Introduction to MongoDB at IGDTUW

Indira Gandhi Delhi Technical University for Women
Speakers:
● Ankur Raina ( ankur.raina@mongodb.com )
● Pooja Gupta ( pooja.gupta@mongodb.com )

900+
employees
About
MongoDB,
Inc.
4,300+
customers
19 offices
worldwide

MongoDB Use Cases
Single View Internet of Things Mobile Real-Time Analytics
Catalog Personalization Content Management

AGENDA
• Introduction to MongoDB
– MongoDB Database
– Document Model
– BSON
– Data Model
– CRUD operations
• Break (10 mins)
• High Availability and Scalability
– Replication
– Sharding
• Break (15 mins)
• Hands-On MongoDB

MongoDB
• Open-source, general purpose document database
• Document: Field and Value pairs
• Similar to JSON objects

Advantages
• Documents (i.e objects) correspond to native datatypes in many
programming languages
• Reduce expensive joins by embedding
• Dynamic schema - change on the fly

Document
Unique Identification - Notice the fields

Document (contd.)
Spot the difference! - Did you notice the flexibility?

JSON? BSON?
• BSON is a binary-encoded serialization of JSON-like documents ( bsonspec.org )
• More data types such as BinData and Date
• Lightweight, Traversable, Efficient

Data Model
• Flexible Schema
• Collections do not enforce Document structure
• Consider the application usage patterns of data
• Normalisation rules do not apply directly!
• References and Embedding
• 16 MB size limit of documents
• Operation are atomic at document level

Let’s insert() a document in the collection

Let’s find() some documents from our collection

New Requirement
• update() PAN numbers of citizens
• Single PAN number of “some” citizens - Not everyone has a PAN card!
• None of our documents has a “pan_card” field

• update() phone numbers of all citizens. Multiple phone numbers.
• Note that we are using an array to store these

New Requirement
• update() complete “permanent address” of citizens
• A field named permanent_address containing sub-fields:
– house_no
– street
– landmark
– locality
– district
– state
– pincode
– map i.e. long-lat

Relational
TABLE 1 : CITIZEN_INFO
id
first_name
last_name
registered_on
pan_card
TABLE 2 :
PHONE_NUMBERS
person_id
phone_number
TABLE 3 :
PERMANENT_ADDRESS
person_id
house_no
street
landmark
locality
pincode
longitude
latitude
TABLE 4:
PINCODE_LOOKUP
pincode
locality
district
state

Doesn’t it look like a natural fit
for this data?

Let’s do some referencing
• The government would like to keep track of criminal records associated with citizens

SQL -> MongoDB
Structured Query Language (SQL) MongoDB Query Language (MQL)
CREATE TABLE insert() / createCollection()
ALTER TABLE - ADD COLUMN update() - $set
ALTER TABLE - DROP COLUMN update() - $unset
CREATE INDEX createIndex()
DROP TABLE drop()
INSERT INTO - VALUES insert()
SELECT find()
UPDATE - SET update() - $set
DELETE remove()
CreateReadUpdateDelete

Introduction to MongoDB at IGDTUW

Citizen Database
Banking Application
Sim Card Subscribers
Gas Connection Subscription
Biometric Details
Example

Pros:
● Most of the software can easily take
advantage of vertical scaling
● Easy to manage and install hardware
within a single machine
Pros:
● Increases performance in small steps as needed
● Can scale out the system as much as you need
Cons:
● Requires substantial financial investment
● Not possible to scale up vertically after a
certain limit
Cons:
● Need to set up the additional servers to handle
the data distribution and parallel processing
capabilities

Hands-On
MongoDB
Download MongoDB Community Server: https://www.mongodb.com/download-center#community

mongo shell
• Download from MongoDB Atlas (MongoDB database-as-a-service)
• Connect to mongo shell - an interactive JavaScript interface to MongoDB
• https://docs.atlas.mongodb.com/getting-started/

Let’s first restore data from an existing dump
Don’t worry, if you don’t get this. Just follow the steps !
• Go to https://github.com/Ankur10gen/SampleDataMongoDB
• Download the zip file and extract it
• cd SampleDataMongoDB-master/mongodump-citizendata_09_10/
mongorestore --db <DBNAME> --host <”ReplicaSetName/Hosts1,Host2,Host3”> --
authenticationDatabase admin --ssl --username admin --password <PASSWORD
e.g.: mongorestore --db demo1 demo1/ --host "Cluster0-shard-0/cluster0-shard-
00-00-ydjii.mongodb.net:27017,cluster0-shard-00-01-
ydjii.mongodb.net:27017,cluster0-shard-00-02-ydjii.mongodb.net:27017" --
authenticationDatabase admin --ssl --username admin --password <PASSWORD>

Great! You have made it! You are ready to use the mongo shell now!
Let’s see which databases exist, connect to a database & see the
collections inside it.
Note: There can be many databases in one mongod deployment and each database can have
several collections.
• show dbs
• use demo1
• show collections

Ex1: Find one citizen with last_name ‘SHARMA’
> db.citizendata.findOne({last_name:"SHARMA"})
SELECT * FROM citizendata WHERE last_name = “SHARMA” LIMIT 1;
Ex2: Find citizens with first_name ‘AJAY’
> db.citizendata.find({"first_name":"AJAY"})
SELECT * FROM citizendata WHERE first_name = “AJAY”
Ex3: Limit the previous result set to 5 documents
> db.citizendata.find({"first_name":"AJAY"}).limit(5)
SELECT * FROM citizendata WHERE first_name = “AJAY” LIMIT 5

Ex4: When was person with "_id" : "678943212601" registered?
> db.citizendata.find( { "_id": "678943212601" } , { "registered_on":1 } )
SELECT registered_on FROM citizendata WHERE _id = "678943212601";
Ex5: Find the count of people with state ‘HARYANA’. Note that state is a
field inside permanent_address.
> db.citizendata.find( { "permanent_address.state": "HARYANA" } ).count()
YOU MAY NEED TO DO A JOIN AND WE DON’T WANT TO GO THERE.
======================================= enjoying?

Ex6: CREATE AN INDEX ON phone_numbers
> db.citizendata.createIndex( { phone_numbers: 1 } )
CREATE INDEX phone_numbers_1 ON citizendata (phone_numbers)
Ex7: Find details of a person with phone_number 8855915314. Note that
phone_numbers is an array type field.
> db.citizendata.find( { "phone_numbers": "8855915314" } ).pretty()
Ex8: Find _id of citizens with first_name REVA or ABEER
> db.citizendata.find( { "first_name": { "$in" : [ "REVA", "ABEER" ] } }, { _id: 1 } )

Ex9: Find the count of people with first_name SANDEEP in each state. We are
using the MongoDB Aggregation Pipeline.
> db.citizendata.aggregate(
[
{ $match : { "first_name":'SANDEEP' } },
{ $group : { _id : "$permanent_address.state", count: {$sum: 1} } }
]
)
In SQL, you’ll use a GROUP BY clause for it. And may be some joins to bring in
this state info from another table.

Ex10: Let’s sort our citizens in descending order with last_name ‘VERMA’ on
the basis of pan_card information using aggregation pipeline and limit our
result set to 10. Project only the phone numbers with NO _id field.
> db.citizendata.aggregate(
[
{ $match : { "last_name":'VERMA' } },
{ $sort : { "pan_card" : -1 } },
{ $project : { "_id": 0, "pan_card":1,"phone_numbers":1 } },
{ $limit : 10 }
]
)

I hope you enjoyed this session!
Share your experience on the social networks! @MongoDB

Introduction to MongoDB at IGDTUW

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