When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.

1. #MDBlocal
From SQL to NoSQL
Changing Your Mindset
SANFRANCISCO
Girish Dusane
Senior Solution Architect

2. #MDBLocal
The mental journey from SQL to MongoDB
• Map terms & concepts
from SQL to MongoDB
• Discover the 4 humongous
advantages of MongoDB
• Change your mindset in 3
key ways

3. #MDBLocal
MongoDB stores data in documents

4. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

5. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

6. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

7. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

8. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

9. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

10. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

11. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

12. #MDBLocal
MongoDB stores data in documents
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}
Relationships?

13. #MDBLocal
Modeling data in MongoDB vs SQL
{
first_name: ”Homer",
last_name: ”Simpson",
cell: ”415123456",
city: ”Springfield",
location: [37.407419, -102.618143],
profession: [”technical supervisor", ”nuclear safety inspector"],
cars: [
{
model: “Plymouth Junkerolla",
year: 1986
},
{
model: ”Plymouth Valiant",
year: 1973
}
]
}

14. #MDBLocal
Modeling data in MongoDB vs SQL
{
first_name: "Randy",
surname: "Marsh",
cell: "415123456",
city: "Southpark",
location: [45.123,47.232],
profession: ["geologist", "hemp farmer"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232
Users

15. #MDBLocal
{
first_name: ”Randy",
surname: "Marsh",
cell: "415123456",
city: "Southpark",
location: [45.123,47.232],
profession: ["geologist", "hemp farmer"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
Modeling data in MongoDB vs SQL
Users
ID user_id profession
10 1 geologist
11 1 hemp farmer
Professions
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232

16. #MDBLocal
{
first_name: ”Randy",
surname: "Marsh",
cell: "415123456",
city: "Southpark",
location: [45.123,47.232],
profession: ["geologist", "hemp farmer"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
Modeling data in MongoDB vs SQL
Users
ID user_id profession
10 1 geologist
11 1 hemp farmer
Professions
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232
ID user_id model year
20 1 Bentley 1973
21 1 Rolls Royce 1965
Cars

17. #MDBLocal
Collections vs Tables
{
first_name: ”Randy",
surname: ”Marsh",
cell: "415123456",
city: ”Southpark",
location: [45.123,47.232],
profession: [”geologist", ”hemp farmer"],
cars: [
{
model: "Plymouth Junkerolla ",
year: 1986
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Sharon",
surname: ”Marsh",
cell: ”415456789",
city: ”Southpark",
profession: [”receptionist"],
}
{
first_name: ”Stan",
surname: ”Marsh",
city: ”Southpark",
school: ”Southpark
Elementary”
}
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232
2 Sharon Marsh 415456789 Southpark NULL NULL
3 Stan Marsh NULL Southpark NULL NULL
UsersUsers

18. #MDBLocal
Collections vs Tables
{
first_name: ”Sharon",
surname: ”Marsh",
cell: ”415456789",
city: ”Southpark",
profession: [”receptionist"],
}
{
first_name: ”Stan",
surname: ”Marsh",
city: ”Southpark",
school: ”Southpark
Elementary”
}
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232
2 Sharon Marsh 415456789 Southpark NULL NULL
3 Stan Marsh NULL Southpark NULL NULL
Users
{
first_name: ”Randy",
surname: ”Marsh",
cell: "415123456",
city: ”Southpark",
location: [45.123,47.232],
profession: [”geologist", ”hemp farmer"],
cars: [
{
model: "Plymouth Junkerolla ",
year: 1986
},
{
model: "Rolls Royce",
year: 1965
}
]
}
Users

