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.

2. From SQL to NoSQL—
Changing Your Mindset
@Lauren_Schaefer, Developer Advocate, MongoDB

3. Parks and Recreation, Season 6, Episode 14

7. Parks and Recreation, Season 6, Episode 14

8. Lauren Schaefer
Developer Advocate, MongoDB
@Lauren_Schaefer

9. From SQL to NoSQL—
Changing Your Mindset

10. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

11. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

12. MongoDB stores data in documents

13. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

14. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

15. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

16. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

17. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

18. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

19. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

20. MongoDB stores data in documents
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

21. Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}

22. Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

23. Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
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

24. Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions
ID user_id model year
20 1 Bentley 1973
21 1 Rolls Royce 1965
Cars
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

25. Modeling data in MongoDB vs SQL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
ID user_id profession
10 1 banking
11 1 finance
12 1 trader
Professions
ID user_id model year
20 1 Bentley 1973
21 1 Rolls Royce 1965
Cars
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
Users

26. Collections vs Tables
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
UsersUsers

27. ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
Collections vs Tables
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
UsersUsers

28. Collections vs Tables
ID first_name surname cell city location_x location_y
1 Paul Miller 447557505611 London 45.123 47.232
2 Lauren Schaefer 1235552222 Lancaster NULL NULL
3 Sydney Schaefer NULL Lancaster NULL NULL
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}
UsersUsers

29. Schemaless
database

30. Schemaless
database
Don’t panic!
Use schema validation.

31. Document Row
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...

32. Document Row(s)
{
...
a: “b”
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

33. Field Column
ID a ...
1 b ...
2 c ...
3 ... ...
{
...
a: “b”
...
}
{
...
a: “c”
...
}

34. Collection Table
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

35. Database Database
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

36. Index Index
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

37. View View
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

38. Embedding Join
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}
ID a ...
1 b ...
2 ... ...
3 ... ...
... d ...
1 e ...
... ... ...

39. Database References Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
... ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

40. $lookup
(Aggregation Pipeline)
Left Outer Join
ID ... ...
1 ... ...
2 ... ...
3 ... ...
... ... ...
1 ... ...
4 ... ...
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

41. $graphLookup
(Aggregation Pipeline)
Recursive Common Table
Expressions
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
{
...
}
{
...
}

42. Multi-Document ACID
Transaction
Multi-Record ACID
Transaction
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...

43. Term mapping summary
x
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

44. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

47. Scale cheaper!
As the size of your database grows, scale horizontally.

48. SQLMongoDB
Query faster!
Stop doing expensive joins to get your data.
ID a ...
1 b ...
2 ... ...
3 ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
... ... ...
{
...
a: “b”,
...
c: {
d: “e”
...
},
...
}

49. SQLMongoDB
Pivot easier!
Easily change the shape of your data as your app evolves.
{
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 ;

50. Program faster!
Documents map to data structures in most popular languages.
Update Your Profile

51. 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

52. Program faster!
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
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

53. Program faster!
# 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})
# 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]
}
### Pull the info from the Professions
### table & put it in the user dict.
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

54. 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": [40.762, -73.979],
"professions": ["Manager",
"Engineer"]
}
# 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"]
}

55. Program faster!
# UPDATE THE USER'S PROFILE IN THE DB
### 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})
# UPDATE THE USER'S PROFILE IN THE DB
### 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()
### 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()

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

57. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

60. Embrace document diversity

61. Embrace document diversity
The Polymorphic Pattern
{
first_name: "Paul",
surname: "Miller",
cell: "447557505611",
city: "London",
location: [45.123,47.232],
profession: ["banking", "finance", "trader"],
cars: [
{
model: "Bentley",
year: 1973
},
{
model: "Rolls Royce",
year: 1965
}
]
}
{
first_name: ”Lauren",
surname: ”Schaefer",
cell: ”1235552222",
city: ”Lancaster",
profession: [”software engineer", ”developer advocate"],
}
{
first_name: ”Sydney",
surname: ”Schaefer",
city: ”Lancaster",
school: ”Daisy’s Daycare”
}

62. Embrace document diversity
The Outlier Pattern
{
_id: ”Lauren_Schaefer",
displayName: ”Lauren Schaefer",
numFollowers: 1310
followers: [
“naomi_pen”,
“kenwalger”,
“mylynn”
...
]
}
{
_id: ”Nick_Offerman",
displayName: ”Nick Offerman",
numFollowers: 1730332
followers: [
“c_hotaling”,
“IAmJerdog”,
“ChloeCondon”
...
],
has_extras: true
}
{
_id: ”Nick_Offerman_1",
twitter_id: “Nick_Offerman”,
is_overflow: true,
followers: [
“StephenAtHome”,
“TheEllenShow”,
“hulu”
...
]
}

63. Data that is accessed together should
be stored together

64. Data that is accessed together should
be stored together

65. 0
20000
40000
60000
80000
100000
120000
1985 2017
Storage vs Developer Costs
Storage Cost per GB Developer Salary
Data that is accessed together should
be stored together

66. 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”
}
]
}
Data that is accessed together should
be stored together

67. Tread carefully with transactions
Relying on transactions is a bad design smell.
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}
{
...
}

68. Make the leap from SQL to MongoDB

69. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
x
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

70. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB

71. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
1. Scale cheaper

72. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
1. Scale cheaper
2. Program faster
# 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})
# 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]
}

73. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
1. Scale cheaper
2. Program faster
3. Query faster

74. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
1. Scale cheaper
2. Program faster
3. Query faster
4. Pivot easier

75. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways

76. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways
1. Embrace document diversity

77. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways
1. Embrace document diversity
2. Data that is accessed together should be stored together

78. Make the leap from SQL to MongoDB
1. Map terms & concepts from SQL to MongoDB
2. Discover the 4 humongous advantages of MongoDB
3. Change your mindset in 3 key ways
1. Embrace document diversity
2. Data that is accessed together should be stored together
3. Tread carefully with transactions

79. Don’t be Ron Swanson
(in this particular case)

80. Change your mindset &
get the full value of MongoDB
Don’t be Ron Swanson

81. Additional resources on data modeling
patterns
• Advanced Schema Design Patterns (webinar)
• Building with Patterns: A Summary (blog series)
• M320: Data Modeling (MongoDB University Course – brand new!)

82. Additional resources
• The MongoDB Docs
• JSON Schema Validation – Locking down your model the smart way
• JSON Schema Validation - Checking Your Arrays
• M121: The MongoDB Aggregation Framework

83. Don’t be Ron Swanson
(in this particular case)
Change your mindset and get the
full value of MongoDB
Change your mindset &
get the full value of MongoDB
Get the slides on my Twitter page:
@Lauren_Schaefer

84. Thank You!