Introduction to MongoDB at IGDTUW
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The document serves as an introduction to MongoDB, detailing its structure as an open-source document database and exploring its functionality through various use cases and hands-on exercises. It covers topics such as CRUD operations, data modeling with BSON, high availability, and scalability. Additionally, practical examples guide users through connecting to databases, querying data, creating indexes, and using aggregation pipelines.
![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 } )](https://image.slidesharecdn.com/introductiontomongodb-igdtuw1-171020112726/85/Introduction-to-MongoDB-at-IGDTUW-44-320.jpg)
![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.](https://image.slidesharecdn.com/introductiontomongodb-igdtuw1-171020112726/85/Introduction-to-MongoDB-at-IGDTUW-45-320.jpg)
![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 }
]
)](https://image.slidesharecdn.com/introductiontomongodb-igdtuw1-171020112726/85/Introduction-to-MongoDB-at-IGDTUW-46-320.jpg)
Introduction to MongoDB at IGDTUW
- 1. Indira Gandhi Delhi Technical University for Women Speakers: ● Ankur Raina ( [email protected] ) ● Pooja Gupta ( [email protected] )
- 3. MongoDB Use Cases Single View Internet of Things Mobile Real-Time Analytics Catalog Personalization Content Management
- 4. 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
- 6. MongoDB • Open-source, general purpose document database • Document: Field and Value pairs • Similar to JSON objects
- 7. Advantages • Documents (i.e objects) correspond to native datatypes in many programming languages • Reduce expensive joins by embedding • Dynamic schema - change on the fly
- 8. Document Unique Identification - Notice the fields
- 9. Document (contd.) Spot the difference! - Did you notice the flexibility?
- 10. 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
- 11. 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
- 12. Let’s insert() a document in the collection
- 13. Let’s find() some documents from our collection
- 14. 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
- 15. • update() phone numbers of all citizens. Multiple phone numbers. • Note that we are using an array to store these
- 16. 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
- 18. 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
- 19. Doesn’t it look like a natural fit for this data?
- 20. Let’s do some referencing • The government would like to keep track of criminal records associated with citizens
- 21. Executables
- 22. 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
- 25. Citizen Database Banking Application Sim Card Subscribers Gas Connection Subscription Biometric Details Example
- 26. Replica Set
- 34. 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
- 38. Hands-On MongoDB Download MongoDB Community Server: https://www.mongodb.com/download-center#community
- 39. 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/
- 40. 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>
- 41. 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
- 42. 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
- 43. 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?
- 44. 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 } )
- 45. 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.
- 46. 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 } ] )
- 47. I hope you enjoyed this session! Share your experience on the social networks! @MongoDB