19. #MDBLocal
{
first_name: ”Randy",
surname: ”Marsh",
cell: "415123456",
city: ”Southpark",
location: [45.123,47.232],
profession: [”geologist", ”hemp farmer"],
cars: [
{
model: "Plymouth Junkerolla ",
year: 1986
},
{
model: "Rolls Royce",
year: 1965
}
]
}
Users
Collections vs Tables
{
first_name: ”Sharon",
surname: ”Marsh",
cell: ”415456789",
city: ”Southpark",
profession: [”receptionist"],
}
{
first_name: ”Stan",
surname: ”Marsh",
city: ”Southpark",
school: ”Southpark
Elementary”
}
ID first_name surname cell city location_x location_y
1 Randy Marsh 415123456 Southpark 45.123 47.232
2 Sharon Marsh 415456789 Southpark NULL NULL
3 Stan Marsh NULL Southpark NULL NULL
Users

20. #MDBLocal
Flexible Schema or Schemaless

21. #MDBLocal
Flexible Schema or Schemaless
Don’t panic!
Use schema validation.

22. #MDBLocal
Map terms from SQL to MongoDB
ID a ...
1 b ...
2 ... ...
3 ... ...
{
...
a: “b”
...
}
Row Document

23. #MDBLocal
Map terms from SQL to MongoDB
{
...
a: “b”
...
}
Row(s) Document
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

24. #MDBLocal
Map terms from SQL to MongoDB
Column Field
{
...
a: “b”
...
}
{
...
a: “c”
...
}
ID a ...
1 b ...
2 c ...
3 ... ...

25. #MDBLocal
Map terms from SQL to MongoDB
Table Collection
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...

26. #MDBLocal
Map terms from SQL to MongoDB
Database Database
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

27. #MDBLocal
Map terms from SQL to MongoDB
Index Index
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

28. #MDBLocal
Map terms from SQL to MongoDB
View View
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...

29. #MDBLocal
Map terms from SQL to MongoDB
Join Embedding
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... d ...
1 e ...
... ... ...

30. #MDBLocal
Map terms from SQL to MongoDB
Join Linking
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

31. #MDBLocal
Map terms from SQL to MongoDB
Left Outer Join $lookup
(Aggregation Pipeline)
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
4 ... ...

32. #MDBLocal
Map terms from SQL to MongoDB
Recursive Common Table
Expressions
$graphLookup
(Aggregation Pipeline)
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...

33. #MDBLocal
Map terms from SQL to MongoDB
Multi-Record ACID Transaction Multi-Record ACID Transaction
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

34. #MDBLocal
Summary of term mapping
Row Column Table Database Index Join Join Left Outer
Join
Recursive
Common Table
Expressions
View Transaction
Document Field Collection Database Index Embedding
Database
References $lookup $graphLookup View Transaction

35. #MDBLocal
The mental journey from SQL to MongoDB
• Map terms & concepts
from SQL to MongoDB
• Discover the 4 humongous
advantages of MongoDB
• Change your mindset in 3
key ways

36. #MDBLocal
Scale Cheaper
As the size of your database grows, scale horizontally.

37. #MDBLocal
Program Faster
Documents map to data structures in most popular languages.
Update Your Profile

38. #MDBLocal
Program Faster
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions

39. import pymongo
from pymongo import MongoClient
# CONNECT TO THE DB
client = MongoClient()
client =
pymongo.MongoClient("mongodb+srv://
username:password@cluster0nsdia.
mongodb.net/test?retryWrites=true")
db = client.fabapp
# THE ID OF THE USER WHOSE PROFILE WE WILL
BE RETRIEVING AND UPDATING
userId = 1
Program faster!
import mysql.connector
# CONNECT TO THE DB
mydb = mysql.connector.connect(
host="localhost",
user=”username",
passwd=”password",
database=”fabapp”
)
mycursor = mydb.cursor()
# THE ID OF THE USER WHOSE PROFILE WE WILL
BE RETRIEVING AND UPDATING
userId = 1

