
(Primary)
Node 2
(Primary)
Node 3
(Secondary)
Heartbeat
Replication
REPLICA SET - RECOVERY
Node 2
(Primary)
Node 3
(Secondary)
Heartbeat
Replication
Node 1
(Recovery)
Replication
REPLICA SET - RECOVERED
Node 2
(Primary)
Node 3
(Secondary)
Heartbeat
Replication
Node 1
(Secondary)
Replication
ELASTIC SCALABILITY
ELASTIC SCALABILITY WITH AUTOMATIC SHARDING
• Increase or decrease capacity as you go
• Automatic load balancing
• Three types of sharding
– Hash-based
– Range-based
– Tag-aware
QUERY ROUTING
• Multiple query optimization models
• Each of the sharding options are
appropriate for different apps / use
cases
DESIGNED FOR PERFORMANCE
Better Data Locality In-Memory Caching In-Place Updates
vs.
Relational MongoDB
PERFORMANCE AT SCALE
Top 5 Marketing Firm Government Agency Top 5 Investment Bank
Data Key / Value 10+ fields, arrays, nested documents 20+ fields, arrays, nested documents
Queries• Key-based• 1-100 docs/query• 80/20 read/write
• Compound queries• Range queries• MapReduce• 20/80 read/write
• Compound queries• Range queries• 50/50 read/write
Servers ~250 ~50 4
Operations / Second
1,200,000 500,000 30,000
PERFORMANCE AT SCALE
Cluster Scale Performance Scale Data Scale
Entertainment Co.
1400 servers 250M Ticks / Sec Petabytes
Asian Internet Co.
1000+ servers 300K+ Ops / Sec 10s of billions of objects
250+ servers Fed Agency 500K+ Ops / Sec 13B documents
SECURITY
ENTERPRISE-GRADE SECURITY
*Included with MongoDB Enterprise Advanced
BUSINESS NEEDS SECURITY FEATURES
Authentication SCRAM, LDAP*, Kerberos*, x.509 Certificates
Authorization Built-in Roles, User-Defined Roles, Field-Level Redaction
Auditing* Admin, DML, DDL, Role-based
Encryption Network: SSL (with FIPS 140-2), Disk: Encrypted Storage Engine* or Partner Solutions
Questions ?For any questions drop me line at [email protected]
CONTACT US
• Development Center :Habilelabs Pvt. Ltd.4th Floor, I.G.M. Senior Secondary Public School Campus,Sec-93 Agarwal Farm, Mansarovar, Jaipur(Raj.) – 302020
• Email : [email protected]
• Web : https://habilelabs.io
• Telephone: +91-9828247415 / +91-9887992695