40. # GET THE USER'S PROFILE INFORMATION
### Pull the info from the Users table & put
it in the user dictionary
sql = "Select * FROM Users WHERE Users.ID=%s”
values = (userId,)
mycursor.execute(sql, values)
result = mycursor.fetchone()
user = {
"first_name": result[1],
"surname": result[2],
"cell": result[3],
"city": result[4],
"location_x": result[5],
"location_y": result[6]
}
# GET THE USER'S PROFILE INFORMATION
## We can pull all of the info from the same
document since we used embedding
user = db['Users'].find_one({"_id": userId})
Program faster!
### Pull the info from the Professions table
& put it in the user dictionary
sql = "Select * FROM Professions WHERE
Professions.user_id=%s”
values = (userId,)
mycursor.execute(sql, values)
results = mycursor.fetchall()
professions = []
for result in results:
professions.append(result[2])
user["professions"] = professions

41. # UPDATE THE USER DICTIONARY BASED ON USER
INPUT IN THE APP
### We'll update the user dictionary
manually for simplicity
user = {
"first_name": "NewFirst",
"surname": "NewSurname",
"cell": "123-456-7890",
"city": "NewCity",
"location": [40.762, -73.979],
"professions": [”Manager", "Engineer"]
}
Program faster!
# UPDATE THE USER DICTIONARY BASED ON USER
INPUT IN THE APP
### We'll update the user dictionary
manually for simplicity
user = {
"first_name": "NewFirst",
"surname": "NewSurname",
"cell": "123-456-7890",
"city": "NewCity",
"location_x": 40.762,
"location_y": 73.979,
"professions": [”Manager", "Engineer"]
}

42. # UPDATE THE USER'S PROFILE IN THE DATABASE
### First update what is stored in the Users
table
sql = "UPDATE Users SET first_name=%s,
surname=%s, cell=%s, city=%s, location_x=%s,
location_y=%s WHERE (ID=%s)"
values = (
user["first_name"],
user["surname"],
user["cell"],
user["city"],
user["location_x"],
user["location_y"],
userId)
mycursor.execute(sql, values)
mydb.commit()
# UPDATE THE USER'S PROFILE IN THE DATABASE
### Since the user's data is stored in a
single document, we only have to make one
update
result = db['Users'].update_one(
{"_id": userId}, {"$set": user})
Program faster!
### Delete existing records in Professions
table and add new ones
sql = "DELETE FROM Professions WHERE
user_id=%s”
values = (userId,)
mycursor.execute(sql, values)
mydb.commit()
if(len(user["professions"]) > 0):
sql = "INSERT INTO Professions
(user_id,profession) VALUES (%s, %s)”
values = []
for profession in user["professions"]:
values.append((userId, profession))
mycursor.executemany(sql,values)
mydb.commit()

43. 30 lines of code
Program faster!
73 lines of code

44. #MDBLocal
Query Faster
Stop doing expensive joins to get your data.
SQLMongoDB
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}

45. #MDBLocal
Pivot easier
Easily change the shape of your data as your app evolves.
SQLMongoDB
{
a: “b”,
c: “one”,
e: “f”
}
{
a: “b”,
c: 1,
new: “no biggee”
}
ALTER TABLE `mydb`.`letters_table`
DROP COLUMN `e`,
ADD COLUMN `New` VARCHAR(45) NULL AFTER `C`,
CHANGE COLUMN `C` `C` INT NULL DEFAULT NULL ;

46. #MDBLocal
Summary - 4 advantages of MongoDB
• Scale cheaper
• Program faster
• Query faster
• Pivot faster

47. #MDBLocal
The mental journey from SQL to MongoDB
• Map terms & concepts
from SQL to MongoDB
• Discover the 4 humongous
advantages of MongoDB
• Change your mindset in 3
key ways

48. #MDBLocal
Not all documents in a collection need to have the same fields

49. #MDBLocal
Not all documents in a collection need to have the same fields
The Polymorphic Pattern
{
first_name: ”Randy",
surname: ”Marsh",
cell: "415123456",
city: ”Southpark",
location: [45.123,47.232],
profession: [”geologist", ”hemp farmer"],
cars: [
{
model: "Plymouth Junkerolla ",
year: 1986
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Sharon",
surname: ”Marsh",
cell: ”415456789",
city: ”Southpark",
profession: [”receptionist"],
}
{
first_name: ”Stan",
surname: ”Marsh",
city: ”Southpark",
school: ”Southpark
Elementary”
}

50. #MDBLocal
Not all documents in a collection need to have the same fields
The Outlier Pattern
{
_id: ”@eric",
displayName: ”Eric Cartman",
numFollowers: 1310
followers: [
“stan”,
“kenny”,
“kyle”
...
]
}
{
_id: ”@jerry",
displayName: ”Jerry Seinfeld",
numFollowers: 1730332
followers: [
“LarryDavid”,
“GeorgeCostanza”,
“Elaine”
...
],
has_extras: true
}
{
_id: ”@jerry_1",
displayName: “Jerry Seinfeld”,
is_overflow: true,
followers: [
“StephenAtHome”,
“TheEllenShow”,
“hulu”
...
]
}

51. #MDBLocal
Data that is accessed together is stored together

52. #MDBLocal
Data that is accessed together is stored together

53. #MDBLocal
Data that is accessed together is stored together
0
20000
40000
60000
80000
100000
120000
1985 2017
Storage vs Developer Costs
Storage Cost per GB Developer Salary

54. #MDBLocal
Data that is accessed together is stored together
Don’t normalize your data for the sake of normalizing it.
{
a: “b”,
c: {
d: “e”
...
},
f: [“g”, “h”, “i”],
j: [
{
k: “l”
},
{
m: “n”
}
]
}

55. #MDBLocal
Stop using transactions regularly
Relying on transactions is a bad design smell
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

56. #MDBLocal
Summary – Changing your mindset
• Not all documents in a collection need to have the
same fields
• Data that is accessed together should be stored
together
• Stop using transactions regularly

57. #MDBLocal
The mental journey from SQL to MongoDB

58. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Row Column Table Database Index Join Join Left Outer
Join
Recursive
Common Table
Expressions
View Transaction
Document Field Collection Database Index Embedding
Database
References $lookup $graphLookup View Transaction

59. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB

60. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
• Scale cheaper

61. #MDBLocal
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
• Scale cheaper
• Program faster
The mental journey from SQL to MongoDB
# UPDATE THE USER'S PROFILE IN THE DATABASE
### First update what is stored in the Users table
sql = "UPDATE Users SET first_name=%s, surname=%s,
cell=%s, city=%s, location_x=%s, location_y=%s WHERE
(ID=%s)"
values = (
user["first_name"],
user["surname"],
user["cell"],
user["city"],
user["location_x"],
user["location_y"],
userId)
# UPDATE THE USER'S PROFILE IN THE DATABASE
### Since the user's data is stored in a single
document, we only have to make one update
result = db['Users'].update_one(
{"_id": userId}, {"$set": user})

62. #MDBLocal
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
• Scale cheaper
• Program faster
• Query faster
The mental journey from SQL to MongoDB

63. #MDBLocal
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
• Scale cheaper
• Program faster
• Query faster
• Pivot easier
The mental journey from SQL to MongoDB

64. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
Change your mindset in 3 key ways

65. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
Change your mindset in 3 key ways
• Not all documents in a collection need to have the same fields

66. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
Change your mindset in 3 key ways
• Not all documents in a collection need to have the same fields
• Data that is accessed together should be stored together

67. #MDBLocal
The mental journey from SQL to MongoDB
Map terms & concepts from SQL to MongoDB
Discover the 4 humongous advantages of MongoDB
Change your mindset in 3 key ways
• Not all documents in a collection need to have the same fields
• Data that is accessed together should be stored together
• Stop using transactions regularly

68. THANK YOU